wehub-resource-sync
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
"""
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The ``mlflow.openai`` module provides an API for logging and loading OpenAI models.
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Credential management for OpenAI on Databricks
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. warning::
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Specifying secrets for model serving with ``MLFLOW_OPENAI_SECRET_SCOPE`` is deprecated.
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Use `secrets-based environment variables <https://docs.databricks.com/en/machine-learning/model-serving/store-env-variable-model-serving.html>`_
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instead.
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When this flavor logs a model on Databricks, it saves a YAML file with the following contents as
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``openai.yaml`` if the ``MLFLOW_OPENAI_SECRET_SCOPE`` environment variable is set.
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.. code-block:: yaml
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OPENAI_API_BASE: {scope}:openai_api_base
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OPENAI_API_KEY: {scope}:openai_api_key
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OPENAI_API_KEY_PATH: {scope}:openai_api_key_path
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OPENAI_API_TYPE: {scope}:openai_api_type
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OPENAI_ORGANIZATION: {scope}:openai_organization
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- ``{scope}`` is the value of the ``MLFLOW_OPENAI_SECRET_SCOPE`` environment variable.
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- The keys are the environment variables that the ``openai-python`` package uses to
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configure the API client.
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- The values are the references to the secrets that store the values of the environment
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variables.
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When the logged model is served on Databricks, each secret will be resolved and set as the
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corresponding environment variable. See https://docs.databricks.com/security/secrets/index.html
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for how to set up secrets on Databricks.
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"""
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from mlflow.openai.autolog import autolog
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from mlflow.openai.constant import FLAVOR_NAME
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from mlflow.version import IS_TRACING_SDK_ONLY
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__all__ = ["autolog", "FLAVOR_NAME"]
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# Import model logging APIs only if mlflow skinny or full package is installed,
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# i.e., skip if only mlflow-tracing package is installed.
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if not IS_TRACING_SDK_ONLY:
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from mlflow.openai.model import (
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_load_pyfunc,
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load_model,
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log_model,
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save_model,
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)
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__all__ += [
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"load_model",
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"log_model",
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"save_model",
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"_load_pyfunc",
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]
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