[RyanPCo]

HPT.init defaulted self.device to torch.device('cuda') and moved the
freshly built policy there. Under submitit every rank sees all 8 GPUs, so all
8 ranks resolved that to cuda:0 and built their policy on the same device:
8 x 3.34B, which OOMs during construction. The 1B arm fit and survived, which
made the failure look config-specific.

PI already builds on CPU and leaves self.device unset for Lightning to fill in
through ModelWrapper.on_fit_start / on_validation_start. HPT now does the same:
no device is chosen in init and nothing leaves CPU there, so the existing
device property -- which already propagates to the HPTModel it holds -- is the
only place a device is ever set.

Verified on 8xH100 with the 3.34B hpt_3b arch-comparison arm: every rank
reports CUDA_VISIBLE_DEVICES [0..7], all 8 reach fit start and train an epoch
without an OOM.
🤖 Generated with Claude Code

https://claude.ai/code/session_01MrpKGcZMHFDQ4ded57hqga

[RyanPCo]

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• hpt: build the policy on CPU and let Lightning place it #681 👈 (View in Graphite)
• algo: RDT (Robotics Diffusion Transformer) on the HPT chassis #680 : 1 other dependent PR (#682 )
• pi: initialize from PaliGemma with a scratch action expert #677 : 1 other dependent PR (#678 )
• perf: vectorized image augs and a torch.compile knob for HPT and Pi; Pi clips gradients like openpi #676
• loader: one zarr read per key, not one per keymap entry #674
• eval: build the pinned-episode video batches only where they are used #673
• checkpoints: last.ckpt as a symlink, not a second full copy #672
• trainHydra: metric_frames_per_episode: auto, and warn on empty prompts #671
• transforms: per-step wrist framing for proprio history #670
• viz: one keypoint renderer, the horizon trace #669
• configs: history and backbone-LR knobs on the keypoint wrist HPT model #668
• trainer: ddp_bf16_compress, DDP with a bf16 gradient all-reduce #667
• eval: overlay videos play at the source rate under DDP #666
• hpt: build the policy on CPU and let Lightning place it #664 : 1 other dependent PR (#665 )
• hpt: never re-initialise pretrained stems; hash-check them; mask text padding #645 : 1 other dependent PR (#646 )
• obs: proprio history (K proprio steps, one token each, learned time embedding) #650
• loss: mask the repeat-last action padding out of the HPT loss #649
• resnet: keep a frozen backbone in eval mode; give backbones their own LR group #652
• augs: per-sample image augmentation for HPT train-mode augs #651
• eval: deterministic validation (seeded prompts + flow sampling); log-only grad-norm MAD #644
• data: mecka_fold_flagship_top3_hpt_6d, the 3-operator flagship train split #643
• val: subsampled metric loaders + pinned contiguous video loaders #642
• infra: mecka 6D / keypoint recipes, 300M models, defaults, and the rollout revert #640
• eval: layout-aware frame-invariant metrics, and a train-split viz head #659
• data: stop letting one bad episode or a rotation channel take a run down #658
• norm: guard degenerate channels, horizon pooling, and no rot6d normalization #657
• keypoints: the 144-D wrist-first hand-keypoint action becomes the human default #656
• mecka: fix the double-mirrored LEFT wrist frame, and correct existing zarrs #655
• 6d: PI goes 6D-only; Eva and Human gain the continuous-rotation modes #654
• 6d: continuous rotation representation, bimanual layouts, shared norm helpers #653
• train: give each Slurm slurm job gets its own cache dir to avoid conflict #638 : 1 other dependent PR (#639 )
• write git version into the run dir for reproducibility #637
• train: don't load PI base weights on resume and verify that checkpoint path exists #636
• configs: declare proprio and actions dims once per HPT model config #634 : 1 other dependent PR (#635 )
• cache norm stats for data configs to minimize recomputation #633
• import cloud packages lazily to support local training #631 : 1 other dependent PR (#632 )
• cache dataset resolver for faster training initialization #630
• validate episode hashes in data config before training #628
• modify pi camera map to match data #629
• add tests to ensure training works between every model and data vendor #626
• run CI tests on every PR #625
• add env variables for user-specific paths #624
• build: set pyproject and uv as the dependency managers #623
• fix(configs): fix paths for data hydra yaml configs #622
• refactor(algo): delete ACT #627 : 1 other dependent PR (#621 )
• tests: create dedicated test folder with unit and integration tests #620
• add pyright type checker #619
• main

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[RyanPCo]

Superseded. The hpt: build the policy on CPU and let Lightning place it commit has been folded into #664, which now carries both the CPU build and the device propagation as one change — #664 alone was only half the fix, since it kept the torch.device('cuda') default that causes the 8-ranks-on-cuda:0 OOM.

What was left on this branch is the slurm GPU-orphan-reap commit, which #682 already carries. Closing in favour of #682.