---
description: First steps on a Slinky instant cluster, logging in, checking the cluster, submitting batch and interactive Slurm jobs.
revision_date: 27.07.2026
---

# Getting started

## Log in

As the `ubuntu` admin, SSH to the cluster's public IP:

```bash
ssh ubuntu@<cluster-ip>
```

The Slurm commands on the login node (`sinfo`, `squeue`, `sbatch`, `srun`, `scancel`, `scontrol`, `sacct`, `salloc`, `sacctmgr`) are thin wrappers that execute inside the Slurm login pod. They behave like the native commands, with two caveats:

* they need kubectl credentials (a readable `~/.kube/config`), which only the `ubuntu` admin and root have by default, and
* they run inside the login pod as its root user, so jobs submitted this way are owned and accounted as root.

Additional users are provisioned with [`slinky-user-add`](https://docs.verda.com/clusters/instant-clusters/slinky/users/) and connect over seamless SSH (port `2222`), where they are themselves and their jobs run under their own identity. Seamless SSH is off by default — enable it with `slinky-user-add --enable-seamless`, otherwise only port `22` is open. `srun id` shows which identity a shell submits as.

## Check the cluster

```bash
sinfo
```

Worker nodes appear as `slinky-0`, `slinky-1`, ... in the `all` partition and should be `idle` (or `mix`/`alloc` when busy).

## Run your first job

Each worker has 8 GPUs, so a 16-GPU job spans two nodes:

```bash
srun --gpus=16 nvidia-smi -L
```

!!! warning
    Steps only see the GPUs they request. With no `--gpus` (or `--gres=gpu:N`), `nvidia-smi` reports `No devices found.` inside the step. See [Shared jail](https://docs.verda.com/clusters/instant-clusters/slinky/shared-jail/).

## Batch jobs

Write the script under `/home` (it is shared with the workers) and submit from there:

```bash
cat > /home/ubuntu/hello.sbatch <<'EOF'
#!/bin/bash
#SBATCH --job-name=hello
#SBATCH --nodes=2
#SBATCH --gres=gpu:8
#SBATCH --time=00:05:00
srun hostname
srun nvidia-smi -L
EOF

sbatch /home/ubuntu/hello.sbatch
```

Output lands in `slurm-<jobid>.out` next to where you submitted. Use `squeue` for active jobs, `sacct` for finished ones. The full command reference is in the [Slurm documentation](https://slurm.schedmd.com/man_index.html), which applies to Slinky unchanged.

## Interactive sessions

```bash
srun --nodes=1 --gres=gpu:1 --time=00:15:00 --pty bash
```

drops you into a shell on a worker with one GPU bound. For multi-node interactive work, hold an allocation with [`salloc`](https://slurm.schedmd.com/salloc.html) and run `srun` inside it.

## Where to put things

* **`/home`** is shared everywhere: Python environments (`python3 -m venv`), job scripts, container images ([SIF files](https://docs.verda.com/clusters/instant-clusters/slinky/containers/)). Build once, use from every job.
* **`/shared`** is a world-writable dataset area on the same shared filesystem: one stable path for team data, identical on the login pod and inside every job.
* **`/local`** inside a step is the worker's local NVMe: a per-node cache for datasets and checkpoints. It persists across jobs and node restarts, so a re-run landing on the same node finds its data already there.
* **`/tmp`** inside a step is private to the job, also on the NVMe: use it for heavy temporary I/O. It is emptied when the job ends; keep anything valuable on `/local` or `/home`.
* **HPC-X modules** are available inside steps from `/opt/hpcx`: `srun bash -lc 'module load hpcx && ...'`.

## Next steps

* Add your team: [User management](https://docs.verda.com/clusters/instant-clusters/slinky/users/)
* Run containers: [Containers (Apptainer)](https://docs.verda.com/clusters/instant-clusters/slinky/containers/)
* Watch the queue and node health: [Observability](https://docs.verda.com/clusters/instant-clusters/slinky/observability/)
* Worked examples: [Tutorials](https://docs.verda.com/clusters/instant-clusters/slinky/tutorials/)