aws / aws/sagemaker-python-sdk
Add ml.p5e.48xlarge to EFA instance lists in sagemaker-train and sagemaker-core
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説明
# Add ml.p5e.48xlarge to EFA instance lists in sagemaker-train and sagemaker-core
## Description
The `SM_EFA_NCCL_INSTANCES` and `SM_EFA_RDMA_INSTANCES` lists in the sagemaker-python-sdk are missing `ml.p5e.48xlarge`, causing NCCL hangs during distributed training initialization on P5e instances when using the SDK's container drivers.
Additionally, `ml.p5.48xlarge` is missing from `SM_EFA_RDMA_INSTANCES` (it's only in `SM_EFA_NCCL_INSTANCES`).
## Current State
```python
SM_EFA_NCCL_INSTANCES = [
"ml.g4dn.8xlarge",
"ml.g4dn.12xlarge",
"ml.g5.48xlarge",
"ml.p3dn.24xlarge",
"ml.p4d.24xlarge",
"ml.p4de.24xlarge",
"ml.p5.48xlarge",
"ml.trn1.32xlarge",
]
SM_EFA_RDMA_INSTANCES = [
"ml.p4d.24xlarge",
"ml.p4de.24xlarge",
"ml.trn1.32xlarge",
]
```
## Expected State
```python
SM_EFA_NCCL_INSTANCES = [
"ml.g4dn.8xlarge",
"ml.g4dn.12xlarge",
"ml.g5.48xlarge",
"ml.p3dn.24xlarge",
"ml.p4d.24xlarge",
"ml.p4de.24xlarge",
"ml.p5.48xlarge",
"ml.p5e.48xlarge", # ADD
"ml.trn1.32xlarge",
]
SM_EFA_RDMA_INSTANCES = [
"ml.p4d.24xlarge",
"ml.p4de.24xlarge",
"ml.p5.48xlarge", # ADD
"ml.p5e.48xlarge", # ADD
"ml.trn1.32xlarge",
]
```
## Impact
Without these entries, the SDK's container drivers don't set the required EFA environment variables (`FI_PROVIDER=efa`, `FI_EFA_USE_DEVICE_RDMA=1`, `RDMAV_FORK_SAFE=1`) for P5e instances, causing NCCL to hang during collective initialization in multi-node distributed training.
## Related
- sagemaker-training-toolkit issue: https://github.com/aws/sagemaker-training-toolkit/issues/240
- sagemaker-training-toolkit PR: https://github.com/aws/sagemaker-training-toolkit/pull/241
- P5e instances use EFA with RDMA support, same as P4d/P4de/P5
## Questions
1. Is there a specific process for testing EFA/instance-specific changes on actual hardware before merging?
2. Should integration tests be added for P5e EFA configuration, or are unit tests sufficient?
コントリビューションガイド
調査の方向性
まず、sagemaker-train と sagemaker-core コンポーネントにある SM_EFA_NCCL_INSTANCES と SM_EFA_RDMA_INSTANCES の定義を見つけます。両方のリストをこの issue で期待されている状態と比較し、その後、関連する unit-test のカバレッジを確認します。完了とは、両方の P5 インスタンスが適切な EFA リストに含まれていることを意味します。
索引モデルが issue の本文から書いたものです。
評価
- 技術スタック
- aws, python
- 領域
- cloud, machine-learning
- issue の種類
- バグ
- 難易度
- 2/5
- 見積もり時間
- 1〜3時間
- 活発さ
- 停滞
- 明瞭さ
- 明確に書かれている
- 初心者へのやさしさ
- 58/100