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Model Serving API

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RunnerBase

RunnerBase()
Base class for model registering.
This base class introduces 5 static methods as followings:
  • predict: Make prediction with given data and model. This method must be overridden. The data is given from the result of preprocess_data, and the return value of this method will be passed to postprocess_data before serving.
  • save_model: Save the model into a file. Return value of this method will be given to the load_model method on model loading. If this method is overriden, load_model must be overriden as well.
  • load_model: Load the model from a file.
  • preprocess_data: Preprocess the data before prediction. It converts the API input data to the model input data.
  • postprocess_data: Postprocess the data after prediction. It converts the model output data to the API output data.
Check each method's docstring for more information.
Methods:

.postprocess_data

vessl.postprocess_data(
data: ModelOutputDataType
)
Postprocess the given data.
The data processed by this method will be given to the user.
Args
  • data : Data to be postprocessed.
Returns Postprocessed data that will be given to the user.

.load_model

vessl.load_model(
props: Union[Dict[str, str], None], artifacts: Dict[str, str]
)
Load the model instance from file.
props is given from the return value of save_model, and artifacts is given from the register_model method.
If the save_model is not overriden, props will be None
Args
  • props (dict | None) : Data that was returned by save_model. If save_model is not overriden, this will be None.
  • artifacts (dict) : Data that is given by register_model function.
Returns Model instance.

.predict

vessl.predict(
model: ModelType, data: ModelInputDataType
)
Make prediction with given data and model.
Args
  • model (model_instance) : Model instance.
  • data : Data to be predicted.
Returns Prediction result.

.save_model

vessl.save_model(
model: ModelType
)
Save the given model instance into file.
Return value of this method will be given to first argument of load_model on model loading.
Args
  • model (model_instance) : Model instance to save.
Returns (dict) Data that will be passed to load_model on model loading. Must be a dictionary with key and value both string.

.preprocess_data

vessl.preprocess_data(
data: InputDataType
)
Preprocess the given data.
The data processed by this method will be given to the model.
Args
  • data : Data to be preprocessed.
Returns Preprocessed data that will be given to the model.
​

register_model

vessl.register_model(
repository_name: str, model_number: Union[int, None], runner_cls: RunnerBase,
model_instance: Union[ModelType, None] = None, requirements: List[str] = None,
artifacts: Dict[str, str] = None, **kwargs
)
Register the given model for serving. If you want to override the default organization, then pass organization_name as **kwargs.
Args
  • repository_name (str) : Model repository name.
  • model_number (int | None) : Model number. If None, new model will be created. In such case, model_instance must be given.
  • runner_cls (RunnerBase) : Runner class that includes code for serving.
  • model_instance (ModelType | None) : Model instance. If None, runner_cls must override load_model method. Defaults to None.
  • requirements (List[str]) : Python requirements for the model. Defaults to [].
  • artifacts (Dict[str, str]) : Artifacts to be uploaded. Key is the path to artifact in local filesystem, and value is the path in the model volume. Only trailing asterisk(*) is allowed for glob pattern. Defaults to {}.
Example
  • "model.pt", "checkpoints/": "checkpoints/"},
register_model(
repository_name="my-model",
model_number=1,
runner_cls=MyRunner,
model_instance=model_instance,
requirements=["torch", "torchvision"],
)
​

register_torch_model

vessl.register_torch_model(
repository_name: str, model_number: Union[int, None], model_instance: ModelType,
preprocess_data = None, postprocess_data = None, requirements: List[str] = None,
**kwargs
)
Register the given torch model instance for model serving. If you want to override the default organization, then pass organization_name as **kwargs.
Args
  • repository_name (str) : Model repository name.
  • model_number (int | None) : Model number. If None, new model will be created.
  • model_instance (model_instance) : Torch model instance.
  • preprocess_data (callable) : Function that will preprocess data. Defaults to identity function.
  • postprocess_data (callable) : Function that will postprocess data. Defaults to identity function.
  • requirements (list) : List of requirements. Defaults to [].
Example
  • x : int(x), postprocess_data=lambda x: {"prediction": x}, requirements=["torch", "torchvision"],
vessl.register_model(
repository_name="my-model",
model_number=1,
model_instance=model_instance,
)
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Last modified 6mo ago