SageMaker MLflow Plugin

This plugin generates Signature V4 headers in each outgoing request to the Amazon SageMaker with MLflow capability, determines the URL of capability to connect to tracking servers, and registers models to the SageMaker Model Registry. It generates a token with the SigV4 Algorithm that the service will use to conduct Authentication and Authorization using AWS IAM.

To install this plugin (lightweight, depends on mlflow-skinny):

pip install sagemaker-mlflow

To install with the full mlflow dependency set:

pip install sagemaker-mlflow[full]

To install from source:

pip install .

By default, the plugin signs requests using credentials from the boto3 default credential chain (environment variables, shared config, instance role, etc.). Callers that need to sign with a specific boto3.Session — for example a non-default profile or per-tenant credentials in a shared process — can inject one without mutating os.environ:

import boto3
import mlflow
import sagemaker_mlflow

custom = boto3.Session(profile_name="my-profile")

with sagemaker_mlflow.use_session(custom):
    mlflow.MlflowClient().search_experiments(max_results=1)

use_session is a context manager scoped to the current thread / asyncio task; the previous session is restored on exit (including on exception). sagemaker_mlflow.set_session(session) is also available for setting a default that lasts for the rest of the context. Resolution order inside AuthBoto: explicit boto3_session= kwarg → use_session/set_session → boto3.Session().

Set SAGEMAKER_PRESIGNED_URL_UPLOAD_ENABLED=true to route recognized S3 artifact uploads through URLs issued by the MLflow tracking server. The tracking-server request uses SageMaker authentication, while the file is streamed directly to the returned S3 URL so those uploads do not require direct S3 write credentials.

export SAGEMAKER_PRESIGNED_URL_UPLOAD_ENABLED=true

Once a presigned upload is attempted, request or PUT failures propagate to the caller; there is no silent fallback to direct S3. When the setting is disabled, the repository retains the standard MLflow direct-S3 behavior.

When the setting is enabled and the repository has a tracking URI, artifact roots that cannot be identified as run or logged-model targets fail before any server or S3 request; they do not fall back to direct S3. Repositories without a tracking URI retain the standard MLflow direct-S3 behavior.

Warning: Trace payload and attachment uploads are not currently supported when SAGEMAKER_PRESIGNED_URL_UPLOAD_ENABLED=true and a tracking URI is configured. Trace logging fails closed before any server or S3 request and does not fall back to direct S3. To use trace logging until the server supports a trace_id upload scope, disable the setting and ensure that the client has direct S3 write permissions.

Presigned uploads for MLflow 3 logged-model artifacts (log_model) require both a client containing logged-model scope support and a tracking server containing mlflow/mlflow#24765. Upgrading only one side does not enable the flow: an older client still sends the model ID as run_id, while a newer client sends model_id, which an older server does not support.

setup.py Contains the primary entry points for the sdk. install_requires Installs mlflow-skinny (lightweight) by default. The [full] extra installs the full mlflow package. entry_points Contains the entry points for the sdk. See https://mlflow.org/docs/latest/plugins.html#defining-a-plugin for more details.

To run tests using tox, run:

pip install tox

Installing tox will enable users to run multi-environment tests. On the other hand, if running individual tests in a single environment, feel free to continue to use pytest instead.

tox -e flake8,black-check,typing,twine

tox -e black-format

tox --skip-env "black.*|flake8|typing|twine" -- test/unit

tox --skip-env "black.*|flake8|typing|twine" -- test/integration

tox.ini contains support for:

• Python 3.9: mlflow 2.8., 2.9., 2.10., 2.11., 2.12., 2.13., 2.16.*, 3.0.0
• Python 3.10/3.11: mlflow 2.8., 2.9., 2.10., 2.11., 2.12., 2.13., 2.16.*, 3.0.0, 3.4.0, 3.10.0

To add test environments on tox for additional versions of python or mlflow, modify the environment configs in envlist, as well as deps and depends in [testenv].