# Manual Checkpoint/Restore

For CPU workloads, no additional configuration is required. With Cedana running on your cluster, you can start by deploying this sample stateful reinforcement learning job (running [Stable Baselines 3](https://github.com/DLR-RM/stable-baselines3)).

## Deploy

```yaml
# test-pod.yaml
apiVersion: v1
kind: Pod
metadata:
  name: cedana-sample-ppo-sb3
  labels:
    app: cedana-sample-ppo-sb3
spec:
  restartPolicy: Never
  containers:
    - name: cedana-sample-container
      image: "cedana/cedana-samples:latest"
      command: ["python3", "/app/cpu_smr/rl/ppo_sb3.py"]
      resources:
        requests:
          cpu: "1"
        limits:
          cpu: "1"
```

{% hint style="info" %}
Note that for any sort of automation (resume on failure), it might make more sense to use [Kubernetes Jobs](https://kubernetes.io/docs/concepts/workloads/controllers/job/). See [checkpoint/restoring Jobs](https://docs.cedana.ai/cedana-kubernetes/jobs) for more information.
{% endhint %}

Deploy this pod to your cluster using:

```bash
kubectl apply -f test-pod.yaml
```

## Checkpoint

You can either create a heartbeat policy to automatically checkpoint at regular intervals, or you can manually checkpoint this pod on the [Pods Page](https://ui.cedana.com/monitoring/pods).

## Restore

You can manually restore the workload on the [Checkpoints Page](https://ui.cedana.com/checkpoints).

{% hint style="info" %}
Automated restores are currently best performed *within* the context of the Kubernetes lifecycle, where we integrate cleanly. For example, if you're using a kind (e.g. [Kubernetes Deployment](https://kubernetes.io/docs/concepts/workloads/controllers/deployment/)) that automatically reschedules the pod on node failure or eviction, it will restore from the latest checkpoint instead of starting from scratch. Check [checkpoint/restoring Jobs](https://docs.cedana.ai/cedana-kubernetes/jobs) for more information.
{% endhint %}

## Example

Below you can find an example of this workflow:

{% embed url="<https://youtu.be/J_yEUqt66Rw>" %}

If you've made it this far, congratulations! You've successfully used Cedana to move a stateful workload between nodes and have it pick up work where it left off.

Take a look at the left sidebar to see more examples, such as [GPU Save/Migrate/Resume on Kubernetes](https://github.com/cedana/cedana-gitbook/blob/main/examples/gpu-smr-on-kubernetes.md).