wehub-resource-sync
151 lines
5.0 KiB
Python
151 lines
5.0 KiB
Python
import os
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from typing import Any
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import yaml
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from mlflow.exceptions import MlflowException
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from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
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__mlflow_model_config__ = None
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class ModelConfig:
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"""
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ModelConfig used in code to read a YAML configuration file or a dictionary.
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Args:
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development_config: Path to the YAML configuration file or a dictionary containing the
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configuration. If the configuration is not provided, an error is raised
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.. code-block:: python
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:caption: Example usage in model code
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from mlflow.models import ModelConfig
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# Load the configuration from a dictionary
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config = ModelConfig(development_config={"key1": "value1"})
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print(config.get("key1"))
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.. code-block:: yaml
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:caption: yaml file for model configuration
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key1: value1
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another_key:
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- value2
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- value3
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.. code-block:: python
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:caption: Example yaml usage in model code
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from mlflow.models import ModelConfig
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# Load the configuration from a file
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config = ModelConfig(development_config="config.yaml")
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print(config.get("key1"))
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When invoking the ModelConfig locally in a model file, development_config can be passed in
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which would be used as configuration for the model.
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.. code-block:: python
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:caption: Example to use ModelConfig when logging model as code: agent.py
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import mlflow
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from mlflow.models import ModelConfig
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config = ModelConfig(development_config={"key1": "value1"})
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class TestModel(mlflow.pyfunc.PythonModel):
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def predict(self, context, model_input, params=None):
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return config.get("key1")
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mlflow.models.set_model(TestModel())
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But this development_config configuration file will be overridden when logging a model.
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When no model_config is passed in while logging the model, an error will be raised when
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trying to load the model using ModelConfig.
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Note: development_config is not used when logging the model.
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.. code-block:: python
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:caption: Example to use agent.py to log the model: deploy.py
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model_config = {"key1": "value2"}
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with mlflow.start_run():
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model_info = mlflow.pyfunc.log_model(
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name="model", python_model="agent.py", model_config=model_config
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)
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loaded_model = mlflow.pyfunc.load_model(model_info.model_uri)
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# This will print "value2" as the model_config passed in while logging the model
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print(loaded_model.predict(None))
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"""
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def __init__(self, *, development_config: str | dict[str, Any] | None = None):
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config = globals().get("__mlflow_model_config__", None)
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# Here mlflow_model_config have 3 states:
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# 1. None, this means if the mlflow_model_config is None, use development_config if
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# available
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# 2. "", Empty string, this means the users explicitly didn't set the model config
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# while logging the model so if ModelConfig is used, it should throw an error
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# 3. A valid path, this means the users have set the model config while logging the
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# model so use that path
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if config is not None:
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self.config = config
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else:
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self.config = development_config
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if not self.config:
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raise FileNotFoundError(
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"Config file is not provided which is needed to load the model. "
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"Please provide a valid path."
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)
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if not isinstance(self.config, dict) and not os.path.isfile(self.config):
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raise FileNotFoundError(f"Config file '{self.config}' not found.")
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def _read_config(self):
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"""Reads the YAML configuration file and returns its contents.
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Raises:
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FileNotFoundError: If the configuration file does not exist.
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yaml.YAMLError: If there is an error parsing the YAML content.
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Returns:
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dict or None: The content of the YAML file as a dictionary, or None if the
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config path is not set.
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"""
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if isinstance(self.config, dict):
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return self.config
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with open(self.config) as file:
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try:
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return yaml.safe_load(file)
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except yaml.YAMLError as e:
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raise MlflowException(
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f"Error parsing YAML file: {e}", error_code=INVALID_PARAMETER_VALUE
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)
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def to_dict(self):
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"""Returns the configuration as a dictionary."""
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return self._read_config()
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def get(self, key):
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"""Gets the value of a top-level parameter in the configuration."""
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config_data = self._read_config()
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if config_data and key in config_data:
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return config_data[key]
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else:
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raise KeyError(f"Key '{key}' not found in configuration: {config_data}.")
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def _set_model_config(model_config):
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globals()["__mlflow_model_config__"] = model_config
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