How to: Anyscale jobs
Now we're ready to execute our ML workloads. We've decided to combine them all together into one [job](./deploy/jobs/workloads.yaml) but we could have also created separate jobs for each workload (train, evaluate, etc.) We'll start by editing the `$GITHUB_USERNAME` slots inside our [`workloads.yaml`](./deploy/jobs/workloads.yaml) file:
```yaml
runtime_env:
  working_dir: .
  upload_path: s3://madewithml/$GITHUB_USERNAME/jobs  # <--- CHANGE USERNAME (case-sensitive)
  env_vars:
    GITHUB_USERNAME: $GITHUB_USERNAME  # <--- CHANGE USERNAME (case-sensitive)
```

The `runtime_env` here specifies that we should upload our current `working_dir` to an S3 bucket so that all of our workers when we execute an Anyscale Job have access to the code to use. The `GITHUB_USERNAME` is used later to save results from our workloads to S3 so that we can retrieve them later (ex. for serving).

Now we're ready to submit our job to execute our ML workloads:
```bash
anyscale job submit deploy/jobs/workloads.yaml
```