Azure Machine Learning inferencing server.
Project description
Check our official documentation AzureML Inference Server - Docs.
Please note, the AzureML Inference Server is now open source! The repo is available here: AzureML Inference Server - Github.
Changelog
1.2.2 (2024-05-14)
Enhancements
Update Werkzeug from <3.0.0 to >=3.0.3
1.2.1 (2024-04-25)
Enhancements
Update gunicorn from 20.1.0 to 22.0.0
1.2.0 (2024-04-02)
Azureml_Inference_Server_Http 1.2.0 (2024-04-02)
Enhancements
Add python 3.11 support
1.1.0 (2024-02-26)
Azureml_Inference_Server_Http 1.1.0 (2024-02-26)
Breaking Changes
Azmlinfsrv will be migrating to Pydantic 2.0 in version 1.1.0. This will be a breaking change for any Pydantic 1.0 code.
1.0.0 (2023-09-21)
Breaking Changes
Deprecate Python 3.7 Support and improve documentation
0.8.4.2 (2023-09-07)
Azureml_Inference_Server_Http 0.8.4.2 (2023-09-07)
Fixes
Updated InferenceSchema from ~=1.5.0 to ~=1.7.0 tp support MLflow NCD
0.8.4.1 (2023-08-09)
Fixes
Fixed pydantic warning at the server startup.
0.8.4 (2023-04-19)
Features
Added ability to configure seperate dedicated health check port.
Fixes
Restored streaming for score response.
Updated the error message related to scoring script not found.
0.8.3 (2023-03-23)
Fixes
Fixed the issue related to compatibility with flask1.
0.8.2 (2023-03-20)
Enhancements
Warning message will be logged if there are extra keys in the config file which are not supported by the server.
0.8.1 (2023-03-06)
Features
Supports loading server config from a json file. Added support for a new env variable AZUREML_CONFIG_FILE. Refer to README for detailed usage.
Enhancements
Exception details will not be returned in the HTTP response. Check the server logs for details.
0.8.0 (2022-12-15)
Breaking Changes
Drop support for Python 3.6
Enhancements
All error responses will be in json. (Format: {‘message’: <error_message>})
Loggers can be configured by users through a logging.json file in AML_APP_ROOT or alongside the entry script.
Log message default format has been updated. (Format: “<UTC DATE> <UTC TIME> <LOG LEVEL CHAR> [<PID>] <LOGGER NAME> - <MESSAGE>”)
0.7.7 (2022-11-01)
Fixes
Upgrade inference-schema dependency to support Python 3.9
0.7.6 (2022-09-13)
Fixes
AML_APP_ROOT variable is now defaulted to the current working directory
AZUREML_ENTRY_SCRIPT is now set to an absolute path to the entry script
0.7.5 (2022-08-16)
Breaking Changes
The header for Client Request ID is renamed from x-client-request-id to x-ms-client-request-id.
Server will no longer throw an error when both x-ms-request-id and x-request-id are provided. Going forward, x-ms-request-id will be treated as the Client Request ID. However, it is still considered deprecated and users are recommended to use x-ms-client-request-id for Client Request ID.
When neither x-ms-request-id or x-ms-client-request-id is set, the server copies the value of x-request-id to x-ms-request-id. This is done to preserve backwards compatability, ensuring that x-ms-request-id is not empty. No value is logged to AppInsights as “Client Request Id”.
When only x-ms-request-id is set, the server returns x-ms-request-id and x-ms-client-request-id set to the value. This value is logged to AppInsights as “Client Request Id”.
When only x-ms-client-request-id is set, the server returns x-ms-request-id and x-ms-client-request-id set to the value. This value is logged to AppInsights as “Client Request Id”.
When both x-ms-request-id and x-ms-client-request-id are set, the values are returned in the respective headers. However, only the value from x-ms-client-request-id is logged to AppInsights as “Client Request Id”.
0.7.4 (2022-07-29)
Fixes
Fix an issue where the server would require arguments that have default values in run().
0.7.3 (2022-07-18)
Features
CORS can be enabled with the environment variable AML_CORS_ORIGINS. Refer to README for detailed usage.
Server can now be started with python -m azureml_inference_server_http in additional to azmlinfsrv.
OPTIONS calls are modified to return 200 OK instead of the previous 405 Method not allowed.
Users can bring their own swaggers by placing swagger2.json and swagger3.json in AML_APP_ROOT.
Enhancements
Swaggers are always generated now, regardless whether the user’s run() function is decorated with inference-schema.
The x-request-id and x-client-request-id headers are now limited to 100 characters.
Fixes
Fixed an issue that prevents the server from cleanly exiting when the scoring script cannot be initialized. If AppInsights is not enabled, users may see AttributeError: 'AppInsightsClient' object has no attribute 'logger'.
0.7.2 (2022-06-06)
Enhancements
Added support for Flask 2.1.
The server now responds with a 400 Bad Request when it finds invalid inputs.
0.7.1 (2022-05-10)
Deprecation
The “x-ms-request-id” header is deprecated and is being replaced by “x-request-id”. Until “x-ms-request-id” is removed, the server will accept either header and respond with both headers set to the same request id. Providing two request ids through the headers is not allowed and will be responded with a Bad Request.
Enhancements
Added support for Flask 2.0. A compatibility layer is introduced to ensure this upgrade doesn’t break users who use @rawhttp as the methods on the Flask request object have slightly changed. Specifically,
request.headers.has_keys() was removed
request.json throws an exception if the content-type is not “application/json”. Previously it returns None.
The compatibility layer restores these functionalities to their previous behaviors. However, this compatibility layer will be removed in a future date and users are encouraged to audit their score scripts today. To see if your score script is ready for Flask 2, run the server with the environment variable AML_FLASK_ONE_COMPATIBILITY set to false.
Flask’s full changelog can be found here: https://flask.palletsprojects.com/en/2.1.x/changes/
Added support for the “x-request-id” and “x-client-request-id” headers. A new GUID is generated for “x-request-id” if one is not provided. These values are echoed back to the client in the response headers.
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