from gradient import DeploymentsClientapi_key='YOUR_API_KEY'deployment_client = DeploymentsClient(api_key)
Get a model to deploy by either selecting experiment output or filtering based on some criteria from a project
model = models_client.list(experiment_id = experiment_id)[0]
deployment_param = {"deployment_type" : "Tensorflow Serving on K8s","image_url": "tensorflow/serving:latest-gpu","name": "sdk_tutorial","machine_type": "K80","instance_count": 2,"model_id" : model.id}
deployment_id = deployment_client.create(**deploy_param)
deployment_client.start(deployment_id)
deployment_client.stop(deployment_id)