Add GPU e2e testing with lambda-gha #64
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- `tests/e2e_train.py`: CLI for seeded training on sample data (5 epochs, 8 channels, 2 residual blocks). Verifies val_loss matches expected value. - `tests/expected_loss.txt`: expected val_loss (0.613389) for regression - `tests/test_e2e_train.py`: pytest wrapper - `examples/e2e_training_demo.ipynb`: notebook version with training curve plot Training is fully deterministic (47% val_loss improvement over 5 epochs). Add notebook deps: `nbconvert`, `ipykernel`, `papermill`. Co-Authored-By: Claude Opus 4.5 <[email protected]>
- `.github/workflows/gpu-e2e.yml`: workflow_dispatch workflow using Open-Athena/lambda-gha for ephemeral Lambda Labs GPU instances - `tests/e2e_train.py`: add `--gpu` flag to enable GPU acceleration Workflow spins up A10 GPU by default, runs training test, then instance self-terminates. Requires LAMBDA_API_KEY, GH_SA_TOKEN, LAMBDA_SSH_PRIVATE_KEY secrets and LAMBDA_SSH_KEY_NAMES variable. Co-Authored-By: Claude Opus 4.5 <[email protected]>
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Summary
.github/workflows/gpu-e2e.ymlfor GPU-based e2e training tests--gpuflag totests/e2e_train.pyfor GPU accelerationUses Open-Athena/lambda-gha to spin up ephemeral Lambda Labs GPU instances.
Required Setup
Secrets:
LAMBDA_API_KEYGH_SA_TOKENLAMBDA_SSH_PRIVATE_KEYVariables:
LAMBDA_SSH_KEY_NAMESTest plan
workflow_dispatchafter merge--gpuflag🤖 Generated with Claude Code