Model Naming Requirements
Model names map onto Kubernetes objects, and must be DNS compliant. The strings for model names must be ASCII alpha-numeric characters or dash (-) only.
_ are not allowed.
Wallaroo supports TensorFlow models by containerizing the model and running as an image.
|Supported File Types||SavedModel format as .zip file|
IMPORTANT NOTEThese requirements are not for Tensorflow Keras models, only for non-Keras Tensorflow models in the SavedModel format. For Tensorflow Keras deployment in Wallaroo, see the Tensorflow Keras requirements.
TensorFlow File Format
TensorFlow models are .zip file of the SavedModel format. For example, the Aloha sample TensorFlow model is stored in the directory
├── 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/
ML models that meet the Tensorflow and SavedModel format will run as Wallaroo Native runtimes by default.
See the SavedModel guide for full details.
Uploading TensorFlow Models
TensorFlow models are uploaded to Wallaroo through the Wallaroo Client
Upload TensorFlow Model Parameters
The following parameters are required for TensorFlow models. Tensorflow models are native runtimes in Wallaroo, so the
output_schema parameters are optional.
||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.|
||The path to the model file being uploaded.|
||Set as the
||The input schema in Apache Arrow schema format.|
||The output schema in Apache Arrow schema format.|
||Not required for native runtimes.
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
For example, the following example is of uploading a TensorFlow ML Model to a Wallaroo instance.
from wallaroo.framework import Framework model = wl.upload_model(model_name, model_file_name, framework=Framework.TENSORFLOW )
Pipeline Deployment Configurations
Pipeline configurations are dependent on whether the model is converted to the Native Runtime space, or Containerized Model Runtime space.
This model will always run in the native runtime space.
Native Runtime Pipeline Deployment Configuration Example
The following configuration allocates 0.25 CPU and 1 Gi RAM to the native runtime models for a pipeline.
deployment_config = DeploymentConfigBuilder() .cpus(0.25) .memory('1Gi') .build()