aws / aws/sagemaker-python-sdk
Sagemaker Endpoint vanishing without traces
- 主要言語
- Python
- スター
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- フォーク
- 1.3k
- 平均マージ
- 1日 22時間
- マージ済み PR(30日)
- 35
説明
**Describe the bug**
I'm currently using Sagemaker to host a custom ML model deployed to two accounts, homolog, and production. Both endpoints have the same entry point code and were deployed the same day. The homologation version suddenly disappeared on June 28th, leaving no traces besides the last HealthCheck ping on CloudWatch. After searching CloudTrail logs to see what could have happened, there was nothing out of the ordinary: deployed the endpoint and that was it. No delete command coming from anywhere.
I thought of it as a bug and promptly redeployed the model, on June 29th, assuming it wouldn't happen again. The issue is that on July 3rd the endpoint vanished without traces again. Same thing, no delete, no update, no renaming of anything on CloudTrail, and the only proof that it was ever on running were the CloudWatch logs and the CreateEndpoint entry on CloudTrail.
**To reproduce**
Couldn't reproduce the bug willingly. I couldn't gather any evidence that could lead me to the cause of the problem.
**Expected behavior**
For it not to vanish
**Screenshots or logs**
**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: 2.72.1
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: SKLearn
- **Framework version**: 0.23
- **Python version**: 3.6
- **CPU or GPU**: ml.t2.medium
- **Custom Docker image (Y/N)**: N
**Additional context**
Some more information:
- The homologation(testing) endpoint wasn't called all that often and had big gaps between calls, they would only happen when we were testing something.
- It was deployed through an AWS Sagemaker Notebook using the Sagemaker SDK for Python
- First time the delta between a request and going offline was 8 hours, and the second time the delta was 48h.
コントリビューションガイド
調査の方向性
まず AWS SageMaker Notebook からの SageMaker Python SDK 2.72.1 のデプロイを確認し、次に 6 月 28 日と 7 月 3 日の消失について endpoint の CloudTrail および CloudWatch の記録を比較します。完了の条件は、記録された delete や update がないまま endpoint がどのように消失したのかを説明する、再現可能な原因または十分な証拠を特定することです。
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, machine-learning, python
- 領域
- cloud, machine-learning
- issue の種類
- バグ
- 難易度
- 5/5
- 見積もり時間
- 1週間以上
- 活発さ
- 停滞
- 明瞭さ
- 説明が足りない
- 初心者へのやさしさ
- 20/100