firebase_admin.ml module

Firebase ML module.

This module contains functions for creating, updating, getting, listing, deleting, publishing and unpublishing Firebase ML models.

Classes

ListModelsPage

class firebase_admin.ml.ListModelsPage(list_models_func, list_filter, page_size, page_token, app)

Bases: object

Represents a page of models in a Firebase project.

Provides methods for traversing the models included in this page, as well as retrieving subsequent pages of models. The iterator returned by iterate_all() can be used to iterate through all the models in the Firebase project starting from this page.

get_next_page()

Retrieves the next page of models if available.

Returns:

Next page of models, or None if this is the last page.

Return type:

ListModelsPage

iterate_all()

Retrieves an iterator for Models.

Returned iterator will iterate through all the models in the Firebase project starting from this page. The iterator will never buffer more than one page of models in memory at a time.

Returns:

An iterator of Model instances.

Return type:

iterator

property has_next_page

True if more pages are available.

property list_filter

The filter string used to filter the models.

property models

A list of Models from this page.

property next_page_token

Token identifying the next page of results.

Model

class firebase_admin.ml.Model(display_name=None, tags=None, model_format=None)

Bases: object

A Firebase ML Model object.

Parameters:

  • display_name – The display name of your model - used to identify your model in code.

  • tags – Optional list of strings associated with your model. Can be used in list queries.

  • model_format – A subclass of ModelFormat. (e.g. TFLiteFormat) Specifies the model details.

as_dict(for_upload=False)

Returns a serializable representation of the object.

classmethod from_dict(data, app=None)

Create an instance of the object from a dict.

wait_for_unlocked(max_time_seconds=None)

Waits for the model to be unlocked. (All active operations complete)

Parameters:

max_time_seconds – The maximum number of seconds to wait for the model to unlock. (None for no limit)

Raises:

exceptions.DeadlineExceeded – If max_time_seconds passed and the model is still locked.

property create_time

The time the model was created.

property display_name

The model’s display name, used to refer to the model in code and in the Firebase console.

property etag

The entity tag (ETag) of the model resource.

property locked

True if the Model object is locked by an active operation.

property model_format

The model’s ModelFormat object, which represents the model’s format and storage location.

property model_hash

SHA256 hash of the model binary.

property model_id

The model’s ID, unique to the project.

property published

True if the model is published and available for clients to download.

property tags

Tag strings, used for filtering query results.

property update_time

The time the model was last updated.

property validation_error

Validation error message.

ModelFormat

class firebase_admin.ml.ModelFormat

Bases: object

Abstract base class representing a Model Format such as TFLite.

as_dict(for_upload=False)

Returns a serializable representation of the object.

TFLiteAutoMlSource

class firebase_admin.ml.TFLiteAutoMlSource(auto_ml_model, app=None)

Bases: TFLiteModelSource

TFLite model source representing a tflite model created with AutoML.

AutoML model support is deprecated and will be removed in the next major version.

as_dict(for_upload=False)

Returns a serializable representation of the object.

property auto_ml_model

Resource name of the model, created by the AutoML API or Cloud console.

TFLiteFormat

class firebase_admin.ml.TFLiteFormat(model_source=None)

Bases: ModelFormat

Model format representing a TFLite model.

Parameters:

model_source – A TFLiteModelSource sub class. Specifies the details of the model source.

as_dict(for_upload=False)

Returns a serializable representation of the object.

classmethod from_dict(data)

Create an instance of the object from a dict.

property model_source

The TF Lite model’s location.

property size_bytes

The size in bytes of the TF Lite model.

TFLiteGCSModelSource

class firebase_admin.ml.TFLiteGCSModelSource(gcs_tflite_uri, app=None)

Bases: TFLiteModelSource

TFLite model source representing a tflite model file stored in GCS.

as_dict(for_upload=False)

Returns a serializable representation of the object.

classmethod from_keras_model(keras_model, model_file_name='firebase_ml_model.tflite', bucket_name=None, app=None)

Creates a Tensor Flow Lite model from the keras model, and uploads the model to GCS.

Parameters:

  • keras_model – A tf.keras model.

  • model_file_name – The name that the tflite model will be saved as in Cloud Storage.

  • bucket_name – The name of an existing bucket. None to use the default bucket configured in the app.

  • app – Optional. A Firebase app instance (or None to use the default app)

Returns:

The source created from the keras_model

Return type:

TFLiteGCSModelSource

Raises:

ImportError – If the Tensor Flow or Cloud Storage Libraries have not been installed.

classmethod from_saved_model(saved_model_dir, model_file_name='firebase_ml_model.tflite', bucket_name=None, app=None)

Creates a Tensor Flow Lite model from the saved model, and uploads the model to GCS.

Parameters:

  • saved_model_dir – The saved model directory.

  • model_file_name – The name that the tflite model will be saved as in Cloud Storage.

  • bucket_name – The name of an existing bucket. None to use the default bucket configured in the app.

  • app – Optional. A Firebase app instance (or None to use the default app)

Returns:

The source created from the saved_model_dir

Return type:

TFLiteGCSModelSource

Raises:

ImportError – If the Tensor Flow or Cloud Storage Libraries have not been installed.

classmethod from_tflite_model_file(model_file_name, bucket_name=None, app=None)

Uploads the model file to an existing Google Cloud Storage bucket.

Parameters:

  • model_file_name – The name of the model file.

  • bucket_name – The name of an existing bucket. None to use the default bucket configured in the app.

  • app – A Firebase app instance (or None to use the default app).

Returns:

The source created from the model_file

Return type:

TFLiteGCSModelSource

Raises:

ImportError – If the Cloud Storage Library has not been installed.

property gcs_tflite_uri

URI of the model file in Cloud Storage.

TFLiteModelSource

class firebase_admin.ml.TFLiteModelSource

Bases: object

Abstract base class representing a model source for TFLite format models.

as_dict(for_upload=False)

Returns a serializable representation of the object.

Functions

create_model

firebase_admin.ml.create_model(model, app=None)

Creates a model in the current Firebase project.

Parameters:

  • model – An ml.Model to create.

  • app – A Firebase app instance (or None to use the default app).

Returns:

The model that was created in Firebase ML.

Return type:

Model

delete_model

firebase_admin.ml.delete_model(model_id, app=None)

Deletes a model from the current project.

Parameters:

  • model_id – The id of the model you wish to delete.

  • app – A Firebase app instance (or None to use the default app).

get_model

firebase_admin.ml.get_model(model_id, app=None)

Gets the model specified by the given ID.

Parameters:

  • model_id – The id of the model to get.

  • app – A Firebase app instance (or None to use the default app).

Returns:

The requested model.

Return type:

Model

list_models

firebase_admin.ml.list_models(list_filter=None, page_size=None, page_token=None, app=None)

Lists the current project’s models.

Parameters:

  • list_filter – a list filter string such as tags:'tag_1'. None will return all models.

  • page_size – A number between 1 and 100 inclusive that specifies the maximum number of models to return per page. None for default.

  • page_token – A next page token returned from a previous page of results. None for first page of results.

  • app – A Firebase app instance (or None to use the default app).

Returns:

A (filtered) list of models.

Return type:

ListModelsPage

publish_model

firebase_admin.ml.publish_model(model_id, app=None)

Publishes a Firebase ML model.

A published model can be downloaded to client apps.

Parameters:

  • model_id – The id of the model to publish.

  • app – A Firebase app instance (or None to use the default app).

Returns:

The published model.

Return type:

Model

unpublish_model

firebase_admin.ml.unpublish_model(model_id, app=None)

Unpublishes a Firebase ML model.

Parameters:

  • model_id – The id of the model to unpublish.

  • app – A Firebase app instance (or None to use the default app).

Returns:

The unpublished model.

Return type:

Model

update_model

firebase_admin.ml.update_model(model, app=None)

Updates a model’s metadata or model file.

Parameters:

  • model – The ml.Model to update.

  • app – A Firebase app instance (or None to use the default app).

Returns:

The updated model.

Return type:

Model