Wallaroo SDK Essentials Guide: Model Uploads and Registrations: TensorFlow Keras

How to upload and use TensorFlow Keras ML Models with Wallaroo

Model Naming Requirements

Model names map onto Kubernetes objects, and must be DNS compliant. The strings for model names must be lower case ASCII alpha-numeric characters or dash (-) only. . and _ are not allowed.

Wallaroo supports TensorFlow/Keras models by containerizing the model and running as an image.

Parameter	Description
Web Site	https://www.tensorflow.org/api_docs/python/tf/keras/Model
Supported Libraries	`tensorflow==2.9.3` `keras==2.9.0`
Framework	`Framework.KERAS` aka `keras`
Supported File Types	SavedModel format as .zip file and HDF5 format

During the model upload process, Wallaroo optimizes models by converting them to the Wallaroo Native Runtime, if possible, or running the model directly in the Wallaroo Containerized Runtime. See the Model Deploy for details on how to configure pipeline resources based on the model’s runtime.

TensorFlow Keras SavedModel Format

TensorFlow Keras SavedModel models are .zip file of the SavedModel format. For example, the Aloha sample TensorFlow model is stored in the directory alohacnnlstm:

├── saved_model.pb
└── variables
    ├── variables.data-00000-of-00002
    ├── variables.data-00001-of-00002
    └── variables.index

This is compressed into the .zip file alohacnnlstm.zip with the following command:

zip -r alohacnnlstm.zip alohacnnlstm/

See the SavedModel guide for full details.

TensorFlow Keras H5 Format

Wallaroo supports the H5 for Tensorflow Keras models.

Uploading TensorFlow Models

TensorFlow Keras models are uploaded to Wallaroo through the Wallaroo Client upload_model method.

Upload TensorFlow Model Parameters

The following parameters are required for TensorFlow keras models. Note that while some fields are considered as optional for the upload_model method, they are required for proper uploading of a TensorFlow Keras model to Wallaroo.

Parameter	Type	Description
`name`	`string` (Required)	The name of the model. Model names are unique per workspace. Models that are uploaded with the same name are assigned as a new version of the model.
`path`	`string` (Required)	The path to the model file being uploaded.
`framework`	`string` (Required)	Set as the `Framework.KERAS`.
`input_schema`	`pyarrow.lib.Schema` (Required)	The input schema in Apache Arrow schema format.
`output_schema`	`pyarrow.lib.Schema` (Required)	The output schema in Apache Arrow schema format.
`convert_wait`	`bool` (Optional) (Default: True)	True: Waits in the script for the model conversion completion. False: Proceeds with the script without waiting for the model conversion process to display complete.
`arch`	wallaroo.engine_config.Architecture	The architecture the model is deployed to. If a model is intended for deployment to an `ARM` architecture, it must be specified during this step. Values include: `X86` (Default): x86 based architectures. `ARM`: ARM based architectures.

Once the upload process starts, the model is containerized by the Wallaroo instance. This process may take up to 10 minutes.

Upload TensorFlow Model Return

upload_model returns a wallaroo.model_version.ModelVersion object with the following fields.

Field	Type	Description
`name`	String	The name of the model.
`version`	String	The model version as a unique UUID.
`file_name`	String	The file name of the model as stored in Wallaroo.
`SHA`	String	The hash value of the model file.
`Status`	String	The status of the model.
`image_path`	String	The image used to deploy the model in the Wallaroo engine.
`last_update_time`	DateTime	When the model was last updated.

Upload TensorFlow Model Example

The following example is of uploading a tensorflow Keras Model to a Wallaroo instance.

input_schema = pa.schema([
    pa.field('input', pa.list_(pa.float64(), list_size=10))
])
output_schema = pa.schema([
    pa.field('output', pa.list_(pa.float64(), list_size=32))
])

model = wl.upload_model('keras-sequential-single-io', 
                        'models/model-auto-conversion_keras_single_io_keras_sequential_model.h5', 
                        framework=Framework.KERAS, 
                        input_schema=input_schema, 
                        output_schema=output_schema)

Waiting for model loading - this will take up to 10.0min.
Model is pending loading to a native runtime.
Model is pending loading to a container runtime..
Model is attempting loading to a container runtime.....................successful

Ready