> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/ccluster/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/ccluster/_mcp/server. # Batch Jobs > Run containerized batch workloads to completion Jobs let you run any containerized workload as a **batch task** that runs to completion — model training, fine-tuning, data preprocessing, evaluation, or any one-off GPU job. Unlike inference or LLM deployments, jobs are batch workloads with **no endpoint or ingress**: they start, run your container until it finishes (or fails), and then stop. CCluster runs your container as a Kubernetes Job, so you can control how many times it runs, how many run in parallel, and how failures are retried. ## 1. Configure your job Under the **Job Information** section, provide the core details for your workload: * **Job Name** — a unique name for the job. It must start with a lowercase letter, contain only lowercase letters, digits, or hyphens (`-`), end with an alphanumeric character, and be at most 20 characters. * **Container Image URL** — the container image to run (e.g., `nvcr.io/nvidia/base/ubuntu`). * **Container Tag** — the image tag (e.g., `latest`). * **Image Pull Secret Username / Password** — credentials for private images. Leave both empty for public images; if you provide one, you must provide the other. API deployments can instead [reference an existing image pull secret](/ccluster/resources/custom-image#use-an-existing-image-pull-secret) using `image_pull_secret_name`. ## 2. Set optional details Expand the **Optional Details** section to tune how the job executes. All of these fields are optional and fall back to sensible defaults. * **Completions** — the number of pods that must complete successfully for the job to be considered done. Defaults to `1`. Use a value greater than `1` for indexed, multi-pod batch work. * **Parallelism** — the maximum number of pods that run at the same time. Defaults to `1` and must be **less than or equal to Completions**. * **Backoff Limit** — the number of retries before the job is marked as failed. Defaults to `3` and must be `0` or greater. * **Active Deadline Seconds** — the maximum time (in seconds) the job may run before it is terminated. Must be `1` or greater. Leave empty for no deadline. * **Command** — the entrypoint command and arguments for the container (e.g., `/bin/sh -c python3 "train.py"`). If left empty, the image's default entrypoint is used. * **Enable logging** — toggle to collect and view logs from the job's pods. * **Environment variables** — pass additional environment variables to the container (e.g., `HF_TOKEN`). * **Annotations** — attach custom key/value metadata to the job. * **Config file** — mount a file into the container. Provide **Filename**, **Mount Path (directory)**, and **File Content**. Use this for startup scripts or small configs your command can read. > **Note** > > The environment variable names `JOB_COMPLETION_INDEX` and `JOB_COMPLETIONS` are reserved by the platform and cannot be used for your own variables. CCluster runs jobs in **indexed** completion mode and automatically injects both variables into every replica so each one can determine which slice of work it owns: > > * **`JOB_COMPLETION_INDEX`** — the unique, zero-based index assigned to each replica, ranging from `0` to `Completions - 1`. Use it to partition or shard work across replicas (for example, to pick which data split a replica processes). > * **`JOB_COMPLETIONS`** — the total number of completions configured for the job (the **Completions** value). It is the same on every replica, letting each one know the total size of the pool. > **Tip** > > For building and deploying your own custom container image on NVIDIA CCluster, see [Deploying Custom Models](/ccluster/resources/custom-image). ## 3. Select the cluster and hardware Choose the regional cluster and GPU hardware instance that best fits your workload, then click **Deploy**. NVIDIA CCluster provides several managed clusters and GPU instance types to run your job on. > **Tip** > > You can integrate your own private cluster into CCluster through bring-your-own-infrastructure support. To get started, open a support request through your NVIDIA CCluster support channel. ## 4. Monitor your job Once created, your job appears in the deployments listing view alongside its current status. Click into the job to view its details page, including per-pod status and, when **Enable logging** is turned on, the logs from each pod. While pods are running, the **Terminal** tab opens an in-browser shell. Jobs are not revision-aware and have no pause/resume or replica autoscaling. See [Managing Deployments](/ccluster/resources/managing-deployments). Because jobs are batch workloads, there is no endpoint to call — the job runs until your container exits. A job is considered complete once the requested number of **Completions** finish successfully, and failed pods are retried up to the **Backoff Limit** (or until the **Active Deadline Seconds** is reached). ## What's next #### [Managing Deployments](/ccluster/resources/managing-deployments) Follow logs and use the in-console Terminal while the job is running. #### [Deploying Custom Models](/ccluster/resources/custom-image) Learn how to build and deploy your own custom container images. #### [Compute Instance](/ccluster/apps/compute) Launch an on-demand GPU instance with SSH access for interactive work.