wehub-resource-sync
86 lines
2.5 KiB
ReStructuredText
86 lines
2.5 KiB
ReStructuredText
Python Package Anti-Tampering with MLflow
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-----------------------------------------
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This directory contains an MLflow project showing how to harden the ML supply chain, and in particular
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how to protect against Python package tampering by enforcing
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`hash checks <https://pip.pypa.io/en/latest/cli/pip_install/#hash-checking-mode>`_ on packages.
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Running this Example
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^^^^^^^^^^^^^^^^^^^^
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First, install MLflow (via ``pip install mlflow``).
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The model is trained locally by running:
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.. code-block:: bash
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mlflow run .
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At the end of the training, note the run ID (say ``e651fcd4dab140a2bd4d3745a32370ac``).
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The model is served locally by running:
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.. code-block:: bash
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mlflow models serve -m runs:/e651fcd4dab140a2bd4d3745a32370ac/model
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Inference is performed by sending JSON POST requests to http://localhost:5000/invocations:
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.. code-block:: bash
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curl -X POST -d "{\"dataframe_split\": {\"data\":[[0.0199132142,0.0506801187,0.1048086895,0.0700725447,-0.0359677813,-0.0266789028,-0.0249926566,-0.002592262,0.0037117382,0.0403433716]]}}" -H "Content-Type: application/json" http://localhost:5000/invocations
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Which returns ``[235.11371081266924]``.
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Structure of this MLflow Project
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. code-block:: yaml
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name: mlflow-supply-chain-security
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channels:
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- nodefaults
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dependencies:
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- python=3.9
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- pip
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- pip:
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- --require-hashes
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- -r requirements.txt
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This ensures that all the package requirements referenced in ``requirements.txt`` have been pinned through both version and hash:
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.. code-block:: text
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mlflow==1.20.2 \
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--hash=sha256:963c22532e82a93450674ab97d62f9e528ed0906b580fadb7c003e696197557c \
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--hash=sha256:b15ff0c7e5e64f864a0b40c99b9a582227315eca2065d9f831db9aeb8f24637b
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numpy==1.21.4 \
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--hash=sha256:0b78ecfa070460104934e2caf51694ccd00f37d5e5dbe76f021b1b0b0d221823 \
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...
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That same conda environment is referenced when logging the model in ``train.py`` so the environment matches during inference:
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.. code-block:: python
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mlflow.sklearn.log_model(
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model,
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name="model",
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signature=mlflow.models.infer_signature(X_train[:10], y_train[:10]),
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input_example=X_train[:10],
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conda_env="conda.yaml",
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)
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The package requirements are managed in ``requirements.in``:
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.. code-block:: text
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pandas==1.3.2
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scikit-learn==0.24.2
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mlflow==1.20.2
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They are compiled using ``pip-tools`` to resolve all the package dependencies, their versions, and their hashes:
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.. code-block:: bash
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pip install pip-tools
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pip-compile --generate-hashes --output-file=requirements.txt requirements.in
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