Editing Tutorials:Run the example container on the cluster (section)

== Checking in on the job ==

We first check if our container is running.

<syntaxhighlight lang="bash">
> kubectl get pods
# somewhere in the output you should see a line like this:
NAME             READY   STATUS    RESTARTS   AGE
your-username-tf-mnist-xxxx   1/1     Running   0          7s
</syntaxhighlight>

Now that you now the name of the pod, you can check in on the logs:

<syntaxhighlight lang="bash">
# replace xxxx with the code from get pods.
> kubectl logs your-username-tf-mnist-xxxx
# this should show the console output of your python program
</syntaxhighlight>

or get some more information about the job, the node the pod was placed on etc.

<syntaxhighlight lang="bash">
> kubectl describe job your-username-tf-mnist
# replace xxxx with the code from get pods.
> kubectl describe pod your-username-tf-mnist-xxxx
</syntaxhighlight>



You can also open a shell in the running container, just as with docker:

<syntaxhighlight lang="bash">
> kubectl exec -it your-username-tf-mnist-xxxx /bin/bash
root@tf-mnist-xxxxx:/workspace# nvidia-smi
Tue Jun 18 14:25:00 2019       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.67       Driver Version: 418.67       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla V100-SXM3...  On   | 00000000:E7:00.0 Off |                    0 |
| N/A   39C    P0    68W / 350W |  30924MiB / 32480MiB |      6%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+
root@tf-mnist-xxxxx:/workspace# ls /application/
nn.py  run.sh  tf-mnist.py
root@tf-mnist-xxxxx:/workspace# 
</syntaxhighlight>