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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()
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.
classmethod from_dict ( data app = None ) Create an instance of the object from a dict.
as_dict ( for_upload = False ) Returns a serializable representation of the object.
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
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.
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.
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.
classmethod from_dict ( data ) Create an instance of the object from a dict.
as_dict ( for_upload = False ) Returns a serializable representation of the object.
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.
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.
as_dict ( for_upload = False ) Returns a serializable representation of the object.
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: 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:
get_model firebase_admin.ml. get_model ( model_id app = None ) Gets the model specified by the given ID.
Parameters: 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'
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: Returns: The published model.
Return type: Model
unpublish_model firebase_admin.ml. unpublish_model ( model_id app = None ) Unpublishes a Firebase ML model.
Parameters: 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: Returns: The updated model.
Return type: Model
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Last updated 2025-07-17 UTC.
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