# Parallel experiments

Submit several jobs, then wait for all of them. `run()` returns immediately, so
submission is fast.

```python
import runcompute

rates = [1e-4, 3e-4, 1e-3]
with runcompute.Client() as client:
    jobs = [
        client.run(
            name=f"lr-{lr}",
            image="ghcr.io/acme/train:v4",
            command=["python", "train.py", "--lr", str(lr)],
            min_vram=24, work_units=2, budget=6,
        )
        for lr in rates
    ]
    finished = [job.wait(progress=False) for job in jobs]

for lr, job in zip(rates, finished):
    print(lr, job.status.value, f"${job.spend:.2f}")
```

Each job has its own budget. To cap the whole sweep, divide the total you are
willing to spend by the number of jobs.

List what is still running:

```bash
runcompute list active
```