state_dict, or the model's parameters. If you saved the model's layers as well, you do not have to redefine the layers.)
vessl.register_model. We specify the repository name and number, pass
MyRunneras the runner class we will use for serving, and list any requirements to install.
vessl.manifest.yaml, which stores metadata and
vessl.runner.pkl, which stores the runner binary. Your model has been registered and is ready for serving.
vessl.register_modelto register a new model as well:
vessl.manifest.yaml, which stores metadata,
vessl.runner.pkl, which stores the runner binary, and
vessl.model.pkl, which stores the trained model. Your model has been registered and is ready for serving.
postprocess_data- the other methods are autogenerated.
failed, there was a problem serving your model. (Most likely, it will be because the Python version of the image did not match the version you used to register your model.) You can also view the logs and system metrics of the pod, as well as connect to the pod using SSH.