Exporting with Jupyter

Exporting GPT-2 Model with Jupyter
Screenshot
Introduction
This example leverages a Deployment to always maintain one instances of Tensorflow with Jupyter. Each instance, in Kubernetes terminology called a Pod is allocated one GPU.
The Kubenetes Control-Plane in the CoreWeave Cloud will ensure that there are one instance (Pods) of Tensorflow Jupyter running at all times. The Control-Plane will reserve GPU, CPU and RAM on CoreWeaves compute nodes. Pods in the same deployment can be scheduled on the same or multiple physical nodes, depending on resource availability. If co-location of Pods is required for some reason, ie. shared ephemeral or block storage, this can be controlled with affinity rules.
The example Deployment does showcase some node affinity rules. These select a mid range GPU (8, 10 or 11GB of VRAM) which is useful when working with smaller models in Jupyter.
Service
A Service is included to show how to publish a Deployment to the public Internet. The Service publishes the web interface of Jupyter to the Internet on port 8888.
Persistent Storage
A Persistent Volume is allocated to persist user uploaded Notebooks and a ReadWriteMany type storage is used to write the model to a network filesystem that can be accessed by multiple containers at teh same time. The allocation is done via a Persistent Volume Claim requesting the storage size and backing storage type (SSD, HDD). This volume claim is then mounted to the /tf/notebooks directory in the Pod definition. Utilizing a persistent volume ensures that files persist even if the node currently running the Pod fails.
Getting Started
After installing kubectl and adding your CoreWeave Cloud access credentials, the following steps will deploy the Ethminer Deployment and service.
1.Apply the resources. This can be used to both create and update existing manifests
 $ kubectl apply -f model-storage-pvc.yaml
 persistentvolumeclaim/model-storage created
 $ kubectl apply -f jupyter-pvc.yaml
 persistentvolumeclaim/jupyter-pv-claim
 $ kubectl apply -f tensorflow-deployment.yaml
 deployment.apps/tensorflow-jupyter configured
 $ kubectl apply -f tensorflow-service.yaml
 service/tensorflow-jupyter configured
`
2.List pods to see the Deployment working to instantiate all our requested instances
 $ kubectl get pods
 NAME                                 READY   STATUS              RESTARTS   AGE
 tensorflow-jupyter-6794bcb465-4czqb  0/1     ContainerCreating   0          2s
3.After a little while, all pods should transition to the Running state
$ kubectl get pods
NAME                                  READY   STATUS    RESTARTS   AGE
tensorflow-jupyter-6794bcb465-4czqb   1/1     Running   0          6s
4.The Deployment will also show that all desired Pods are up and running
 $ kubectl get deployment
 NAME                 READY   UP-TO-DATE   AVAILABLE   AGE
 tensorflow-jupyter   1/1     1            1           73m
5.Describing a Pod will help troubleshoot a Pod that does not want to start and gives other relevant information about the Pod
 $ kubectl describe pod tensorflow-jupyter-6794bcb465-4czqb
 ....
 Events:
   Type    Reason                  Age   From                     Message
   ----    ------                  ----  ----                     -------
   Normal  Scheduled               54s   default-scheduler        Successfully assigned tenant-test/tensorflow-jupyter-6794bcb465-4czqb to g04c225
   Normal  SuccessfulAttachVolume  54s   attachdetach-controller  AttachVolume.Attach succeeded for volume "pvc-66fa0887-a4c4-4245-a4e2-02200f640fea"
   Normal  Pulling                 50s   kubelet, g04c225         Pulling image "tensorflow/tensorflow:1.15.0-py3-jupyter"
   Normal  Pulled                  24s   kubelet, g04c225         Successfully pulled image "tensorflow/tensorflow:1.15.0-py3-jupyter"
   Normal  Created                 19s   kubelet, g04c225         Created container miner
   Normal  Started                 18s   kubelet, g04c225         Started container miner
6.The logs can be viewed to retrieve the Jupyter login token
```shell $ kubectl logs tensorflow-jupyter-6794bcb465-4czqb
_ /__ __/ /__ **** / __ _ _ _/ _ _/ / /_ _ | /| / / / / / / / /( )/ // / / __/ / / // / |/ |/ / // __/// //// \/// // // _/_/|_/
WARNING: You are running this container as root, which can cause new files in
mounted volumes to be created as the root user on your host machine.
​
To avoid this, run the container by specifying your user's userid:
​
$ docker run -u $(id -u):$(id -g) args...
​
[I 14:09:12.985 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
[I 14:09:13.153 NotebookApp] Serving notebooks from local directory: /tf
[I 14:09:13.153 NotebookApp] The Jupyter Notebook is running at:
[I 14:09:13.153 NotebookApp] http://tensorflow-jupyter-6794bcb465-4czqb:8888/?token=a71eb39261e6ef01bdec8867c2c051b0b3aaf31545bfbb84
[I 14:09:13.153 NotebookApp]  or http://127.0.0.1:8888/?token=a71eb39261e6ef01bdec8867c2c051b0b3aaf31545bfbb84
```
1.To get the public IP assigned to the service, simply list all services
$ kubetl get service                                                                                                                                                                                                                               git:(master↓3|…
NAME                 TYPE           CLUSTER-IP       EXTERNAL-IP     PORT(S)          AGE
tensorflow-jupyter   LoadBalancer   10.134.100.173   64.79.105.199   8888:30947/TCP   30s
```
You can now now access Jupyter on http://EXTERNAL-IP:8888 using the login token from the log.
2.Once logged into Jupyter, upload the gpt-2.ipynb from this repository.
3.Run the notebook, this will save the model for serving in /models, which is where the shared filesystem PWC is mounted.
4.You can now proceed to deploy the PVC based service​

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Exporting GPT-2 Model with Jupyter