diff --git a/data/MP/sweep_splits/gga+u_split_sweep12k.json b/data/MP/sweep_splits/gga+u_split_sweep12k.json
new file mode 100644
index 00000000..838dde7b
--- /dev/null
+++ b/data/MP/sweep_splits/gga+u_split_sweep12k.json
@@ -0,0 +1 @@
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diff --git a/data/MP/sweep_splits/gga_split_sweep12k.json b/data/MP/sweep_splits/gga_split_sweep12k.json
new file mode 100644
index 00000000..92afa884
--- /dev/null
+++ b/data/MP/sweep_splits/gga_split_sweep12k.json
@@ -0,0 +1 @@
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+# LR / weight-decay sweep across width (W32 / W64 / W96)
+
+Status: stages A+B COMPLETE (launched 2026-07-30/31, finished 2026-07-31 —
+16 + 15 trials, all succeeded; results below). Stage C not launched.
+
+## Results (see W&B `mp-gga-ggau-lrwd`; aggregate with scripts/review_lrwd_sweep.py)
+
+Best val NMAE% on the 8-epoch/12K proxy. Noise bar from the three verbatim
+anchor reruns: **±0.04–0.07 points** (1.914→1.968, 1.592→1.665, 1.524→1.566).
+
+| lr | W32 | W64 | W96 |
+|---------|-------|-------|-------|
+| 2.5e-4 | 2.330 | 1.998 | 1.943 |
+| 5e-4 | 2.133 | 1.841 | 1.836 |
+| 1e-3 | 1.935 | 1.708 | 1.757 |
+| 2e-3 | **1.914** | 1.759 | **1.524** |
+| 4e-3 | 1.954 | **1.592** | 1.562 |
+| 8e-3 | — | 1.580 | — |
+
+| wd @ lr* (AdamW) | W32 | W64 | W96 |
+|---------|-------|-------|-------|
+| 0 (+rerun) | 1.914 / 1.968 | 1.592 / 1.665 | 1.524 / 1.566 |
+| 1e-4 | 1.968 | 1.631 | 1.535 |
+| 1e-3 | 1.995 | 1.610 | 1.640 |
+| 1e-2 | 1.970 | 1.654 | 1.549 |
+| cross (lr*/2, 1e-3) | 1.904 | 1.638 | 1.731 |
+
+Conclusions:
+
+1. **lr\* does not shrink with width** — every width sits in a flat 2–4e-3
+   basin (differences at/inside the noise bar). The production lr=1e-3 is
+   suboptimal at all widths, and the penalty grows with width: ~1% relative
+   at W32, ~7% at W64, ~13% at W96. Extrapolation to W128/W160: use 2e-3.
+2. **Weight decay is neutral across 1e-4–1e-2 at every width** — even on
+   this subset, where overfitting bites ~9× earlier than at full scale, so
+   at 111K it has even less room to help. Recommend keeping **wd=0**
+   (continuity with all incumbents); wd up to 1e-2 is demonstrably safe if
+   ever wanted for other reasons. (Only outlier: W96 @ 1e-3 = 1.640,
+   marginally past the bar; with 1e-2 neutral on both sides it reads as an
+   unlucky draw, not a trend.)
+3. W64's stage-A curiosities dissolved: the 8e-3 "win" (1.580 vs 1.592) and
+   the 2e-3 dip (1.759; cross term at same lr with wd landed 1.638) are
+   both within run-to-run noise.
+4. Width ordering at tuned recipes is unchanged: W96 1.524 < W64 1.592 <
+   W32 1.914.
+
+**Stage C recipes**: W32 (2e-3, 0), W64 (4e-3, 0), W96 (2e-3, 0) — with
+2e-3 defensible everywhere given the flat basin.
+
+## Why
+
+Every rung of the width ladder (`mp-gga-ggau-width`: W64, W96, W128, W160)
+trains with the same recipe — `lr: 0.001`, `weight_decay: 0.0` — inherited
+from the June W64 run. For Adam in standard parametrization the optimal LR
+typically *shrinks* as width grows (roughly ∝ 1/width in the μP limit), so
+the wider rungs are plausibly mistuned and the width-scaling conclusions
+conflate capacity with recipe mismatch. W96's 17.8% win over W64 survived
+that handicap; the question is whether the gaps (and the W128/W160 verdicts)
+change once each width gets its own recipe.
+
+This sweep tunes LR and WD independently at widths 32, 64, 96, then uses the
+per-width optima to (a) re-examine the width ranking and (b) extrapolate a
+recipe for W128/W160 rather than sweeping at those (much more expensive)
+widths.
+
+## Trial design (proxy task)
+
+Full production runs are ~5 days; sweep trials compress two axes and change
+nothing else:
+
+- **Data**: same staged capped dataset (111,257 ids). Train is subsampled to
+  ~12K structures (frac 0.1112 of each functional's capped train split, so
+  the 76/24 gga/gga+u mix is preserved: 9,139 + 2,862). **Validation is the
+  untouched production val set** (847 + 262 = 1,109), so trial `val_loss` is
+  on the same yardstick as the production curves. The subset lives in two
+  small split JSONs checked into the repo (`data/MP/sweep_splits/`) and
+  referenced by repo-relative path — they ride along in the submit.sh bundle,
+  so no bucket upload and no interaction with the `.staged.ok` warm-node
+  skip.
+- **Schedule**: `epochs: 8`, `warmup_length: 1` — each trial runs its own
+  complete warmup+cosine schedule, compressed. Comparing LRs mid-schedule is
+  misleading; comparing completed short schedules preserves ranking much
+  better. 8 epochs × 12K ≈ 24K optimizer steps ≈ 0.9 production epochs.
+- **Hardware/batch**: 1 node, GB200x4, DDP, batch 1/GPU — the *same
+  effective batch (4) as production*, so the tuned LR transfers directly.
+  (A single-GPU trial would change the effective batch and invalidate it.)
+- Everything else identical to `config_gga_gga+u_w96.yaml`: bf16-mixed, no
+  activation checkpointing, Adam betas (0.9, 0.99), offline W&B + sidecar.
+
+Per-trial wall-clock (from measured production step times): W96 ≈ 4 h,
+W64 ≈ 3 h, W32 ≈ 2–2.5 h.
+
+## Stage A — LR grid at wd = 0
+
+`lr ∈ {2.5e-4, 5e-4, 1e-3, 2e-3, 4e-3}` × `width ∈ {32, 64, 96}` = **15
+trials, ≈ 50 node-hours**. Factor-2 spacing matches the flatness of Adam LR
+basins; the 16× range is centered on the incumbent 1e-3 and wide enough for
+a ~3× shift across 32→96.
+
+Decision rule per width: `lr*` = argmin of final-epoch `val_loss` (sanity:
+best-epoch val and curve shape agree; a diverged/NaN trial is a top-edge
+signal, likely at 4e-3 for the wider models). **If a width's optimum lands
+on a grid edge, extend one more factor-2 point before moving on.** The old
+Optuna tier-1 result (lr ≈ 3.5e-3 best for W32, different data/precision)
+hints W32 may press the top edge.
+
+## Stage B — WD grid at lr*
+
+Weight decay uses **AdamW** (`optimizer: adamw`, added to
+`lightning.py`) — the incumbent plain Adam applies `weight_decay` as
+coupled L2, which gets rescaled per-parameter by the adaptive denominator
+and makes values neither interpretable nor comparable across widths. At
+wd = 0 the two optimizers are identical, so nothing about existing runs or
+Stage A changes.
+
+Per width, 5 trials:
+
+- `wd ∈ {1e-4, 1e-3, 1e-2}` at `lr*`
+- one cross term `(lr*/2, wd=1e-3)` to catch LR–WD interaction (in AdamW the
+  effective per-step decay is `lr·wd`, so a strong-WD winner may prefer a
+  lower LR)
+- a **verbatim rerun of the Stage-A anchor** `(lr*, wd=0)` — the run-to-run
+  noise bar that decides whether Stage B differences are real
+
+**15 trials, ≈ 50 node-hours.**
+
+Caveat to carry into analysis: on a 12K subset, overfitting appears ~9×
+earlier than on the full set, so the subset will overstate how much WD
+helps. Treat Stage B as establishing the *tolerance and trend* (does wd hurt
+below some threshold? does λ_eff = lr·wd stay constant across widths?), and
+confirm the absolute choice in Stage C. If a large wd wins on the subset,
+prefer the largest wd that is *neutral-or-better* rather than the argmin.
+
+## Stage C — full-scale confirmation
+
+One run per width at the tuned `(lr*, wd*)`: full 111K dataset, production
+config, **10–12 epoch budget** (~45 h W96, ~32 h W64 on GB200x4). Compare
+val_loss at matched epochs against the incumbent curves, which already exist
+at lr=1e-3/wd=0:
+
+- W64: `mp-gga-ggau-width` run `zz3oecp7` (16-epoch best 0.008398)
+- W96: run `z21di7sl` / `revived-energy-7` (ep10 0.008336 … ep27 0.006901)
+
+W32 has no incumbent in this campaign; run it only if the trend fit needs
+the third full-scale point.
+
+## Analysis
+
+- Fit `log lr*` vs `log width` → slope α; predict lr for W128/W160.
+  (Expected α between 0 and −1.) Same check on wd: is `lr*·wd*`
+  width-stable?
+- Re-examine the width ranking at matched epochs with tuned recipes — this
+  feeds the width-ablation writeup.
+- All sweep runs log to W&B project **`mp-gga-ggau-lrwd`**. Run names are
+  auto-generated `w{width}_{MMDD-HHMM}` (restart-segment convention), so
+  aggregate by `config.lr` / `config.weight_decay` /
+  `config.model.n_channels` — same pattern as
+  `scripts/review_width_ablation.py`.
+
+## Runbook
+
+```bash
+# 1. Get the production split files locally (small; either source works)
+rclone copy cw:mp/chg_datasets/functionals/gga/split_capped.json /tmp/sweep/gga/
+rclone copy cw:mp/chg_datasets/functionals/gga+u/split_capped.json /tmp/sweep/gga+u/
+#   or: scp della:/scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga/split_capped.json ...
+
+# 2. Generate the sweep splits (checked into the branch for provenance)
+uv run python scripts/coreweave/make_sweep_split.py \
+    /tmp/sweep/gga/split_capped.json data/MP/sweep_splits/gga_split_sweep12k.json --frac 0.1112
+uv run python scripts/coreweave/make_sweep_split.py \
+    "/tmp/sweep/gga+u/split_capped.json" "data/MP/sweep_splits/gga+u_split_sweep12k.json" --frac 0.1112
+
+# 3. Stage A configs (already generated in src/electrai/configs/MP/sweep_lrwd/)
+uv run python scripts/coreweave/gen_lrwd_sweep.py --stage a
+#    ... submit the printed submit.sh lines from a clean worktree; jobs run at
+#    batch priority and won't starve the live W128 run.
+
+# 4. After Stage A: regenerate for Stage B with the real per-width winners
+uv run python scripts/coreweave/gen_lrwd_sweep.py --stage b \
+    --best-lr 32=<lr*> 64=<lr*> 96=<lr*>
+```
+
+Each config filename stem doubles as the checkpoint namespace
+(`run_training.sh` derives ckpt dirs from the stem), so trials never
+cross-resume; the Stage-B noise rerun gets a `_rep2` stem for the same
+reason.
+
+## Known limitations
+
+- 24K steps ≈ 0.9 production epochs per trial: LR ranking at short horizon
+  with a completed cosine is a standard, usually-reliable proxy, but a
+  near-tie between adjacent LRs at a width should be broken toward the
+  *lower* LR (long-horizon optima drift down, not up).
+- WD conclusions from the subset overstate regularization benefit (see
+  Stage B caveat); Stage C is the arbiter.
+- The scheduler steps per epoch, so an 8-epoch cosine has coarse resolution;
+  all trials share it, so comparisons are fair.
+
+## W128 withheld-test-set evaluation (Aug 5–7, 2026)
+
+Checkpoint `w128_ckpt_epoch55_val0.005785.ckpt` (stage-C W128 at lr 2e-3,
+epoch 55) evaluated on the withheld `test` split of `split_capped.json` —
+never seen in training or validation. Della jobs 12057752 (GGA) + 12111587
+(GGA+U), fp32, single A100-80GB; config
+`src/electrai/configs/MP/config_gga_gga+u_w128_test_della.yaml`; per-sample
+CSVs in `/scratch/gpfs/ROSENGROUP/bb9080/w128_test_eval/results{,_padsu}/`.
+
+| Subset | n | mean NMAE | median | p90 | p99 | max | share <1% |
+|---|---|---|---|---|---|---|---|
+| GGA | 1700 | 0.480% | 0.373% | 0.900% | 1.842% | 2.95% | 92.0% |
+| GGA+U (PADS) | 524 | 0.483% | 0.397% | 0.857% | 1.794% | 3.36% | 94.5% |
+| **Combined** | **2224** | **0.481%** | 0.379% | 0.890% | 1.832% | 3.36% | 92.6% |
+
+**Combined test NMAE 0.481% is under the 0.5% ChargE3Net threshold** and
+consistent with the 0.579% val loss (val is bf16-on-GB200; test is fp32).
+
+Provenance notes uncovered during this eval:
+
+- The split files existed only in the buckets; they were staged from
+  `s3://oa-electrai` to `/scratch/gpfs/ROSENGROUP/bb9080/w128_test_eval/`
+  and their test/validation indices verified byte-identical to the
+  training-time splits (via `data/MP/sweep_splits/*_sweep12k.json`
+  pass-through).
+- **The training data's `gga+u` inputs are PADS, not SAD.** Della's
+  `functionals/gga+u_sad` and `gga+u_pads` share identical filelists and
+  labels; only inputs differ. Evaluating with SAD inputs gives ~10% NMAE
+  across the whole GGA+U test set (the model degrades SAD inputs below
+  their own 7–9% input error); PADS inputs give 0.48%. Probe: Della job
+  12111541. Any "GGA+U" result from the gga-ggau width campaign is a
+  PADS-input result.
+- bf16-mixed autocast OOMs this checkpoint on A100 (a ~75 GiB allocation
+  inside a decoder conv on a 108³ sample, Della job 12049722) — evaluate at
+  fp32 on Della, matching the throughput benchmark (job 12046200).
diff --git a/job_w128_test_eval.slurm b/job_w128_test_eval.slurm
new file mode 100644
index 00000000..1e24ee9c
--- /dev/null
+++ b/job_w128_test_eval.slurm
@@ -0,0 +1,52 @@
+#!/bin/bash
+#SBATCH --job-name=w128-test-eval
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=64G
+#SBATCH --gres=gpu:1
+#SBATCH --constraint=gpu80
+#SBATCH --time=04:00:00
+#SBATCH --output=/scratch/gpfs/ROSENGROUP/bb9080/logs/w128-test-eval-%j.out
+
+# Evaluate the W128 lr2e-3 checkpoint (epoch 55, val 0.005785) on the withheld
+# GGA + GGA+U test set (split_capped.json test keys: 1700 + 524 samples).
+# Config: src/electrai/configs/MP/config_gga_gga+u_w128_test_della.yaml
+
+module purge
+module load anaconda3/2025.6
+conda activate electrai
+module load proxy/default
+export PATH=$HOME/.local/bin:$PATH
+# Variable grid sizes fragment the caching allocator; expandable segments
+# lets large per-sample activations reuse freed reserved memory.
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+
+cd /scratch/gpfs/ROSENGROUP/bb9080/electrai
+
+EVAL=/scratch/gpfs/ROSENGROUP/bb9080/w128_test_eval
+CKPT_SRC=/scratch/gpfs/ROSENGROUP/bb9080/checkpoints/w128_ckpt_epoch55_val0.005785.ckpt
+
+mkdir -p ${EVAL}/ckpt ${EVAL}/results
+# test entrypoint expects <ckpt_path>/last.ckpt
+ln -sfn ${CKPT_SRC} ${EVAL}/ckpt/last.ckpt
+
+echo "=== W128 test-set eval ==="
+echo "Checkpoint: ${CKPT_SRC}"
+echo "Results:    ${EVAL}/results"
+echo "Start:      $(date)"
+echo ""
+
+srun uv run --no-sync python ./src/electrai/entrypoints/main.py test \
+    --config src/electrai/configs/MP/config_gga_gga+u_w128_test_della.yaml
+
+echo ""
+echo "End: $(date)"
+
+METRICS=${EVAL}/results/metrics.csv
+if [ ! -f ${METRICS} ]; then
+    echo "ERROR: metrics.csv not found at ${METRICS}"
+    exit 1
+fi
+N_ROWS=$(tail -n +2 ${METRICS} | wc -l)
+echo "metrics.csv rows: ${N_ROWS} (expected 2224)"
diff --git a/modal/benchmark.py b/modal/benchmark.py
new file mode 100644
index 00000000..bcf82777
--- /dev/null
+++ b/modal/benchmark.py
@@ -0,0 +1,278 @@
+"""Modal GPU benchmark for electrai.
+
+Runs a configurable training benchmark on Modal GPUs with data from the
+electrai-data Volume, logs metrics to WandB, and reports wall-clock time.
+
+Two data sources on the Volume:
+- "s3" (default): 205 samples from s3://openathena/electrai/ (≤25MB, matches EC2 benchmark)
+- "dataset_4": 2,885 samples from Globus/Della dataset_4 (large grids, needs A100 for prod config)
+
+Usage:
+    # Default: 50 samples from S3 set, 5 epochs, 32ch/16blk, L4 (matches EC2 benchmark)
+    modal run modal/benchmark.py
+
+    # Dataset_4 with tiny model on L4
+    modal run modal/benchmark.py --dataset dataset_4 --channels 8 --residual-blocks 2
+
+    # Production model on A100 with dataset_4
+    modal run modal/benchmark.py --dataset dataset_4 --gpu A100 --channels 32 --residual-blocks 16
+
+    # All S3 samples
+    modal run modal/benchmark.py --samples 0
+
+    # Quick smoke test
+    modal run modal/benchmark.py --samples 10 --epochs 2 --channels 8 --residual-blocks 2
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import modal
+
+ROOT = Path(__file__).parent.parent
+
+data_volume = modal.Volume.from_name("electrai-data")
+
+image = (
+    modal.Image.debian_slim(python_version="3.12")
+    .apt_install("git")
+    .pip_install_from_pyproject(
+        str(ROOT / "pyproject.toml"), optional_dependencies=["dev"]
+    )
+    .add_local_dir(str(ROOT / "src"), remote_path="/root/electrai/src", copy=True)
+    .add_local_dir(
+        str(ROOT / "scripts"), remote_path="/root/electrai/scripts", copy=True
+    )
+    .add_local_file(
+        str(ROOT / "pyproject.toml"),
+        remote_path="/root/electrai/pyproject.toml",
+        copy=True,
+    )
+    .run_commands("cd /root/electrai && pip install --no-deps -e .")
+)
+
+app = modal.App("electrai-benchmark", image=image)
+
+# Data roots on the Volume
+DATASETS = {
+    # Mirrors s3://openathena/electrai/ — same data as EC2 gpu-benchmark.yml
+    # Note: S3 uses input/ but RhoRead expects data/, so we symlink
+    "s3": {
+        "root": "/data/s3/openathena/electrai",
+        "input_dir": "input",  # S3 naming
+        "max_file_size": 25,  # matches EC2 benchmark default
+    },
+    # Globus/Della dataset_4 — 2,885 samples, large grids
+    "dataset_4": {
+        "root": "/data/mp/chg_datasets/dataset_4",
+        "input_dir": "data",  # Della naming
+        "max_file_size": 100,
+    },
+}
+
+
+@app.function(
+    gpu="L4",
+    volumes={"/data": data_volume},
+    secrets=[modal.Secret.from_name("wandb-credentials")],
+    timeout=7200,
+    retries=0,
+)
+def run_benchmark(
+    epochs: int = 5,
+    channels: int = 32,
+    residual_blocks: int = 16,
+    samples: int = 50,
+    max_file_size: float = -1,
+    seed: int = 42,
+    wandb_project: str = "elf-net-ci",
+    gpu_type: str = "L4",
+    dataset: str = "s3",
+    local_copy: bool = False,
+):
+    """Run benchmark and return results."""
+    import logging
+    import sys
+
+    log = logging.getLogger(__name__)
+    logging.basicConfig(level=logging.INFO)
+
+    sys.path.insert(0, "/root/electrai/scripts")
+    from e2e_train import run_training
+
+    ds = DATASETS[dataset]
+    ds_root = Path(ds["root"])
+    input_dir = ds["input_dir"]
+
+    # Use dataset-specific default if max_file_size not explicitly set
+    if max_file_size < 0:
+        max_file_size = ds["max_file_size"]
+
+    # Build filelist from files on disk (S3 set has no mp_filelist.txt)
+    data_dir = ds_root / input_dir
+    all_ids = sorted(p.stem for p in data_dir.glob("*.CHGCAR"))
+    log.info("Dataset %r: %d total samples in %s", dataset, len(all_ids), data_dir)
+
+    # Filter by file size (avoid OOM on large grids)
+    if max_file_size > 0:
+        max_bytes = int(max_file_size * 1024 * 1024)
+        eligible = [
+            sid
+            for sid in all_ids
+            if (data_dir / f"{sid}.CHGCAR").stat().st_size <= max_bytes
+        ]
+        log.info(
+            "File size filter: %d/%d eligible (<=%.0fMB)",
+            len(eligible),
+            len(all_ids),
+            max_file_size,
+        )
+    else:
+        eligible = all_ids
+
+    # Select samples: first N (lexicographic, matching s3_sync.py behavior)
+    if 0 < samples < len(eligible):
+        subset = eligible[:samples]
+        log.info("Selected first %d/%d eligible samples", samples, len(eligible))
+    else:
+        subset = eligible
+        samples = len(subset)
+        log.info("Using all %d eligible samples", samples)
+
+    if not subset:
+        raise ValueError(
+            f"No eligible samples (dataset={dataset}, total={len(all_ids)}, "
+            f"max_file_size={max_file_size}MB)"
+        )
+
+    data_root = "/tmp/benchmark_data"
+    Path(data_root).mkdir(parents=True, exist_ok=True)
+
+    if local_copy:
+        # Copy selected samples to local disk (Volume I/O is ~15x slower)
+        import shutil
+
+        local_data = Path(data_root) / "data"
+        local_label = Path(data_root) / "label"
+        local_data.mkdir(parents=True, exist_ok=True)
+        local_label.mkdir(parents=True, exist_ok=True)
+        for sid in subset:
+            shutil.copy2(
+                ds_root / input_dir / f"{sid}.CHGCAR", local_data / f"{sid}.CHGCAR"
+            )
+            shutil.copy2(
+                ds_root / "label" / f"{sid}.CHGCAR", local_label / f"{sid}.CHGCAR"
+            )
+        log.info("Copied %d samples to local disk", len(subset))
+    else:
+        # Symlink to volume (slower I/O but no copy overhead for large datasets)
+        data_link = Path(data_root) / "data"
+        if not data_link.exists():
+            data_link.symlink_to(ds_root / input_dir)
+        label_link = Path(data_root) / "label"
+        if not label_link.exists():
+            label_link.symlink_to(ds_root / "label")
+        log.info("Using volume directly (no local copy)")
+
+    Path(data_root, "mp_filelist.txt").write_text("\n".join(subset) + "\n")
+
+    # Always use gradient checkpointing (32ch/16blk needs it even for ≤25MB files)
+    use_grad_ckpt = True
+
+    log.info(
+        "Benchmark: gpu=%s, epochs=%d, channels=%d, blocks=%d, samples=%d, "
+        "dataset=%s, grad_ckpt=%s",
+        gpu_type,
+        epochs,
+        channels,
+        residual_blocks,
+        samples,
+        dataset,
+        use_grad_ckpt,
+    )
+
+    # Set WandB run name and env vars for platform tagging
+    import os
+    import time
+
+    os.environ["INSTANCE_TYPE"] = f"modal-{gpu_type}"
+    # Set workflow-like name so WandB run name is descriptive.
+    # GHA sets GITHUB_RUN_NUMBER; for local runs, use timestamp.
+    os.environ["GITHUB_WORKFLOW"] = "Modal Benchmark"
+    if "GITHUB_RUN_NUMBER" not in os.environ:
+        os.environ["GITHUB_RUN_NUMBER"] = time.strftime("%y%m%d-%H%M")
+
+    results = run_training(
+        channels=channels,
+        residual_blocks=residual_blocks,
+        epochs=epochs,
+        seed=seed,
+        gpu=True,
+        gradient_checkpoint=use_grad_ckpt,
+        data_root=data_root,
+        max_file_size=0,  # already filtered above
+        wandb_project=wandb_project,
+        verbose=True,
+    )
+
+    log.info("val_loss: %.6f", results["final_val_loss"])
+    log.info("train_loss: %.6f", results["final_train_loss"])
+    log.info("Wallclock: %.1fs", results["wallclock_s"])
+    log.info("GPU: %s (Modal)", gpu_type)
+
+    return results
+
+
+@app.local_entrypoint()
+def main(
+    gpu: str = "L4",
+    epochs: int = 5,
+    channels: int = 32,
+    residual_blocks: int = 16,
+    samples: int = 50,
+    max_file_size: float = -1,
+    seed: int = 42,
+    wandb_project: str = "elf-net-ci",
+    dataset: str = "s3",
+    local_copy: bool = False,
+):
+    import logging
+
+    logging.basicConfig(level=logging.INFO)
+    log = logging.getLogger(__name__)
+
+    benchmark_fn = run_benchmark
+    if gpu != "L4":
+        benchmark_fn = run_benchmark.with_options(gpu=gpu)
+
+    results = benchmark_fn.remote(
+        epochs=epochs,
+        channels=channels,
+        residual_blocks=residual_blocks,
+        samples=samples,
+        max_file_size=max_file_size,
+        seed=seed,
+        wandb_project=wandb_project,
+        gpu_type=gpu,
+        dataset=dataset,
+        local_copy=local_copy,
+    )
+
+    log.info(
+        "Benchmark complete: val_loss=%.6f, wallclock=%.1fs on %s",
+        results["final_val_loss"],
+        results["wallclock_s"],
+        gpu,
+    )
+
+    # Print parseable output for GHA summary
+    wandb_url = results.get("wandb_run_url") or ""
+    print(f"BENCHMARK_VAL_LOSS={results['final_val_loss']:.6f}")  # noqa: T201
+    print(f"BENCHMARK_TRAIN_LOSS={results['final_train_loss']:.6f}")  # noqa: T201
+    print(f"BENCHMARK_WALLCLOCK={results['wallclock_s']:.0f}")  # noqa: T201
+    print(f"BENCHMARK_GPU={gpu}")  # noqa: T201
+    print(f"BENCHMARK_DATASET={dataset}")  # noqa: T201
+    print(f"BENCHMARK_SAMPLES={samples}")  # noqa: T201
+    if wandb_url:
+        print(f"BENCHMARK_WANDB_URL={wandb_url}")  # noqa: T201
diff --git a/modal/ci.py b/modal/ci.py
new file mode 100644
index 00000000..0cd9da96
--- /dev/null
+++ b/modal/ci.py
@@ -0,0 +1,93 @@
+"""Modal GPU CI for electrai e2e training test."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import modal
+
+ROOT = Path(__file__).parent.parent
+
+# Dependencies read from pyproject.toml (shared with train.py, populate_volume.py)
+image = (
+    modal.Image.debian_slim(python_version="3.12")
+    .apt_install("git")
+    .pip_install_from_pyproject(
+        str(ROOT / "pyproject.toml"), optional_dependencies=["dev"]
+    )
+    .add_local_dir(str(ROOT / "src"), remote_path="/root/electrai/src", copy=True)
+    .add_local_dir(
+        str(ROOT / "scripts"), remote_path="/root/electrai/scripts", copy=True
+    )
+    .add_local_dir(str(ROOT / "tests"), remote_path="/root/electrai/tests", copy=True)
+    .add_local_dir(str(ROOT / "data"), remote_path="/root/electrai/data", copy=True)
+    .add_local_file(
+        str(ROOT / "pyproject.toml"),
+        remote_path="/root/electrai/pyproject.toml",
+        copy=True,
+    )
+    .run_commands("cd /root/electrai && pip install --no-deps -e .")
+)
+
+app = modal.App("electrai-ci", image=image)
+
+
+@app.function(gpu="L4", timeout=600, retries=0)
+def run_e2e_test(epochs: int = 5, check: bool = True):
+    """Run e2e training test on GPU."""
+    import json
+    import logging
+    import sys
+
+    log = logging.getLogger(__name__)
+
+    sys.path.insert(0, "/root/electrai/scripts")
+    from e2e_train import run_training
+
+    results = run_training(epochs=epochs, gpu=True, verbose=True)
+    log.info("Platform: %s", results["platform"])
+    log.info("Final val_loss: %.6f", results["final_val_loss"])
+    if results["final_train_loss"] is not None:
+        log.info("Final train_loss: %.6f", results["final_train_loss"])
+    log.info("Wallclock: %.1fs", results["wallclock_s"])
+
+    if check:
+        expected_file = Path("/root/electrai/tests/expected_values.json")
+        expected_values = json.loads(expected_file.read_text())
+        platform = results["platform"]
+        if platform not in expected_values:
+            raise ValueError(
+                f"No expected values for platform {platform!r}, "
+                f"available: {list(expected_values.keys())}"
+            )
+        expected = expected_values[platform]
+        if expected.get("final_val_loss") is None:
+            raise ValueError(f"Expected values for {platform!r} are null")
+        expected_val_loss = expected["final_val_loss"]
+        diff = abs(results["final_val_loss"] - expected_val_loss)
+        tolerance = 0.001
+        if diff > tolerance:
+            raise AssertionError(
+                f"val_loss {results['final_val_loss']:.6f} differs from expected "
+                f"{expected_val_loss:.6f} by {diff:.6f} (tolerance: {tolerance})"
+            )
+        log.info(
+            "PASS: val_loss matches expected within tolerance (%.6f <= %f)",
+            diff,
+            tolerance,
+        )
+
+    return results
+
+
+@app.local_entrypoint()
+def main(epochs: int = 5, check: bool = True):
+    import logging
+
+    logging.basicConfig(level=logging.INFO)
+    results = run_e2e_test.remote(epochs=epochs, check=check)
+    logging.getLogger(__name__).info(
+        "Results: val_loss=%.6f in %.1fs",
+        results["final_val_loss"],
+        results["wallclock_s"],
+    )
diff --git a/modal/globus_load.py b/modal/globus_load.py
new file mode 100644
index 00000000..2c495df7
--- /dev/null
+++ b/modal/globus_load.py
@@ -0,0 +1,103 @@
+"""Load della data onto the electrai-data Modal Volume via Globus.
+
+Runs Globus Connect Personal (GCP) inside a CPU-only Modal container with the
+`electrai-data` Volume mounted at /data, registered as a personal endpoint using
+a setup key generated in the Globus web UI. GCP makes only outbound connections,
+so it works behind Modal's NAT. You then initiate the della -> this endpoint
+transfer from the Globus file manager, writing into /data; the Volume is
+committed periodically and on exit.
+
+Steps:
+    1. In the Globus web UI (https://app.globus.org), create a Globus Connect
+       Personal collection and copy its setup key.
+    2. Start the endpoint host (runs up to ~24h):
+           modal run modal/globus_load.py --setup-key "<KEY>"
+    3. In the Globus file manager (https://app.globus.org/file-manager) transfer:
+           source: ROSENGROUP share, .../mp/chg_datasets/functionals/{gga,gga+u}
+           dest:   this endpoint, path /data/mp/chg_datasets/functionals/
+       The data/label entries are symlinks into rho_gga{,+u}; confirm Globus
+       follows them (lands real .zarr stores). If it copies symlinks instead,
+       transfer rho_gga and rho_gga+u and adjust the config roots.
+    4. When the Globus task shows complete, stop this run (ctrl-C / `modal app
+       stop electrai-globus-load`).
+"""
+
+from __future__ import annotations
+
+import modal
+
+app = modal.App("electrai-globus-load")
+
+data_volume = modal.Volume.from_name("electrai-data", create_if_missing=True)
+
+VOLUME_ROOT = "/data"
+GCP_URL = (
+    "https://downloads.globus.org/globus-connect-personal/linux/stable/"
+    "globusconnectpersonal-latest.tgz"
+)
+
+image = (
+    modal.Image.debian_slim()
+    .apt_install("wget", "ca-certificates", "tar")
+    .run_commands(
+        "cd /opt && wget -q "
+        f"{GCP_URL}"
+        " -O gcp.tgz && tar xzf gcp.tgz && rm gcp.tgz "
+        "&& mv globusconnectpersonal-* globusconnectpersonal"
+    )
+)
+
+
+@app.function(
+    image=image,
+    volumes={VOLUME_ROOT: data_volume},
+    timeout=86400,  # 24h max: one transfer window
+    cpu=4.0,
+)
+def host_endpoint(setup_key: str, run_hours: float = 23.5, commit_every_s: int = 300):
+    import logging
+    import subprocess
+    import time
+    from pathlib import Path
+
+    log = logging.getLogger(__name__)
+    logging.basicConfig(level=logging.INFO)
+
+    gcp = "/opt/globusconnectpersonal/globusconnectpersonal"
+
+    # Non-interactive endpoint registration using the web-UI setup key.
+    log.info("Registering Globus Connect Personal endpoint...")
+    subprocess.run([gcp, "-setup", "--setup-key", setup_key], check=True)
+
+    # Grant read/write to the mounted Volume (path,writable,shareable).
+    cfg_dir = Path.home() / ".globusonline" / "lta"
+    cfg_dir.mkdir(parents=True, exist_ok=True)
+    (cfg_dir / "config-paths").write_text(f"{VOLUME_ROOT},1,0\n")
+    log.info("config-paths -> %s,1,0", VOLUME_ROOT)
+
+    # Start GCP in the background (outbound-only).
+    log.info("Starting Globus Connect Personal...")
+    proc = subprocess.Popen([gcp, "-start"])
+    log.info(
+        "Endpoint up. Initiate the della -> %s transfer in the Globus web UI now.",
+        VOLUME_ROOT,
+    )
+
+    deadline = time.time() + run_hours * 3600
+    try:
+        while time.time() < deadline:
+            time.sleep(commit_every_s)
+            data_volume.commit()  # persist whatever has landed so far
+            status = subprocess.run(
+                [gcp, "-status"], capture_output=True, text=True, check=False
+            )
+            log.info("globus status: %s", (status.stdout or status.stderr).strip())
+    finally:
+        proc.terminate()
+        data_volume.commit()
+        log.info("Stopped GCP and committed Volume.")
+
+
+@app.local_entrypoint()
+def main(setup_key: str, run_hours: float = 23.5):
+    host_endpoint.remote(setup_key=setup_key, run_hours=run_hours)
diff --git a/modal/populate_volume.py b/modal/populate_volume.py
new file mode 100644
index 00000000..d3d5ce71
--- /dev/null
+++ b/modal/populate_volume.py
@@ -0,0 +1,218 @@
+"""Populate Modal Volume with training data from S3, packing zarr stores.
+
+Modal Volumes are capped at ~500K inodes per volume. Our zarr v3 layout has
+~8 inodes per `<id>.zarr/` (3 files + 5 directory entries) times ~226K stores
+is ~1.8M inodes — well over the cap.
+
+To fit, each S3-side `<prefix>/.../<id>.zarr/{zarr.json,
+charge_density_total/zarr.json, charge_density_total/c/0/0/0}` is packed into
+a single `<prefix>/.../<id>.zarr.zip` on the Volume (zarr's `ZipStore` reads
+it as a normal store). Non-zarr S3 keys (filelists, split files) pass through
+unchanged. Net: ~226K + a few standalone files ≈ ~230K inodes — well under
+the cap.
+
+The matching loader change (auto-detect `.zarr.zip` via ZipStore) lives in
+`src/electrai/dataloader/utils.py:load_zarr`.
+"""
+
+from __future__ import annotations
+
+import modal
+
+app = modal.App("electrai-populate")
+volume = modal.Volume.from_name("electrai-data", create_if_missing=True)
+
+
+WORKERS = 32
+BATCH = 2000  # commit every BATCH item completions (stores or standalones)
+
+
+@app.function(
+    image=modal.Image.debian_slim(python_version="3.12").pip_install("boto3"),
+    volumes={"/data": volume},
+    secrets=[modal.Secret.from_name("oa-electrai-read")],
+    timeout=86400,  # 24h
+    retries=0,
+    cpu=4.0,
+    memory=4096,
+)
+def sync_s3(
+    bucket: str = "oa-electrai",
+    prefix: str = "mp/chg_datasets",
+    dest: str = "/data/mp/chg_datasets",
+    wipe_first: bool = False,
+):
+    """Sync S3 -> Volume, packing each `<id>.zarr/` into `<id>.zarr.zip`.
+
+    If `wipe_first=True`, recursively removes `dest` before populating to free
+    inodes — required when re-populating after a previous unpacked attempt.
+    """
+    import logging
+    import shutil
+    import zipfile
+    from concurrent.futures import ThreadPoolExecutor, as_completed
+    from pathlib import Path
+
+    import boto3
+    from botocore.config import Config
+
+    log = logging.getLogger(__name__)
+    logging.basicConfig(level=logging.INFO)
+
+    s3 = boto3.client(
+        "s3",
+        config=Config(max_pool_connections=WORKERS + 8, retries={"max_attempts": 5}),
+    )
+
+    if wipe_first:
+        target = Path(dest)
+        if target.exists():
+            log.info("Wiping %s (this can take a few minutes for many inodes)…", target)
+            shutil.rmtree(target)
+            volume.commit()
+            log.info("Wipe complete; committed.")
+
+    # ---- list S3 ---------------------------------------------------------
+    paginator = s3.get_paginator("list_objects_v2")
+    objects = [
+        obj
+        for page in paginator.paginate(Bucket=bucket, Prefix=prefix)
+        for obj in page.get("Contents", [])
+    ]
+    total_bytes = sum(o["Size"] for o in objects)
+    log.info("Listed %d S3 objects, %.1f GiB", len(objects), total_bytes / (1024**3))
+
+    # ---- group by .zarr/ store ------------------------------------------
+    # Each store-key is the S3 prefix up to and including `.zarr`. Inner name
+    # within the zip is the remainder after `.zarr/`.
+    stores: dict[str, list[tuple[dict, str]]] = {}
+    standalones: list[dict] = []
+    for obj in objects:
+        key = obj["Key"]
+        if ".zarr/" in key:
+            store_key, inner = key.split(".zarr/", 1)
+            store_key = store_key + ".zarr"
+            stores.setdefault(store_key, []).append((obj, inner))
+        else:
+            standalones.append(obj)
+    log.info(
+        "Grouping: %d zarr stores to pack, %d standalone files",
+        len(stores),
+        len(standalones),
+    )
+
+    # ---- error log on volume --------------------------------------------
+    err_log_path = Path(dest).parent / "_populate_errors.log"
+    err_log_path.parent.mkdir(parents=True, exist_ok=True)
+    err_log = err_log_path.open("a")
+    err_log.write(f"\n=== run start {bucket}/{prefix} (packed) ===\n")
+    err_log.flush()
+
+    # ---- workers --------------------------------------------------------
+    def pack_store(store_key: str, parts: list[tuple[dict, str]]):
+        rel = store_key[len(prefix) :].lstrip("/")
+        zip_path = Path(dest) / (rel + ".zip")
+        if zip_path.exists() and zip_path.stat().st_size > 0:
+            return ("skip", None)
+        try:
+            zip_path.parent.mkdir(parents=True, exist_ok=True)
+            tmp = zip_path.with_name(zip_path.name + ".tmp")
+            with zipfile.ZipFile(tmp, "w", zipfile.ZIP_STORED) as z:
+                for obj, inner in parts:
+                    body = s3.get_object(Bucket=bucket, Key=obj["Key"])["Body"].read()
+                    z.writestr(inner, body)
+            tmp.rename(zip_path)
+            return ("dl", None)
+        except Exception as e:
+            return ("err", f"{store_key}: {e!r}")
+
+    def copy_standalone(obj: dict):
+        key = obj["Key"]
+        rel = key[len(prefix) :].lstrip("/")
+        local_path = Path(dest) / rel
+        if local_path.exists() and local_path.stat().st_size == obj["Size"]:
+            return ("skip", None)
+        try:
+            local_path.parent.mkdir(parents=True, exist_ok=True)
+            tmp = local_path.with_name(local_path.name + ".tmp")
+            with tmp.open("wb") as f:
+                s3.download_fileobj(bucket, key, f)
+            tmp.rename(local_path)
+            return ("dl", None)
+        except Exception as e:
+            return ("err", f"{key}: {e!r}")
+
+    # ---- process: stores first (the bulk), then standalones --------------
+    work: list[tuple[str, object]] = [("store", item) for item in stores.items()] + [
+        ("standalone", obj) for obj in standalones
+    ]
+    total = len(work)
+    log.info("Total work items: %d", total)
+
+    packed = skipped = errors = seen = 0
+    try:
+        with ThreadPoolExecutor(max_workers=WORKERS) as pool:
+            for start in range(0, total, BATCH):
+                batch = work[start : start + BATCH]
+                futures = []
+                for kind, payload in batch:
+                    if kind == "store":
+                        store_key, parts = payload
+                        futures.append(pool.submit(pack_store, store_key, parts))
+                    else:
+                        futures.append(pool.submit(copy_standalone, payload))
+                for future in as_completed(futures):
+                    res, msg = future.result()
+                    seen += 1
+                    if res == "dl":
+                        # pack_store returns "dl" too; we count both as "packed"
+                        # for stores and "copied" for standalones; cheaper to
+                        # just bucket together.
+                        packed += 1
+                    elif res == "skip":
+                        skipped += 1
+                    else:
+                        errors += 1
+                        err_log.write(msg + "\n")
+                        if errors <= 50:
+                            log.warning("error: %s", msg)
+                err_log.flush()
+                volume.commit()
+                log.info(
+                    "Progress: %d/%d (done %d, skipped %d, errors %d) — committed",
+                    seen,
+                    total,
+                    packed,
+                    skipped,
+                    errors,
+                )
+    finally:
+        err_log.close()
+
+    log.info("Done: %d packed/copied, %d skipped, %d errors", packed, skipped, errors)
+    volume.commit()
+
+
+@app.local_entrypoint()
+def main(
+    bucket: str = "oa-electrai",
+    prefix: str = "mp/chg_datasets",
+    dest: str = "/data/mp/chg_datasets",
+    wipe_first: bool = False,
+):
+    """Fire-and-forget: spawn sync_s3 and return so the remote run survives any
+    local CLI disconnect. Pair with `modal run --detach`; monitor via the Modal
+    web UI or `modal app logs <app-id>`.
+
+    Pass `--wipe-first` on the first packed run to clear any unpacked partial
+    state from previous attempts (frees inodes).
+    """
+    import logging
+
+    logging.basicConfig(level=logging.INFO)
+    fc = sync_s3.spawn(bucket=bucket, prefix=prefix, dest=dest, wipe_first=wipe_first)
+    log = logging.getLogger(__name__)
+    log.info(
+        "Spawned sync_s3 FunctionCall id=%s (wipe_first=%s)", fc.object_id, wipe_first
+    )
+    log.info("Monitor at https://modal.com/apps (look for electrai-populate)")
diff --git a/modal/prep_volume.py b/modal/prep_volume.py
new file mode 100644
index 00000000..7888ecd3
--- /dev/null
+++ b/modal/prep_volume.py
@@ -0,0 +1,105 @@
+"""Prepare the electrai-data Volume after a Globus transfer.
+
+The Globus transfer lands the real data under .../rho_gga and .../rho_gga+u plus
+the small per-functional metadata (mp_filelist.txt, split.json). This helper:
+
+  1. (Re)creates the data/label symlinks the loader expects under
+     functionals/{gga,gga+u}/ pointing at the matching rho_* dirs.
+  2. Builds subset smoke filelists (mp_filelist_smoke.txt) used by
+     config_gga_gga+u_f32_smoke.yaml.
+  3. Sanity-checks that the first id of each filelist resolves to a real .zarr.
+  4. Commits the Volume explicitly (no reliance on shell auto-commit).
+
+Idempotent: if Globus already landed real data/label dirs (i.e. it followed the
+symlinks), those are left untouched.
+
+Usage:
+    modal run modal/prep_volume.py            # 200-sample smoke filelists
+    modal run modal/prep_volume.py --smoke-n 50
+"""
+
+from __future__ import annotations
+
+import modal
+
+app = modal.App("electrai-prep")
+
+data_volume = modal.Volume.from_name("electrai-data", create_if_missing=True)
+
+VOLUME_ROOT = "/data"
+# functional dir name -> real data dir name (both under mp/chg_datasets/)
+FUNCTIONALS = {"gga": "rho_gga", "gga+u": "rho_gga+u"}
+
+image = modal.Image.debian_slim()
+
+
+@app.function(image=image, volumes={VOLUME_ROOT: data_volume}, timeout=900)
+def prep(smoke_n: int = 200):
+    import logging
+    from pathlib import Path
+
+    log = logging.getLogger(__name__)
+    logging.basicConfig(level=logging.INFO)
+
+    base = Path(VOLUME_ROOT) / "mp" / "chg_datasets"
+
+    for func, rho in FUNCTIONALS.items():
+        fdir = base / "functionals" / func
+        filelist = fdir / "mp_filelist.txt"
+        if not filelist.exists():
+            raise FileNotFoundError(
+                f"{filelist} missing — transfer functionals/{func}/mp_filelist.txt "
+                "(and split.json) before running prep."
+            )
+
+        # 1. data/label symlinks -> ../../rho_*/{data,label}
+        for sub in ("data", "label"):
+            link = fdir / sub
+            target = Path("../..") / rho / sub  # relative to fdir
+            real = base / rho / sub
+            if not real.exists():
+                raise FileNotFoundError(
+                    f"{real} missing — transfer the {rho} data dir before running prep."
+                )
+            if link.is_symlink():
+                link.unlink()
+                link.symlink_to(target)
+                log.info("relinked %s -> %s", link, target)
+            elif link.is_dir():
+                log.info(
+                    "%s is a real dir (Globus followed symlinks); leaving as-is", link
+                )
+            else:
+                link.symlink_to(target)
+                log.info("linked %s -> %s", link, target)
+
+        # 2. smoke filelist
+        ids = filelist.read_text().splitlines()
+        smoke = fdir / "mp_filelist_smoke.txt"
+        smoke.write_text("\n".join(ids[:smoke_n]) + "\n")
+        log.info("wrote %s (%d ids)", smoke, min(smoke_n, len(ids)))
+
+        # 3. sanity check: first id resolves to a `.zarr.zip` (packed) or
+        # `.zarr/` (unpacked) store under data/.
+        first = ids[0]
+        store_dir = fdir / "data" / f"{first}.zarr"
+        store_zip = fdir / "data" / f"{first}.zarr.zip"
+        if not (store_zip.exists() or store_dir.exists()):
+            raise FileNotFoundError(
+                f"Neither {store_zip} nor {store_dir} found — "
+                "data/ symlink or transfer is incomplete."
+            )
+        log.info(
+            "OK: %s resolves (%d total ids)",
+            store_zip if store_zip.exists() else store_dir,
+            len(ids),
+        )
+
+    # 4. persist
+    data_volume.commit()
+    log.info("Volume committed.")
+
+
+@app.local_entrypoint()
+def main(smoke_n: int = 200):
+    prep.remote(smoke_n=smoke_n)
diff --git a/modal/train.py b/modal/train.py
new file mode 100644
index 00000000..68e88800
--- /dev/null
+++ b/modal/train.py
@@ -0,0 +1,206 @@
+"""Modal training entrypoint for electrai.
+
+Run real training experiments on Modal GPUs with data from the
+electrai-data Volume and checkpoints persisted to electrai-checkpoints.
+
+Handles both single-dataset and multi-dataset (`datasets:` list) configs.
+Della absolute paths in the config are remapped onto the Volume mount, and
+checkpoints are namespaced by `run_name` so the entrypoint auto-resumes from
+`<ckpt>/last.ckpt` across successive (24h-capped) invocations.
+
+Usage:
+    # Multi-dataset experiment config on a single A100 (80GB)
+    modal run modal/train.py --config src/electrai/configs/MP/config_gga_gga+u_f32.yaml
+
+    # Short subset smoke run
+    modal run modal/train.py --config src/electrai/configs/MP/config_gga_gga+u_f32_smoke.yaml
+
+    # Pick a different GPU (e.g. multi-GPU DDP once throughput is known)
+    modal run modal/train.py --config <cfg> --gpu "A100-80GB:8"
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import modal
+
+ROOT = Path(__file__).parent.parent
+
+# Persistent volumes
+data_volume = modal.Volume.from_name("electrai-data", create_if_missing=True)
+ckpt_volume = modal.Volume.from_name("electrai-checkpoints", create_if_missing=True)
+
+# Dependencies read from pyproject.toml (shared with ci.py, populate_volume.py)
+image = (
+    modal.Image.debian_slim(python_version="3.12")
+    .apt_install("git")
+    .pip_install_from_pyproject(
+        str(ROOT / "pyproject.toml"), optional_dependencies=["dev"]
+    )
+    .add_local_dir(str(ROOT / "src"), remote_path="/root/electrai/src", copy=True)
+    .add_local_dir(
+        str(ROOT / "examples"), remote_path="/root/electrai/examples", copy=True
+    )
+    .add_local_file(
+        str(ROOT / "pyproject.toml"),
+        remote_path="/root/electrai/pyproject.toml",
+        copy=True,
+    )
+    .run_commands("cd /root/electrai && pip install --no-deps -e .")
+)
+
+app = modal.App("electrai-train", image=image)
+
+# Della ROSENGROUP share root and where it is mounted on Modal. Config paths are
+# written as della absolute paths; they are rewritten onto the Volume here.
+DELLA_SHARE_PREFIX = "/scratch/gpfs/ROSENGROUP/common/globus_share_OA"
+VOLUME_ROOT = "/data"
+CKPT_ROOT = "/checkpoints"
+DEFAULT_GPU = "A100-80GB"
+
+
+def _remap_path(path: str | None) -> str | None:
+    """Rewrite a della share path onto the Volume mount. Idempotent."""
+    if not path:
+        return path
+    if path.startswith(VOLUME_ROOT):
+        return path
+    if path.startswith(DELLA_SHARE_PREFIX):
+        rel = path[len(DELLA_SHARE_PREFIX) :].lstrip("/")
+        return str(Path(VOLUME_ROOT) / rel)
+    return path
+
+
+def _remap_data_paths(cfg: dict) -> dict:
+    """Remap every dataset root/split_file in a config onto the Volume."""
+    data = cfg.get("data", {})
+    for ds in data.get("datasets") or []:
+        ds["root"] = _remap_path(ds.get("root"))
+        ds["split_file"] = _remap_path(ds.get("split_file"))
+    if data.get("root"):
+        data["root"] = _remap_path(data["root"])
+    if data.get("split_file"):
+        data["split_file"] = _remap_path(data["split_file"])
+    return cfg
+
+
+def _dataset_roots(cfg: dict) -> list[str]:
+    data = cfg.get("data", {})
+    if data.get("datasets"):
+        return [ds["root"] for ds in data["datasets"]]
+    if data.get("root"):
+        return [data["root"]]
+    return []
+
+
+@app.function(
+    gpu=DEFAULT_GPU,
+    volumes={VOLUME_ROOT: data_volume, CKPT_ROOT: ckpt_volume},
+    secrets=[modal.Secret.from_name("wandb-credentials")],
+    timeout=86400,  # 24h (Modal max); resume across runs via <ckpt>/last.ckpt
+    retries=0,
+)
+def train(config_json: str, gpu_type: str = DEFAULT_GPU):
+    """Run training with the given config (as JSON, converted to YAML remotely)."""
+    import json
+    import logging
+    import subprocess
+    import sys
+
+    import yaml
+
+    log = logging.getLogger(__name__)
+    logging.basicConfig(level=logging.INFO)
+
+    cfg = json.loads(config_json)
+
+    config_path = Path("/tmp/config.yaml")
+    with config_path.open("w") as f:
+        yaml.dump(cfg, f, default_flow_style=False)
+
+    log.info("GPU: %s", gpu_type)
+    log.info("Checkpoint dir: %s", cfg.get("ckpt_path"))
+
+    # Verify each dataset filelist exists on the Volume and log sample counts.
+    total = 0
+    for root in _dataset_roots(cfg):
+        fp = Path(root)
+        if not fp.exists():
+            raise FileNotFoundError(f"Filelist not found on Volume: {fp}")
+        n = len(fp.read_text().strip().splitlines())
+        total += n
+        log.info("Dataset %s: %d samples", root, n)
+    log.info("Total samples: %d", total)
+
+    # Auto-resume: the entrypoint loads <ckpt_path>/last.ckpt if present.
+    ckpt_dir = Path(cfg.get("ckpt_path", CKPT_ROOT))
+    ckpt_dir.mkdir(parents=True, exist_ok=True)
+    last_ckpt = ckpt_dir / "last.ckpt"
+    if last_ckpt.exists():
+        log.info("Resuming from %s", last_ckpt)
+    else:
+        log.info("No checkpoint found, starting from scratch")
+
+    result = subprocess.run(
+        [
+            sys.executable,
+            "-m",
+            "electrai.entrypoints.main",
+            "train",
+            "--config",
+            str(config_path),
+        ],
+        cwd="/root/electrai",
+        check=False,
+    )
+
+    # Persist checkpoints
+    ckpt_volume.commit()
+
+    if result.returncode != 0:
+        raise RuntimeError(f"Training failed with exit code {result.returncode}")
+
+    log.info("Training complete. Checkpoints saved to electrai-checkpoints volume.")
+
+
+@app.local_entrypoint()
+def main(config: str, gpu: str = DEFAULT_GPU):
+    import json
+    import logging
+    import subprocess
+
+    logging.basicConfig(level=logging.INFO)
+    log = logging.getLogger(__name__)
+
+    # Load the YAML locally without importing electrai (yaml may not be in the
+    # modal CLI env). Pass the path as argv to avoid shell/string interpolation.
+    loaded = subprocess.run(
+        [
+            "python3",
+            "-c",
+            "import sys, yaml, json; "
+            "print(json.dumps(yaml.safe_load(open(sys.argv[1]))))",
+            config,
+        ],
+        capture_output=True,
+        text=True,
+        check=True,
+    )
+    cfg = json.loads(loaded.stdout)
+
+    # Remap della paths onto the Volume and pin checkpoints to the ckpt Volume,
+    # namespaced by run so multiple experiments don't collide on last.ckpt.
+    cfg = _remap_data_paths(cfg)
+    run_name = cfg.get("run_name", "run")
+    cfg["ckpt_path"] = f"{CKPT_ROOT}/{run_name}"
+
+    config_json = json.dumps(cfg, indent=2)
+    log.info("Config:\n%s", config_json)
+
+    # Fire-and-forget so multi-day training survives any local CLI disconnect.
+    # Pair with `modal run --detach`; monitor via `modal app logs <app-id>` or
+    # the Modal web UI. See [[modal-long-running-detach-spawn]].
+    fc = train.with_options(gpu=gpu).spawn(config_json=config_json, gpu_type=gpu)
+    log.info("Spawned train FunctionCall id=%s on %s", fc.object_id, gpu)
+    log.info("Monitor at https://modal.com/apps (look for electrai-train)")
diff --git a/pyproject.toml b/pyproject.toml
index ca0865c6..aeddaa2c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -62,6 +62,17 @@ repository = "https://github.com/Quantum-Accelerators/electrai"
 documentation = "https://quantum-accelerators.github.io/electrai/"
 changelog = "https://github.com/Quantum-Accelerators/electrai/blob/main/CHANGELOG.md"
 
+# PyPI's linux-aarch64 torch wheels are CPU-only; Grace/GB200 nodes need the
+# SBSA CUDA builds (+cu128) from the PyTorch index.
+[tool.uv.sources]
+torch = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux' and platform_machine == 'aarch64'" }]
+torchvision = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux' and platform_machine == 'aarch64'" }]
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true
+
 [tool.setuptools.package-data]
 electrai = ["py.typed"]
 
@@ -183,6 +194,9 @@ skip-magic-trailing-comma = true
 "scripts/benchmark_*.py" = [
     "T201", # print used (CLI script logging)
 ]
+"scripts/coreweave/*.py" = [
+    "T201", # print used (CLI script logging)
+]
 "inference_benchmark/charge3net/**" = [
     # Reference copies of files that live in the charge3net fork; not
     # subject to electrai's lint policy.
diff --git a/scripts/coreweave/count_voxel_bands.py b/scripts/coreweave/count_voxel_bands.py
new file mode 100644
index 00000000..2ebc44a5
--- /dev/null
+++ b/scripts/coreweave/count_voxel_bands.py
@@ -0,0 +1,58 @@
+"""Count capped-dataset structures above the per-width cuDNN kernel cliffs.
+
+cuDNN's fast conv engines fall back to reference kernels (~400x slower) when
+the U-Net decoder-concat tensor exceeds 2^31 elements. For concat channels
+2C, the per-structure voxel ceiling is 2^31 / (2 * n_channels). This script
+reads zarr shape metadata for every id in the capped filelists (data must be
+staged locally; run on a warm node) and reports how many structures exceed
+each width's ceiling — i.e. the data cost of a width-specific tighter cap.
+"""
+
+from __future__ import annotations
+
+import statistics
+from multiprocessing import Pool
+from pathlib import Path
+
+import zarr
+
+STAGE_ROOT = Path("/uv/cache/electrai/mp/chg_datasets/functionals")
+CEILINGS = {
+    "W192 (2*192 ch concat)": 2**31 // 384,  # 5,592,405 voxels
+    "W256 (2*256 ch concat)": 2**31 // 512,  # 4,194,304 voxels
+}
+CURRENT_CAP = 5_832_000
+
+
+def voxels(args):
+    root, mpid = args
+    try:
+        g = zarr.open_group(str(root / "data" / f"{mpid}.zarr"), mode="r")
+        s = g["charge_density_total"].shape
+        return s[0] * s[1] * s[2]
+    except Exception:
+        return -1
+
+
+def main():
+    tasks = []
+    for func in ("gga", "gga+u"):
+        root = STAGE_ROOT / func
+        ids = (root / "mp_filelist_capped.txt").read_text().split()
+        tasks += [(root, i) for i in ids]
+
+    with Pool(32) as p:
+        sizes = p.map(voxels, tasks, chunksize=256)
+
+    ok = [s for s in sizes if s > 0]
+    print(f"total {len(sizes)}, unreadable {len(sizes) - len(ok)}")
+    print(f"max voxels: {max(ok)} (current cap {CURRENT_CAP})")
+    print(f"mean voxels: {sum(ok) / len(ok):.0f}, median: {statistics.median(ok):.0f}")
+    for label, ceiling in CEILINGS.items():
+        over = sum(1 for s in ok if s > ceiling)
+        print(f"over {label} ceiling {ceiling}: {over} ({100 * over / len(ok):.3f}%)")
+    print("COUNT DONE")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/coreweave/dry_run.py b/scripts/coreweave/dry_run.py
new file mode 100644
index 00000000..9231ea7c
--- /dev/null
+++ b/scripts/coreweave/dry_run.py
@@ -0,0 +1,124 @@
+"""Dry run for the CoreWeave W96 campaign.
+
+Validates, on one GB200, everything the real run depends on except multi-GPU:
+  1. the staged dataset (filelists, symlink layout, zarr reads via RhoRead),
+  2. dataloader batch structure and read throughput,
+  3. the W96 memory envelope: forward+backward+Adam step at the grid-size cap
+     (180^3, the largest structure the capped filelists allow) in bf16,
+     with activation checkpointing off.
+
+Run after scripts/coreweave/stage_data.sh:
+  uv run --no-sync python scripts/coreweave/dry_run.py \
+      --config src/electrai/configs/MP/config_gga_gga+u_w96.yaml
+"""
+
+from __future__ import annotations
+
+import argparse
+import time
+from pathlib import Path
+
+import torch
+import yaml
+from hydra.utils import instantiate
+
+
+def describe(obj):
+    if isinstance(obj, torch.Tensor):
+        return f"Tensor{tuple(obj.shape)} {obj.dtype}"
+    if isinstance(obj, dict):
+        return {k: describe(v) for k, v in obj.items()}
+    if isinstance(obj, (list, tuple)):
+        return [describe(v) for v in obj]
+    return repr(obj)[:60]
+
+
+def check_data(cfg, n_batches):
+    dm = instantiate(cfg["data"])
+    dm.setup("fit")
+    train = dm.train_dataloader()
+    val = dm.val_dataloader()
+    print(f"train batches: {len(train)}, val batches: {len(val)}")
+
+    start = time.monotonic()
+    for i, batch in enumerate(train):
+        if i == 0:
+            print("batch structure:", describe(batch))
+        if i + 1 >= n_batches:
+            break
+    dt = time.monotonic() - start
+    print(f"read {n_batches} train batches in {dt:.1f}s ({dt / n_batches:.2f}s/batch)")
+
+
+def check_memory(cfg, grids=(128, 180), iters=1):
+    model = instantiate(cfg["model"]).cuda()
+    n_params = sum(p.numel() for p in model.parameters())
+    print(f"model params: {n_params / 1e6:.1f}M")
+    opt = torch.optim.Adam(model.parameters(), lr=1e-3)
+
+    # 180^3 = 5,832,000 voxels: the exact cap from cap_filelists.py, i.e. the
+    # worst case any capped structure can present. With iters > 1 the first
+    # iteration absorbs cuDNN algorithm search (benchmark mode) and the last
+    # one is the steady state.
+    total = torch.cuda.get_device_properties(0).total_memory / 2**30
+    for n in grids:
+        shape = (n, n, n)
+        torch.cuda.reset_peak_memory_stats()
+        x = torch.randn(1, 1, *shape, device="cuda")
+        times = []
+        for _ in range(iters):
+            start = time.monotonic()
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                loss = model(x).float().square().mean()
+            loss.backward()
+            opt.step()
+            opt.zero_grad(set_to_none=True)
+            torch.cuda.synchronize()
+            times.append(time.monotonic() - start)
+        peak = torch.cuda.max_memory_allocated() / 2**30
+        print(
+            f"grid {shape}: fwd+bwd+step {' / '.join(f'{t:.2f}s' for t in times)}, "
+            f"peak {peak:.1f} GiB / {total:.1f} GiB",
+            flush=True,
+        )
+        del x, loss
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", required=True)
+    parser.add_argument("--n-batches", type=int, default=10)
+    parser.add_argument("--grids", default="128,180", help="comma-separated cube edges")
+    parser.add_argument("--iters", type=int, default=1, help="timed steps per grid")
+    parser.add_argument("--cudnn-benchmark", action="store_true")
+    parser.add_argument(
+        "--skip-data", action="store_true", help="model/memory probe only"
+    )
+    parser.add_argument(
+        "--n-channels", type=int, help="override model.n_channels (width probes)"
+    )
+    args = parser.parse_args()
+
+    with Path(args.config).open() as f:
+        cfg = yaml.safe_load(f)
+    if args.n_channels:
+        cfg["model"]["n_channels"] = args.n_channels
+        print(f"n_channels override: {args.n_channels}")
+
+    print("== gpu ==")
+    print(
+        torch.cuda.get_device_name(0), "capability", torch.cuda.get_device_capability(0)
+    )
+    if args.cudnn_benchmark:
+        torch.backends.cudnn.benchmark = True
+        print("cudnn.benchmark = True")
+    if not args.skip_data:
+        print("== data ==")
+        check_data(cfg, args.n_batches)
+    print("== memory envelope ==")
+    check_memory(cfg, grids=[int(g) for g in args.grids.split(",")], iters=args.iters)
+    print("DRY RUN PASSED")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/coreweave/gen_lrwd_sweep.py b/scripts/coreweave/gen_lrwd_sweep.py
new file mode 100755
index 00000000..2ea1c717
--- /dev/null
+++ b/scripts/coreweave/gen_lrwd_sweep.py
@@ -0,0 +1,138 @@
+#!/usr/bin/env python3
+"""Generate per-trial configs for the LR/weight-decay width sweep.
+
+Derives each trial from the CoreWeave production config
+(config_gga_gga+u_w96.yaml) with only these deltas: width, lr, weight_decay,
+optimizer, the compressed 8-epoch schedule, the 12K sweep split files, and
+per-trial run/ckpt names. Design rationale: docs/lr_wd_width_sweep.md.
+
+Stage A — LR grid at wd=0:
+
+    uv run python scripts/coreweave/gen_lrwd_sweep.py --stage a
+
+Stage B — WD grid at the per-width best LR from stage A, plus one
+(lr*/2, wd=1e-3) cross term and a verbatim rerun of the stage-A anchor to
+measure run-to-run noise:
+
+    uv run python scripts/coreweave/gen_lrwd_sweep.py --stage b \
+        --best-lr 32=0.002 64=0.001 96=0.0005
+
+Configs land in src/electrai/configs/MP/sweep_lrwd/; matching submit.sh
+commands are printed but NOT executed.
+"""
+
+from __future__ import annotations
+
+import argparse
+import copy
+from pathlib import Path
+
+import yaml
+
+REPO = Path(__file__).resolve().parents[2]
+BASE_CONFIG = REPO / "src/electrai/configs/MP/config_gga_gga+u_w96.yaml"
+OUT_DIR = REPO / "src/electrai/configs/MP/sweep_lrwd"
+
+WIDTHS = [32, 64, 96]
+STAGE_A_LRS = [2.5e-4, 5e-4, 1e-3, 2e-3, 4e-3]
+STAGE_B_WDS = [1e-4, 1e-3, 1e-2]
+
+SWEEP_EPOCHS = 8
+SWEEP_WARMUP = 1
+WB_PROJECT = "mp-gga-ggau-lrwd"
+# Repo-relative: bundled in the submit.sh zip and read from the job workdir,
+# so warm nodes whose .staged.ok marker skips the bucket sync still see them.
+SWEEP_SPLITS = {
+    "gga+u": "data/MP/sweep_splits/gga+u_split_sweep12k.json",
+    "gga": "data/MP/sweep_splits/gga_split_sweep12k.json",
+}
+
+
+def fmt(x: float) -> str:
+    return f"{x:g}"
+
+
+def make_trial(base: dict, width: int, lr: float, wd: float, suffix: str = "") -> str:
+    cfg = copy.deepcopy(base)
+    cfg["model"]["n_channels"] = width
+    cfg["lr"] = lr
+    cfg["weight_decay"] = wd
+    cfg["optimizer"] = "adamw" if wd > 0 else "adam"
+    cfg["epochs"] = SWEEP_EPOCHS
+    cfg["warmup_length"] = SWEEP_WARMUP
+    cfg["wb_pname"] = WB_PROJECT
+    for ds in cfg["data"]["datasets"]:
+        functional = next(f for f in SWEEP_SPLITS if f"/{f}/" in ds["root"])
+        ds["split_file"] = SWEEP_SPLITS[functional]
+
+    stem = f"sweep_lrwd_w{width}_lr{fmt(lr)}_wd{fmt(wd)}{suffix}"
+    cfg["run_name"] = stem
+    # Own stem => own ckpt namespace; a reused stem would resume from the
+    # earlier trial's last.ckpt in the bucket.
+    cfg["ckpt_path"] = f"/uv/cache/electrai/checkpoints/{stem}"
+
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    path = OUT_DIR / f"{stem}.yaml"
+    with path.open("w") as fp:
+        fp.write(
+            f"# Generated by scripts/coreweave/gen_lrwd_sweep.py from {BASE_CONFIG.name}\n"
+        )
+        fp.write(
+            "# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.\n"
+        )
+        yaml.safe_dump(cfg, fp, sort_keys=False)
+    return stem
+
+
+def parse_best_lr(pairs: list[str]) -> dict[int, float]:
+    best = {}
+    for pair in pairs:
+        w, _, lr = pair.partition("=")
+        best[int(w)] = float(lr)
+    missing = set(WIDTHS) - set(best)
+    if missing:
+        raise SystemExit(f"--best-lr missing widths: {sorted(missing)}")
+    return best
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--stage", choices=["a", "b"], required=True)
+    parser.add_argument(
+        "--best-lr",
+        nargs="*",
+        default=[],
+        metavar="WIDTH=LR",
+        help="stage b: per-width best LR from stage a, e.g. 32=0.002",
+    )
+    args = parser.parse_args()
+
+    with BASE_CONFIG.open() as fp:
+        base = yaml.safe_load(fp)
+
+    stems = []
+    if args.stage == "a":
+        stems = [
+            make_trial(base, width, lr, 0.0) for width in WIDTHS for lr in STAGE_A_LRS
+        ]
+    else:
+        best = parse_best_lr(args.best_lr)
+        for width in WIDTHS:
+            lr = best[width]
+            stems.extend(make_trial(base, width, lr, wd) for wd in STAGE_B_WDS)
+            stems.append(make_trial(base, width, lr / 2, 1e-3))
+            stems.append(make_trial(base, width, lr, 0.0, suffix="_rep2"))
+
+    print(f"Wrote {len(stems)} configs to {OUT_DIR}\n")
+    print("Submit (from a clean worktree, one job per trial):")
+    for stem in stems:
+        job = "el-" + stem.removeprefix("sweep_lrwd_").replace(".", "p").replace(
+            "_", "-"
+        )
+        print(
+            f"  scripts/coreweave/submit.sh src/electrai/configs/MP/sweep_lrwd/{stem}.yaml {job}"
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/coreweave/make_sweep_split.py b/scripts/coreweave/make_sweep_split.py
new file mode 100755
index 00000000..23e705e9
--- /dev/null
+++ b/scripts/coreweave/make_sweep_split.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+"""Subsample the train indices of a split JSON for cheap sweep trials.
+
+Reads a split file as consumed by electrai.dataloader.split.split_data
+({"train": [indices...], "validation": [...], ...}), draws a deterministic
+random fraction of the *train* indices, and writes a new split file with
+every other key (validation, test) copied through untouched — so val_loss
+from sweep trials stays directly comparable to full-dataset runs.
+
+Run once per functional against a local copy of split_capped.json, e.g.:
+
+    rclone copy cw:mp/chg_datasets/functionals/gga/split_capped.json /tmp/gga/
+    uv run python scripts/coreweave/make_sweep_split.py \
+        /tmp/gga/split_capped.json data/MP/sweep_splits/gga_split_sweep12k.json \
+        --frac 0.1112
+
+Using the same --frac for gga and gga+u keeps the 76/24 functional mix of
+the full capped train set.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import random
+from pathlib import Path
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("infile", type=Path, help="source split JSON")
+    parser.add_argument("outfile", type=Path, help="destination split JSON")
+    parser.add_argument(
+        "--frac",
+        type=float,
+        required=True,
+        help="fraction of train indices to keep (0, 1]",
+    )
+    parser.add_argument("--seed", type=int, default=42)
+    args = parser.parse_args()
+
+    if not 0 < args.frac <= 1:
+        parser.error(f"--frac must be in (0, 1], got {args.frac}")
+
+    with args.infile.open() as fp:
+        splits = json.load(fp)
+
+    train = splits["train"]
+    n_keep = round(len(train) * args.frac)
+    rng = random.Random(args.seed)
+    # Sorted so epoch iteration order is index order, matching full-set splits
+    kept = sorted(rng.sample(train, n_keep))
+
+    out = {**splits, "train": kept}
+    args.outfile.parent.mkdir(parents=True, exist_ok=True)
+    with args.outfile.open("w") as fp:
+        json.dump(out, fp)
+
+    other = {k: len(v) for k, v in splits.items() if k != "train"}
+    print(f"{args.infile} -> {args.outfile}")
+    print(f"  train: {len(train)} -> {n_keep} (frac={args.frac}, seed={args.seed})")
+    print(f"  passed through unchanged: {other}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/coreweave/run_training.sh b/scripts/coreweave/run_training.sh
new file mode 100755
index 00000000..dd544f29
--- /dev/null
+++ b/scripts/coreweave/run_training.sh
@@ -0,0 +1,143 @@
+#!/bin/bash
+# Training wrapper for Iris jobs on the CoreWeave GB200 cluster.
+#
+# Sequence: install rclone -> stage dataset (idempotent) -> restore last.ckpt
+# from CAIOS if the node has none -> start a background checkpoint sync loop
+# -> torchrun. Lightning resumes from $CKPT_DIR/last.ckpt automatically when
+# present, so preempted jobs continue from at most CKPT_SYNC_S + the 20-min
+# checkpoint interval behind.
+#
+# Env (all optional):
+#   CONFIG       training config                  [config_gga_gga+u_w96.yaml]
+#   NPROC        GPUs on this node                [4]
+#   CKPT_DIR     local checkpoint dir (must match ckpt_path in CONFIG)
+#   CKPT_S3      CAIOS checkpoint prefix          [s3://rhoarnet-us-east-08a/checkpoints/gga_gga+u_w96]
+#   CKPT_SYNC_S  sync interval seconds            [600]
+#   STAGE_*      see stage_data.sh
+#
+# AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY must hold CAIOS credentials;
+# WANDB_API_KEY is required for wandb_mode: online.
+set -euo pipefail
+
+REPO_ROOT=$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)
+cd "$REPO_ROOT"
+
+CONFIG=${CONFIG:-src/electrai/configs/MP/config_gga_gga+u_w96.yaml}
+NPROC=${NPROC:-4}
+# Default checkpoint locations derive from the config filename stem so that a
+# different config (e.g. w128) can never accidentally resume another run's
+# incompatible last.ckpt.
+RUN_STEM=$(basename "$CONFIG" .yaml)
+RUN_STEM=${RUN_STEM#config_}
+CKPT_DIR=${CKPT_DIR:-/uv/cache/electrai/checkpoints/$RUN_STEM}
+CKPT_S3=${CKPT_S3:-s3://rhoarnet-us-east-08a/checkpoints/$RUN_STEM}
+CKPT_SYNC_S=${CKPT_SYNC_S:-600}
+STAGE_ENDPOINT=${STAGE_ENDPOINT:-http://cwlota.com}
+CKPT_REMOTE="cw:${CKPT_S3#s3://}"
+
+if ! command -v rclone >/dev/null 2>&1; then
+    RCDIR=$(mktemp -d)
+    case "$(uname -m)" in
+        x86_64) RCARCH=amd64 ;;
+        aarch64) RCARCH=arm64 ;;
+        *)
+            echo "run_training: unsupported arch: $(uname -m)" >&2
+            exit 1
+            ;;
+    esac
+    python3 - "$RCARCH" "$RCDIR" <<'PYEOF'
+import io
+import sys
+import urllib.request
+import zipfile
+
+url = f"https://downloads.rclone.org/rclone-current-linux-{sys.argv[1]}.zip"
+zipfile.ZipFile(io.BytesIO(urllib.request.urlopen(url).read())).extractall(sys.argv[2])
+PYEOF
+    RCBIN=$(echo "$RCDIR"/rclone-*-linux-"$RCARCH")
+    chmod +x "$RCBIN/rclone"
+    export PATH="$RCBIN:$PATH"
+fi
+
+# CAIOS remote, configured via env: virtual-host addressing is mandatory
+export RCLONE_CONFIG_CW_TYPE=s3
+export RCLONE_CONFIG_CW_PROVIDER=Other
+export RCLONE_CONFIG_CW_ENV_AUTH=true
+export RCLONE_CONFIG_CW_ENDPOINT="$STAGE_ENDPOINT"
+export RCLONE_CONFIG_CW_REGION=default
+export RCLONE_CONFIG_CW_FORCE_PATH_STYLE=false
+
+bash scripts/coreweave/stage_data.sh
+
+# Restore for resume. Lightning versions the save_last file (last-v1.ckpt,
+# last-v2.ckpt, ...) whenever an earlier run's last.ckpt already exists, but
+# resume always reads last.ckpt — so fetch all last*.ckpt and promote the
+# newest before launch. ALWAYS reconcile against the bucket with --update
+# (copy only when the bucket file is newer): after several preemption
+# bounces a retry can land on a node whose local last.ckpt is DAYS stale,
+# and trusting it rolled a run back six epochs (2026-08-01, W160).
+mkdir -p "$CKPT_DIR"
+rclone copy "$CKPT_REMOTE" "$CKPT_DIR" --include 'last*.ckpt' --update --transfers 4 || true
+NEWEST=$(ls -t "$CKPT_DIR"/last*.ckpt 2>/dev/null | head -1 || true)
+if [[ -n "$NEWEST" && "$NEWEST" != "$CKPT_DIR/last.ckpt" ]]; then
+    cp -f "$NEWEST" "$CKPT_DIR/.last_promote_tmp"
+    mv -f "$CKPT_DIR/.last_promote_tmp" "$CKPT_DIR/last.ckpt"
+    echo "run_training: promoted $(basename "$NEWEST") -> last.ckpt"
+fi
+
+# Guard: never resume from a checkpoint meaningfully older than the bucket's.
+# A transient fetch failure above (swallowed by || true) can otherwise let a
+# stale node copy win promotion — that silently rolled W128 back ~20 epochs
+# on 2026-08-05. Failing hard makes the Iris retry refetch instead.
+REMOTE_TS=$(rclone lsl "$CKPT_REMOTE" --include 'last.ckpt' 2>/dev/null | awk '{print $2" "$3}' | head -1)
+if [[ -n "$REMOTE_TS" && -f "$CKPT_DIR/last.ckpt" ]]; then
+    remote_s=$(date -d "$REMOTE_TS" +%s 2>/dev/null || echo 0)
+    local_s=$(stat -c %Y "$CKPT_DIR/last.ckpt" 2>/dev/null || echo 0)
+    if ((remote_s > 0 && local_s > 0 && remote_s - local_s > 600)); then
+        echo "run_training: local last.ckpt is $((remote_s - local_s))s staler than bucket — refusing stale resume" >&2
+        exit 1
+    fi
+fi
+if [[ -f "$CKPT_DIR/last.ckpt" ]]; then
+    echo "run_training: resuming from last.ckpt"
+else
+    echo "run_training: fresh start"
+fi
+
+ckpt_sync() {
+    rclone copy "$CKPT_DIR" "$CKPT_REMOTE" --transfers 4 || true
+}
+# wandb runs offline (online init hits the viewer flags=null TypeError and a
+# rank-0 crash deadlocks DDP); `wandb sync` uses a different API path and works
+wandb_sync() {
+    for d in "$REPO_ROOT"/wandb/offline-run-*; do
+        [ -d "$d" ] && uv run --no-sync wandb sync "$d" >/dev/null 2>&1
+    done
+    return 0
+}
+(while true; do
+    sleep "$CKPT_SYNC_S"
+    ckpt_sync
+    wandb_sync
+done) &
+SYNC_PID=$!
+
+export PYTORCH_ALLOC_CONF=expandable_segments:True
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+export PYTHONPATH="$REPO_ROOT"
+
+uv run --no-sync torchrun --standalone --nproc_per_node="$NPROC" \
+    src/electrai/entrypoints/main.py train --config "$CONFIG" &
+TRAIN_PID=$!
+trap 'kill -TERM "$TRAIN_PID" 2>/dev/null || true' TERM INT
+
+set +e
+wait "$TRAIN_PID"
+RC=$?
+set -e
+
+kill "$SYNC_PID" 2>/dev/null || true
+ckpt_sync
+wandb_sync
+echo "run_training: exited rc=$RC (final checkpoint + wandb sync done)"
+exit "$RC"
diff --git a/scripts/coreweave/stage_data.sh b/scripts/coreweave/stage_data.sh
new file mode 100755
index 00000000..5006f46c
--- /dev/null
+++ b/scripts/coreweave/stage_data.sh
@@ -0,0 +1,89 @@
+#!/bin/bash
+# Stage the charge-density dataset from CAIOS object storage to node-local NVMe.
+#
+# Runs as the first step of an Iris task on the CoreWeave GB200 cluster.
+# Idempotent: a marker file written after a successful sync lets preemption
+# retries that land on a warm node (the cache dir is a hostPath that survives
+# pod restarts) skip the download entirely. The dataset is immutable, so the
+# marker is trusted unless STAGE_FORCE=1.
+#
+# Uses rclone: CAIOS requires virtual-host addressing for list operations,
+# which s5cmd cannot emit (it is path-style only against custom endpoints).
+#
+# STAGE_ROOT lives under /uv/cache because that is the ONLY host-persistent
+# mount Iris task pods get (hostPath /mnt/local/iris-cache/uv-cache). Writing
+# anywhere else lands on the container overlay and counts against the pod's
+# ephemeral-storage limit, which kills the pod mid-stage (exit 137).
+#
+# Env (all optional):
+#   STAGE_BUCKET    source bucket                  [rhoarnet-us-east-08a]
+#   STAGE_PREFIX    bucket prefix to mirror        [mp/chg_datasets]
+#   STAGE_ROOT      local destination root         [/uv/cache/electrai]
+#   STAGE_ENDPOINT  S3 endpoint                    [http://cwlota.com]
+#   STAGE_FORCE     1 = re-sync even if marker present
+#
+# AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY must hold CAIOS credentials
+# (passed to the Iris job with -e; they override the cluster-injected ones).
+set -euo pipefail
+
+STAGE_BUCKET=${STAGE_BUCKET:-rhoarnet-us-east-08a}
+STAGE_PREFIX=${STAGE_PREFIX:-mp/chg_datasets}
+STAGE_ROOT=${STAGE_ROOT:-/uv/cache/electrai}
+STAGE_ENDPOINT=${STAGE_ENDPOINT:-http://cwlota.com}
+DEST="$STAGE_ROOT/$STAGE_PREFIX"
+MARKER="$DEST/.staged.ok"
+
+if [[ -f "$MARKER" && "${STAGE_FORCE:-0}" != "1" ]]; then
+    echo "stage_data: marker present ($(cat "$MARKER")), skipping sync"
+    exit 0
+fi
+
+if ! command -v rclone >/dev/null 2>&1; then
+    RCDIR=$(mktemp -d)
+    case "$(uname -m)" in
+        x86_64) RCARCH=amd64 ;;
+        aarch64) RCARCH=arm64 ;;
+        *)
+            echo "stage_data: unsupported arch: $(uname -m)" >&2
+            exit 1
+            ;;
+    esac
+    python3 - "$RCARCH" "$RCDIR" <<'PYEOF'
+import io
+import sys
+import urllib.request
+import zipfile
+
+url = f"https://downloads.rclone.org/rclone-current-linux-{sys.argv[1]}.zip"
+zipfile.ZipFile(io.BytesIO(urllib.request.urlopen(url).read())).extractall(sys.argv[2])
+PYEOF
+    RCBIN=$(echo "$RCDIR"/rclone-*-linux-"$RCARCH")
+    chmod +x "$RCBIN/rclone"
+    export PATH="$RCBIN:$PATH"
+fi
+
+# CAIOS remote, configured via env: virtual-host addressing is mandatory
+export RCLONE_CONFIG_CW_TYPE=s3
+export RCLONE_CONFIG_CW_PROVIDER=Other
+export RCLONE_CONFIG_CW_ENV_AUTH=true
+export RCLONE_CONFIG_CW_ENDPOINT="$STAGE_ENDPOINT"
+export RCLONE_CONFIG_CW_REGION=default
+export RCLONE_CONFIG_CW_FORCE_PATH_STYLE=false
+
+mkdir -p "$DEST"
+echo "stage_data: syncing s3://$STAGE_BUCKET/$STAGE_PREFIX -> $DEST"
+start=$(date +%s)
+rclone copy "cw:$STAGE_BUCKET/$STAGE_PREFIX" "$DEST" \
+    --transfers 96 --checkers 128 --size-only --fast-list \
+    --stats 60s --stats-one-line --log-level NOTICE
+
+# RhoRead resolves data/ and label/ as siblings of the filelist, so the
+# functionals dirs need the same symlink shim prep_data.sh created on Lambda.
+ln -sfn ../../rho_gga/data "$DEST/functionals/gga/data"
+ln -sfn ../../rho_gga/label "$DEST/functionals/gga/label"
+ln -sfn "../../rho_gga+u/data" "$DEST/functionals/gga+u/data"
+ln -sfn "../../rho_gga+u/label" "$DEST/functionals/gga+u/label"
+
+n_files=$(find "$DEST" -type f | wc -l)
+echo "stage_data: staged $n_files files in $(($(date +%s) - start))s"
+date -u +"%Y-%m-%dT%H:%M:%SZ $n_files files" >"$MARKER"
diff --git a/scripts/coreweave/submit.sh b/scripts/coreweave/submit.sh
new file mode 100755
index 00000000..8ba6f417
--- /dev/null
+++ b/scripts/coreweave/submit.sh
@@ -0,0 +1,47 @@
+#!/bin/bash
+# Submit a CoreWeave training job through Iris. Run from the LAPTOP, with the
+# repo checkout you want bundled as the current directory (use a clean
+# worktree: the bundle is a zip of cwd, and stray large files bloat it).
+#
+# Usage:
+#   scripts/coreweave/submit.sh <config-path> <job-name> [extra iris args...]
+# Example:
+#   scripts/coreweave/submit.sh src/electrai/configs/MP/config_gga_gga+u_w128.yaml \
+#       electrai-w128-gga-ggau
+#
+# Env overrides:
+#   MARIN_REPO   marin checkout providing the iris client  [~/code/marin]
+#   GPUS         iris --gpu spec                           [GB200x4]
+#   CPUS/MEMORY/DISK                                       [64 / 200GB / 60GB]
+#
+# Credentials are read locally (CAIOS keys from the `coreweave` AWS profile,
+# W&B key from ~/.netrc) and passed as job env vars; nothing is printed.
+set -euo pipefail
+
+CONFIG=${1:?usage: submit.sh <config> <job-name> [extra iris args...]}
+JOB_NAME=${2:?usage: submit.sh <config> <job-name> [extra iris args...]}
+shift 2
+
+MARIN_REPO=${MARIN_REPO:-$HOME/code/marin}
+GPUS=${GPUS:-GB200x4}
+CPUS=${CPUS:-64}
+MEMORY=${MEMORY:-200GB}
+DISK=${DISK:-60GB}
+MAX_RETRIES=${MAX_RETRIES:-25}
+
+[[ -f $CONFIG ]] || {
+    echo "submit: config not found in cwd bundle: $CONFIG" >&2
+    exit 1
+}
+
+KUBECONFIG=${KUBECONFIG:-$HOME/.kube/config-coreweave} \
+    uv run --project "$MARIN_REPO" --package marin-iris \
+    iris --cluster=cw-us-east-08a job run \
+    --enable-extra-resources --gpu "$GPUS" --cpu "$CPUS" --memory "$MEMORY" --disk "$DISK" \
+    --priority batch --max-retries "$MAX_RETRIES" --job-name "$JOB_NAME" --no-wait \
+    -e AWS_ACCESS_KEY_ID "$(aws configure get aws_access_key_id --profile coreweave)" \
+    -e AWS_SECRET_ACCESS_KEY "$(aws configure get aws_secret_access_key --profile coreweave)" \
+    -e WANDB_API_KEY "$(awk '/machine api.wandb.ai/{f=1} f && /password/{print $2; exit}' ~/.netrc)" \
+    -e CONFIG "$CONFIG" \
+    "$@" \
+    -- bash -lc 'cd "${IRIS_WORKDIR:-/app}" && bash scripts/coreweave/run_training.sh'
diff --git a/scripts/lambda/MONITOR.md b/scripts/lambda/MONITOR.md
new file mode 100644
index 00000000..105d975c
--- /dev/null
+++ b/scripts/lambda/MONITOR.md
@@ -0,0 +1,129 @@
+# Monitoring an in-flight Lambda training run
+
+A 100-epoch run is ~26 days. Babysitting that with a human-driven status check
+is impractical, so this doc describes the lightweight monitor we've been using:
+an hourly LLM-driven check using a fixed prompt + a one-shot status script.
+
+The companion script `monitor_status.sh` produces a single snapshot you can
+read directly; the LLM prompt fires the same snapshot hourly and decides
+whether anything needs attention.
+
+## What "healthy" looks like (the 3-part liveness rule)
+
+Training is healthy iff **all three** are true:
+
+| Signal | Threshold | What it confirms |
+|---|---|---|
+| (a) `train.log` mtime | within 60s of now | log file actively being written |
+| (b) All GPUs (4 on lambda2, 8 on multi-node) | ≥ 80% util | trainer actively computing |
+| (c) `last.ckpt` mtime | within 8h of now | per-epoch checkpoint advance |
+
+**Only flag a stall if at least 2 of the 3 fail.** Single-signal failures are
+almost always false alarms.
+
+## Why each non-obvious signal exists
+
+- **Don't trust the visible step counter in `tail train.log`.** The log is
+  multi-GB and grows by ~1 MB/min. Each progress update is a `\r`-overwritten
+  line, but each overwrite also appends a fresh log entry. The "last 200 lines"
+  from a `tail` is therefore a snapshot of ~1 minute of training and looks
+  identical between hourly checks even when training is fine. The mtime of the
+  file itself is the trustworthy freshness signal.
+- **GPU util is the most direct signal training is doing real work.** All four
+  H100s should be 90-100% busy. A dataloader stall drops them; a dead trainer
+  drops them; a NCCL hang drops them.
+- **`last.ckpt` mtime is the per-epoch advance marker.** Lightning writes it
+  at the end of each validation, so it cleanly tells you "we finished epoch
+  N". With 6-7h epochs, 8h is the right threshold.
+
+## False alarms we've already learned
+
+- `tail` on a fast-writing log returns a snapshot that looks stale between
+  consecutive hourly checks. Use mtime + GPU util instead.
+- `stat <full_path>` can return ENOENT on NFS/virtiofs while `ls <dir>` of
+  the same path works. Don't conclude "file missing" from one `stat`.
+- "Backup log entry stuck" can just mean the 10-min cadence hasn't fired yet.
+  Check against the loop's cadence, not against arbitrary timestamps.
+
+## Setup: the hourly cron monitor
+
+Inside a Claude Code session, the recurring task is scheduled via `CronCreate`
+with this prompt (verbatim, hourly at minute `:27` to avoid herd timing):
+
+```text
+check the Lambda H100:4 full training run for config_gga_gga+u_f32 on lambda2.
+
+Run on lambda2 via `ssh lambda2`:
+1. `tmux ls 2>&1 | head -3` — verify `electrai-train` session alive
+   (expect 3 windows: train, backup, wandb-sync)
+2. `date -Iseconds; stat -c '%y %n' \
+     /lambda/nfs/<NFS>/checkpoints/train.log \
+     /lambda/nfs/<NFS>/checkpoints/<RUN>/last.ckpt 2>&1`
+   — liveness via mtimes (NOT step counter)
+3. `nvidia-smi --query-gpu=utilization.gpu --format=csv,noheader`
+   — all should be 90-100%
+4. `tmux capture-pane -t electrai-train:train -p -S -100 2>&1 \
+     | tr '\r' '\n' | grep -oE 'Epoch [0-9]+: *[0-9]+%.*it/s.*' | tail -2`
+   — current epoch/step/it/s from live tmux pane
+5. `ls -la /lambda/nfs/<NFS>/checkpoints/<RUN>/ 2>&1 | tail -6`
+   — checkpoint files
+6. `tail -3 /lambda/nfs/<NFS>/checkpoints/backup.log`
+   — S3 ckpt backup loop (10-min cadence)
+7. `tail -8 /lambda/nfs/<NFS>/checkpoints/wandb-sync.log`
+   — wandb-sync loop; expect periodic "Syncing: ... done." lines
+
+Liveness rule (avoid false alarms): training is healthy iff
+(a) train.log mtime within 60s of now AND
+(b) all GPUs >= 80% util AND
+(c) last.ckpt mtime within 8h of now.
+The step counter in the tail of train.log is NOT a reliable freshness
+signal. Only flag a stall if at least 2 of those three signals show
+>threshold staleness.
+
+Report concisely:
+- Current epoch + step + it/s (from tmux pane)
+- Latest val_loss_epoch (and delta vs previous)
+- New checkpoint files since last check
+- wandb-sync state: time of last "Syncing: ... done." vs now
+- Any errors / restarts (training exits != 0; wandb sync failures)
+- ETA estimate: epochs remaining × current epoch wall-clock
+
+If the strict liveness rule (a&b&c) fails OR a checkpoint hasn't
+advanced for >8h OR wandb-sync has logged failures for >2 consecutive
+cycles, surface the failure and consider intervention.
+```
+
+For multi-node (see [PORT_PLAN.md](PORT_PLAN.md) Phase 7), add a second SSH
+target for the worker node and check its GPUs + tmux separately.
+
+## Ad-hoc usage
+
+```sh
+bash scripts/lambda/monitor_status.sh                    # default: lambda2, full_gga_gga+u_f32
+bash scripts/lambda/monitor_status.sh lambda3-a          # different host
+bash scripts/lambda/monitor_status.sh lambda3-a smoke_gga_gga+u_f32_smoke
+```
+
+## Intervention thresholds
+
+| Symptom | Action |
+|---|---|
+| Liveness rule (a+b+c) fails for 1 cycle | wait a cycle — likely transient |
+| Liveness rule fails for 2+ cycles | ssh in, check `ps aux | grep python`, GPU XID errors in `dmesg` |
+| `last.ckpt` >8h old | ssh in, check if validation is hung; consider killing + resuming from S3 mirror |
+| wandb-sync fails for 2+ cycles | check `wandb-sync.log`; if backend is down, sync will catch up later — don't intervene unless training itself is also failing |
+| Worker node unreachable (multi-node only) | DDP will time out and trainer will auto-resume; verify worker GPU/tmux when reachable again |
+| `nvidia-smi` reports XID errors | hardware issue; preserve state via S3, file Lambda support ticket, consider switching instances |
+
+## What this monitor doesn't do
+
+- It doesn't auto-restart anything. The `while true` resume loop in
+  `run_training.sh` handles trainer crashes; the wandb-sync and backup
+  windows have their own loops too. The monitor's job is to surface
+  failures the operator should investigate.
+- It doesn't watch loss curves for divergence — that's wandb's job. The
+  monitor checks that wandb is *receiving* data, not that the data is
+  good.
+- It doesn't track Lambda billing. Cost is roughly $11.96/hr (4×) or
+  $47.84/hr (16×); back-of-envelope from "epochs left × wall-clock per
+  epoch × $/hr".
diff --git a/scripts/lambda/MONITOR_EC2.md b/scripts/lambda/MONITOR_EC2.md
new file mode 100644
index 00000000..e52a46d4
--- /dev/null
+++ b/scripts/lambda/MONITOR_EC2.md
@@ -0,0 +1,177 @@
+# EC2 resident monitor — active operator for the Lambda run
+
+This is the **active** counterpart to [MONITOR.md](MONITOR.md). MONITOR.md
+describes an *observe-only* hourly check (it explicitly "doesn't auto-restart
+anything"). This doc describes a dedicated always-on EC2 box running a resident
+Claude agent that uses the same liveness rule and snapshot script, but **also
+attempts a bounded set of safe fixes on its own and escalates to Slack only when
+a fix fails or the situation is outside its allowed actions.**
+
+Use one or the other, not both pointed at the same run.
+
+## Why a separate box
+The MONITOR.md cron lives *inside* a Claude Code session — it needs a live
+session running somewhere (a laptop). A 100-epoch run is ~26 days, so the
+watcher must be durable and independent of both your laptop and the training
+box. A tiny EC2 instance gives you: 24/7 uptime, co-location with
+`s3://oa-electrai` (us-east-1), an IAM instance role instead of static keys, and
+a blast radius separate from the GPUs it babysits.
+
+## Components (all in `scripts/lambda/`)
+| File | Role |
+|---|---|
+| `monitor_status.sh` | one-shot read-only snapshot of one node (existing) |
+| `monitor_status_all.sh` | wraps the above over every node in `$HOSTS` (head + worker) |
+| `monitor_agent_prompt.md` | the operator prompt fed to Claude each tick |
+| `monitor_loop.sh` | resident supervisor loop (run by systemd) |
+| `monitor_settings.json` | Claude permission allowlist (fail-closed) |
+| `monitor.env.example` | EnvironmentFile template (targets + secrets) |
+| `monitor_watchdog.sh` | independent "who watches the watcher" check |
+| `systemd/electrai-monitor.service` | runs the loop, `Restart=always` |
+| `systemd/electrai-monitor-watchdog.{service,timer}` | runs the watchdog every 30 min |
+
+### How a tick works
+1. The loop prepends a live **CONTEXT** header (HOSTS, RUN, journal path,
+   maintenance flag, snapshot command) to `monitor_agent_prompt.md`.
+2. Claude reads the tail of the **journal** (its memory across ticks), runs
+   `monitor_status_all.sh`, judges health by MONITOR.md's 3-part liveness rule,
+   and either does nothing, remediates, or escalates.
+3. Claude's reply is appended to the journal. Continuity is the journal, **not**
+   the conversation buffer — that's what makes this survivable over 26 days and
+   across restarts/token refreshes.
+4. A successful tick touches the **heartbeat** file; the watchdog keys off that,
+   so a silently failing Claude (expired token, quota, network) still gets
+   caught even though the journal header updates every tick.
+
+## REMEDIATION RUNBOOK (the agent reads this section)
+
+The training script already self-heals transient crashes (`run_training.sh` runs
+the trainer in a `while true; … sleep 30` loop and Lightning auto-resumes from
+`last.ckpt`). So the agent's autonomous scope is deliberately narrow: the cases
+the self-heal loop **can't** cover. Confirm a real stall with the liveness rule
+(>= 2 of 3 failing) before acting.
+
+### Allowed autonomous actions (safe, idempotent, reversible)
+| Situation | Action |
+|---|---|
+| `electrai-train` tmux session absent entirely | relaunch: `ssh <head> 'cd ~/electrai && bash scripts/lambda/run_training.sh full'` (auto-resumes) |
+| A loop window died (backup or wandb-sync window gone, training fine) | restart just that window in the existing session |
+| Train alive but wedged >= 2 cycles (GPUs idle + `train.log` stale → NCCL hang) | kill + relaunch the train window once (`NCCL_ASYNC_ERROR_HANDLING=1` should make resume clean) |
+| (multi-node) worker tmux died but host reachable | restart the worker training command |
+| Single-cycle / single-signal failure | wait one tick — likely transient (per MONITOR.md) |
+
+### Circuit breaker
+At most **2** autonomous restart actions for the same issue within a rolling
+**60-minute** window — count them from your journal recall. On the 3rd
+occurrence, stop remediating and **escalate** instead of thrashing.
+
+### Escalate-only → Slack (never autonomous)
+| Situation | Why escalate |
+|---|---|
+| `nvidia-smi` / `dmesg` XID hardware errors | hardware; preserve state, human picks whether to switch instances (cost) |
+| Worker host *unreachable* / instance down (not just tmux) | provisioning is a cost decision for a human |
+| `last.ckpt` still > 8h old after one allowed restart | validation genuinely hung; needs a look |
+| Anything risking checkpoint/data loss; disk full | irreversible |
+| Persistent failure past the circuit breaker | stop and page |
+| Its own auth failures (Claude token / wandb / AWS / SSH) | the agent can't fix itself — surfaced by the watchdog |
+| wandb-sync failing while training is healthy | **log only, do not escalate** (per MONITOR.md) |
+
+When unsure of the root cause, do not act on the cluster — escalate.
+
+### Maintenance mode
+During the H100:4 → H100:16 cutover (see [PORT_PLAN.md](PORT_PLAN.md)) there is a
+lot of expected churn. Pause autonomous remediation:
+```sh
+touch  ~/.config/electrai-monitor/MAINTENANCE   # observe + journal only
+rm     ~/.config/electrai-monitor/MAINTENANCE   # resume normal operation
+```
+
+## Provisioning the box
+
+### 1. Instance
+- `t4g.medium` (2 vCPU / 4 GB, ARM) — no GPU; Claude runs on Anthropic's
+  servers, the box only SSHes/parses/loops. ~$24/mo.
+- Ubuntu 24.04 LTS, **us-east-1** (co-located with `s3://oa-electrai`).
+- 30 GB gp3 EBS. Allocate an **Elastic IP** (stable address for your SSH in and
+  for allowlisting on the Lambda boxes).
+- Security group: inbound SSH (22) **from your IP only**; outbound all.
+
+### 2. IAM instance role (no static keys)
+Attach a role with **read-only** access to the checkpoint prefix — restores
+happen on the Lambda box with its own creds, so the monitor never needs write:
+```
+s3:GetObject, s3:ListBucket on arn:aws:s3:::oa-electrai (prefix checkpoints/lambda/*)
+```
+
+### 3. Software
+```sh
+sudo apt-get update && sudo apt-get install -y git tmux awscli jq curl
+curl -LsSf https://astral.sh/uv/install.sh | sh           # optional, parity with Lambda
+# Node LTS + Claude Code (per current install docs), then:
+claude setup-token        # interactive once — binds your Max plan (long-lived token in ~/.claude)
+git clone -b betsy/gga-gga+u-f32 git@github.com:Quantum-Accelerators/electrai.git ~/electrai
+```
+
+### 4. SSH to the Lambda boxes (dedicated, revocable key)
+Do **not** copy your personal `id_ed25519_lambda`. Mint a key just for the
+monitor and authorize it on each Lambda node:
+```sh
+ssh-keygen -t ed25519 -f ~/.ssh/id_ed25519_monitor -N ''
+# add ~/.ssh/id_ed25519_monitor.pub to ~/.ssh/authorized_keys on lambda2 (and lambda3-a/-b)
+```
+Replicate the host aliases in `~/.ssh/config` (`lambda2`, `lambda3-a`,
+`lambda3-b`) pointing at `id_ed25519_monitor`, then verify:
+```sh
+ssh lambda2 nvidia-smi
+bash ~/electrai/scripts/lambda/monitor_status.sh lambda2
+```
+Consider locking the Lambda nodes' SSH inbound to the EC2 Elastic IP + your laptop.
+
+### 5. Config + secrets
+```sh
+mkdir -p ~/.config/electrai-monitor ~/.local/state/electrai-monitor
+cp ~/electrai/scripts/lambda/monitor.env.example ~/.config/electrai-monitor/monitor.env
+chmod 600 ~/.config/electrai-monitor/monitor.env
+# edit: HOSTS, RUN, SLACK_WEBHOOK_URL, WANDB_API_KEY
+```
+
+### 6. Install the services
+```sh
+sudo cp ~/electrai/scripts/lambda/systemd/electrai-monitor*.service /etc/systemd/system/
+sudo cp ~/electrai/scripts/lambda/systemd/electrai-monitor-watchdog.timer /etc/systemd/system/
+sudo systemctl daemon-reload
+sudo systemctl enable --now electrai-monitor.service
+sudo systemctl enable --now electrai-monitor-watchdog.timer
+journalctl -u electrai-monitor -f          # or: tail -f ~/.local/state/electrai-monitor/journal.md
+```
+
+### 7. Smoke test (before trusting it)
+1. Confirm a healthy tick lands a `STATUS: HEALTHY` block in the journal.
+2. At a safe moment, `ssh lambda2 'tmux kill-session -t electrai-train'` and
+   confirm the agent detects the stall, relaunches the run, and logs `STATUS:
+   ACTED`. Watch that the trainer resumes from `last.ckpt`.
+3. Force an escalate-only case (e.g. set `HOSTS` to an unreachable host) and
+   confirm a Slack message arrives.
+4. Stop `electrai-monitor.service` and confirm the watchdog Slacks within its
+   interval.
+
+## Cost
+~$24/mo instance + ~$3/mo EBS + negligible egress. Claude runs on your Max
+subscription token, so no per-token API bill — but a continuous loop on Opus
+consumes Max usage; tune `MONITOR_INTERVAL` (default 15 min) and `MONITOR_MODEL` (default
+Opus) if you bump Max limits. Trivial next to the ~$11.96/hr (4×) / ~$47.84/hr
+(16×) training spend.
+
+## Multi-node cutover checklist
+1. `touch ~/.config/electrai-monitor/MAINTENANCE` before the cutover.
+2. Edit `monitor.env`: `HOSTS="lambda3-a lambda3-b"`, `RUN="full_gga_gga+u_f32_16x"`.
+3. Add `lambda3-a` / `lambda3-b` to `~/.ssh/config` and authorize the monitor key.
+4. (Already in `monitor_settings.json`: ssh allow entries for both.)
+5. `sudo systemctl restart electrai-monitor` and confirm a healthy multi-node tick.
+6. `rm ~/.config/electrai-monitor/MAINTENANCE` to re-enable remediation.
+
+## Open items before go-live
+- **Sign-off on the allowed-action list above** — this is the one place the
+  monitor touches a live, expensive run.
+- Confirm the Max-plan token survives unattended (the watchdog catches expiry,
+  but plan for periodic `claude setup-token` refresh).
diff --git a/scripts/lambda/MULTINODE.md b/scripts/lambda/MULTINODE.md
new file mode 100644
index 00000000..60152b13
--- /dev/null
+++ b/scripts/lambda/MULTINODE.md
@@ -0,0 +1,302 @@
+# Multi-node Lambda training runbook (`scripts/lambda/MULTINODE.md`)
+
+Drop-in 2-node companion to the single-node runbook (`README.md`). Same data
+layout, same configs, same tmux session structure -- just two Lambda
+instances coordinating over Ethernet via `torchrun` + NCCL.
+
+The training entrypoint (`src/electrai/entrypoints/train.py`) already reads
+`LOCAL_WORLD_SIZE` / `WORLD_SIZE` from the environment and derives
+`num_nodes` for the Lightning Trainer. We don't touch model code; this is
+purely a launcher + ops doc.
+
+> **Heads-up.** Each Lambda instance has its own `/lambda/nfs/<uuid>`
+> filesystem -- they are NOT cross-mounted. Data and checkpoints live on
+> each node's local NVMe / NFS independently. Only the S3 checkpoint backup
+> on the head node is shared state.
+
+## Topology
+
+```
+                            29500/tcp + NCCL data
+        Lambda instance A  <----------------------->  Lambda instance B
+        (head, NODE_RANK=0)       100 Gbps eth      (worker, NODE_RANK=1)
+        8 x H100 80GB SXM                            8 x H100 80GB SXM
+        /lambda/nfs/<A-uuid>                         /lambda/nfs/<B-uuid>
+        /home/ubuntu (NVMe)                          /home/ubuntu (NVMe)
+```
+
+- **Rendezvous:** torchrun static (`--master_addr` + `--master_port=29500`).
+  Simpler than `c10d` for two known-IP nodes and matches the runbook's
+  "head node IP" mental model. Swap for `--rdzv_backend=c10d` if/when we
+  want elastic worker restarts (not needed today).
+- **Global batch size goes from 4 -> 16.** The Lightning `Trainer` sees
+  `devices="auto"` (= 8) and `num_nodes=2`. See "LR scaling" below.
+
+---
+
+## Pre-flight checklist (in order)
+
+### 1. Provision a second Lambda instance
+
+Pick the same `gpu_8x_h100` SKU. After it boots, SSH in as `ubuntu@<ip>`.
+
+### 2. One-time env setup on the new node
+
+Same as single-node:
+
+```bash
+curl -fsSL https://raw.githubusercontent.com/Quantum-Accelerators/electrai/betsy/gga-gga+u-f32/scripts/lambda/setup.sh | bash
+```
+
+Then, on the new node:
+
+```bash
+aws configure                     # use the betsy-della creds (read + ckpt write)
+echo "export WANDB_API_KEY='...'" >> ~/.bashrc
+```
+
+### 3. Sync data to the new node's NVMe (~1-3 h, parallel to step 4 below)
+
+```bash
+bash ~/electrai/scripts/lambda/data_sync.sh
+bash ~/electrai/scripts/lambda/prep_data.sh
+```
+
+Each node needs its own full copy of the data on its own NVMe. There is
+no cross-node sharing.
+
+### 4. (Head node, in parallel) stage a known-good checkpoint on the head
+
+If you're switching mid-run from the existing 4 GPU run, first cleanly stop
+training so `last.ckpt` is consistent:
+
+```bash
+# inside the existing electrai-train tmux session on the head node:
+tmux send-keys -t electrai-train:train C-c   # graceful stop
+# wait for Lightning to checkpoint + exit; verify last.ckpt timestamp is fresh
+ls -lh $CKPT_ROOT/last.ckpt
+```
+
+Then sync the checkpoint to the worker so any rank can resume:
+
+```bash
+# head node -> worker node (run on head):
+rsync -avz $CKPT_ROOT/last.ckpt ubuntu@<worker-ip>:$CKPT_ROOT/
+```
+
+(Lightning only restores from `last.ckpt` on the global-rank-0 process, so
+strictly the worker doesn't need it -- but having both copies makes
+disaster recovery cheaper.)
+
+### 5. Find each node's PRIVATE / internal IP
+
+We need the IP that the OTHER node actually routes packets to -- usually a
+private 10.x or 172.x address on the 100 Gbps NIC, NOT the public NAT'd
+address Lambda lists in their console.
+
+On each node:
+
+```bash
+# show all interfaces and their addresses
+ip -brief addr
+
+# the IP you want is on the high-bandwidth NIC (look for the 100Gb one,
+# usually enp* or ens* with a 10.x or 172.x address)
+```
+
+Pick the head node's private IP -- call it `HEAD_IP` for the rest of this
+doc. Both nodes will use it as `MASTER_ADDR`.
+
+### 6. Network connectivity check
+
+From the **worker** node:
+
+```bash
+ping -c 3 $HEAD_IP                    # latency sanity (should be < 1 ms)
+nc -zv $HEAD_IP 29500                 # MASTER_PORT reachable?
+```
+
+If `nc` fails, check Lambda's per-instance firewall (cloud-init may have
+left iptables open by default, but verify with `sudo iptables -L -n`).
+
+### 7. Find the right `NCCL_SOCKET_IFNAME`
+
+The launcher auto-detects this via `ip route get $MASTER_ADDR`. To
+double-check manually on the worker:
+
+```bash
+ip -o route get $HEAD_IP | awk '{for(i=1;i<=NF;i++)if($i=="dev")print $(i+1)}'
+```
+
+That prints the interface name (e.g. `enp25s0`, `ens9`). If it's `lo`,
+`docker0`, or a vlan, something's wrong -- pass `NCCL_SOCKET_IFNAME=...`
+explicitly to the launcher.
+
+### 8. NCCL allreduce smoke test (strongly recommended)
+
+Validates that NCCL can actually talk across the two nodes -- way faster
+to debug here than inside Lightning.
+
+```bash
+# On HEAD:
+NODE_RANK=0 MASTER_ADDR=$HEAD_IP NCCL_IB_DISABLE=1 NCCL_DEBUG=INFO \
+  uv run torchrun --nnodes=2 --node_rank=0 --nproc_per_node=8 \
+    --master_addr=$HEAD_IP --master_port=29500 \
+    scripts/lambda/nccl_test.py
+
+# On WORKER (within ~60s of starting the head):
+NODE_RANK=1 MASTER_ADDR=$HEAD_IP NCCL_IB_DISABLE=1 NCCL_DEBUG=INFO \
+  uv run torchrun --nnodes=2 --node_rank=1 --nproc_per_node=8 \
+    --master_addr=$HEAD_IP --master_port=29500 \
+    scripts/lambda/nccl_test.py
+```
+
+Expect 16 lines of the form `[rank N/16 local=M] allreduce got=120.0
+expected=120.0 ok=True` (sum of `0..15` is 120).
+
+If it hangs at "initializing process group", see "Diagnostics" below.
+
+---
+
+## Smoke test (200 samples, 2 epochs)
+
+Same config as single-node smoke, but with `WANDB_MODE_OVERRIDE=disabled`
+to keep multi-node tests out of wandb.
+
+On both nodes, start within ~60 s of each other:
+
+```bash
+# HEAD:
+NODE_RANK=0 MASTER_ADDR=$HEAD_IP WANDB_MODE_OVERRIDE=disabled \
+  bash ~/electrai/scripts/lambda/run_training_multinode.sh smoke
+
+# WORKER:
+NODE_RANK=1 MASTER_ADDR=$HEAD_IP WANDB_MODE_OVERRIDE=disabled \
+  bash ~/electrai/scripts/lambda/run_training_multinode.sh smoke
+```
+
+Validate:
+
+- `tmux attach -t electrai-train` on each node shows training advancing.
+- Head log shows `world_size=16, num_nodes=2` in Lightning's startup banner.
+- Loss curves match the 4 GPU smoke (within stochasticity); if they don't,
+  something is wrong with DDP gradient sync.
+- ~half the steps per epoch vs single-node 8 GPU (since each rank sees the
+  same per-rank batch but global throughput is 2x).
+
+---
+
+## Full run
+
+Once smoke passes, on both nodes (within ~60 s of each other):
+
+```bash
+# HEAD:
+NODE_RANK=0 MASTER_ADDR=$HEAD_IP WANDB_MODE_OVERRIDE=offline \
+  bash ~/electrai/scripts/lambda/run_training_multinode.sh full
+
+# WORKER:
+NODE_RANK=1 MASTER_ADDR=$HEAD_IP WANDB_MODE_OVERRIDE=offline \
+  bash ~/electrai/scripts/lambda/run_training_multinode.sh full
+```
+
+The head node also runs the `backup` and `wandb-sync` tmux windows
+(workers skip them; only global rank 0 writes checkpoints, and that
+process lives on the head).
+
+---
+
+## LR scaling (open question, flag for review)
+
+Going from 4 -> 16 GPUs means global batch size goes from 4 -> 16
+(`batch_size=1` per rank, see `config_gga_gga+u_f32.yaml`).
+
+The current config has `lr: 0.001`. Linear-scaling rule says `lr * 4 =
+0.004`; square-root rule says `lr * 2 = 0.002`. The conservative starting
+point is somewhere between.
+
+**Recommendation: start with `lr = 0.0025` (2.5x), run 1 epoch, compare
+val loss against the 4 GPU baseline at the same epoch.** If loss is
+clearly higher than the 4 GPU run's epoch 1, drop back to `lr * 2`. If
+loss matches or improves, hold there for a few epochs, then optionally
+push to `lr * 3` for the rest of the campaign.
+
+To override the LR without editing the committed config:
+
+```bash
+# easiest: copy the config and edit lr in the copy
+cp src/electrai/configs/MP/config_gga_gga+u_f32.yaml \
+   src/electrai/configs/MP/config_gga_gga+u_f32_2node.yaml
+sed -i 's/^lr: .*/lr: 0.0025/' \
+   src/electrai/configs/MP/config_gga_gga+u_f32_2node.yaml
+# then point SRC_CFG at the new file (or add a "full2node" MODE).
+```
+
+This is the one piece of config that genuinely needs a 1-epoch
+verification before committing to the full 100-epoch campaign. Don't
+skip it.
+
+---
+
+## Switching from the in-flight 4 GPU run
+
+1. Verify what's running:
+   `tmux ls -t electrai-train` on `lambda2`.
+2. Stop training gracefully so `last.ckpt` is consistent:
+   `tmux send-keys -t electrai-train:train C-c`, then wait for
+   `last.ckpt` mtime to update and the training process to exit.
+3. (Optional, recommended) Bump the S3 backup once so the worker can
+   pull from there if the head dies:
+   `aws s3 sync $CKPT_ROOT s3://oa-electrai/checkpoints/lambda/`
+4. Provision the second instance, follow steps 2-7 above.
+5. Run the smoke test (steps "Smoke test" above) to validate cross-node
+   DDP works on this hardware pairing.
+6. Launch the full multi-node run.
+
+The same `last.ckpt` resumes -- Lightning's `ckpt_path=` argument in
+`train.py` is symmetric across world sizes (DDP just shards the loaded
+state across whatever ranks are present).
+
+---
+
+## Diagnostics (common multi-node failures)
+
+| Symptom | Likely cause | Fix |
+|---|---|---|
+| Hangs at "initializing process group", no NCCL output yet | torchrun rendezvous failed (TCP store). | Check `MASTER_ADDR` is reachable; `nc -zv $MASTER_ADDR 29500` from worker. |
+| `NCCL WARN ... connection closed by remote peer` followed by hang | NCCL picked the wrong NIC (e.g. docker0 or a vlan). | Set `NCCL_SOCKET_IFNAME=<iface>` explicitly. Use the iface from `ip route get $MASTER_ADDR`. |
+| `NCCL timeout` after some training steps | Stragglers / one rank stuck (e.g. dataloader). | Inspect each rank's stack with `py-spy dump --pid <python-pid>`. Often a single bad zarr file. |
+| `world_size` doesn't match `nnodes * nproc_per_node` | One side launched with the wrong `--nnodes`. | Check that both launchers used `NUM_NODES=2`. |
+| `RuntimeError: Address already in use` on head | A previous run left a torchrun TCPStore on 29500. | `pkill -f torchrun; sleep 3` then relaunch. Or pick a different `MASTER_PORT`. |
+| Workers exit instantly with rank 0 unreachable | Wrong `MASTER_ADDR` (used the public IP instead of private). | Re-run step 5 above to pick the private IP. |
+| Loss curves diverge between single-node and multi-node | Effective batch size changed; LR not scaled. | See "LR scaling" above. |
+| `NCCL: failed to bind to interface` | Firewall blocking 29500 or NCCL's chosen port range. | `sudo iptables -L -n`; on Lambda's default cloud-init image this should be open, but verify. |
+| Only one node prints "initializing process group" | Static rendezvous timed out (>~5 min) waiting for the late joiner. | Restart both within ~60 s of each other. |
+
+---
+
+## Tunables (env vars)
+
+Inherited from `run_training.sh`:
+
+| var | default | what |
+|---|---|---|
+| `REPO_DIR` | `~/electrai` | repo location |
+| `DATA_ROOT` | `$NFS_ROOT/data` | local data root |
+| `CKPT_ROOT` | `$NFS_ROOT/checkpoints` | local checkpoint dir |
+| `S3_CKPT_BUCKET` | `oa-electrai` | S3 bucket for ckpt backups |
+| `S3_CKPT_PREFIX` | `checkpoints/lambda-multinode` | ckpt backup prefix |
+| `CKPT_BACKUP_S` | `600` | seconds between backups |
+| `TMUX_SESSION` | `electrai-train` | tmux session name |
+| `WANDB_MODE_OVERRIDE` | (unset) | override `wandb_mode` (`offline` / `disabled`) |
+
+Multi-node specific:
+
+| var | default | what |
+|---|---|---|
+| `NODE_RANK` | (required) | 0 for head, 1+ for workers |
+| `MASTER_ADDR` | (required) | head node's private IP, reachable from all workers |
+| `NUM_NODES` | `2` | total node count |
+| `NPROC_PER_NODE` | `8` | GPUs per node |
+| `MASTER_PORT` | `29500` | torchrun rendezvous port |
+| `NCCL_SOCKET_IFNAME` | (auto) | NIC for NCCL; autodetect via `ip route get $MASTER_ADDR` |
diff --git a/scripts/lambda/PORT_PLAN.md b/scripts/lambda/PORT_PLAN.md
new file mode 100644
index 00000000..3be04d91
--- /dev/null
+++ b/scripts/lambda/PORT_PLAN.md
@@ -0,0 +1,265 @@
+# Plan: port the in-flight H100:4 run to H100:16 (2 nodes × 8 GPUs)
+
+Companion to [MULTINODE.md](MULTINODE.md) — that file is the static operator
+runbook; this file is the migration plan for taking a *running* 4× campaign
+and porting it onto a 2-node 16-GPU cluster with the lowest risk and the
+earliest abort signal.
+
+Status at the time of writing: H100:4 on `lambda2`, ~48h in, ~epoch 7,
+`ckpt_epoch=06_val_loss=0.011800.ckpt` saved. Multi-node scaffolding lives
+in `scripts/lambda/{run_training_multinode.sh, MULTINODE.md, nccl_test.py}`.
+
+## Why this is risky enough to need a plan
+
+Going from 1 node to 2 changes several things at once:
+
+- **DDP world size 4 → 16**, so the effective batch size goes 4 → 16 (bs=1
+  per rank). Need to scale `lr`.
+- **All-reduce hops over Ethernet**, not NVLink. Cheap for our 12M-param
+  model in theory, but NCCL config is hardware-specific.
+- **Doubled failure surface** — two instances means ~2× the probability of
+  hardware/network failure stopping the run.
+- **Capacity uncertain** — Lambda has been showing 8× H100 SXM unavailable
+  on demand. Provisioning two simultaneously may take time.
+
+Goal: don't commit to 16× until we have measured proof it's worth it.
+
+## Decision gates
+
+> **Smoke gate:** the 16× smoke must hit **≥3.5× the 4× run's
+> samples/sec** (≥15.4 samples/sec ≈ ≥3.85 it/s at world-size 16). Below
+> 2.5× → abort.
+>
+> **LR gate:** one 16× epoch resumed from `last.ckpt` with a candidate `lr`
+> must give a `val_loss` no worse than 1.5× the 4× run's value at the same
+> epoch index. If it spikes or plateaus, try a different `lr` (cheap; one
+> epoch each).
+
+Hitting both gates before cutover bounds the downside of a bad migration.
+
+## Phase 1 — Provision (~30 min)
+
+1. Spin up two Lambda 8× H100 SXM instances. Call them `lambda3-a` and
+   `lambda3-b`. Configure `~/.ssh/config` aliases (Identity, ProxyJump if
+   needed, ForwardAgent yes).
+2. On each: clone the repo, check out `betsy/gga-gga+u-f32`, run
+   `scripts/lambda/setup.sh`.
+3. On each: `aws configure` (region `us-east-1`) and append
+   `WANDB_API_KEY` to `~/.bashrc`.
+4. Verify each has a `/lambda/nfs/...` mount and ≥1.4 TB free NVMe.
+
+## Phase 2 — Data on both nodes (~3–4h, parallel)
+
+Each node needs its own NVMe copy — NFS is too slow (we measured 4× drop in
+throughput on the current run). In two parallel terminals:
+
+```sh
+ssh -A lambda3-a 'cd ~/electrai && tmux new-session -d -s data-sync \
+  "bash scripts/lambda/data_sync.sh 2>&1 | tee -a ~/data_sync.log"'
+ssh -A lambda3-b 'cd ~/electrai && tmux new-session -d -s data-sync \
+  "bash scripts/lambda/data_sync.sh 2>&1 | tee -a ~/data_sync.log"'
+```
+
+When each completes (`OK.` line in `~/data_sync.log`), run on **both**:
+
+```sh
+DATA_ROOT=~/data bash ~/electrai/scripts/lambda/prep_data.sh
+```
+
+Do the Phase 3 network prep in parallel while the data syncs.
+
+## Phase 3 — Network & NCCL validation (~30 min)
+
+1. **Find the head node's reachable IP** (private/internal, not the public
+   NAT'd address):
+
+   ```sh
+   ssh lambda3-a 'ip -4 addr show | grep -A1 -E "(ens|enp|eth)" | grep inet'
+   ```
+
+   Pick the 10.x or 172.x address. Set `MASTER_ADDR`.
+
+2. **Confirm worker → head reach on port 29500**:
+
+   ```sh
+   ssh lambda3-a 'nc -l 29500' &
+   ssh lambda3-b "nc -zv $MASTER_ADDR 29500"
+   ```
+
+   Should succeed. If blocked, open the port or pick another and set
+   `MASTER_PORT`.
+
+3. **Find the right NCCL socket interface** (from the worker):
+
+   ```sh
+   ssh lambda3-b "ip route get $MASTER_ADDR" \
+     | awk '{for (i=1;i<=NF;i++) if ($i=="dev") print $(i+1)}'
+   ```
+
+   Set `NCCL_SOCKET_IFNAME` to that interface name.
+
+4. **Run the NCCL smoke** — `scripts/lambda/nccl_test.py` does a
+   cross-node all-reduce. Expect <30s success.
+
+   If it hangs or NCCL errors out, **fix here, not in real training** —
+   debugging NCCL while losing real training time is much harder. Common
+   fixes: wrong `NCCL_SOCKET_IFNAME`, wrong IP family, firewall on the
+   NCCL ephemeral port range.
+
+## Phase 4 — Multi-node smoke (~20 min)
+
+Goal: validate the loader + DDP topology + throughput **before** committing.
+
+```sh
+# on head, separate ssh session per node
+ssh -A lambda3-a 'cd ~/electrai && DATA_ROOT=~/data NUM_NODES=2 NODE_RANK=0 \
+  MASTER_ADDR=10.x.x.x WANDB_MODE_OVERRIDE=disabled \
+  bash scripts/lambda/run_training_multinode.sh smoke'
+
+# on worker, same time
+ssh -A lambda3-b 'cd ~/electrai && DATA_ROOT=~/data NUM_NODES=2 NODE_RANK=1 \
+  MASTER_ADDR=10.x.x.x WANDB_MODE_OVERRIDE=disabled \
+  bash scripts/lambda/run_training_multinode.sh smoke'
+```
+
+What to measure:
+
+- World size = 16 in the Lightning startup log (`Initializing distributed:
+  GLOBAL_RANK: 0, MEMBER: 1/16`)
+- Per-step `it/s` from the live tmux pane on the head node
+- `val_loss` after 2 epochs (expect ~0.10, comparable to the 4× smoke)
+
+**Decision point #1 — smoke gate.** If samples/sec ≥ 15.4 (i.e.
+it/s × 16 ≥ 15.4), continue. If not, abort and stay on 4×.
+
+## Phase 5 — LR-scaling 1-epoch validation (~3h, recommended)
+
+Going 4 → 16 ranks quadruples the effective batch. Standard scaling is
+either √4 = 2× `lr` or linear 4× `lr`. The config is at `lr: 0.001`.
+
+1. **Copy `last.ckpt` from the 4× run to the head node's NFS:**
+
+   ```sh
+   ssh lambda3-a 'mkdir -p /lambda/nfs/<head-nfs>/checkpoints/full_gga_gga+u_f32 \
+     && aws s3 cp s3://oa-electrai/checkpoints/lambda/full_gga_gga+u_f32/last.ckpt \
+     /lambda/nfs/<head-nfs>/checkpoints/full_gga_gga+u_f32/last.ckpt'
+   ```
+
+2. **Make a candidate config** at `configs/MP/config_gga_gga+u_f32_16x.yaml`
+   based on the full config, with:
+   - `lr: 0.0025` (2.5×, the geometric midpoint of √ and linear)
+   - `run_name: gga_gga+u_f32_16x` (fresh wandb run)
+3. **Run 1 epoch** from that ckpt using the new config across both nodes.
+4. **Compare** the resulting `val_loss` to the 4× trajectory at the same
+   epoch index.
+
+**Decision point #2 — LR gate.** If `val_loss` is monotonically descending
+and within 50% of the 4× value at the same epoch, commit. If it spikes or
+plateaus higher, try `lr: 0.004` (4×, linear) or `lr: 0.0015` (1.5×) — each
+is a cheap 1-epoch experiment.
+
+## Phase 6 — Cutover (~10 min)
+
+1. **Stop the 4× run cleanly.** Two options:
+   - Wait for current epoch to end. Worst-case ~6h cost; lossless.
+     Recommended.
+   - Hard-stop now (loses progress within the current epoch; Lightning
+     resumes from the last completed-epoch ckpt anyway).
+
+   ```sh
+   ssh lambda2 'tmux kill-session -t electrai-train'
+   ```
+
+2. Wait for the lambda2 wandb-sync window to flush the final state, then
+   `tmux kill-session -t electrai-train` on lambda2 if not already.
+3. **Launch real 16× training** with the chosen `lr`:
+
+   ```sh
+   # head
+   ssh -A lambda3-a 'cd ~/electrai && DATA_ROOT=~/data NUM_NODES=2 NODE_RANK=0 \
+     MASTER_ADDR=10.x.x.x WANDB_MODE_OVERRIDE=offline \
+     bash scripts/lambda/run_training_multinode.sh full'
+   # worker
+   ssh -A lambda3-b 'cd ~/electrai && DATA_ROOT=~/data NUM_NODES=2 NODE_RANK=1 \
+     MASTER_ADDR=10.x.x.x WANDB_MODE_OVERRIDE=offline \
+     bash scripts/lambda/run_training_multinode.sh full'
+   ```
+
+4. Confirm the head node has 3 tmux windows: train, backup, wandb-sync.
+   Worker has 1: train.
+5. Tear down lambda2 — but leave it up for 30 min as a fallback in case
+   the 16× run shows an early problem.
+
+## Phase 7 — Monitoring the 16× run
+
+Most of the existing hourly cron monitor transfers with minor edits:
+
+| Aspect | H100:4 (lambda2) | H100:16 (lambda3-a head + lambda3-b worker) |
+|---|---|---|
+| ssh target | `lambda2` | `lambda3-a` (head; canonical state lives here) |
+| GPU check | 4 GPUs ≥ 80% | 8 GPUs on each node ≥ 80% |
+| Per-step check | `tmux capture-pane -t electrai-train:train` | same, on head |
+| Checkpoints | local NFS + S3 | head's NFS + S3 (worker writes nothing) |
+| wandb-sync | on lambda2 | on head (lambda3-a) |
+| **Extra check** | n/a | also `ssh lambda3-b` to confirm worker tmux + GPUs alive |
+
+### New failure modes
+
+- **Worker rank disappears** → DDP times out → head ranks exit → auto-resume
+  starts back from `last.ckpt`. Cron should ssh the worker every cycle and
+  confirm its training tmux + nvidia-smi are alive. Wasted time if the
+  worker is dead a long time.
+- **NCCL silent hang** — training stops moving but no error. Catch via the
+  existing liveness rule (train.log mtime stale + GPUs idle). With
+  `NCCL_ASYNC_ERROR_HANDLING=1` already set in the script, this should
+  surface as a clean exception → auto-resume.
+- **wandb run id changes** from `8yzlii32` to whatever the 16× run's
+  `offline-run-*` directory becomes. Update the wandb-sync log path + cron
+  monitor to the new offline dir on the head.
+
+## Concerns / risks (ranked by likelihood × impact)
+
+1. **HIGH — Multi-node networking misconfig.**
+   `NCCL_SOCKET_IFNAME` and the routable IP require real-hardware
+   verification. **Do not skip the Phase 3 NCCL smoke.** Mitigation:
+   `NCCL_DEBUG=INFO` on first run, ready to revisit.
+2. **HIGH — Throughput doesn't scale enough.**
+   <2.5× of current and we just paid setup time + a few hours of double
+   compute for nothing. The decision gates above catch this in <8h
+   instead of mid-campaign.
+3. **MED — LR scaling lands wrong on first try.**
+   Multi-epoch validation is too expensive; one-epoch is what we can
+   afford. If the model diverges, worst case is one bad epoch (~$300)
+   before we revert.
+4. **MED — Doubled instance failure surface.**
+   Two H100:8 instances means ~2× the probability of one dying mid-run.
+   S3 checkpoint backup already mitigates total data loss; auto-resume
+   handles single failures. We lose at most the in-progress epoch.
+5. **MED — Lambda capacity for the 2nd instance.**
+   Single-node H100:8 has been intermittently unavailable.
+   File a Lambda support ticket in parallel with this plan to gauge
+   timing.
+6. **LOW — wandb dashboard discontinuity.**
+   New run id; the loss curve will look like a fresh run starting at the
+   ckpt-6 loss value. Paper over with wandb run groups, or just accept it.
+7. **LOW — NFS visible only on head.**
+   Worker doesn't need NFS (only rank-0 writes). Already handled in the
+   multi-node script.
+8. **LOW — Cost overrun in setup phase.**
+   Even with everything going wrong, 24h of double-instance dead time
+   ≈ $575. Acceptable.
+
+## When to actually do this
+
+Don't migrate until at least one of these is true:
+
+- Lambda confirms 2× H100:8 instances are available simultaneously
+- val_loss curve on H100:4 plateaus enough that we expect to *need* the
+  full 100 epochs (right now it's monotonic, dropping ~5% epoch-over-epoch
+  — could plateau at any epoch)
+- A specific deadline (e.g. April 2026 funding milestone) forces our hand
+
+If we hit any of these, this plan is ~12h end-to-end from "instances
+provisioned" to "real training resumed at 16×". The pre-drafted scripts
+handle most of the mechanics; the human work is provisioning, NCCL config
+tuning, and the LR validation read.
diff --git a/scripts/lambda/README.md b/scripts/lambda/README.md
new file mode 100644
index 00000000..03c50e12
--- /dev/null
+++ b/scripts/lambda/README.md
@@ -0,0 +1,124 @@
+# Lambda training runbook (`scripts/lambda/`)
+
+End-to-end runbook for training `config_gga_gga+u_f32` on a Lambda Cloud GPU
+instance (4× or 8× H100). Parallel to the Modal pipeline under `modal/` but
+adapted for a reserved VM: data lives on local NVMe, training runs in tmux,
+checkpoints back up to S3 hourly.
+
+The four scripts are idempotent and can be re-run safely.
+
+## Prerequisites
+
+- Lambda H100 (or A100) instance, SSH'able as `ubuntu@…`.
+- AWS credentials with **read** access to `s3://oa-electrai/mp/chg_datasets/*`
+  (the `electrai-modal-reader` IAM user works; or a Lambda-specific read-only
+  user in the same AWS account).
+- Optional but recommended: AWS credentials with **write** to a checkpoint
+  prefix like `s3://oa-electrai/checkpoints/lambda/` so training checkpoints
+  back up off-instance.
+- A `WANDB_API_KEY` for `PrinceOA` (any teammate's key works).
+
+## Step 0 — Boot the instance and SSH in
+
+```bash
+ssh ubuntu@<instance-ip>      # or `ssh lambda` if you set up an SSH config
+```
+
+## Step 1 — One-time env setup
+
+```bash
+# inside the Lambda instance
+curl -fsSL https://raw.githubusercontent.com/Quantum-Accelerators/electrai/betsy/gga-gga+u-f32/scripts/lambda/setup.sh | bash
+#   or, if you've already cloned the repo:
+# cd ~/electrai && bash scripts/lambda/setup.sh
+```
+
+The script installs `uv`, `aws`, and `tmux` (if missing), clones the repo at
+the right branch, runs `uv sync`, and validates credentials. If AWS or wandb
+isn't configured, it prints the exact next command.
+
+After setup:
+- `aws configure` (or export `AWS_ACCESS_KEY_ID` / `AWS_SECRET_ACCESS_KEY`).
+- `export WANDB_API_KEY='...'` in `~/.bashrc`.
+
+## Step 2 — Sync data from S3 (~1–3 h)
+
+```bash
+bash ~/electrai/scripts/lambda/data_sync.sh
+```
+
+Pulls the packed `.zarr.zip` tree (~1.2 TiB, ~226K objects) from
+`s3://oa-electrai/mp/chg_datasets/` to `~/data/mp/chg_datasets/`. Idempotent;
+safe to re-run if interrupted.
+
+## Step 3 — Wire data/label symlinks + smoke filelists
+
+```bash
+bash ~/electrai/scripts/lambda/prep_data.sh
+```
+
+Recreates the `functionals/{gga,gga+u}/{data,label}` symlinks into the
+`rho_*` real dirs, writes `mp_filelist_smoke.txt` (first 200 ids) per
+functional, and sanity-checks that the first id resolves to a real
+`.zarr.zip`.
+
+## Step 4 — Smoke training
+
+```bash
+bash ~/electrai/scripts/lambda/run_training.sh smoke
+```
+
+Launches under tmux session `electrai-train`. Validates the end-to-end
+pipeline (DDP launches, ZipStore loads, wandb logs, checkpoints write) on
+400 samples × 2 epochs in ~10 min. Throughput on Lambda's local NVMe is
+the actual number we use to predict the full-run cost.
+
+Watch:
+```bash
+tmux attach -t electrai-train
+# or
+tail -f ~/checkpoints/train.log
+```
+
+## Step 5 — Full training run
+
+```bash
+bash ~/electrai/scripts/lambda/run_training.sh full
+```
+
+Same tmux pattern, but the *full* config (113K samples × 100 epochs). The
+training loop auto-resumes from `last.ckpt` if a previous run crashed, and a
+sibling tmux window backs the checkpoint dir up to S3 every 10 min so even
+a catastrophic instance loss only sets us back at most one window.
+
+ETA on 4× H100, given the Modal smoke (1.28 it/s at H100:8 → ~5 samples/sec
+on H100:4), is ~22 days for 100 epochs — over the 2-week ceiling. Plan for
+either fewer epochs, more GPUs, or accepting a longer wall-clock. The
+Lambda smoke result decides.
+
+## Tunables (env vars)
+
+| var | default | what |
+|---|---|---|
+| `REPO_DIR` | `~/electrai` | repo location |
+| `DATA_ROOT` | `~/data` | local data root |
+| `CKPT_ROOT` | `~/checkpoints` | local checkpoint dir |
+| `BRANCH` | `betsy/gga-gga+u-f32` | branch to check out |
+| `S3_BUCKET` | `oa-electrai` | S3 source bucket |
+| `S3_PREFIX` | `mp/chg_datasets` | prefix on S3 |
+| `S3_CKPT_BUCKET` | `oa-electrai` | S3 bucket for ckpt backups |
+| `S3_CKPT_PREFIX` | `checkpoints/lambda` | ckpt backup prefix |
+| `CKPT_BACKUP_S` | `600` | seconds between backups |
+| `SMOKE_N` | `200` | ids per functional in smoke filelists |
+| `TMUX_SESSION` | `electrai-train` | tmux session name |
+
+## Recovery
+
+If the instance dies mid-run:
+1. Boot a fresh Lambda H100, run `setup.sh`.
+2. **Skip data_sync.sh and prep_data.sh** if the data is already on this
+   instance's NVMe (re-runs are idempotent but cost time). If on a new
+   instance, run them.
+3. Pull the latest checkpoint from S3:
+   `aws s3 sync s3://oa-electrai/checkpoints/lambda/ ~/checkpoints/`
+4. Re-run `run_training.sh full` — Lightning resumes from `last.ckpt`.
diff --git a/scripts/lambda/cap_filelists.py b/scripts/lambda/cap_filelists.py
new file mode 100755
index 00000000..0f20b0a3
--- /dev/null
+++ b/scripts/lambda/cap_filelists.py
@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+"""Generate a grid-size-capped filelist + remapped split for a functional dir.
+
+Why: the W64 fp16 full-MP run crash-loops because the largest structures
+(charge-density grids up to ~540^3 vs a ~109^3 median) make a single DDP step
+exceed the 30-min NCCL watchdog timeout (and approach the 80 GB OOM ceiling).
+Capping the grid size drops that tail (~1.5% of structures at ~180^3).
+
+Reads  <root>/mp_filelist.txt and <root>/split.json. split.json stores
+POSITIONAL indices into the filelist, so filtering the filelist requires
+remapping the split indices — done here.
+
+Writes <root>/mp_filelist_<suffix>.txt and <root>/split_<suffix>.json.
+
+Usage:
+  python cap_filelists.py <functional_root> [--cap-voxels N] [--suffix capped] [--workers 32]
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from multiprocessing import Pool
+from pathlib import Path
+
+import zarr
+
+
+def _voxels(args):
+    root, i, idx = args
+    d = Path(root) / "data"
+    zp, dp = d / f"{idx}.zarr.zip", d / f"{idx}.zarr"
+    try:
+        if zp.exists():
+            s = zarr.storage.ZipStore(str(zp), mode="r")
+            try:
+                shp = zarr.open_group(s, mode="r")["charge_density_total"].shape
+            finally:
+                s.close()
+        elif dp.exists():
+            shp = zarr.open_group(str(dp), mode="r")["charge_density_total"].shape
+        else:
+            return (i, None)
+        nx, ny, nz = (int(x) for x in shp)
+        return (i, nx * ny * nz)
+    except Exception:
+        return (i, None)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("root")
+    ap.add_argument("--cap-voxels", type=int, default=5_832_000)  # ~180^3
+    ap.add_argument("--suffix", default="capped")
+    ap.add_argument("--workers", type=int, default=32)
+    a = ap.parse_args()
+
+    root = Path(a.root)
+    ids = [
+        line.strip()
+        for line in (root / "mp_filelist.txt").read_text().splitlines()
+        if line.strip()
+    ]
+    n = len(ids)
+    with Pool(a.workers) as p:
+        sizes = dict(
+            p.map(
+                _voxels,
+                [(str(root), i, idx) for i, idx in enumerate(ids)],
+                chunksize=64,
+            )
+        )
+
+    keep = [
+        i for i in range(n) if sizes.get(i) is not None and sizes[i] <= a.cap_voxels
+    ]
+    missing = sum(1 for i in range(n) if sizes.get(i) is None)
+    remap = {old: new for new, old in enumerate(keep)}
+
+    (root / f"mp_filelist_{a.suffix}.txt").write_text(
+        "\n".join(ids[i] for i in keep) + "\n"
+    )
+    split = json.loads((root / "split.json").read_text())
+    new_split = {k: [remap[o] for o in lst if o in remap] for k, lst in split.items()}
+    (root / f"split_{a.suffix}.json").write_text(json.dumps(new_split))
+
+    print(  # noqa: T201
+        f"{root.name}: total={n} keep={len(keep)} drop={n - len(keep)} "
+        f"({100 * (n - len(keep)) / n:.2f}%) missing={missing} "
+        f"cap={a.cap_voxels} (~{a.cap_voxels ** (1 / 3):.0f}^3) "
+        f"splits={ {k: len(v) for k, v in new_split.items()} }"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/lambda/data_sync.sh b/scripts/lambda/data_sync.sh
new file mode 100755
index 00000000..5db01af9
--- /dev/null
+++ b/scripts/lambda/data_sync.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+# Sync packed zarr training data from S3 to local NVMe.
+# Idempotent (aws s3 sync skips files that already exist with matching size).
+
+set -euo pipefail
+
+S3_BUCKET="${S3_BUCKET:-oa-electrai}"
+S3_PREFIX="${S3_PREFIX:-mp/chg_datasets}"
+NFS_ROOT="${NFS_ROOT:-$(ls -d /lambda/nfs/* 2>/dev/null | head -1)}"
+[ -z "$NFS_ROOT" ] && { echo "ERROR: no /lambda/nfs/* mount found; pass NFS_ROOT=... explicitly"; exit 1; }
+DATA_ROOT="${DATA_ROOT:-$NFS_ROOT/data}"
+DEST="$DATA_ROOT/$S3_PREFIX"
+
+mkdir -p "$DEST"
+
+echo "=== syncing s3://$S3_BUCKET/$S3_PREFIX/ -> $DEST ==="
+echo "(this is ~1.2 TiB / ~226K .zarr.zip files; expect 1-3h on Lambda NVMe)"
+echo
+
+# --no-progress keeps the log quiet; --only-show-errors keeps it informative
+# without thousands of "upload: ... -> ..." lines.
+aws s3 sync \
+  "s3://$S3_BUCKET/$S3_PREFIX/" "$DEST/" \
+  --only-show-errors --no-progress
+
+echo
+echo "=== verifying ==="
+# Count files in each major subtree
+for sub in rho_gga rho_gga+u functionals; do
+  count=$(find "$DEST/$sub" -type f 2>/dev/null | wc -l)
+  size=$(du -sh "$DEST/$sub" 2>/dev/null | awk '{print $1}')
+  printf "  %-15s %8s files  %8s\n" "$sub" "$count" "$size"
+done
+
+total=$(find "$DEST" -type f | wc -l)
+total_sz=$(du -sh "$DEST" 2>/dev/null | awk '{print $1}')
+echo "  --------------------------------------"
+printf "  %-15s %8s files  %8s\n" "TOTAL" "$total" "$total_sz"
+
+# Sanity: S3 holds the unpacked zarr layout (~678K files: ~226K stores ×
+# ~3 inner files + standalone files). The .zarr.zip packed form is
+# Modal-Volume-specific; not present on S3.
+if [ "$total" -lt 670000 ]; then
+  echo "WARN: file count looks low; sync may not be complete."
+  exit 1
+fi
+
+echo "OK."
diff --git a/scripts/lambda/monitor.env.example b/scripts/lambda/monitor.env.example
new file mode 100644
index 00000000..4328c5d1
--- /dev/null
+++ b/scripts/lambda/monitor.env.example
@@ -0,0 +1,29 @@
+# EnvironmentFile for the resident monitor. Copy to
+#   ~/.config/electrai-monitor/monitor.env   (chmod 600 — it holds secrets)
+# and edit. NOT committed with real values.
+
+# --- targets ---
+# Single node (H100:4 phase):
+HOSTS="lambda2"
+RUN="full_gga_gga+u_f32"
+# Multi-node (after PORT_PLAN.md cutover) — head first, then worker:
+#   HOSTS="lambda3-a lambda3-b"
+#   RUN="full_gga_gga+u_f32_16x"
+
+# --- cadence / model ---
+MONITOR_INTERVAL="900"          # seconds between ticks (15 min)
+MONITOR_MODEL="claude-opus-4-8"   # Opus: strong reasoning for remediation calls on the live run
+
+# --- paths (defaults are fine) ---
+# MONITOR_JOURNAL="$HOME/.local/state/electrai-monitor/journal.md"
+# MONITOR_HEARTBEAT="$HOME/.local/state/electrai-monitor/heartbeat"
+# MONITOR_MAINT_FLAG="$HOME/.config/electrai-monitor/MAINTENANCE"
+
+# --- secrets ---
+# Slack incoming-webhook used for escalation only:
+SLACK_WEBHOOK_URL="https://hooks.slack.com/services/REPLACE/ME"
+# wandb read key (lets the agent cross-check run heartbeat/metrics if it wants):
+WANDB_API_KEY="REPLACE_ME"
+
+# --- watchdog ---
+WATCHDOG_STALE_S="3600"         # heartbeat must be newer than this (1h)
diff --git a/scripts/lambda/monitor_agent_prompt.md b/scripts/lambda/monitor_agent_prompt.md
new file mode 100644
index 00000000..9cdbc9fd
--- /dev/null
+++ b/scripts/lambda/monitor_agent_prompt.md
@@ -0,0 +1,68 @@
+You are the resident operations monitor for an in-flight ElectrAI training run on
+Lambda Cloud GPUs. You are invoked once per loop tick. Your job: confirm the run
+is healthy, autonomously fix a SMALL, well-defined set of failures, and escalate
+everything else to a human via Slack. The CONTEXT block prepended above this
+prompt gives you the live targets (HOSTS, RUN), the journal path, the maintenance
+flag, and the exact snapshot command for this tick.
+
+Authoritative references — read them, they are the source of truth:
+- `scripts/lambda/MONITOR.md`      → the 3-part liveness rule, the false-alarm
+                                      catalog, and the intervention table.
+- `scripts/lambda/MONITOR_EC2.md`  → the REMEDIATION RUNBOOK (allowed vs
+                                      escalate-only actions) and the circuit breaker.
+
+Do this, in order:
+
+1. RECALL. Read the last ~40 lines of the journal: `tail -n 40 <journal-path>`
+   (path is in the CONTEXT block). This is your memory of prior ticks — recent
+   observations, actions taken, and restart counts. The circuit breaker is
+   enforced by counting your own past actions here, so read it first.
+
+2. PROBE. Run the snapshot command from the CONTEXT block. For multi-node, the
+   worker node legitimately shows NFS/checkpoint errors (only the head holds that
+   state) — judge the worker on its tmux + GPU lines only.
+
+3. JUDGE using MONITOR.md's liveness rule: healthy iff (a) `train.log` mtime
+   < 60s, (b) all GPUs >= 80% util, (c) `last.ckpt` mtime < 8h. Flag a stall ONLY
+   if >= 2 of the 3 fail — single-signal failures are almost always false alarms;
+   wait for the next tick. Honor the documented false alarms (don't trust the
+   `tail` step counter; don't conclude "file missing" from one failed `stat`).
+
+4. ACT:
+   - HEALTHY  → emit one STATUS block (format below) and stop. Take NO action.
+   - MAINTENANCE = yes (see CONTEXT) → observe and report only. Do NOT remediate
+     and do NOT escalate routine churn; the operator is mid-cutover. Say so.
+   - PROBLEM   → consult the REMEDIATION RUNBOOK in MONITOR_EC2.md:
+       * ALLOWED autonomous action AND circuit breaker permits (per your recall)
+         → perform the minimal idempotent fix over ssh, then state exactly what
+         you did and why. Restarts are safe: the trainer auto-resumes from
+         last.ckpt.
+       * ESCALATE-ONLY, or an allowed fix already failed this cycle, or the
+         circuit breaker is tripped, or you are not confident of the root cause
+         → ESCALATE to Slack (below) and stop. Do NOT touch the cluster.
+   - When in doubt, do NOT act on the cluster — escalate. A false restart on a
+     live run is worse than a false page.
+
+ESCALATION (only when the runbook says to): check the CONTEXT line
+"Slack escalation channel".
+   - If it says "configured", push to Slack with a plain one-line curl:
+       curl -fsS -X POST -H 'Content-type: application/json' --data '{"text":"<message>"}' "$SLACK_WEBHOOK_URL"
+     Use the $SLACK_WEBHOOK_URL variable as-is; NEVER print/echo/interpolate its value.
+   - If it says "NOT configured", do NOT curl — the operator is reading the journal
+     directly. Make the escalation impossible to miss: begin your reply with
+       *** ESCALATION (no Slack configured — operator is reading the journal) ***
+   Either way your full reply is journaled, so the diagnosis is never lost.
+   Message (one line): host(s), which of (a/b/c) failed, what you tried, current
+   epoch / it/s / val_loss if known, and the specific ask of the human.
+
+NEVER do autonomously (escalate instead):
+   - delete checkpoints or data, or run `rm`; provision / terminate / switch
+     instances; edit configs or hyperparameters; restore/pull over a run that is
+     actually healthy.
+   - exceed the circuit breaker — stop and page rather than thrash restarts.
+
+RESPONSE FORMAT — your entire reply is appended verbatim to the journal, so keep
+it tight (a few lines):
+   STATUS: HEALTHY | STALL | ACTED | ESCALATED | MAINTENANCE
+   metrics: <epoch/step/it/s; latest val_loss_epoch + delta; ckpt advance; wandb-sync state>
+   action:  <what you did or escalated, with the reason — or "none">
diff --git a/scripts/lambda/monitor_loop.sh b/scripts/lambda/monitor_loop.sh
new file mode 100644
index 00000000..58ef31a2
--- /dev/null
+++ b/scripts/lambda/monitor_loop.sh
@@ -0,0 +1,77 @@
+#!/usr/bin/env bash
+# Resident monitor loop for the EC2 watcher box. Each tick injects a small live
+# CONTEXT header in front of monitor_agent_prompt.md and runs Claude headless;
+# Claude's reply is appended to the journal (its durable memory across ticks).
+#
+# Continuity is by JOURNAL, not by conversation buffer: a 26-day run would blow
+# any context window, and the journal survives restarts and token refreshes.
+#
+# Run under systemd (see systemd/electrai-monitor.service). Config + secrets come
+# from the EnvironmentFile (see monitor.env.example).
+#
+# Heartbeat: only a *successful* tick touches $HEARTBEAT. The watchdog checks that
+# file (not the journal) so a silently failing Claude (expired token, quota,
+# network) is detected even though the journal header still updates each tick.
+
+set -uo pipefail
+
+HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_DIR="${REPO_DIR:-$HOME/electrai}"
+
+# Optional env file (systemd already loads it; this covers manual runs).
+ENV_FILE="${MONITOR_ENV:-$HOME/.config/electrai-monitor/monitor.env}"
+if [ -f "$ENV_FILE" ]; then set -a; . "$ENV_FILE"; set +a; fi
+
+HOSTS="${HOSTS:-lambda2}"
+RUN="${RUN:-full_gga_gga+u_f32}"
+INTERVAL="${MONITOR_INTERVAL:-900}"        # seconds between ticks (15 min)
+JOURNAL="${MONITOR_JOURNAL:-$HOME/.local/state/electrai-monitor/journal.md}"
+HEARTBEAT="${MONITOR_HEARTBEAT:-$HOME/.local/state/electrai-monitor/heartbeat}"
+MAINT_FLAG="${MONITOR_MAINT_FLAG:-$HOME/.config/electrai-monitor/MAINTENANCE}"
+PROMPT_FILE="${MONITOR_PROMPT:-$HERE/monitor_agent_prompt.md}"
+SETTINGS="${MONITOR_SETTINGS:-$HERE/monitor_settings.json}"
+MODEL="${MONITOR_MODEL:-claude-opus-4-8}"
+CLAUDE_BIN="${CLAUDE_BIN:-claude}"
+
+mkdir -p "$(dirname "$JOURNAL")" "$(dirname "$HEARTBEAT")"
+cd "$REPO_DIR" || { echo "FATAL: REPO_DIR=$REPO_DIR missing" >&2; exit 1; }
+
+echo "$(date -Iseconds) monitor_loop start: HOSTS='$HOSTS' RUN='$RUN' interval=${INTERVAL}s model=$MODEL" >> "$JOURNAL"
+
+while true; do
+  ts="$(date -Iseconds)"
+  maint="no"; [ -f "$MAINT_FLAG" ] && maint="yes"
+  slack="configured"
+  if [ -z "${SLACK_WEBHOOK_URL:-}" ] || printf '%s' "${SLACK_WEBHOOK_URL:-}" | grep -qi replace; then
+    slack="NOT configured (journal-only escalation)"
+  fi
+
+  ctx="CONTEXT (injected $ts):
+- Nodes to check (HOSTS): $HOSTS
+- Run name (RUN): $RUN
+- Journal path (tail it for prior state): $JOURNAL
+- Maintenance mode: $maint   (if yes: observe + journal only, do NOT remediate)
+- Snapshot command for this tick (HOSTS and RUN are already exported in your env):
+    bash scripts/lambda/monitor_status_all.sh \"$RUN\"
+- Slack escalation channel: $slack  (webhook, if any, is in env as \$SLACK_WEBHOOK_URL — never print it)
+"
+  prompt="${ctx}
+$(cat "$PROMPT_FILE")"
+
+  echo "" >> "$JOURNAL"
+  echo "=== $ts tick (maint=$maint) ===" >> "$JOURNAL"
+
+  if "$CLAUDE_BIN" -p "$prompt" \
+        --model "$MODEL" \
+        --settings "$SETTINGS" \
+        --permission-mode default \
+        --add-dir "$REPO_DIR" \
+        >> "$JOURNAL" 2>&1; then
+    date -Iseconds > "$HEARTBEAT"
+  else
+    rc=$?
+    echo "$(date -Iseconds) ERROR: claude tick failed rc=$rc (auth? quota? network?) — heartbeat NOT updated" >> "$JOURNAL"
+  fi
+
+  sleep "$INTERVAL"
+done
diff --git a/scripts/lambda/monitor_settings.json b/scripts/lambda/monitor_settings.json
new file mode 100644
index 00000000..de5c8187
--- /dev/null
+++ b/scripts/lambda/monitor_settings.json
@@ -0,0 +1,27 @@
+{
+  "_comment": "Permission allowlist for the resident monitor (loaded via `claude --settings`). In headless `-p` mode, allowlisted Bash prefixes auto-approve and everything else fails closed (no interactive prompt is possible). Containment of remote ssh command CONTENT is by the operator prompt's hard rules + the dedicated revocable SSH key + read-only AWS, NOT by these patterns. Add lambda3-a / lambda3-b ssh entries at multi-node cutover.",
+  "permissions": {
+    "allow": [
+      "Read",
+      "Grep",
+      "Glob",
+      "Bash(tail:*)",
+      "Bash(date:*)",
+      "Bash(bash scripts/lambda/monitor_status_all.sh:*)",
+      "Bash(bash scripts/lambda/monitor_status.sh:*)",
+      "Bash(aws s3 ls:*)",
+      "Bash(ssh lambda:*)",
+      "Bash(ssh lambda2:*)",
+      "Bash(ssh lambda3-a:*)",
+      "Bash(ssh lambda3-b:*)",
+      "Bash(curl:*)"
+    ],
+    "deny": [
+      "Bash(rm:*)",
+      "Bash(aws s3 rm:*)",
+      "Bash(aws s3 cp:*)",
+      "Bash(aws s3 sync:*)",
+      "Bash(aws ec2:*)"
+    ]
+  }
+}
diff --git a/scripts/lambda/monitor_status.sh b/scripts/lambda/monitor_status.sh
new file mode 100755
index 00000000..19d5ec70
--- /dev/null
+++ b/scripts/lambda/monitor_status.sh
@@ -0,0 +1,46 @@
+#!/usr/bin/env bash
+# One-shot status snapshot for an in-flight Lambda training run.
+#
+# Usage:
+#   bash monitor_status.sh [host=lambda2] [run_name=full_gga_gga+u_f32]
+#
+# Designed to pair with the hourly LLM-driven monitor (see MONITOR.md). The
+# output is intentionally machine-greppable plus human-readable.
+
+set -uo pipefail
+
+HOST="${1:-lambda2}"
+RUN="${2:-full_gga_gga+u_f32}"
+SESSION="${SESSION:-electrai-train}"
+
+ssh "$HOST" "
+  NFS_ROOT=\"\${NFS_ROOT:-\$(ls -d /lambda/nfs/* 2>/dev/null | head -1)}\"
+  CKPT_DIR=\"\$NFS_ROOT/checkpoints\"
+  RUN_DIR=\"\$CKPT_DIR/$RUN\"
+
+  echo '=== tmux ==='
+  tmux ls 2>&1 | head -5
+
+  echo '=== now ==='
+  date -Iseconds
+
+  echo '=== mtimes (train.log within 60s of now, last.ckpt within 8h = live) ==='
+  stat -c '%y %n' \"\$CKPT_DIR/train.log\" \"\$RUN_DIR/last.ckpt\" 2>&1
+
+  echo '=== GPU util (all >= 80% = active) ==='
+  nvidia-smi --query-gpu=utilization.gpu --format=csv,noheader
+
+  echo '=== current step (live tmux pane) ==='
+  tmux capture-pane -t $SESSION:train -p -S -100 2>&1 \\
+    | tr '\\r' '\\n' \\
+    | grep -oE 'Epoch [0-9]+: *[0-9]+%.*it/s.*' | tail -2
+
+  echo '=== checkpoints ==='
+  ls -la \"\$RUN_DIR/\" 2>&1 | tail -8
+
+  echo '=== backup log (S3 ckpt mirror, 10-min cadence) ==='
+  tail -3 \"\$CKPT_DIR/backup.log\" 2>&1
+
+  echo '=== wandb-sync log (10-min cadence; expect Syncing: ... done.) ==='
+  tail -8 \"\$CKPT_DIR/wandb-sync.log\" 2>&1
+"
diff --git a/scripts/lambda/monitor_status_all.sh b/scripts/lambda/monitor_status_all.sh
new file mode 100644
index 00000000..0413f323
--- /dev/null
+++ b/scripts/lambda/monitor_status_all.sh
@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+# Multi-host wrapper around monitor_status.sh. Prints a labeled snapshot for
+# every node in HOSTS so the EC2 monitor agent can evaluate head + worker in a
+# single read.
+#
+# Usage:
+#   bash monitor_status_all.sh                                  # HOSTS=lambda2, default run
+#   HOSTS="lambda3-a lambda3-b" bash monitor_status_all.sh full_gga_gga+u_f32_16x
+#
+# Note on multi-node: only the HEAD node holds NFS/checkpoint/wandb-sync state.
+# The worker's snapshot will legitimately show NFS/checkpoint errors — judge the
+# worker on its `=== tmux ===` and `=== GPU util ===` sections only.
+
+set -uo pipefail
+
+HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+RUN="${1:-full_gga_gga+u_f32}"
+HOSTS="${HOSTS:-lambda2}"
+
+for h in $HOSTS; do
+  echo "################## NODE: $h ##################"
+  bash "$HERE/monitor_status.sh" "$h" "$RUN" || echo "(monitor_status.sh exited rc=$? for $h)"
+  echo
+done
diff --git a/scripts/lambda/monitor_watchdog.sh b/scripts/lambda/monitor_watchdog.sh
new file mode 100644
index 00000000..b98ac368
--- /dev/null
+++ b/scripts/lambda/monitor_watchdog.sh
@@ -0,0 +1,41 @@
+#!/usr/bin/env bash
+# Independent watchdog for the resident monitor — NO LLM, no cluster access.
+# "Who watches the watcher": confirms the monitor service is up and that a tick
+# actually SUCCEEDED recently (via the heartbeat file, which only a clean Claude
+# run touches). Pings Slack if not. Run from a systemd timer or cron (~30 min).
+#
+# Catches the failure the monitor itself cannot report: expired Claude token,
+# quota exhaustion, a wedged loop, or the EC2 box itself degrading.
+
+set -uo pipefail
+
+ENV_FILE="${MONITOR_ENV:-$HOME/.config/electrai-monitor/monitor.env}"
+if [ -f "$ENV_FILE" ]; then set -a; . "$ENV_FILE"; set +a; fi
+
+HEARTBEAT="${MONITOR_HEARTBEAT:-$HOME/.local/state/electrai-monitor/heartbeat}"
+SERVICE="${MONITOR_SERVICE:-electrai-monitor.service}"
+STALE_S="${WATCHDOG_STALE_S:-3600}"
+
+problems=()
+
+if command -v systemctl >/dev/null 2>&1; then
+  systemctl is-active --quiet "$SERVICE" || problems+=("service $SERVICE not active")
+fi
+
+if [ -f "$HEARTBEAT" ]; then
+  age=$(( $(date +%s) - $(stat -c %Y "$HEARTBEAT") ))
+  [ "$age" -gt "$STALE_S" ] && problems+=("last successful tick ${age}s ago (> ${STALE_S}s) — Claude may be de-authed / out of quota / wedged")
+else
+  problems+=("heartbeat $HEARTBEAT missing — monitor has never completed a clean tick")
+fi
+
+[ ${#problems[@]} -eq 0 ] && exit 0
+
+msg=":rotating_light: electrai-monitor watchdog on $(hostname): $(printf '%s; ' "${problems[@]}")"
+if [ -n "${SLACK_WEBHOOK_URL:-}" ]; then
+  curl -fsS -X POST -H 'Content-type: application/json' \
+    --data "$(printf '{"text":%s}' "\"${msg//\"/\\\"}\"")" \
+    "$SLACK_WEBHOOK_URL" >/dev/null 2>&1 || true
+fi
+echo "$(date -Iseconds) WATCHDOG: $msg" >&2
+exit 1
diff --git a/scripts/lambda/nccl_test.py b/scripts/lambda/nccl_test.py
new file mode 100644
index 00000000..757bc4be
--- /dev/null
+++ b/scripts/lambda/nccl_test.py
@@ -0,0 +1,58 @@
+"""Minimal cross-node NCCL allreduce smoke test.
+
+Run with torchrun on BOTH nodes (same args as run_training_multinode.sh):
+
+    # head:
+    NODE_RANK=0 MASTER_ADDR=<head-ip> NCCL_DEBUG=INFO \\
+      NCCL_SOCKET_IFNAME=<iface> NCCL_IB_DISABLE=1 \\
+      uv run torchrun --nnodes=2 --node_rank=0 --nproc_per_node=8 \\
+        --master_addr=<head-ip> --master_port=29500 \\
+        scripts/lambda/nccl_test.py
+
+    # worker:
+    NODE_RANK=1 MASTER_ADDR=<head-ip> NCCL_DEBUG=INFO \\
+      NCCL_SOCKET_IFNAME=<iface> NCCL_IB_DISABLE=1 \\
+      uv run torchrun --nnodes=2 --node_rank=1 --nproc_per_node=8 \\
+        --master_addr=<head-ip> --master_port=29500 \\
+        scripts/lambda/nccl_test.py
+
+Verifies process group init + an all_reduce across world_size ranks. If this
+hangs at init, fix the NCCL_SOCKET_IFNAME / firewall before touching the
+real training command.
+"""
+
+from __future__ import annotations
+
+import os
+
+import torch
+import torch.distributed as dist
+
+
+def main() -> None:
+    local_rank = int(os.environ["LOCAL_RANK"])
+    rank = int(os.environ["RANK"])
+    world_size = int(os.environ["WORLD_SIZE"])
+
+    torch.cuda.set_device(local_rank)
+    dist.init_process_group(backend="nccl")
+
+    tensor = torch.full((1,), float(rank), device=f"cuda:{local_rank}")
+    dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
+
+    # Expected sum: 0+1+...+(world_size-1) == world_size*(world_size-1)/2
+    expected = world_size * (world_size - 1) / 2
+    got = tensor.item()
+    ok = abs(got - expected) < 1e-6
+    print(  # noqa: T201
+        f"[rank {rank}/{world_size} local={local_rank}] "
+        f"allreduce got={got} expected={expected} ok={ok}",
+        flush=True,
+    )
+
+    dist.barrier()
+    dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/lambda/prep_data.sh b/scripts/lambda/prep_data.sh
new file mode 100755
index 00000000..fed02dc9
--- /dev/null
+++ b/scripts/lambda/prep_data.sh
@@ -0,0 +1,74 @@
+#!/usr/bin/env bash
+# Recreate the data/label symlinks and build smoke filelists in the local data
+# tree. Lambda equivalent of modal/prep_volume.py.
+
+set -euo pipefail
+
+NFS_ROOT="${NFS_ROOT:-$(ls -d /lambda/nfs/* 2>/dev/null | head -1)}"
+[ -z "$NFS_ROOT" ] && { echo "ERROR: no /lambda/nfs/* mount found; pass NFS_ROOT=... explicitly"; exit 1; }
+DATA_ROOT="${DATA_ROOT:-$NFS_ROOT/data}"
+BASE="$DATA_ROOT/mp/chg_datasets"
+SMOKE_N="${SMOKE_N:-200}"
+
+echo "=== relinking functionals/{gga,gga+u}/{data,label} -> rho_* ==="
+for entry in "gga:rho_gga" "gga+u:rho_gga+u"; do
+  func="${entry%:*}"
+  rho="${entry#*:}"
+  fdir="$BASE/functionals/$func"
+  mkdir -p "$fdir"
+  for sub in data label; do
+    link="$fdir/$sub"
+    target="../../$rho/$sub"
+    real="$BASE/$rho/$sub"
+    if [ ! -d "$real" ]; then
+      echo "  ERROR: $real missing -- data_sync.sh hasn't completed"
+      exit 1
+    fi
+    if [ -L "$link" ]; then rm "$link"; fi
+    if [ -d "$link" ] && [ ! -L "$link" ]; then
+      echo "  $link is a real dir; leaving as-is"
+      continue
+    fi
+    ln -s "$target" "$link"
+    echo "  linked $link -> $target"
+  done
+done
+
+echo
+echo "=== writing smoke filelists (first $SMOKE_N ids) ==="
+for func in gga 'gga+u'; do
+  fdir="$BASE/functionals/$func"
+  fl="$fdir/mp_filelist.txt"
+  smoke="$fdir/mp_filelist_smoke.txt"
+  if [ ! -f "$fl" ]; then
+    echo "  ERROR: $fl missing"
+    exit 1
+  fi
+  head -n "$SMOKE_N" "$fl" > "$smoke"
+  n=$(wc -l < "$smoke")
+  echo "  wrote $smoke ($n ids)"
+done
+
+echo
+echo "=== sanity check (first id of each filelist resolves) ==="
+# Loader auto-detects either packed (.zarr.zip) or unpacked (.zarr/ dir).
+# S3 holds unpacked; Modal Volume held packed; Lambda gets whichever S3 has.
+for func in gga 'gga+u'; do
+  fdir="$BASE/functionals/$func"
+  first=$(head -n 1 "$fdir/mp_filelist.txt")
+  zip="$fdir/data/$first.zarr.zip"
+  store="$fdir/data/$first.zarr"
+  if [ -f "$zip" ]; then
+    fmt="packed (.zarr.zip)"; resolved="$zip"
+  elif [ -d "$store" ]; then
+    fmt="unpacked (.zarr/)"; resolved="$store"
+  else
+    echo "  ERROR: neither $zip nor $store resolves -- transfer incomplete"
+    exit 1
+  fi
+  n=$(wc -l < "$fdir/mp_filelist.txt")
+  echo "  OK ($func, $fmt): $resolved ($n total ids)"
+done
+
+echo
+echo "prep_data complete. DATA_ROOT=$DATA_ROOT"
diff --git a/scripts/lambda/run_training.sh b/scripts/lambda/run_training.sh
new file mode 100755
index 00000000..4370b002
--- /dev/null
+++ b/scripts/lambda/run_training.sh
@@ -0,0 +1,127 @@
+#!/usr/bin/env bash
+# Launch training in a detached tmux session. Auto-resume from last.ckpt; a
+# parallel rclone-style aws sync backs checkpoints up to S3 every CKPT_BACKUP_S.
+#
+# Usage:
+#   bash scripts/lambda/run_training.sh smoke   # subset + 2 epochs
+#   bash scripts/lambda/run_training.sh full    # full 113K + 100 epochs
+
+set -euo pipefail
+
+MODE="${1:-smoke}"   # smoke | full
+REPO_DIR="${REPO_DIR:-$HOME/electrai}"
+NFS_ROOT="${NFS_ROOT:-$(ls -d /lambda/nfs/* 2>/dev/null | head -1)}"
+[ -z "$NFS_ROOT" ] && { echo "ERROR: no /lambda/nfs/* mount found; pass NFS_ROOT=... explicitly"; exit 1; }
+DATA_ROOT="${DATA_ROOT:-$NFS_ROOT/data}"
+CKPT_ROOT="${CKPT_ROOT:-$NFS_ROOT/checkpoints}"
+S3_CKPT_BUCKET="${S3_CKPT_BUCKET:-oa-electrai}"
+S3_CKPT_PREFIX="${S3_CKPT_PREFIX:-checkpoints/lambda}"
+CKPT_BACKUP_S="${CKPT_BACKUP_S:-600}"   # 10 min
+TMUX_SESSION="${TMUX_SESSION:-electrai-train}"
+UV_BIN="${UV_BIN:-$HOME/.local/bin/uv}"
+
+case "$MODE" in
+  smoke) SRC_CFG="src/electrai/configs/MP/config_gga_gga+u_f32_smoke.yaml" ;;
+  full)  SRC_CFG="src/electrai/configs/MP/config_gga_gga+u_f32.yaml" ;;
+  w64)   SRC_CFG="src/electrai/configs/MP/config_gga_gga+u_w64.yaml" ;;
+  *) echo "MODE must be 'smoke', 'full', or 'w64'"; exit 1 ;;
+esac
+
+cd "$REPO_DIR"
+
+# Pull WANDB_API_KEY out of ~/.bashrc. Ubuntu's default ~/.bashrc starts with
+# `case $- in *i*) ;; *) return;; esac` which early-returns for non-interactive
+# shells, so plain `source ~/.bashrc` doesn't help. Grep the export line
+# directly and eval it.
+if [ -z "${WANDB_API_KEY:-}" ] && [ -f "$HOME/.bashrc" ]; then
+  eval "$(grep -E '^[[:space:]]*export WANDB_API_KEY=' "$HOME/.bashrc" | tail -1)"
+fi
+if [ -z "${WANDB_API_KEY:-}" ]; then
+  echo "ERROR: WANDB_API_KEY env var not set."
+  echo "  Add to ~/.bashrc:  export WANDB_API_KEY='...'  then re-run."
+  exit 1
+fi
+
+mkdir -p "$CKPT_ROOT"
+
+# Rewrite the config so dataset paths and ckpt_path point at our local roots.
+# Two source path conventions exist in committed configs:
+#   - della: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/...
+#   - modal: /data/...  (smoke config uses this since it was authored for Modal)
+# Both get rewritten to live under $DATA_ROOT.
+RUNTIME_CFG="$CKPT_ROOT/runtime-config.yaml"
+WANDB_SED=()
+if [ -n "${WANDB_MODE_OVERRIDE:-}" ]; then
+  WANDB_SED=(-e 's|^wandb_mode: .*|wandb_mode: '"$WANDB_MODE_OVERRIDE"'|')
+fi
+sed \
+  -e 's| /scratch/gpfs/ROSENGROUP/common/globus_share_OA/| '"$DATA_ROOT"'/|g' \
+  -e 's| /data/mp/chg_datasets/| '"$DATA_ROOT"'/mp/chg_datasets/|g' \
+  -e 's|^ckpt_path: .*|ckpt_path: '"$CKPT_ROOT"'/${MODE}_${RUN}|' \
+  "${WANDB_SED[@]}" \
+  "$SRC_CFG" \
+  | python3 -c "
+import sys, re
+text = sys.stdin.read()
+# substitute MODE/RUN tokens from env
+text = text.replace('\${MODE}', '$MODE')
+# pull run_name from the config itself for the ckpt subdir
+m = re.search(r'^run_name:\s*(.+)$', text, re.M)
+run = (m.group(1).strip() if m else 'run')
+text = text.replace('\${RUN}', run)
+print(text)
+" > "$RUNTIME_CFG"
+
+# Sanity-print the rewritten paths
+echo "=== runtime config datasets/ckpt ==="
+grep -E '^\s*(root|split_file|ckpt_path):' "$RUNTIME_CFG"
+
+# Pull entity/project from the config so the wandb URL printed below is correct
+# for whichever mode/config was selected.
+WB_ENTITY="$(grep -E '^entity:' "$RUNTIME_CFG" | awk '{print $2}')"
+WB_PROJECT="$(grep -E '^wb_pname:' "$RUNTIME_CFG" | awk '{print $2}')"
+
+# Kill any existing session of the same name
+if tmux has-session -t "$TMUX_SESSION" 2>/dev/null; then
+  echo "killing existing tmux session $TMUX_SESSION"
+  tmux kill-session -t "$TMUX_SESSION"
+fi
+
+# Launch training + backup loop in one tmux session with two windows.
+PYTHONPATH="$REPO_DIR/src${PYTHONPATH:+:$PYTHONPATH}"
+export PYTHONPATH
+
+# Window 1: training. Re-exec on crash so a transient failure doesn't end
+# the session; Lightning auto-resumes from last.ckpt on the next start.
+# - set -o pipefail captures uv's exit code through the `| tee`
+# - uv is invoked by absolute path so we don't depend on a sourced PATH
+TRAIN_CMD="set -o pipefail; cd '$REPO_DIR' && export WANDB_API_KEY='$WANDB_API_KEY' && \
+  while true; do \
+    echo \"\$(date -Iseconds) starting training\" | tee -a $CKPT_ROOT/train.log; \
+    '$UV_BIN' run python -m electrai.entrypoints.main train --config '$RUNTIME_CFG' 2>&1 | tee -a $CKPT_ROOT/train.log; \
+    rc=\$?; \
+    echo \"\$(date -Iseconds) training exited rc=\$rc\" | tee -a $CKPT_ROOT/train.log; \
+    [ \$rc -eq 0 ] && break; \
+    sleep 30; \
+  done"
+
+# Window 2: checkpoint backup loop, every CKPT_BACKUP_S seconds.
+BACKUP_CMD="while true; do \
+  echo \"\$(date -Iseconds) backing up checkpoints\" | tee -a $CKPT_ROOT/backup.log; \
+  aws s3 sync '$CKPT_ROOT' 's3://$S3_CKPT_BUCKET/$S3_CKPT_PREFIX/' --only-show-errors 2>&1 | tee -a $CKPT_ROOT/backup.log; \
+  sleep $CKPT_BACKUP_S; \
+done"
+
+tmux new-session -d -s "$TMUX_SESSION" -n train "$TRAIN_CMD"
+tmux new-window  -t "$TMUX_SESSION" -n backup "$BACKUP_CMD"
+tmux ls
+
+cat <<EOF
+
+Training launched as tmux session: $TMUX_SESSION
+  attach:        tmux attach -t $TMUX_SESSION
+  train log:     tail -f $CKPT_ROOT/train.log
+  backup log:    tail -f $CKPT_ROOT/backup.log
+  wandb:         https://wandb.ai/$WB_ENTITY/$WB_PROJECT
+  S3 ckpt sink:  s3://$S3_CKPT_BUCKET/$S3_CKPT_PREFIX/
+EOF
diff --git a/scripts/lambda/run_training_multinode.sh b/scripts/lambda/run_training_multinode.sh
new file mode 100755
index 00000000..cebb3fcb
--- /dev/null
+++ b/scripts/lambda/run_training_multinode.sh
@@ -0,0 +1,256 @@
+#!/usr/bin/env bash
+# Launch 2-node (or more) DDP training across Lambda Cloud H100 instances.
+# Drop-in companion to run_training.sh (single-node). The training entrypoint
+# already reads LOCAL_WORLD_SIZE / WORLD_SIZE and derives num_nodes for the
+# Lightning Trainer, so we just need torchrun to set the right env on both
+# nodes and a NCCL setup that works over Lambda's 100 Gbps Ethernet (no IB).
+#
+# Usage (run on EACH node, simultaneously):
+#   # head (rank 0):
+#   NODE_RANK=0 MASTER_ADDR=<head-private-ip> \
+#     bash scripts/lambda/run_training_multinode.sh full
+#
+#   # worker (rank 1):
+#   NODE_RANK=1 MASTER_ADDR=<head-private-ip> \
+#     bash scripts/lambda/run_training_multinode.sh full
+#
+# Required env:
+#   NODE_RANK     0 for head, 1..NUM_NODES-1 for workers
+#   MASTER_ADDR   IP of the head node, reachable from all workers (use the
+#                 PRIVATE / internal interface IP, not the public NAT'd one)
+#
+# Optional env (with defaults):
+#   NUM_NODES         2
+#   NPROC_PER_NODE    8                              (1 rank per H100)
+#   MASTER_PORT       29500
+#   NCCL_SOCKET_IFNAME (autodetect via `ip route get $MASTER_ADDR`)
+#   WANDB_MODE_OVERRIDE  (inherit pattern from run_training.sh; "offline"
+#                         for online wandb, "disabled" for smokes)
+#   See MULTINODE.md for the full operator runbook.
+
+set -uo pipefail
+
+MODE="${1:-smoke}"   # smoke | full
+
+# -- required inputs -----------------------------------------------------------
+: "${NODE_RANK:?NODE_RANK is required (0 = head, 1+ = worker)}"
+: "${MASTER_ADDR:?MASTER_ADDR is required (head node private IP)}"
+
+# -- defaults ------------------------------------------------------------------
+NUM_NODES="${NUM_NODES:-2}"
+NPROC_PER_NODE="${NPROC_PER_NODE:-8}"
+MASTER_PORT="${MASTER_PORT:-29500}"
+
+REPO_DIR="${REPO_DIR:-$HOME/electrai}"
+NFS_ROOT="${NFS_ROOT:-$(ls -d /lambda/nfs/* 2>/dev/null | head -1)}"
+[ -z "$NFS_ROOT" ] && { echo "ERROR: no /lambda/nfs/* mount found; pass NFS_ROOT=... explicitly"; exit 1; }
+DATA_ROOT="${DATA_ROOT:-$NFS_ROOT/data}"
+CKPT_ROOT="${CKPT_ROOT:-$NFS_ROOT/checkpoints}"
+S3_CKPT_BUCKET="${S3_CKPT_BUCKET:-oa-electrai}"
+S3_CKPT_PREFIX="${S3_CKPT_PREFIX:-checkpoints/lambda-multinode}"
+CKPT_BACKUP_S="${CKPT_BACKUP_S:-600}"
+TMUX_SESSION="${TMUX_SESSION:-electrai-train}"
+UV_BIN="${UV_BIN:-$HOME/.local/bin/uv}"
+
+# -- pick the right config -----------------------------------------------------
+case "$MODE" in
+  smoke) SRC_CFG="src/electrai/configs/MP/config_gga_gga+u_f32_smoke.yaml" ;;
+  full)  SRC_CFG="src/electrai/configs/MP/config_gga_gga+u_f32.yaml" ;;
+  *) echo "MODE must be 'smoke' or 'full'"; exit 1 ;;
+esac
+
+cd "$REPO_DIR"
+
+# -- WANDB key (same trick as run_training.sh; Ubuntu's ~/.bashrc returns early
+# for non-interactive shells, so we grep the export line directly) -------------
+if [ -z "${WANDB_API_KEY:-}" ] && [ -f "$HOME/.bashrc" ]; then
+  eval "$(grep -E '^[[:space:]]*export WANDB_API_KEY=' "$HOME/.bashrc" | tail -1)"
+fi
+if [ -z "${WANDB_API_KEY:-}" ]; then
+  echo "ERROR: WANDB_API_KEY env var not set."
+  echo "  Add to ~/.bashrc:  export WANDB_API_KEY='...'  then re-run."
+  exit 1
+fi
+
+mkdir -p "$CKPT_ROOT"
+
+# -- auto-detect the NIC that reaches MASTER_ADDR ------------------------------
+# `ip route get <ip>` prints the interface used to reach that IP. On Lambda
+# this is usually the second NIC (the 100 Gbps internal one) -- e.g. `enp...`
+# or `ens...`. If autodetection picks the wrong NIC (e.g. the management
+# interface), set NCCL_SOCKET_IFNAME explicitly.
+if [ -z "${NCCL_SOCKET_IFNAME:-}" ]; then
+  DETECTED_IF=$(ip -o route get "$MASTER_ADDR" 2>/dev/null | awk '{
+    for (i=1;i<=NF;i++) if ($i=="dev") { print $(i+1); exit }
+  }')
+  if [ -n "$DETECTED_IF" ]; then
+    NCCL_SOCKET_IFNAME="$DETECTED_IF"
+    echo "NCCL_SOCKET_IFNAME auto-detected -> $NCCL_SOCKET_IFNAME (route to $MASTER_ADDR)"
+  else
+    echo "WARN: could not auto-detect NIC for $MASTER_ADDR; falling back to NCCL default."
+    echo "  Set NCCL_SOCKET_IFNAME=<iface>  (see MULTINODE.md)."
+  fi
+fi
+
+# -- rewrite the config so dataset paths and ckpt_path point at our local roots
+# Same sed pipeline as run_training.sh; both nodes regenerate this independently
+# (idempotent, same input -> same output). ------------------------------------
+RUNTIME_CFG="$CKPT_ROOT/runtime-config.yaml"
+WANDB_SED=()
+if [ -n "${WANDB_MODE_OVERRIDE:-}" ]; then
+  WANDB_SED=(-e 's|^wandb_mode: .*|wandb_mode: '"$WANDB_MODE_OVERRIDE"'|')
+fi
+sed \
+  -e 's| /scratch/gpfs/ROSENGROUP/common/globus_share_OA/| '"$DATA_ROOT"'/|g' \
+  -e 's| /data/mp/chg_datasets/| '"$DATA_ROOT"'/mp/chg_datasets/|g' \
+  -e 's|^ckpt_path: .*|ckpt_path: '"$CKPT_ROOT"'/${MODE}_${RUN}|' \
+  "${WANDB_SED[@]}" \
+  "$SRC_CFG" \
+  | python3 -c "
+import sys, re
+text = sys.stdin.read()
+text = text.replace('\${MODE}', '$MODE')
+m = re.search(r'^run_name:\s*(.+)$', text, re.M)
+run = (m.group(1).strip() if m else 'run')
+text = text.replace('\${RUN}', run)
+print(text)
+" > "$RUNTIME_CFG"
+
+echo "=== runtime config datasets/ckpt ==="
+grep -E '^\s*(root|split_file|ckpt_path):' "$RUNTIME_CFG"
+
+# -- tmux ----------------------------------------------------------------------
+if tmux has-session -t "$TMUX_SESSION" 2>/dev/null; then
+  echo "killing existing tmux session $TMUX_SESSION"
+  tmux kill-session -t "$TMUX_SESSION"
+fi
+
+PYTHONPATH="$REPO_DIR/src${PYTHONPATH:+:$PYTHONPATH}"
+export PYTHONPATH
+
+# -- NCCL env  -----------------------------------------------------------------
+# Why each var (all of these get passed through tmux into the training shell):
+#   NCCL_IB_DISABLE=1      Lambda Ethernet-only fabric; force the socket
+#                          transport so NCCL doesn't waste time probing IB.
+#   NCCL_SOCKET_IFNAME=... Pin NCCL to the NIC that actually reaches
+#                          MASTER_ADDR (auto-detected above). Without this,
+#                          NCCL may pick docker0 / a vlan and hang.
+#   NCCL_DEBUG=INFO        Keep this loud for the first few multi-node runs;
+#                          drop to WARN once stable to cut log noise.
+#   NCCL_ASYNC_ERROR_HANDLING=1
+#                          Surface NCCL errors as Python exceptions instead
+#                          of silent hangs. Strongly recommended on Ethernet.
+#   NCCL_P2P_LEVEL=NVL     Intra-node: use NVLink only (H100 SXM has NVL).
+#                          Inter-node always falls back to socket regardless.
+NCCL_ENV=(
+  "NCCL_IB_DISABLE=1"
+  "NCCL_DEBUG=INFO"
+  "NCCL_ASYNC_ERROR_HANDLING=1"
+  "NCCL_P2P_LEVEL=NVL"
+)
+if [ -n "${NCCL_SOCKET_IFNAME:-}" ]; then
+  NCCL_ENV+=("NCCL_SOCKET_IFNAME=$NCCL_SOCKET_IFNAME")
+fi
+NCCL_EXPORTS=$(printf 'export %s; ' "${NCCL_ENV[@]}")
+
+# -- training command ----------------------------------------------------------
+# Rendezvous backend choice:
+#   We use torchrun's classic --master_addr / --master_port (a.k.a. "static"
+#   rendezvous). For exactly 2 known-IP nodes this is simpler and friendlier
+#   to firewall config than c10d, and matches the rest of the runbook's
+#   "head node IP" mental model. Switch to `--rdzv_backend=c10d
+#   --rdzv_endpoint=$MASTER_ADDR:$MASTER_PORT --rdzv_id=electrai` if/when we
+#   want elastic restarts (e.g. workers can drop/rejoin) -- not needed today.
+#
+# torchrun sets LOCAL_RANK, RANK, WORLD_SIZE, LOCAL_WORLD_SIZE in each worker
+# process. train.py already reads LOCAL_WORLD_SIZE / WORLD_SIZE to derive
+# num_nodes for the Lightning Trainer.
+#
+# We wrap in a while-loop for the same crash-resume behaviour as the
+# single-node script. Lightning auto-resumes from last.ckpt on restart.
+TORCHRUN_CMD=(
+  "$UV_BIN" run torchrun
+  --nnodes="$NUM_NODES"
+  --node_rank="$NODE_RANK"
+  --nproc_per_node="$NPROC_PER_NODE"
+  --master_addr="$MASTER_ADDR"
+  --master_port="$MASTER_PORT"
+  -m electrai.entrypoints.main train --config "$RUNTIME_CFG"
+)
+# expand to a single shell string for tmux's -d send-keys form
+TORCHRUN_STR="${TORCHRUN_CMD[*]}"
+
+TRAIN_CMD="set -o pipefail; cd '$REPO_DIR' && \
+  export WANDB_API_KEY='$WANDB_API_KEY' && \
+  $NCCL_EXPORTS \
+  while true; do \
+    echo \"\$(date -Iseconds) [node $NODE_RANK/$NUM_NODES] starting training\" | tee -a $CKPT_ROOT/train.log; \
+    $TORCHRUN_STR 2>&1 | tee -a $CKPT_ROOT/train.log; \
+    rc=\$?; \
+    echo \"\$(date -Iseconds) [node $NODE_RANK/$NUM_NODES] training exited rc=\$rc\" | tee -a $CKPT_ROOT/train.log; \
+    [ \$rc -eq 0 ] && break; \
+    sleep 30; \
+  done"
+
+tmux new-session -d -s "$TMUX_SESSION" -n train "$TRAIN_CMD"
+
+# -- head-node-only: ckpt backup + wandb sync windows --------------------------
+# In Lightning DDP only global rank 0 writes checkpoints, and global rank 0
+# is always on the head node (NODE_RANK=0). So workers don't need backup or
+# wandb-sync windows.
+if [ "$NODE_RANK" = "0" ]; then
+  BACKUP_CMD="while true; do \
+    echo \"\$(date -Iseconds) backing up checkpoints\" | tee -a $CKPT_ROOT/backup.log; \
+    aws s3 sync '$CKPT_ROOT' 's3://$S3_CKPT_BUCKET/$S3_CKPT_PREFIX/' --only-show-errors 2>&1 | tee -a $CKPT_ROOT/backup.log; \
+    sleep $CKPT_BACKUP_S; \
+  done"
+  tmux new-window -t "$TMUX_SESSION" -n backup "$BACKUP_CMD"
+
+  # wandb-sync window: only useful when WANDB_MODE_OVERRIDE=offline (the
+  # working pattern for PrinceOA right now -- online login crashes). For
+  # smoke runs with WANDB_MODE_OVERRIDE=disabled there's nothing to sync,
+  # but starting the window is cheap and idempotent.
+  WANDB_SYNC_CMD="cd '$REPO_DIR' && export WANDB_API_KEY='$WANDB_API_KEY' && \
+    while true; do \
+      echo \"\$(date -Iseconds) wandb sync sweep\" | tee -a $CKPT_ROOT/wandb-sync.log; \
+      find . -type d -name 'offline-run-*' -print 2>/dev/null \
+        | xargs -r -n1 '$UV_BIN' run wandb sync 2>&1 \
+        | tee -a $CKPT_ROOT/wandb-sync.log; \
+      sleep 300; \
+    done"
+  tmux new-window -t "$TMUX_SESSION" -n wandb-sync "$WANDB_SYNC_CMD"
+fi
+
+tmux ls
+
+cat <<EOF
+
+Multi-node training launched (node $NODE_RANK of $NUM_NODES).
+  tmux session : $TMUX_SESSION
+  attach       : tmux attach -t $TMUX_SESSION
+  train log    : tail -f $CKPT_ROOT/train.log
+EOF
+
+if [ "$NODE_RANK" = "0" ]; then
+  cat <<EOF
+  backup log   : tail -f $CKPT_ROOT/backup.log
+  wandb-sync   : tail -f $CKPT_ROOT/wandb-sync.log
+  S3 ckpt sink : s3://$S3_CKPT_BUCKET/$S3_CKPT_PREFIX/
+EOF
+fi
+
+cat <<EOF
+
+NCCL env on this node:
+EOF
+printf '  %s\n' "${NCCL_ENV[@]}"
+
+cat <<EOF
+
+If training hangs at "initializing process group", check:
+  1. MASTER_ADDR ($MASTER_ADDR) is reachable from this node ($(hostname))
+     (try:  nc -zv $MASTER_ADDR $MASTER_PORT)
+  2. NCCL_SOCKET_IFNAME is the right NIC (see MULTINODE.md "diagnostics").
+  3. Both nodes were started within ~60s of each other; static rendezvous
+     does not wait forever for late joiners.
+EOF
diff --git a/scripts/lambda/setup.sh b/scripts/lambda/setup.sh
new file mode 100755
index 00000000..9ed0f0b1
--- /dev/null
+++ b/scripts/lambda/setup.sh
@@ -0,0 +1,68 @@
+#!/usr/bin/env bash
+# One-time setup for a fresh Lambda 4x/8x H100 (or A100) instance.
+# Idempotent; safe to re-run.
+
+set -euo pipefail
+
+REPO_URL="${REPO_URL:-git@github.com:Quantum-Accelerators/electrai.git}"
+REPO_DIR="${REPO_DIR:-$HOME/electrai}"
+BRANCH="${BRANCH:-betsy/gga-gga+u-f32}"
+
+echo "=== installing uv ==="
+if ! command -v uv >/dev/null 2>&1; then
+  curl -LsSf https://astral.sh/uv/install.sh | sh
+  export PATH="$HOME/.local/bin:$PATH"
+fi
+uv --version
+
+echo "=== aws cli ==="
+if ! command -v aws >/dev/null 2>&1; then
+  sudo apt-get update -qq
+  sudo apt-get install -y -qq awscli
+fi
+aws --version
+
+echo "=== tmux ==="
+if ! command -v tmux >/dev/null 2>&1; then
+  sudo apt-get install -y -qq tmux
+fi
+tmux -V
+
+echo "=== repo ==="
+if [ ! -d "$REPO_DIR/.git" ]; then
+  git clone "$REPO_URL" "$REPO_DIR"
+fi
+cd "$REPO_DIR"
+git fetch origin
+git checkout "$BRANCH"
+git pull --ff-only origin "$BRANCH"
+
+echo "=== uv sync ==="
+uv sync
+
+echo "=== checking credentials ==="
+if ! aws sts get-caller-identity >/dev/null 2>&1; then
+  echo "AWS creds not configured. Run: aws configure"
+  echo "  (need read access to s3://oa-electrai/mp/chg_datasets/*)"
+  exit 1
+fi
+echo "AWS identity:"
+aws sts get-caller-identity --output text --query 'Arn'
+
+if [ -z "${WANDB_API_KEY:-}" ]; then
+  echo "WARNING: WANDB_API_KEY env var not set."
+  echo "  Add to ~/.bashrc:  export WANDB_API_KEY='...'"
+  echo "  (Or pass inline before run_training.sh.)"
+fi
+
+echo
+echo "=== setup OK ==="
+echo "Repo at  : $REPO_DIR"
+echo "Branch   : $(git rev-parse --abbrev-ref HEAD)"
+echo "Commit   : $(git rev-parse --short HEAD)"
+echo
+echo "Next:"
+echo "  bash scripts/lambda/data_sync.sh       # ~1-3h, 1.2 TiB from S3 to local NVMe"
+echo "  bash scripts/lambda/prep_data.sh       # relink data/label dirs, build smoke filelists"
+echo "  bash scripts/lambda/run_training.sh smoke      # validate end-to-end"
+echo "  bash scripts/lambda/run_training.sh full       # full 100-epoch campaign"
diff --git a/scripts/lambda/systemd/electrai-monitor-watchdog.service b/scripts/lambda/systemd/electrai-monitor-watchdog.service
new file mode 100644
index 00000000..7449afeb
--- /dev/null
+++ b/scripts/lambda/systemd/electrai-monitor-watchdog.service
@@ -0,0 +1,10 @@
+[Unit]
+Description=ElectrAI monitor watchdog (checks the monitor is alive; pings Slack)
+
+[Service]
+Type=oneshot
+User=ubuntu
+Environment=HOME=/home/ubuntu
+Environment=PATH=/usr/local/bin:/usr/bin:/bin
+EnvironmentFile=-/home/ubuntu/.config/electrai-monitor/monitor.env
+ExecStart=/usr/bin/env bash /home/ubuntu/electrai/scripts/lambda/monitor_watchdog.sh
diff --git a/scripts/lambda/systemd/electrai-monitor-watchdog.timer b/scripts/lambda/systemd/electrai-monitor-watchdog.timer
new file mode 100644
index 00000000..1baf162a
--- /dev/null
+++ b/scripts/lambda/systemd/electrai-monitor-watchdog.timer
@@ -0,0 +1,10 @@
+[Unit]
+Description=Run the ElectrAI monitor watchdog every 30 minutes
+
+[Timer]
+OnBootSec=10min
+OnUnitActiveSec=30min
+AccuracySec=1min
+
+[Install]
+WantedBy=timers.target
diff --git a/scripts/lambda/systemd/electrai-monitor.service b/scripts/lambda/systemd/electrai-monitor.service
new file mode 100644
index 00000000..84de003c
--- /dev/null
+++ b/scripts/lambda/systemd/electrai-monitor.service
@@ -0,0 +1,19 @@
+[Unit]
+Description=ElectrAI Lambda training monitor (resident Claude agent)
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+User=ubuntu
+Environment=HOME=/home/ubuntu
+# claude (native installer / npm global), uv, and aws can land in any of these:
+Environment=PATH=/home/ubuntu/.local/bin:/home/ubuntu/.npm-global/bin:/usr/local/bin:/usr/bin:/bin
+WorkingDirectory=/home/ubuntu/electrai
+EnvironmentFile=-/home/ubuntu/.config/electrai-monitor/monitor.env
+ExecStart=/usr/bin/env bash /home/ubuntu/electrai/scripts/lambda/monitor_loop.sh
+Restart=always
+RestartSec=30
+
+[Install]
+WantedBy=multi-user.target
diff --git a/scripts/lambda/train_watchdog.sh b/scripts/lambda/train_watchdog.sh
new file mode 100755
index 00000000..90fa9cc9
--- /dev/null
+++ b/scripts/lambda/train_watchdog.sh
@@ -0,0 +1,100 @@
+#!/usr/bin/env bash
+# One-shot training watchdog for the Lambda W64 run. Run from cron (~10 min).
+#
+# Catches the failure that silently burned ~2 days on 2026-07-13: training went
+# to NaN but kept "running" (GPUs busy, session up), and save_top_k masked it so
+# the best-checkpoint list looked frozen rather than alarming.
+#
+# Detects, on each tick:
+#   NaN    - sustained nan train_loss (2 strikes, ~20 min) -> ALERT + stop run
+#   STALL  - train.log not updated in STALL_S while session up -> ALERT
+#   DOWN   - tmux session gone -> ALERT
+#
+# Emits: ALERT.txt in the checkpoint dir (mirrored to S3 by the backup loop),
+# a line in ~/train_watchdog.log, and a Slack post if SLACK_WEBHOOK_URL is set.
+# De-dups Slack so an unchanged condition doesn't ping every tick.
+#
+# Install:  (echo '*/10 * * * * bash $HOME/electrai/scripts/lambda/train_watchdog.sh') | crontab -
+set -uo pipefail
+
+ENV_FILE="${WATCHDOG_ENV:-$HOME/.config/electrai-monitor/monitor.env}"
+[ -f "$ENV_FILE" ] && { set -a; . "$ENV_FILE"; set +a; }
+
+SESSION="${WATCHDOG_SESSION:-electrai-train}"
+NFS_ROOT="${NFS_ROOT:-$(ls -d /lambda/nfs/* 2>/dev/null | head -1)}"
+CKPT_ROOT="${CKPT_ROOT:-$NFS_ROOT/checkpoints}"
+LOG="${WATCHDOG_LOG:-$CKPT_ROOT/train.log}"
+STALL_S="${WATCHDOG_STALL_S:-2700}"              # 45 min without a log write
+NAN_STRIKES_MAX="${WATCHDOG_NAN_STRIKES:-2}"     # consecutive nan ticks before acting
+STOP_ON_NAN="${WATCHDOG_STOP_ON_NAN:-1}"
+ALERT_FILE="$CKPT_ROOT/ALERT.txt"
+WLOG="$HOME/train_watchdog.log"
+STATE_DIR="${WATCHDOG_STATE:-$HOME/.local/state/electrai-watchdog}"
+mkdir -p "$STATE_DIR"
+STRIKE_FILE="$STATE_DIR/nan_strikes"
+LASTMSG_FILE="$STATE_DIR/last_alert"
+
+ts() { date -Iseconds; }
+
+alert() {
+  local kind="$1"; shift
+  local msg="[$kind] $*"
+  printf '%s %s\n' "$(ts)" "$msg" | tee -a "$WLOG" > "$ALERT_FILE"
+  # Slack, de-duplicated on (kind+msg)
+  if [ -n "${SLACK_WEBHOOK_URL:-}" ] && [ "$msg" != "$(cat "$LASTMSG_FILE" 2>/dev/null)" ]; then
+    local safe; safe=$(printf 'electrai W64 watchdog (%s): %s' "$(hostname)" "$msg" | tr -d '"\n\r' | cut -c1-400)
+    curl -fsS -X POST -H 'Content-type: application/json' \
+      --data "{\"text\":\":rotating_light: $safe\"}" "$SLACK_WEBHOOK_URL" >/dev/null 2>&1 || true
+  fi
+  printf '%s' "$msg" > "$LASTMSG_FILE"
+}
+
+ok() { printf '%s OK %s\n' "$(ts)" "$*" >> "$WLOG"; rm -f "$ALERT_FILE"; : > "$LASTMSG_FILE"; }
+
+# --- DOWN: session gone ---
+if ! tmux has-session -t "$SESSION" 2>/dev/null; then
+  alert DOWN "tmux session '$SESSION' is gone -- training not running"
+  exit 2
+fi
+
+# --- STALL: log not written recently ---
+if [ -f "$LOG" ]; then
+  age=$(( $(date +%s) - $(stat -c %Y "$LOG") ))
+  if [ "$age" -gt "$STALL_S" ]; then
+    alert STALL "train.log not updated in ${age}s (> ${STALL_S}s) -- stalled/hung"
+    exit 3
+  fi
+else
+  alert DOWN "train.log missing at $LOG"
+  exit 2
+fi
+
+# --- NaN: sustained nan in the newest run's recent train_loss values ---
+start=$(grep -n "starting training" "$LOG" 2>/dev/null | tail -1 | cut -d: -f1); start="${start:-1}"
+recent=$(tail -n +"$start" "$LOG" 2>/dev/null | tr '\r' '\n' | grep -oE 'train_loss_step=[0-9na.]+' | tail -30)
+last=$(printf '%s' "$recent" | tail -1)
+nan_n=$(printf '%s\n' "$recent" | grep -c 'nan')
+
+# Real divergence: latest value nan AND >=25/30 recent are nan (startup shows
+# nan only briefly before numeric losses appear, so it won't hold across ticks).
+if [ -n "$last" ] && printf '%s' "$last" | grep -q 'nan' && [ "$nan_n" -ge 25 ]; then
+  strikes=$(( $(cat "$STRIKE_FILE" 2>/dev/null || echo 0) + 1 ))
+  echo "$strikes" > "$STRIKE_FILE"
+  printf '%s NaN-strike %s/%s (nan %s/30, last=%s)\n' "$(ts)" "$strikes" "$NAN_STRIKES_MAX" "$nan_n" "$last" >> "$WLOG"
+  if [ "$strikes" -ge "$NAN_STRIKES_MAX" ]; then
+    if [ "$STOP_ON_NAN" = "1" ]; then
+      tmux kill-session -t "$SESSION" 2>/dev/null
+      pkill -9 -f "[v]env/bin/python3 -m electrai" 2>/dev/null
+      alert NAN-STOPPED "Sustained NaN over ${strikes} ticks -- STOPPED the run to stop compute waste. Recover: relaunch from newest good ckpt_epoch=* (NOT last.ckpt)."
+    else
+      alert NAN "Sustained NaN over ${strikes} ticks -- training is producing NaN."
+    fi
+    exit 4
+  fi
+  exit 0
+fi
+
+# healthy
+echo 0 > "$STRIKE_FILE"
+ok "last=${last:-<none>} nan=${nan_n}/30 logage=${age}s"
+exit 0
diff --git a/scripts/review_lrwd_sweep.py b/scripts/review_lrwd_sweep.py
new file mode 100755
index 00000000..b74e2ffb
--- /dev/null
+++ b/scripts/review_lrwd_sweep.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+"""Aggregate the LR/WD width-sweep W&B runs into per-(width, lr, wd) results.
+
+Preemption restarts create a fresh W&B run per segment (no explicit id=), so
+runs are grouped by (n_channels, lr, weight_decay) from run config and merged
+per epoch before picking best/final val_loss. val_loss is NormMAE; x100 = %.
+
+    uv run python scripts/review_lrwd_sweep.py [project]
+"""
+# ruff: noqa: T201
+
+from __future__ import annotations
+
+import sys
+
+import wandb
+import wandb.sdk.lib.server as _wandb_server
+
+# Server returns "flags": null, crashing the viewer query inside Api() login
+# (same wandb bug that forces offline mode + sidecar sync on the cluster).
+_orig_query = _wandb_server.Server.query_with_timeout
+
+
+def _query_tolerant(self, *args, **kwargs):
+    try:
+        _orig_query(self, *args, **kwargs)
+    except TypeError:
+        self._flags = {}
+
+
+_wandb_server.Server.query_with_timeout = _query_tolerant
+
+ENTITY = "PrinceOA"
+PROJECT = sys.argv[1] if len(sys.argv) > 1 else "mp-gga-ggau-lrwd"
+
+
+def main() -> None:
+    api = wandb.Api()
+    runs = api.runs(f"{ENTITY}/{PROJECT}")
+
+    trials: dict[tuple[int, float, float, str], dict[int, float]] = {}
+    for run in runs:
+        cfg = run.config
+        model = cfg.get("model") or {}
+        width = model.get("n_channels")
+        lr = cfg.get("lr")
+        wd = cfg.get("weight_decay", 0.0)
+        if width is None or lr is None:
+            print(f"  (skipping run {run.name}: no width/lr in config)")
+            continue
+        # The _rep2 noise-bar reruns share (width, lr, wd) with their stage-A
+        # twins; the config run_name (the config stem) keeps them apart while
+        # still merging preemption-restart segments, which share it.
+        rep = "rep2" if str(cfg.get("run_name", "")).endswith("_rep2") else ""
+        per_epoch = trials.setdefault((width, float(lr), float(wd), rep), {})
+        for h in run.scan_history(keys=["epoch", "val_loss_epoch"]):
+            ep, val = h.get("epoch"), h.get("val_loss_epoch")
+            if ep is None or val is None:
+                continue
+            # Restart segments can re-log an epoch; keep the better value
+            per_epoch[int(ep)] = min(val, per_epoch.get(int(ep), float("inf")))
+
+    print(
+        f"{'width':>5} {'lr':>8} {'wd':>7} {'rep':>4} {'epochs':>6} {'best val':>9} "
+        f"{'best%':>6} {'final val':>9} {'@ep':>3}"
+    )
+    best_by_width: dict[int, tuple[float, float]] = {}
+    for (width, lr, wd, rep), per_epoch in sorted(trials.items()):
+        if not per_epoch:
+            continue
+        best_ep = min(per_epoch, key=per_epoch.get)
+        last_ep = max(per_epoch)
+        best = per_epoch[best_ep]
+        print(
+            f"{width:>5} {lr:>8g} {wd:>7g} {rep:>4} {len(per_epoch):>6} {best:>9.6f} "
+            f"{best * 100:>6.3f} {per_epoch[last_ep]:>9.6f} {best_ep:>3}"
+        )
+        if (
+            wd == 0.0
+            and not rep
+            and best < best_by_width.get(width, (float("inf"),))[0]
+        ):
+            best_by_width[width] = (best, lr)
+
+    if best_by_width:
+        args = " ".join(f"{w}={lr:g}" for w, (_, lr) in sorted(best_by_width.items()))
+        print(f"\nBest LR per width (wd=0 trials): {args}")
+        print(
+            f"Stage B: uv run python scripts/coreweave/gen_lrwd_sweep.py "
+            f"--stage b --best-lr {args}"
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/electrai/configs/MP/config_gga_gga+u_f32.yaml b/src/electrai/configs/MP/config_gga_gga+u_f32.yaml
new file mode 100644
index 00000000..dde48c02
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_f32.yaml
@@ -0,0 +1,48 @@
+# Dataset / loader parameters
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+    - root: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga/mp_filelist.txt
+      split_file: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga/split.json
+      dataset_id: 1
+    - root: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga+u/mp_filelist.txt
+      split_file: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga+u/split.json
+      dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+
+# Model
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+
+# Training parameters
+precision: 32
+epochs: 100
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+
+# Weights and biases
+wandb_mode: online
+entity: PrinceOA
+wb_pname: mp-large-scale
+run_name: gga_gga+u_f32
+
+# checkpoints
+ckpt_path: /scratch/gpfs/ROSENGROUP/ho0950/electrai/examples/MP/experiments/chgcar/functionals/gga_gga+u_f32/checkpoints
+
+# set HF as well
diff --git a/src/electrai/configs/MP/config_gga_gga+u_f32_smoke.yaml b/src/electrai/configs/MP/config_gga_gga+u_f32_smoke.yaml
new file mode 100644
index 00000000..3c0427b5
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_f32_smoke.yaml
@@ -0,0 +1,56 @@
+# Smoke test for config_gga_gga+u_f32 on Modal: subset filelists + few epochs.
+# Validates data wiring, DDP/checkpoint path, and wandb logging before the full run.
+#
+# Create the subset filelists on the Volume first (after the Globus transfer):
+#   modal run modal/prep_volume.py        # also (re)links data/label, commits the Volume
+#
+# Paths are already in Volume (/data) form, so train.py's della->Volume remap is a no-op.
+
+# Dataset / loader parameters
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+    - root: /data/mp/chg_datasets/functionals/gga/mp_filelist_smoke.txt
+      split_file: null
+      val_frac: 0.1
+      dataset_id: 1
+    - root: /data/mp/chg_datasets/functionals/gga+u/mp_filelist_smoke.txt
+      split_file: null
+      val_frac: 0.1
+      dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+
+# Model
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+
+# Training parameters
+precision: 32
+epochs: 2
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+
+# Weights and biases
+wandb_mode: online
+entity: PrinceOA
+wb_pname: mp-large-scale
+run_name: gga_gga+u_f32_smoke
+
+# checkpoints (overridden by modal/train.py to /checkpoints/<run_name>)
+ckpt_path: /checkpoints/gga_gga+u_f32_smoke
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w128.yaml b/src/electrai/configs/MP/config_gga_gga+u_w128.yaml
new file mode 100644
index 00000000..0b265397
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w128.yaml
@@ -0,0 +1,70 @@
+# Width-128 point of the width ablation. IDENTICAL to config_gga_gga+u_w96.yaml
+# (same capped GGA + GGA+U dataset/splits, bf16-mixed, lr 0.001, warmup 4) with
+# a SINGLE delta so the only variable vs W96 is width:
+#   - model.n_channels: 96 -> 128
+# Compare against W96 on this exact dataset (W&B mp-gga-ggau-width run
+# `revived-energy-7`/z21di7sl lineage: val_loss 0.007929 at epoch 12) and W64
+# (best 0.008398 at epoch 16, run zz3oecp7).
+#
+# Memory: ~193M params; activations ~4/3 of W96's measured 28.2 GiB peak at the
+# 180^3 cap -> ~40-45 GiB expected, no activation checkpointing needed on
+# GB200 (185 GiB). Verify with scripts/coreweave/dry_run.py before launch.
+# Compute: conv FLOPs ~(128/96)^2 = 1.78x W96 per step; wall-clock increase is
+# smaller because larger channel counts improve GPU occupancy (W96 MFU ~10%).
+# Reuses the same *_capped filelists/splits as W64/W96; do NOT re-cap.
+#
+# Paths target the CoreWeave marin-us-east-08a cluster: dataset staged from the
+# rhoarnet-us-east-08a CAIOS bucket to node-local NVMe by
+# scripts/coreweave/stage_data.sh (run before training; run_training.sh does).
+# Tracked in W&B project `mp-gga-ggau-width`.
+
+# Dataset / loader parameters
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+    - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+      split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga/split_capped.json
+      dataset_id: 1
+    - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+      split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/split_capped.json
+      dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+
+# Model
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 128
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+
+# Training parameters
+precision: bf16-mixed
+epochs: 100
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+
+# Weights and biases — OFFLINE + sidecar sync: online wandb.init crashes with
+# the viewer flags=null TypeError and a rank-0 death silently deadlocks DDP.
+# run_training.sh re-syncs the offline run dirs every CKPT_SYNC_S seconds.
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-width
+run_name: gga_gga+u_w128
+
+# checkpoints: node-local NVMe (survives same-node pod restarts); synced to and
+# restored from the CAIOS bucket by run_training.sh (path derived from this
+# config's filename stem — keep them consistent)
+ckpt_path: /uv/cache/electrai/checkpoints/gga_gga+u_w128
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w128_lr2e-3.yaml b/src/electrai/configs/MP/config_gga_gga+u_w128_lr2e-3.yaml
new file mode 100644
index 00000000..ead2777a
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w128_lr2e-3.yaml
@@ -0,0 +1,41 @@
+# W128 at the sweep-tuned lr 2e-3 (vs the live 1e-3 run, W&B mp-gga-ggau-width).
+# Logs to its OWN W&B project so the two W128 runs stay separate. Same
+# 100-epoch cosine and capped dataset as the incumbent -> matched-epoch
+# comparison against gga_gga+u_w128 tells us what 1e-3 left on the table.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga/split_capped.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/split_capped.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 128
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 100
+lr: 0.002
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-w128-lr2e3
+run_name: config_gga_gga+u_w128_lr2e-3
+ckpt_path: /uv/cache/electrai/checkpoints/gga_gga+u_w128_lr2e-3
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w128_test_della.yaml b/src/electrai/configs/MP/config_gga_gga+u_w128_test_della.yaml
new file mode 100644
index 00000000..eb651c3f
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w128_test_della.yaml
@@ -0,0 +1,55 @@
+# Test-set eval of the W128 lr2e-3 checkpoint (epoch 55, val 0.005785) on Della.
+# Withheld test set = the `test` key of split_capped.json (GGA 1700 + GGA+U 524),
+# staged from s3://oa-electrai into bb9080 scratch and remapped onto the UNCAPPED
+# COMMON filelists (byte-identical to the bucket's) so the shared functionals
+# dirs are used read-only. gga+u on Della is named gga+u_sad (filelist verified
+# identical to the bucket's gga+u).
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga/mp_filelist.txt
+    split_file: /scratch/gpfs/ROSENGROUP/bb9080/w128_test_eval/gga/split_capped_on_uncapped.json
+    dataset_id: 1
+  # gga+u_pads, NOT gga+u_sad: the trained model's gga+u inputs are PADS.
+  # Probe job 12111541 on 3 test samples: PADS 0.29-1.15% NMAE vs SAD
+  # 8.6-14.5% (worse than raw SAD input error) — the staged CoreWeave "gga+u"
+  # data was PADS content. Labels/filelists are identical between variants.
+  - root: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga+u_pads/mp_filelist.txt
+    split_file: /scratch/gpfs/ROSENGROUP/bb9080/w128_test_eval/gga+u/split_capped_on_uncapped.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 0
+  val_workers: 4
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 128
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+# fp32, matching the proven W128 throughput benchmark on this checkpoint/GPU
+# (job 12046200, peak 45.5 GB). bf16-mixed autocast OOM'd on A100: a single
+# ~75 GiB allocation inside a decoder conv on a 108^3 sample (job 12049722) —
+# pathological cuDNN algo/workspace for bf16 5^3 convs on A100 (training ran
+# bf16 fine on GB200, a different GPU/cuDNN).
+precision: 32
+epochs: 100
+lr: 0.002
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+wandb_mode: disabled
+entity: PrinceOA
+wb_pname: mp-gga-ggau-w128-lr2e3
+run_name: w128_epoch55_test_eval
+ckpt_path: /scratch/gpfs/ROSENGROUP/bb9080/w128_test_eval/ckpt
+save_pred: false
+log_dir: /scratch/gpfs/ROSENGROUP/bb9080/w128_test_eval/results
+out_dir: /scratch/gpfs/ROSENGROUP/bb9080/w128_test_eval/results/preds
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w160.yaml b/src/electrai/configs/MP/config_gga_gga+u_w160.yaml
new file mode 100644
index 00000000..8feb75be
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w160.yaml
@@ -0,0 +1,61 @@
+# Width-160 point of the width ablation. Same recipe as W96/W128 (capped
+# GGA + GGA+U dataset/splits, bf16-mixed, lr 0.001, warmup 4) with a SINGLE
+# delta: model.n_channels -> 160 (~302M params). This is the WIDEST width
+# that clears the cuDNN large-tensor kernel cliff on the UNMODIFIED capped
+# dataset (measured 2026-07-22: W160@180^3 = 2.36s/step, 48.3 GiB peak;
+# W176@180^3 = 590s/step — cliff between 1.87e9 and 2.05e9 concat elements).
+# Full comparability with W64/W96/W128: same filelists, do NOT re-cap.
+# Est ~8.5-9h/epoch on 4x GB200 (~7 days to a W96-style plateau).
+# lr 0.001 / warmup 4 kept for single-variable comparability; note the
+# optimizer regime is increasingly untuned at this scale.
+# Tracked in W&B project `mp-gga-ggau-width`.
+# Dataset / loader parameters
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+    - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+      split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga/split_capped.json
+      dataset_id: 1
+    - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+      split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/split_capped.json
+      dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+
+# Model
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 160
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+
+# Training parameters
+precision: bf16-mixed
+epochs: 100
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+
+# Weights and biases — OFFLINE + sidecar sync: online wandb.init crashes with
+# the viewer flags=null TypeError and a rank-0 death silently deadlocks DDP.
+# run_training.sh re-syncs the offline run dirs every CKPT_SYNC_S seconds.
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-width
+run_name: gga_gga+u_w160
+
+# checkpoints: node-local NVMe (survives same-node pod restarts); synced to and
+# restored from the CAIOS bucket by run_training.sh (path derived from this
+# config's filename stem — keep them consistent)
+ckpt_path: /uv/cache/electrai/checkpoints/gga_gga+u_w160
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w192.yaml b/src/electrai/configs/MP/config_gga_gga+u_w192.yaml
new file mode 100644
index 00000000..21807344
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w192.yaml
@@ -0,0 +1,58 @@
+# Width-192 point of the width ablation. Same recipe as W96/W128 (capped
+# GGA + GGA+U, bf16-mixed, lr 0.001, warmup 4) with n_channels: 192 (~435M
+# params). Motivated by the W256 kernel cliff: cuDNN heuristics select
+# pathological conv kernels for 256-channel convs on grids >~150^3 (1495s/step
+# at 180^3), and benchmark-mode search is unusable with this dataset's
+# variable shapes — W192 is the next rung IF it stays on the fast side of the
+# cliff. Validate with scripts/coreweave/dry_run.py --grids 128,160,180
+# --iters 2 in heuristic mode before any launch. Do NOT re-cap the filelists.
+# Dataset / loader parameters
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+    - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+      split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga/split_capped.json
+      dataset_id: 1
+    - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+      split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/split_capped.json
+      dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+
+# Model
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 192
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+
+# Training parameters
+precision: bf16-mixed
+epochs: 100
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+
+# Weights and biases — OFFLINE + sidecar sync: online wandb.init crashes with
+# the viewer flags=null TypeError and a rank-0 death silently deadlocks DDP.
+# run_training.sh re-syncs the offline run dirs every CKPT_SYNC_S seconds.
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-width
+run_name: gga_gga+u_w192
+
+# checkpoints: node-local NVMe (survives same-node pod restarts); synced to and
+# restored from the CAIOS bucket by run_training.sh (path derived from this
+# config's filename stem — keep them consistent)
+ckpt_path: /uv/cache/electrai/checkpoints/gga_gga+u_w192
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w256.yaml b/src/electrai/configs/MP/config_gga_gga+u_w256.yaml
new file mode 100644
index 00000000..0b878f3a
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w256.yaml
@@ -0,0 +1,58 @@
+# Width-256 EXPLORATORY point of the width ablation. Same recipe as W96/W128
+# (capped GGA + GGA+U, bf16-mixed, lr 0.001, warmup 4) with n_channels: 256.
+# ~774M params. Projected from measured W96/W128 scaling: peak ~85 GiB at the
+# 180^3 cap (fits GB200 185 GiB without activation checkpointing), conv FLOPs
+# 4x W128 -> est 12-16h/epoch on 4x GB200. CAVEAT: at this scale lr 0.001 /
+# warmup 4 may be the wrong optimizer regime — a launch is a deliberate
+# decision, not implied by this config existing. Validate with
+# scripts/coreweave/dry_run.py first. Do NOT re-cap the filelists.
+# Dataset / loader parameters
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+    - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+      split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga/split_capped.json
+      dataset_id: 1
+    - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+      split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/split_capped.json
+      dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+
+# Model
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 256
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+
+# Training parameters
+precision: bf16-mixed
+epochs: 100
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+
+# Weights and biases — OFFLINE + sidecar sync: online wandb.init crashes with
+# the viewer flags=null TypeError and a rank-0 death silently deadlocks DDP.
+# run_training.sh re-syncs the offline run dirs every CKPT_SYNC_S seconds.
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-width
+run_name: gga_gga+u_w256
+
+# checkpoints: node-local NVMe (survives same-node pod restarts); synced to and
+# restored from the CAIOS bucket by run_training.sh (path derived from this
+# config's filename stem — keep them consistent)
+ckpt_path: /uv/cache/electrai/checkpoints/gga_gga+u_w256
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w32_lr2e-3.yaml b/src/electrai/configs/MP/config_gga_gga+u_w32_lr2e-3.yaml
new file mode 100644
index 00000000..c55de657
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w32_lr2e-3.yaml
@@ -0,0 +1,43 @@
+# Stage C of the LR/WD width sweep (docs/lr_wd_width_sweep.md): full-scale
+# confirmation at the tuned recipe. Identical to the incumbent production
+# config INCLUDING the 100-epoch cosine -- only lr differs -- so val curves
+# compare against the incumbent at matched epochs. Budget: halt manually
+# around epoch 10-12 (do not set epochs lower: that would compress the
+# cosine and flatter this run vs the incumbent).
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga/split_capped.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/split_capped.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 100
+lr: 0.002
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-width
+run_name: config_gga_gga+u_w32_lr2e-3
+ckpt_path: /uv/cache/electrai/checkpoints/gga_gga+u_w32_lr2e-3
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w64.yaml b/src/electrai/configs/MP/config_gga_gga+u_w64.yaml
new file mode 100644
index 00000000..560ff2a2
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w64.yaml
@@ -0,0 +1,60 @@
+# Width-64 variant of the mp-large-scale run (config_gga_gga+u_f32.yaml).
+# Same GGA + GGA+U hyperparameters, with four deltas:
+#   - model.n_channels: 32 -> 64  (width 64)
+#   - precision: 32 -> bf16-mixed (fp32 OOMs at W64; fp16 overflowed to NaN at
+#     epoch 3 on 2026-07-13, so use bf16 for its fp32-range exponent)
+#   - warmup_length: 1 -> 4
+#   - grid-size cap: uses *_capped filelists/splits (structures with grids
+#     >5,832,000 voxels ~180^3 dropped, ~1.5%). Without the cap, the largest
+#     structures (up to ~540^3) make a single DDP step exceed the 30-min NCCL
+#     watchdog and crash-loop. Regenerate with scripts/lambda/cap_filelists.py.
+# Tracked in W&B project `mp-gga-ggau-width`.
+
+# Dataset / loader parameters
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+    - root: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+      split_file: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga/split_capped.json
+      dataset_id: 1
+    - root: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+      split_file: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/chg_datasets/functionals/gga+u/split_capped.json
+      dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+
+# Model
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+
+# Training parameters
+precision: bf16-mixed
+epochs: 100
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+
+# Weights and biases
+wandb_mode: online
+entity: PrinceOA
+wb_pname: mp-gga-ggau-width
+run_name: gga_gga+u_w64
+
+# checkpoints (overridden by scripts/lambda/run_training.sh to $CKPT_ROOT/<mode>_<run>)
+ckpt_path: ./checkpoints/gga_gga+u_w64
+
+# set HF as well
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w64_lr4e-3.yaml b/src/electrai/configs/MP/config_gga_gga+u_w64_lr4e-3.yaml
new file mode 100644
index 00000000..ae0cd94b
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w64_lr4e-3.yaml
@@ -0,0 +1,43 @@
+# Stage C of the LR/WD width sweep (docs/lr_wd_width_sweep.md): full-scale
+# confirmation at the tuned recipe. Identical to the incumbent production
+# config INCLUDING the 100-epoch cosine -- only lr differs -- so val curves
+# compare against the incumbent at matched epochs. Budget: halt manually
+# around epoch 10-12 (do not set epochs lower: that would compress the
+# cosine and flatter this run vs the incumbent).
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga/split_capped.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/split_capped.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 100
+lr: 0.004
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-width
+run_name: config_gga_gga+u_w64_lr4e-3
+ckpt_path: /uv/cache/electrai/checkpoints/gga_gga+u_w64_lr4e-3
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w96.yaml b/src/electrai/configs/MP/config_gga_gga+u_w96.yaml
new file mode 100644
index 00000000..50142b9e
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w96.yaml
@@ -0,0 +1,72 @@
+# Width-96 point of the width ablation. IDENTICAL to config_gga_gga+u_w64.yaml
+# (same capped GGA + GGA+U dataset/splits, bf16-mixed, lr 0.001, warmup 4) with
+# a SINGLE delta so the only variable vs W64 is width:
+#   - model.n_channels: 64 -> 96
+# Compare against the W64 result on this exact dataset: val NMAE 0.84%
+# (ckpt_epoch=16_val_loss=0.008398), W&B mp-gga-ggau-width run zz3oecp7.
+#
+# Memory: W96 activations ~1.5x W64 (~115 GB), which OOMs an 80 GB A100/H100.
+# This run targets a GB200 (185 GiB visible/GPU), so it fits WITHOUT activation
+# checkpointing -> `use_checkpoint` stays off, matching W64 exactly. If ever run
+# on 80 GB GPUs, set model.use_checkpoint: True (costs ~30% compute).
+# Reuses the same *_capped filelists/splits as W64 (validated 111,257 ids);
+# do NOT re-cap, or the W64 comparison breaks.
+#
+# Paths target the CoreWeave marin-us-east-08a cluster: the dataset is staged
+# from the rhoarnet-us-east-08a CAIOS bucket to node-local NVMe by
+# scripts/coreweave/stage_data.sh, which also creates the functionals/<f>/
+# {data,label} symlinks RhoRead needs. Run stage_data.sh before training.
+# Tracked in W&B project `mp-gga-ggau-width`.
+
+# Dataset / loader parameters
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+    - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+      split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga/split_capped.json
+      dataset_id: 1
+    - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+      split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/split_capped.json
+      dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+
+# Model
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+
+# Training parameters
+precision: bf16-mixed
+epochs: 100
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+
+# Weights and biases — OFFLINE + sidecar sync: online wandb.init crashes with
+# the viewer flags=null TypeError (same wandb bug as lambda2; confirmed on
+# GB200 2026-07-18) and the rank-0 death silently deadlocks DDP.
+# run_training.sh re-syncs the offline run dirs every CKPT_SYNC_S seconds.
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-width
+run_name: gga_gga+u_w96
+
+# checkpoints: node-local NVMe (survives same-node pod restarts); synced to and
+# restored from the CAIOS bucket by the CoreWeave run wrapper
+ckpt_path: /uv/cache/electrai/checkpoints/gga_gga+u_w96
+
+# set HF as well
diff --git a/src/electrai/configs/MP/config_gga_gga+u_w96_lr2e-3.yaml b/src/electrai/configs/MP/config_gga_gga+u_w96_lr2e-3.yaml
new file mode 100644
index 00000000..30ce4748
--- /dev/null
+++ b/src/electrai/configs/MP/config_gga_gga+u_w96_lr2e-3.yaml
@@ -0,0 +1,43 @@
+# Stage C of the LR/WD width sweep (docs/lr_wd_width_sweep.md): full-scale
+# confirmation at the tuned recipe. Identical to the incumbent production
+# config INCLUDING the 100-epoch cosine -- only lr differs -- so val curves
+# compare against the incumbent at matched epochs. Budget: halt manually
+# around epoch 10-12 (do not set epochs lower: that would compress the
+# cosine and flatter this run vs the incumbent).
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga/split_capped.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/split_capped.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 100
+lr: 0.002
+weight_decay: 0.0
+warmup_length: 4
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-width
+run_name: config_gga_gga+u_w96_lr2e-3
+ckpt_path: /uv/cache/electrai/checkpoints/gga_gga+u_w96_lr2e-3
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.00025_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.00025_wd0.yaml
new file mode 100644
index 00000000..dbbcd22b
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.00025_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.00025
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w32_lr0.00025_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w32_lr0.00025_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.0005_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.0005_wd0.yaml
new file mode 100644
index 00000000..dd39944b
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.0005_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.0005
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w32_lr0.0005_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w32_lr0.0005_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.001_wd0.001.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.001_wd0.001.yaml
new file mode 100644
index 00000000..75d7185c
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.001_wd0.001.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.001
+weight_decay: 0.001
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w32_lr0.001_wd0.001
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w32_lr0.001_wd0.001
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.001_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.001_wd0.yaml
new file mode 100644
index 00000000..d1cf47ba
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.001_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w32_lr0.001_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w32_lr0.001_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.0001.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.0001.yaml
new file mode 100644
index 00000000..c942a9e1
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.0001.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.0001
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w32_lr0.002_wd0.0001
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w32_lr0.002_wd0.0001
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.001.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.001.yaml
new file mode 100644
index 00000000..8dc20598
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.001.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.001
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w32_lr0.002_wd0.001
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w32_lr0.002_wd0.001
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.01.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.01.yaml
new file mode 100644
index 00000000..4702236e
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.01.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.01
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w32_lr0.002_wd0.01
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w32_lr0.002_wd0.01
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.yaml
new file mode 100644
index 00000000..c7189368
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w32_lr0.002_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w32_lr0.002_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0_rep2.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0_rep2.yaml
new file mode 100644
index 00000000..6ef2e287
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.002_wd0_rep2.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w32_lr0.002_wd0_rep2
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w32_lr0.002_wd0_rep2
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.004_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.004_wd0.yaml
new file mode 100644
index 00000000..1c52ab16
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w32_lr0.004_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 32
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.004
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w32_lr0.004_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w32_lr0.004_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.00025_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.00025_wd0.yaml
new file mode 100644
index 00000000..769add74
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.00025_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.00025
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.00025_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.00025_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.0005_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.0005_wd0.yaml
new file mode 100644
index 00000000..f6221379
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.0005_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.0005
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.0005_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.0005_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.001_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.001_wd0.yaml
new file mode 100644
index 00000000..44eb4fa3
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.001_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.001_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.001_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.002_wd0.001.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.002_wd0.001.yaml
new file mode 100644
index 00000000..7f7d3eef
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.002_wd0.001.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.001
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.002_wd0.001
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.002_wd0.001
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.002_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.002_wd0.yaml
new file mode 100644
index 00000000..87e1d6b1
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.002_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.002_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.002_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.0001.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.0001.yaml
new file mode 100644
index 00000000..c020d6a6
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.0001.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.004
+weight_decay: 0.0001
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.004_wd0.0001
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.004_wd0.0001
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.001.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.001.yaml
new file mode 100644
index 00000000..57eec956
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.001.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.004
+weight_decay: 0.001
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.004_wd0.001
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.004_wd0.001
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.01.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.01.yaml
new file mode 100644
index 00000000..76d5dbfe
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.01.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.004
+weight_decay: 0.01
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.004_wd0.01
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.004_wd0.01
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.yaml
new file mode 100644
index 00000000..422b3e37
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.004
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.004_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.004_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0_rep2.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0_rep2.yaml
new file mode 100644
index 00000000..df8a6ea2
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.004_wd0_rep2.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.004
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.004_wd0_rep2
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.004_wd0_rep2
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.008_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.008_wd0.yaml
new file mode 100644
index 00000000..7ae605ab
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w64_lr0.008_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 64
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.008
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w64_lr0.008_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w64_lr0.008_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.00025_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.00025_wd0.yaml
new file mode 100644
index 00000000..9ca19f75
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.00025_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.00025
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w96_lr0.00025_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w96_lr0.00025_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.0005_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.0005_wd0.yaml
new file mode 100644
index 00000000..de10e352
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.0005_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.0005
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w96_lr0.0005_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w96_lr0.0005_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.001_wd0.001.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.001_wd0.001.yaml
new file mode 100644
index 00000000..67522f52
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.001_wd0.001.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.001
+weight_decay: 0.001
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w96_lr0.001_wd0.001
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w96_lr0.001_wd0.001
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.001_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.001_wd0.yaml
new file mode 100644
index 00000000..df3ea370
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.001_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.001
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w96_lr0.001_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w96_lr0.001_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.0001.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.0001.yaml
new file mode 100644
index 00000000..e710713a
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.0001.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.0001
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w96_lr0.002_wd0.0001
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w96_lr0.002_wd0.0001
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.001.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.001.yaml
new file mode 100644
index 00000000..ea0b4330
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.001.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.001
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w96_lr0.002_wd0.001
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w96_lr0.002_wd0.001
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.01.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.01.yaml
new file mode 100644
index 00000000..269578ad
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.01.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.01
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w96_lr0.002_wd0.01
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w96_lr0.002_wd0.01
+optimizer: adamw
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.yaml
new file mode 100644
index 00000000..1223cc94
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w96_lr0.002_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w96_lr0.002_wd0
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0_rep2.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0_rep2.yaml
new file mode 100644
index 00000000..08ece595
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.002_wd0_rep2.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.002
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w96_lr0.002_wd0_rep2
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w96_lr0.002_wd0_rep2
+optimizer: adam
diff --git a/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.004_wd0.yaml b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.004_wd0.yaml
new file mode 100644
index 00000000..375133ad
--- /dev/null
+++ b/src/electrai/configs/MP/sweep_lrwd/sweep_lrwd_w96_lr0.004_wd0.yaml
@@ -0,0 +1,40 @@
+# Generated by scripts/coreweave/gen_lrwd_sweep.py from config_gga_gga+u_w96.yaml
+# Deltas: width/lr/wd/optimizer, 8-epoch schedule, 12K sweep splits.
+data:
+  _target_: electrai.dataloader.dataset.RhoRead
+  datasets:
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga_split_sweep12k.json
+    dataset_id: 1
+  - root: /uv/cache/electrai/mp/chg_datasets/functionals/gga+u/mp_filelist_capped.txt
+    split_file: data/MP/sweep_splits/gga+u_split_sweep12k.json
+    dataset_id: 2
+  precision: f32
+  batch_size: 1
+  train_workers: 8
+  val_workers: 2
+  pin_memory: false
+  drop_last: false
+  augmentation: false
+  random_seed: 42
+model:
+  _target_: electrai.model.resunet.ResUNet3D
+  in_channels: 1
+  out_channels: 1
+  n_channels: 96
+  n_residual_blocks: 1
+  kernel_size: 5
+  depth: 2
+precision: bf16-mixed
+epochs: 8
+lr: 0.004
+weight_decay: 0.0
+warmup_length: 1
+beta1: 0.9
+beta2: 0.99
+wandb_mode: offline
+entity: PrinceOA
+wb_pname: mp-gga-ggau-lrwd
+run_name: sweep_lrwd_w96_lr0.004_wd0
+ckpt_path: /uv/cache/electrai/checkpoints/sweep_lrwd_w96_lr0.004_wd0
+optimizer: adam
diff --git a/src/electrai/dataloader/dataset.py b/src/electrai/dataloader/dataset.py
index a4845f51..27d1ba7f 100644
--- a/src/electrai/dataloader/dataset.py
+++ b/src/electrai/dataloader/dataset.py
@@ -200,15 +200,19 @@ def __init__(self, datapath: str, precision: str, augmentation: bool, **kwargs):
         if not member_list:
             raise ValueError(f"Filelist at {datapath} is empty.")
         self.member_list = member_list
-        # Detect zarr vs CHGCAR by checking which extension the first entry has
+        # Detect zarr vs CHGCAR; zarr may be unpacked (`<id>.zarr/`) or packed
+        # (`<id>.zarr.zip`) — load_zarr handles both.
         first = member_list[0]
-        if (self.root / "data" / f"{first}.zarr").exists():
+        data_dir = self.root / "data"
+        if (data_dir / f"{first}.zarr.zip").exists() or (
+            data_dir / f"{first}.zarr"
+        ).exists():
             self.fmt = "zarr"
-        elif (self.root / "data" / f"{first}.CHGCAR").exists():
+        elif (data_dir / f"{first}.CHGCAR").exists():
             self.fmt = "chgcar"
         else:
             raise ValueError(
-                f"No .zarr or .CHGCAR file found for '{first}' in {self.root / 'data'}"
+                f"No .zarr(.zip) or .CHGCAR file found for '{first}' in {data_dir}"
             )
 
     def __len__(self):
diff --git a/src/electrai/dataloader/utils.py b/src/electrai/dataloader/utils.py
index 9c346883..c99debf6 100644
--- a/src/electrai/dataloader/utils.py
+++ b/src/electrai/dataloader/utils.py
@@ -40,18 +40,39 @@ def load_numpy_rho(
 
 
 def load_zarr(root: str | bytes | os.PathLike, index: str):
+    """Read a zarr store as either a directory tree (`<id>.zarr/`) or a single
+    packed zip (`<id>.zarr.zip`). Volumes capped by inode count (e.g. Modal)
+    benefit greatly from the packed form — one inode per store instead of ~8.
+    """
     import zarr
 
-    def _read(path):
-        z = zarr.open_group(str(path), mode="r")
-        if "structure" not in z.attrs:
-            raise KeyError(f"'structure' attribute missing from zarr store at {path}")
-        arr = np.array(z["charge_density_total"])
-        volume = json.loads(z.attrs["structure"])["lattice"]["volume"]
-        return arr / volume
+    def _read(zarr_dir: Path, zarr_zip: Path):
+        store = None
+        if zarr_zip.exists():
+            store = zarr.storage.ZipStore(str(zarr_zip), mode="r")
+            z = zarr.open_group(store, mode="r")
+        elif zarr_dir.exists():
+            z = zarr.open_group(str(zarr_dir), mode="r")
+        else:
+            raise FileNotFoundError(f"No zarr store at {zarr_zip} or {zarr_dir}")
+        try:
+            if "structure" not in z.attrs:
+                raise KeyError(
+                    f"'structure' attribute missing from zarr store at "
+                    f"{zarr_zip if zarr_zip.exists() else zarr_dir}"
+                )
+            arr = np.array(z["charge_density_total"])
+            volume = json.loads(z.attrs["structure"])["lattice"]["volume"]
+            return arr / volume
+        finally:
+            if store is not None:
+                store.close()
 
-    data = _read(Path(root) / "data" / f"{index}.zarr")
-    label = _read(Path(root) / "label" / f"{index}.zarr")
+    root = Path(root)
+    data = _read(root / "data" / f"{index}.zarr", root / "data" / f"{index}.zarr.zip")
+    label = _read(
+        root / "label" / f"{index}.zarr", root / "label" / f"{index}.zarr.zip"
+    )
     return data, label
 
 
diff --git a/src/electrai/entrypoints/train.py b/src/electrai/entrypoints/train.py
index d841519e..649b6fa7 100644
--- a/src/electrai/entrypoints/train.py
+++ b/src/electrai/entrypoints/train.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import os
+from datetime import UTC, datetime, timedelta
 from pathlib import Path
 from types import SimpleNamespace
 
@@ -9,6 +10,7 @@
 from hydra.utils import instantiate
 from lightning.pytorch import Trainer
 from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint
+from lightning.pytorch.strategies import DDPStrategy
 
 from electrai.lightning import LightningGenerator
 
@@ -40,8 +42,15 @@ def train(args):
     if wandb_mode != "disabled":
         from lightning.pytorch.loggers import WandbLogger
 
+        # Short width-first run name (w128_0729-1912): auto-generated names
+        # made restart segments of different-width runs indistinguishable,
+        # and full config-derived names were too long for the project view.
+        model_cfg = getattr(cfg, "model", None) or {}
+        n_ch = model_cfg.get("n_channels") if isinstance(model_cfg, dict) else None
+        stamp = datetime.now(UTC).strftime("%m%d-%H%M")
+        run_name = f"w{n_ch}_{stamp}" if n_ch else getattr(cfg, "run_name", None)
         wandb_logger = WandbLogger(
-            project=cfg.wb_pname, entity=cfg.entity, config=vars(cfg)
+            project=cfg.wb_pname, entity=cfg.entity, name=run_name, config=vars(cfg)
         )
     else:
         wandb_logger = None
@@ -53,12 +62,25 @@ def train(args):
         save_top_k=2,
         mode="min",
         filename="ckpt_{epoch:02d}_{val_loss:.6f}",
-        save_last=True,
+    )
+
+    # Frequent resume checkpoint. An epoch here is tens of thousands of steps
+    # and can crash mid-way (e.g. a large sample tripping the NCCL watchdog), so
+    # save `last.ckpt` periodically; a restart then resumes instead of redoing
+    # the epoch from scratch. `val_loss` only exists at epoch end, so this is a
+    # separate monitor-less callback (save_top_k=0 -> last.ckpt only).
+    # Step-based, NOT train_time_interval: the wall-clock trigger needs a DDP
+    # broadcast to align ranks and their clocks can disagree about when the
+    # interval fired, which deadlocked a 4-rank run mid-epoch (ranks in
+    # mismatched collectives; NCCL watchdog never fires). Step counts are
+    # identical on every rank, so no alignment collective is needed.
+    last_ckpt_cb = ModelCheckpoint(
+        dirpath=ckpt_path, every_n_train_steps=2000, save_top_k=0, save_last=True
     )
 
     lr_monitor = LearningRateMonitor(logging_interval="epoch")
 
-    callbacks = [checkpoint_cb, lr_monitor]
+    callbacks = [checkpoint_cb, last_ckpt_cb, lr_monitor]
 
     hf_cfg = getattr(cfg, "hf", None)
     if hf_cfg and hf_cfg.get("repo_id"):
@@ -82,7 +104,11 @@ def train(args):
         precision=cfg.precision,
         devices="auto",
         num_nodes=num_nodes,
-        strategy="ddp",
+        # Raise the NCCL collective timeout from the 30-min default: a single
+        # large sample's forward/backward can stall the all-reduce on the other
+        # ranks past 30 min and abort the whole group. The grid-size cap keeps
+        # steps short; this is a safety net for the occasional slow one.
+        strategy=DDPStrategy(timeout=timedelta(hours=2)),
         log_every_n_steps=1,
         gradient_clip_val=getattr(cfg, "gradient_clip_value", 1.0),
     )
diff --git a/src/electrai/lightning.py b/src/electrai/lightning.py
index c1123880..fc5b6916 100644
--- a/src/electrai/lightning.py
+++ b/src/electrai/lightning.py
@@ -59,7 +59,14 @@ def _loss_calculation(self, batch):
         return loss
 
     def configure_optimizers(self):
-        optimizer = torch.optim.Adam(
+        # "adam" applies weight_decay as coupled L2 (rescaled by the adaptive
+        # denominator); "adamw" decays weights directly. At weight_decay=0 the
+        # two are identical, so existing configs are unaffected.
+        opt_name = (getattr(self.cfg, "optimizer", None) or "adam").lower()
+        if opt_name not in ("adam", "adamw"):
+            raise ValueError(f"Unknown optimizer '{opt_name}': use 'adam' or 'adamw'")
+        opt_cls = torch.optim.AdamW if opt_name == "adamw" else torch.optim.Adam
+        optimizer = opt_cls(
             self.model.parameters(),
             lr=float(self.cfg.lr),
             weight_decay=float(self.cfg.weight_decay),
diff --git a/uv.lock b/uv.lock
index abf58e23..e203a0b4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4,9 +4,11 @@ requires-python = ">=3.11, <3.14"
 resolution-markers = [
     "python_full_version >= '3.12' and sys_platform == 'win32'",
     "python_full_version < '3.12' and sys_platform == 'win32'",
-    "python_full_version >= '3.12' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux'",
     "python_full_version >= '3.12' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version < '3.12' and sys_platform == 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux'",
     "python_full_version < '3.12' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 
@@ -574,12 +576,16 @@ name = "cuda-bindings"
 version = "12.9.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder", marker = "sys_platform == 'linux'" },
+    { name = "cuda-pathfinder" },
 ]
 wheels = [
+    { url = "https://files.pythonhosted.org/packages/a9/2b/ebcbb60aa6dba830474cd360c42e10282f7a343c0a1f58d24fbd3b7c2d77/cuda_bindings-12.9.4-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a6a429dc6c13148ff1e27c44f40a3dd23203823e637b87fd0854205195988306", size = 11840604, upload-time = "2025-10-21T14:51:34.565Z" },
     { url = "https://files.pythonhosted.org/packages/45/e7/b47792cc2d01c7e1d37c32402182524774dadd2d26339bd224e0e913832e/cuda_bindings-12.9.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c912a3d9e6b6651853eed8eed96d6800d69c08e94052c292fec3f282c5a817c9", size = 12210593, upload-time = "2025-10-21T14:51:36.574Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/c2/65bfd79292b8ff18be4dd7f7442cea37bcbc1a228c1886f1dea515c45b67/cuda_bindings-12.9.4-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:694ba35023846625ef471257e6b5a4bc8af690f961d197d77d34b1d1db393f56", size = 11760260, upload-time = "2025-10-21T14:51:40.79Z" },
     { url = "https://files.pythonhosted.org/packages/a9/c1/dabe88f52c3e3760d861401bb994df08f672ec893b8f7592dc91626adcf3/cuda_bindings-12.9.4-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fda147a344e8eaeca0c6ff113d2851ffca8f7dfc0a6c932374ee5c47caa649c8", size = 12151019, upload-time = "2025-10-21T14:51:43.167Z" },
+    { url = "https://files.pythonhosted.org/packages/05/8b/b4b2d1c7775fa403b64333e720cfcfccef8dcb9cdeb99947061ca5a77628/cuda_bindings-12.9.4-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cf8bfaedc238f3b115d957d1fd6562b7e8435ba57f6d0e2f87d0e7149ccb2da5", size = 11570071, upload-time = "2025-10-21T14:51:47.472Z" },
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     { name = "pyyaml" },
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-    { name = "torchvision" },
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-    { name = "torchvision", specifier = ">=0.24.0" },
+    { name = "torch", marker = "platform_machine != 'aarch64' or sys_platform != 'linux'", specifier = "~=2.10.0" },
+    { name = "torch", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'", specifier = "~=2.10.0", index = "https://download.pytorch.org/whl/cu128" },
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     { name = "pyyaml" },
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     { name = "tqdm" },
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 source = { registry = "https://pypi.org/simple" }
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+    { name = "nvidia-nvjitlink-cu12" },
 ]
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@@ -1700,6 +1718,7 @@ name = "nvidia-curand-cu12"
 version = "10.3.9.90"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
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@@ -1708,11 +1727,12 @@ name = "nvidia-cusolver-cu12"
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-    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cusparse-cu12" },
+    { name = "nvidia-nvjitlink-cu12" },
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 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
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@@ -1732,6 +1753,7 @@ name = "nvidia-cusparselt-cu12"
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@@ -1740,6 +1762,7 @@ name = "nvidia-nccl-cu12"
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@@ -1756,6 +1780,7 @@ name = "nvidia-nvshmem-cu12"
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     { name = "lightning-utilities" },
     { name = "packaging" },
     { name = "pyyaml" },
-    { name = "torch" },
+    { name = "torch", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' or sys_platform != 'linux'" },
+    { name = "torch", version = "2.10.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
     { name = "torchmetrics" },
     { name = "tqdm" },
     { name = "typing-extensions" },
@@ -2747,6 +2774,14 @@ wheels = [
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 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
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+    "python_full_version < '3.12' and sys_platform != 'linux' and sys_platform != 'win32'",
+]
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     { name = "filelock" },
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+    { name = "fsspec" },
+    { name = "jinja2" },
+    { name = "networkx" },
+    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cuda-cupti-cu12" },
+    { name = "nvidia-cuda-nvrtc-cu12" },
+    { name = "nvidia-cuda-runtime-cu12" },
+    { name = "nvidia-cudnn-cu12" },
+    { name = "nvidia-cufft-cu12" },
+    { name = "nvidia-cufile-cu12" },
+    { name = "nvidia-curand-cu12" },
+    { name = "nvidia-cusolver-cu12" },
+    { name = "nvidia-cusparse-cu12" },
+    { name = "nvidia-cusparselt-cu12" },
+    { name = "nvidia-nccl-cu12" },
+    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvshmem-cu12" },
+    { name = "nvidia-nvtx-cu12" },
+    { name = "setuptools", marker = "python_full_version >= '3.12'" },
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+]
+
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 name = "torchmetrics"
 version = "1.8.2"
@@ -2807,7 +2883,8 @@ dependencies = [
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     { name = "numpy" },
     { name = "packaging" },
-    { name = "torch" },
+    { name = "torch", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' or sys_platform != 'linux'" },
+    { name = "torch", version = "2.10.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
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