aws / aws/sagemaker-python-sdk
LocalPipelineSession Mutates sagemaker_client by Injecting _pipelines Attribute
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説明
# Bug: `LocalPipelineSession` Mutates `sagemaker_client` by Injecting `_pipelines` Attribute
## PySDK Version
* [x] PySDK V3 (3.x)
* [ ] PySDK V2 (2.x)
---
## Describe the bug
`LocalPipelineSession` dynamically adds a `_pipelines` attribute to the `sagemaker_client` object:
```python
if not hasattr(self.sagemaker_client, "_pipelines"):
self.sagemaker_client._pipelines = {}
```
This modifies the client instance by attaching internal state that is unrelated to the client’s intended responsibility.
Because `sagemaker_client` is typically expected to behave like a boto-style service client, mutating it in this way introduces hidden state and side effects.
---
## Why this is problematic
This approach can cause several issues:
* **Breaks encapsulation**
Pipeline state is stored on the service client rather than on the session or pipeline objects that own the state.
* **Unexpected side effects**
If multiple `LocalPipelineSession` instances share the same client object, they will also share the same `_pipelines` registry.
* **Potential attribute collision**
`_pipelines` could conflict with future attributes added to the client implementation.
* **Harder debugging and maintenance**
Attaching internal state to an external object makes the code harder to reason about and maintain.
In general, AWS SDK components avoid mutating externally provided clients and instead maintain internal state within the session or service wrapper.
---
## To reproduce
Example demonstrating shared pipeline state when using the same client object:
```python
from sagemaker.workflow.pipeline_context import LocalPipelineSession
session1 = LocalPipelineSession()
session2 = LocalPipelineSession()
client = session1.sagemaker_client
# session2 reuses the same client
session2.sagemaker_client = client
session1.sagemaker_client._pipelines["pipelineA"] = "A"
print(session2.sagemaker_client._pipelines)
```
Output:
```
{'pipelineA': 'A'}
```
Both sessions share the same `_pipelines` state because it was attached to the client object.
---
## Expected behavior
Pipeline state should be owned by the session or pipeline classes, not by the `sagemaker_client`.
A safer design would store pipelines inside the session instance:
```python
class LocalPipelineSession(LocalSession):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._local_pipelines = {}
```
This ensures:
* Pipeline state is isolated per session
* No mutation of external client objects
* Clear ownership of internal state
---
## System information
* **SageMaker Python SDK version**: main branch
* **Python version**: 3.9+
* **CPU or GPU**: CPU
* **Custom Docker image (Y/N)**: N
---
コントリビューションガイド
調査の方向性
sagemaker.workflow.pipeline_context の LocalPipelineSession から始め、_pipelines レジストリがどこで初期化され、使用されているかを追跡します。Issue に記載されている共有クライアントの再現を確認し、その後、パイプラインの状態がセッションごとに分離され、外部の sagemaker_client が変更されなくなっていることを検証します。
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
- machine-learning
- issue の種類
- バグ
- 難易度
- 4/5
- 見積もり時間
- 3〜5日
- 活発さ
- 停滞
- 明瞭さ
- おおむね明確
- 初心者へのやさしさ
- 45/100