unionml.model.Model#

class unionml.model.Model(name='model', init=None, *, dataset, hyperparameter_config=None)#

Initialize a UnionML Model.

The term UnionML Model refers to the specification of a model, which the user defines through the functional entrypoints, e.g. unionml.model.Model.trainer(). The term model object is used to refer to some instance of model from a machine learning framework such as the subclasses of the BaseEstimator class in sklearn, Module in pytorch, etc.

Parameters

name (str) – name of the model
init (Union[Type, Callable, None]) – a class or callable that produces a model object (e.g. an sklearn estimator) when invoked.
dataset (Dataset) – a UnionML Dataset object to bind to the model.
hyperparameter_config (Optional[Dict[str, Type]]) –
A dictionary mapping hyperparameter names to types. This is used to determine the hyperparameter names and types associated with the model object produced by the init argument. For example:
```
>>> {
...    "hyperparameter1": int,
...    "hyperparameter2": str,
...    "hyperparameter3": float,
... }
```

Methods

`evaluator`	Register a function for producing metrics for given model object.
`init`	Register a function for initializing a model object.
`load`	Load a model object from disk.
`loader`	Register a function for deserializing a model object to disk.
`predict`	Generate predictions locally.
`predict_from_features_task`	Create a Flyte task for generating predictions from a model object.
`predict_from_features_workflow`	Create a Flyte prediction workflow using raw features.
`predict_task`	Create a Flyte task for generating predictions from a model object.
`predict_workflow`	Create a Flyte prediction workflow using features from the `dataset.reader` as the data source.
`predictor`	Register a function that generates predictions from a model object.
`remote`	Configure the `unionml.Model` for remote backend deployment.
`remote_deploy`	Deploy model services to a Flyte backend.
`remote_fetch_predictions`	Fetch predictions from a Flyte execution.
`remote_list_model_versions`	Lists all the model versions of this UnionML app, in reverse chronological order.
`remote_load`	Load a `ModelArtifact` based on the provided Flyte execution.
`remote_predict`	Generate predictions on a remote Flyte backend.
`remote_train`	Train a model object on a remote Flyte backend.
`remote_wait`	Wait for a `FlyteWorkflowExecution` to complete and returns the execution's output.
`save`	Save the model object to disk.
`saver`	Register a function for serializing a model object to disk.
`serve`	Create a FastAPI serving app.
`train`	Train a model object locally
`train_task`	Create a Flyte task for training a model object.
`train_workflow`	Create a Flyte training workflow for model object training.
`trainer`	Register a function for training a model object.

Attributes

`artifact`	Model artifact associated with the `unionml.Model` .
`config_file`	Path to the config file associated with the Flyte backend.
`dockerfile`	Path to Docker file used to package the UnionML app.
`hyperparameter_type`	Hyperparameter type of the model object based on the `init` function signature.
`model_type`	rtype `Type`
`predict_from_features_workflow_name`	Name of the prediction workflow used to generate predictions from raw features.
`predict_workflow_name`	Name of the prediction workflow used to generate predictions from the `dataset.reader` .
`registry`	Docker registry used to push UnionML app.
`train_workflow_name`	Name of the training workflow.
`trainer_params`	Parameters used to create a Flyte workflow for model object training.

property artifact: Optional[unionml.model.ModelArtifact]#

Model artifact associated with the unionml.Model .

Return type: Optional[ModelArtifact]

property config_file: Optional[str]#

Path to the config file associated with the Flyte backend.

Return type: Optional[str]

property dockerfile: Optional[str]#

Path to Docker file used to package the UnionML app.

Return type: Optional[str]

evaluator(fn)#: Register a function for producing metrics for given model object.

property hyperparameter_type: Type#

Hyperparameter type of the model object based on the init function signature.

Return type: Type

init(fn)#: Register a function for initializing a model object.

load(file, *args, **kwargs)#: Load a model object from disk.

loader(fn)#: Register a function for deserializing a model object to disk.

predict(features=None, **reader_kwargs)#

Generate predictions locally.

You can either pass this function raw features via the features argument or you can pass in keyword arguments that will be forwarded to the unionml.Dataset.reader() method as the feature source.

Parameters

features (Optional[Any]) –
Raw features that are pre-processed by the :py:class:unionml.Dataset methods in the following order:
- unionml.dataset.Dataset.feature_loader()
- unionml.dataset.Dataset.feature_transformer()
reader_kwargs – keyword arguments that correspond to the unionml.Dataset.reader() method signature.

predict_from_features_task()#

Create a Flyte task for generating predictions from a model object.

This is used in the Flyte workflow produced by predict_from_features_workflow.

predict_from_features_workflow()#: Create a Flyte prediction workflow using raw features.

property predict_from_features_workflow_name#: Name of the prediction workflow used to generate predictions from raw features.

predict_task()#

Create a Flyte task for generating predictions from a model object.

This is used in the Flyte workflow produced by predict_workflow.

predict_workflow()#: Create a Flyte prediction workflow using features from the dataset.reader as the data source.

property predict_workflow_name#: Name of the prediction workflow used to generate predictions from the dataset.reader .

predictor(fn=None, **predict_task_kwargs)#: Register a function that generates predictions from a model object.

property registry: Optional[str]#

Docker registry used to push UnionML app.

Return type: Optional[str]

remote(registry=None, image_name=None, dockerfile='Dockerfile', config_file=None, project=None, domain=None)#

Configure the unionml.Model for remote backend deployment.

Parameters

registry (Optional[str]) – Docker registry used to push UnionML app.
image_name (Optional[str]) – image name to give to the Docker image associated with the UnionML app.
dockerfile (str) – path to the Dockerfile used to package the UnionML app.
config_file (Optional[str]) – path to the flytectl config to use for deploying your UnionML app to a Flyte backend.
project (Optional[str]) – deploy your app to this Flyte project name.
project – deploy your app to this Flyte domain name.

remote_deploy(app_version=None)#: Deploy model services to a Flyte backend.

remote_fetch_predictions(execution)#

Fetch predictions from a Flyte execution.

Parameters: execution (FlyteWorkflowExecution) – a Flyte workflow execution, which is the output of remote_predict(..., wait=False) .
Return type: Any

remote_list_model_versions(app_version=None, limit=10)#

Lists all the model versions of this UnionML app, in reverse chronological order.

Parameters

app_version (Optional[str]) – if provided, lists the model versions associated with this app version. By default, this uses the current git sha of the repo, which versions your UnionML app.
limit (int) – limit the number results to fetch.

Return type

List[str]

remote_load(execution)#

Load a ModelArtifact based on the provided Flyte execution.

Parameters: execution (FlyteWorkflowExecution) – a Flyte workflow execution, which is the output of remote_train(..., wait=False) .

remote_predict(app_version=None, model_version=None, wait=True, *, features=None, **reader_kwargs)#

Generate predictions on a remote Flyte backend.

You can either pass this function raw features via the features argument or you can pass in keyword arguments that will be forwarded to the unionml.Dataset.reader() method as the feature source.

Parameters

app_version (Optional[str]) – if provided, executes a prediction job using the specified UnionML app version. By default, this uses the current git sha of the repo, which versions your UnionML app.
model_version (Optional[str]) – if provided, executes a prediction job using the specified model version. By default, this uses the latest Flyte execution id as the model version.
wait (bool) – if True, this is a synchronous operation, returning a ModelArtifact. Otherwise, this function returns a FlyteWorkflowExecution.
features (Optional[Any]) –
Raw features that are pre-processed by the :py:class:unionml.Dataset methods in the following order:
- unionml.dataset.Dataset.feature_loader()
- unionml.dataset.Dataset.feature_transformer()
reader_kwargs – keyword arguments that correspond to the unionml.Dataset.reader() method signature.

Return type

Union[Any, FlyteWorkflowExecution]

remote_train(app_version=None, wait=True, *, hyperparameters=None, loader_kwargs=None, splitter_kwargs=None, parser_kwargs=None, trainer_kwargs=None, **reader_kwargs)#

Train a model object on a remote Flyte backend.

Parameters

app_version (Optional[str]) – if provided, executes a training job using the specified UnionML app version. By default, this uses the current git sha of the repo, which versions your UnionML app.
wait (bool) – if True, this is a synchronous operation, returning a ModelArtifact. Otherwise, this function returns a FlyteWorkflowExecution.
hyperparameters (Optional[Dict[str, Any]]) – a dictionary mapping hyperparameter names to values. This is passed into the init callable to initialize a model object.
loader_kwargs (Optional[Dict[str, Any]]) – key-word arguments to pass to the registered unionml.Dataset.loader function. This will override any defaults set in the function definition.
splitter_kwargs (Optional[Dict[str, Any]]) – key-word arguments to pass to the registered unionml.Dataset.splitter function. This will override any defaults set in the function definition.
parser_kwargs (Optional[Dict[str, Any]]) – key-word arguments to pass to the registered unionml.Dataset.parser function. This will override any defaults set in the function definition.
trainer_kwargs (Optional[Dict[str, Any]]) – a dictionary mapping training parameter names to values. There training parameters are determined by the keyword-only arguments of the model.trainer function.
reader_kwargs – keyword arguments that correspond to the unionml.Dataset.reader() method signature.

Return type

Union[ModelArtifact, FlyteWorkflowExecution]

remote_wait(execution, **kwargs)#

Wait for a FlyteWorkflowExecution to complete and returns the execution’s output.

Return type: Any

save(file, *args, **kwargs)#: Save the model object to disk.

saver(fn)#: Register a function for serializing a model object to disk.

serve(app, remote=False, model_version='latest')#

Create a FastAPI serving app.

Parameters: app (FastAPI) – A FastAPI app to use for model serving.

train(hyperparameters=None, loader_kwargs=None, splitter_kwargs=None, parser_kwargs=None, trainer_kwargs=None, **reader_kwargs)#

Train a model object locally

Parameters

hyperparameters (Optional[Dict[str, Any]]) – a dictionary mapping hyperparameter names to values. This is passed into the init callable to initialize a model object.
loader_kwargs (Optional[Dict[str, Any]]) – key-word arguments to pass to the registered unionml.Dataset.loader function. This will override any defaults set in the function definition.
splitter_kwargs (Optional[Dict[str, Any]]) – key-word arguments to pass to the registered unionml.Dataset.splitter function. This will override any defaults set in the function definition.
parser_kwargs (Optional[Dict[str, Any]]) – key-word arguments to pass to the registered unionml.Dataset.parser function. This will override any defaults set in the function definition.
trainer_kwargs (Optional[Dict[str, Any]]) – a dictionary mapping training parameter names to values. There training parameters are determined by the keyword-only arguments of the model.trainer function.
reader_kwargs – keyword arguments that correspond to the unionml.Dataset.reader() method signature.

Return type

Tuple[Any, Any]

train_task()#

Create a Flyte task for training a model object.

This is used in the Flyte workflow produced by train_workflow.

train_workflow()#: Create a Flyte training workflow for model object training.

property train_workflow_name#: Name of the training workflow.

trainer(fn=None, **train_task_kwargs)#: Register a function for training a model object.

property trainer_params: Dict[str, inspect.Parameter]#

Parameters used to create a Flyte workflow for model object training.

Return type: Dict[str, Parameter]