---
description: Access your Kubernetes Instant Cluster, run a first multi-node NCCL test, and pull container images from registries.
revision_date: 27.07.2026
---

# Getting started

## Accessing the cluster

SSH into the jumphost and use `kubectl`. Admin credentials are pre-configured at:

```
/root/.kube/config
/home/ubuntu/.kube/config
```

`k9s` and `helm` are also available out of the box. Verify access with:

```bash
kubectl get nodes
```

You should see the service node (control plane) and your GPU workers in `Ready` status. Confirm the GPUs are schedulable:

```bash
kubectl get nodes -o custom-columns='NODE:.metadata.name,GPU:.status.allocatable.nvidia\.com/gpu'
```

Each worker should report `8`.

!!! note "Run `kubectl` on the jumphost"
    The API server listens on the cluster's private network only, and its certificate is issued for the service node's internal name and address. It is not reachable from your workstation, so run `kubectl` on the jumphost rather than copying the kubeconfig elsewhere.

## Running your first job: NCCL all_reduce test

A pre-configured example job is available on the jumphost. It runs an [NCCL all_reduce_perf](https://github.com/NVIDIA/nccl-tests) benchmark across 2 nodes, the standard way to verify that the cluster's InfiniBand networking is healthy. The example is rendered for your cluster's hardware (including the correct `NCCL_IB_PKEY` setting, see [InfiniBand and NCCL configuration](https://docs.verda.com/clusters/instant-clusters/kubernetes/workloads/#infiniband-and-nccl-configuration)). A full-cluster variant that spans every worker node is provided alongside it.

**Submit the job:**

```console
$ kubectl create -f /home/ubuntu/verda_k8s_all_reduce_perf_2_nodes.yml
mpijob.kubeflow.org/nccl-test-2n-wcq4s created
```

**Check pod status:**

```console
$ kubectl get pods
NAME                                READY   STATUS      RESTARTS   AGE
nccl-test-2n-8cnbc-launcher-przrf   0/1     Completed   4          30m
```

> Downloading the container image on all workers may take a few minutes on first run.

**View the results:**

```console
$ kubectl logs -f nccl-test-2n-8cnbc-launcher-przrf | tail -10

  4294967296    1073741824     float     sum      -1  9230.25  465.31  872.46       0  9221.35  465.76  873.31       0
  8589934592    2147483648     float     sum      -1  18376.5  467.44  876.45       0  18337.6  468.43  878.31       0
# Out of bounds values : 0 OK
# Avg bus bandwidth    : 827.441
#
# Collective test concluded: all_reduce_perf
#

=== NCCL test completed ===
```

**Understanding the output:** the key metric is **Avg bus bandwidth** how fast GPUs collectively communicate across the InfiniBand fabric. A healthy cluster reaches **600+ GB/s** for large message sizes. Significantly lower numbers may indicate a network issue. The `#wrong` columns should read `0`, anything else indicates data corruption and warrants a support ticket.

## Monitoring jobs

Use standard `kubectl` commands to monitor your workloads:

```bash
# List all pods and their status
kubectl get pods

# Follow logs from a specific pod
kubectl logs -f <pod-name>

# Describe a pod for detailed status and events
kubectl describe pod <pod-name>

# List all MPIJobs
kubectl get mpijobs

# Live resource usage (served by the pre-installed metrics-server)
kubectl top nodes
kubectl top pods
```

For cluster-level dashboards, health checks and profiling, see [Observability](https://docs.verda.com/clusters/instant-clusters/kubernetes/observability/).

## Container registry

Your container images are pulled by containerd on the worker nodes. It is recommended to use authenticated access when pulling images.

Verda provides a managed container registry — see [Container Registries](https://docs.verda.com/storage/container-registry/) for setup instructions. To pull from a private registry, create a pull secret and reference it in your pod specs:

```bash
kubectl create secret docker-registry my-registry \
  --docker-server=vccr.io \
  --docker-username=<user> \
  --docker-password=<token>
```

```yaml
spec:
  imagePullSecrets:
    - name: my-registry
```

For quick experimentation, public images from NVIDIA NGC (`nvcr.io/nvidia/...`) and Docker Hub can also be used.

## Next steps

* [Running multi-node workloads](https://docs.verda.com/clusters/instant-clusters/kubernetes/workloads/) — write your own MPIJobs and PyTorch training jobs.
* [Job queueing (Kueue)](https://docs.verda.com/clusters/instant-clusters/kubernetes/queueing/) — queue and prioritize work instead of hand-scheduling it.
* [Storage](https://docs.verda.com/clusters/instant-clusters/kubernetes/storage/) — pick the right volume type for datasets, caches and checkpoints.