Deploy your Notebook

NOTE: This notebook requires Kaptain SDK 1.3.x or later.

This is a continuation of the Multi-Cluster Tutorial use case example. Ensure you have successfully completed the steps in Build your Notebook in your source cluster.

What You Need

To run this notebook, ensure your target deployment cluster notebook server is configured similar to the source cluster as specified in the Prerequisites, which includes these steps:

1. Create a Docker secret and an AWS credentials secret.

2. Create a PodDefault configuration referencing the created secrets.

3. Launch a Jupyter notebook server with said PodDefault configuration.

You will be able to open this notebook after launching the notebook server.

Ensure You Are Ready to Start

Before proceeding, verify that the notebook server was configured and launched correctly:

1. Ensure that the docker secret is mounted. You should not see an error:

   CODE

   %%sh
   ls -la ~/.docker/config.json

   Output:

   CODE

   lrwxrwxrwx 1 root istio 18 Oct  6 07:45 /home/kubeflow/.docker/config.json -> ..data/config.json

2. Verify that the AWS environment variables are set. You should see AWS_ACCESS_KEY_ID, AWS_REGION, and AWS_SECRET_ACCESS_KEY:

   CODE

   %%sh
   set | egrep ^AWS_ | cut -f 1 -d '='

   Output:

   CODE

   AWS_ACCESS_KEY_ID
   AWS_REGION
   AWS_SECRET_ACCESS_KEY

Initialize Model Configuration

1. Before loading the model, provide some configuration to the Kaptain SDK, so it is able to recognize Docker and S3:

   CODE

   from kaptain.config import Config
   from kaptain.platform.config.s3 import S3ConfigurationProvider
   from kaptain.platform.config.docker import DockerConfigurationProvider
   from kaptain.platform.model_util import ModelUtil
   
   config = Config(
     docker_config_provider=DockerConfigurationProvider.default(),
     storage_config_provider=S3ConfigurationProvider.from_env(),
   )

2. Load the stored model state from S3:

   CODE

   # Replace model_uri = "..." with the value obtained from the previous notebook when running `model.meta().saved_model_uri`
   model = Model.load_from_json(model_uri = "s3://kaptain/models/dev/mnist/trained/b69dc6f6e3c246858cf43a1eba8be5f5/0001", config = config)

   Output:

   CODE

   [I 221006 08:39:36 model_util:67] Loading model state from s3://kaptain/models/dev/mnist/trained/b69dc6f6e3c246858cf43a1eba8be5f5/0001.

3. Deploy the model:

   CODE

   model.deploy(cpu="1", memory="2G", replace=True)

Congratulations, you have completed this tutorial!