aws / aws/sagemaker-python-sdk

ModelBuilder.deploy(update_endpoint=True) raises AttributeError on Session.create_endpoint_config()

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描述

**PySDK Version**

- [ ] PySDK V2 (2.x)
- [x] PySDK V3 (3.x)

**Describe the bug**

`ModelBuilder.deploy(update_endpoint=True)` raises `AttributeError: 'Session' object has no attribute 'create_endpoint_config'`.

In the installed V3 packages, `ModelBuilder._deploy_core_endpoint()` calls `self.sagemaker_session.create_endpoint_config(...)` on the update path, but `sagemaker.core.helper.session_helper.Session` does not implement that method.

**To reproduce**

Run the following script:

```python
from types import SimpleNamespace

import boto3
from sagemaker.core.helper.session_helper import Session
from sagemaker.serve.model_builder import ModelBuilder

builder = ModelBuilder(
s3_model_data_url={
"S3DataSource": {
"S3Uri": "s3://example-bucket/model/",
"S3DataType": "S3Prefix",
"CompressionType": "None",
}
},
image_uri="123456789012.dkr.ecr.us-west-2.amazonaws.com/example:latest",
role_arn="arn:aws:iam::123456789012:role/SageMakerExecutionRole",
sagemaker_session=Session(boto_session=boto3.Session(region_name="us-west-2")),
instance_type="ml.g5.xlarge",
env_vars={},
)

builder.model_name = "demo-model"
builder.region = "us-west-2"
builder.endpoint_name = "demo-endpoint"
builder.built_model = SimpleNamespace(model_name="demo-model")

builder._deploy_core_endpoint(
endpoint_name="demo-endpoint",
instance_type="ml.g5.xlarge",
initial_instance_count=1,
wait=False,
update_endpoint=True,
)
```

**Steps**

1. Create a Python environment with the V3 SageMaker packages listed below.
2. Save the script above.
3. Run `python minimal_repro_model_builder_update_endpoint.py`.

**Expected behavior**

One of the following should happen:

- `ModelBuilder.deploy(update_endpoint=True)` should work with the default `Session` implementation used by V3 packages.
- Or `ModelBuilder` should use a session method that actually exists on `sagemaker.core.helper.session_helper.Session`.

**Screenshots or logs**

Actual error:

```text
AttributeError: 'Session' object has no attribute 'create_endpoint_config'
```

I also confirmed locally that:

- `hasattr(Session, "create_endpoint_config") == False`
- `ModelBuilder._deploy_core_endpoint()` contains a call to `self.sagemaker_session.create_endpoint_config(...)`

In my real deployment code, this was triggered from `ModelBuilder.build(...); ModelBuilder.deploy(..., update_endpoint=True)` while updating an existing SageMaker endpoint.

**System information**

- **SageMaker Python SDK version**: V3 packages; `sagemaker-core==2.5.0`, `sagemaker-serve==1.5.0`
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: N/A for repro; issue is in `ModelBuilder` update flow
- **Framework version**: N/A
- **Python version**: 3.14.3
- **CPU or GPU**: Repro itself is local and does not require either; production usage was GPU-backed SageMaker endpoint deployment
- **Custom Docker image (Y/N)**: N

**Additional context**

- Avoiding `update_endpoint=True` and recreating the endpoint instead works around the issue.
- This looks related to the ongoing V3 API/session transition, and may be adjacent to issue #5336, but this report is specifically about the missing `create_endpoint_config()` method on the session object used by `ModelBuilder`.

贡献指南

打开贡献指南

调研方向

首先使用最小脚本复现失败,然后检查 ModelBuilder._deploy_core_endpoint 和 sagemaker.core.helper.session_helper.Session,重点关注 update_endpoint 路径以及缺失的 create_endpoint_config 调用。运行复现脚本并验证使用默认 V3 Session 实现更新现有 endpoint 可以正常工作,以确认修复有效。

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评估

技术栈
aws, python
领域
cloud, machine-learning
Issue 类型
缺陷
难度
3/5
预计耗时
1-2 天
活跃度
冷清
描述清晰度
基本清楚
新手友好度
58/100

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