***

title: NGC Developer Guide
description: >-
Everything you need to know about NVIDIA GPU Cloud (NGC) - the hub for
GPU-optimized AI software.
--------------------------

NGC is NVIDIA's hub for GPU-optimized AI software, serving as a GPU-optimized container registry for GPU-accelerated containers, pre-trained models, and AI toolkits specifically optimized for NVIDIA hardware.

Every asset in NGC is tested for security and validated across different GPUs for performance and scalability. This isn't just a repository - it's a curated, enterprise-grade catalog.

## What's in the NGC Catalog?

| Asset Type             | What You Get                  | Example Use Case                  |
| ---------------------- | ----------------------------- | --------------------------------- |
| **Containers**         | GPU-optimized Docker images   | PyTorch, TensorFlow, RAPIDS       |
| **Pre-trained Models** | Ready-to-deploy AI models     | NeMo LLMs, computer vision models |
| **Helm Charts**        | Kubernetes deployment configs | Deploy inference services on K8s  |
| **Resources**          | Datasets, Jupyter notebooks   | Training data, example workflows  |
| **SDKs**               | Domain-specific toolkits      | Healthcare, autonomous vehicles   |
| **Collections**        | Curated bundles               | End-to-end AI pipelines           |

Browse the catalog at [catalog.ngc.nvidia.com](https://catalog.ngc.nvidia.com/).

## Quick Start

<Steps>
  <Step title="Create an NGC Account">
    Go to [ngc.nvidia.com](https://ngc.nvidia.com) and sign up with your NVIDIA account (or corporate SSO).
  </Step>

  <Step title="Generate an API Key">
    In the NGC web UI:

    1. Click your profile → Setup
    2. Generate API Key
    3. Save it securely - you'll only see it once!
  </Step>

  <Step title="Install NGC CLI">
    ```bash
    # Using pip (recommended)
    pip install ngc-cli

    # Or download directly
    wget -O ngc https://api.ngc.nvidia.com/v2/resources/nvidia/ngc-apps/ngc_cli/versions/3.31.0/files/ngccli_linux.zip
    unzip ngccli_linux.zip
    chmod +x ngc-cli/ngc
    export PATH=$PATH:$(pwd)/ngc-cli
    ```
  </Step>

  <Step title="Configure Authentication">
    ```bash
    # Interactive setup
    ngc config set

    # Or set environment variables
    export NGC_API_KEY=<your-api-key>
    export NGC_ORG=<your-org>  # Optional: for enterprise users
    ```
  </Step>

  <Step title="Pull Your First Container">
    ```bash
    # Pull PyTorch container optimized for GPUs
    docker pull nvcr.io/nvidia/pytorch:24.01-py3

    # Run it
    docker run --gpus all -it nvcr.io/nvidia/pytorch:24.01-py3
    ```
  </Step>
</Steps>

## Core Developer Workflows

### Pulling Containers

```bash
# Format: nvcr.io/<organization>/<repository>:<tag>

# NVIDIA's public containers
docker pull nvcr.io/nvidia/pytorch:24.01-py3
docker pull nvcr.io/nvidia/tensorflow:24.01-tf2-py3
docker pull nvcr.io/nvidia/tritonserver:24.01-py3

# Partner containers (ISV)
docker pull nvcr.io/isv-ngc-partner/company/container:version
```

### Downloading Models

```bash
# List available models
ngc registry model list

# Download a specific model
ngc registry model download-version nvidia/nemo/megatron_gpt_345m:1.0

# Download to specific directory
ngc registry model download-version nvidia/nemo/megatron_gpt_345m:1.0 --dest ./models/
```

### Using Resources (Datasets, Notebooks)

```bash
# List resources
ngc registry resource list

# Download a resource
ngc registry resource download-version nvidia/tao/tao_getting_started:1.0
```

### Working with Helm Charts

```bash
# Add NGC Helm repo
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia

# Search for charts
helm search repo nvidia

# Install a chart (e.g., GPU Operator)
helm install gpu-operator nvidia/gpu-operator \
  --set driver.enabled=true \
  --namespace gpu-operator \
  --create-namespace
```

## NGC CLI Power Commands

```bash
# Search the catalog
ngc registry image list --format_type csv | grep pytorch

# Get detailed info about an image
ngc registry image info nvidia/pytorch:24.01-py3

# List all versions of a model
ngc registry model list --org nvidia --name nemo

# Check your orgs and teams
ngc org list
ngc team list

# Upload to private registry (enterprise)
ngc registry image push my-org/my-team/my-container:v1.0

# Scripting: JSON output for automation
ngc registry image list --format_type json | jq '.[] | .name'
```

## Registry Architecture

```mermaid
%%{init: {'themeVariables': {'fontFamily': 'monospace'}}}%%
flowchart LR
    A["nvcr.io<br />(pull/push)"] --> B["Reverse Proxy<br />(auth/authz)"]
    B --> C["Registry Server<br />(Docker API)"]
    D["layers.nvcr.io"] --> E["Container Blobs (US)"]
    F["ngc.download.nvidia.com"] --> G["Container Blobs (Global)"]
    H["authn.nvidia.com"] --> I["Authentication Service"]
    style A text-align:left
    style B text-align:left
    style C text-align:left
    style D text-align:left
    style E text-align:left
    style F text-align:left
    style G text-align:left
    style H text-align:left
    style I text-align:left
```

**Key URLs:**

* **nvcr.io** - Main registry for pulling containers
* **catalog.ngc.nvidia.com** - Web UI for browsing
* **api.ngc.nvidia.com** - REST API endpoint

## Deployment Options

| Environment    | How to Use NGC                                              |
| -------------- | ----------------------------------------------------------- |
| **Local/DGX**  | Direct `docker pull nvcr.io/...`                            |
| **AWS**        | NGC containers on EC2 GPU instances, SageMaker integration. |
| **GCP**        | NGC containers on Compute Engine with GPUs.                 |
| **Azure**      | NGC containers on Azure VMs with NVIDIA GPUs.               |
| **Kubernetes** | NGC Helm charts, GPU Operator.                              |
| **Edge**       | NGC containers optimized for Jetson.                        |

## Private Registry (Enterprise)

For enterprise users, NGC offers private registries to host your own containers and models:

```bash
# Push to private registry
docker tag my-container:latest nvcr.io/my-org/my-team/my-container:v1.0
docker push nvcr.io/my-org/my-team/my-container:v1.0

# Manage access
ngc org user add --org my-org --email colleague@company.com --role REGISTRY_USER
```

**Roles:**

* `REGISTRY_USER` - Can pull from private registry
* `REGISTRY_READ` - Read-only access
* `ADMIN` - Full management access

## Integration with NVIDIA Tools

| Tool                        | NGC Integration                           |
| --------------------------- | ----------------------------------------- |
| **NVIDIA AI Workbench**     | Pull models/containers directly from NGC. |
| **NeMo**                    | Pre-trained LLMs hosted on NGC.           |
| **Triton Inference Server** | Container and model hosting.              |
| **TAO Toolkit**             | Pre-trained vision models.                |
| **RAPIDS**                  | GPU-accelerated data science containers.  |
| **DeepStream**              | Video analytics containers.               |

## Best Practices

<Accordion title="Monthly Updates">
  NVIDIA updates containers monthly. Use specific tags (`:24.01-py3`) in production, not `:latest`.
</Accordion>

<Accordion title="Container Scanning">
  All NGC containers pass security scanning. Check the "Security" tab in the catalog for CVE reports.
</Accordion>

<Accordion title="Offline Environments">
  Download containers and models to a local registry for air-gapped deployments.
</Accordion>

<Accordion title="CI/CD Integration">
  Use NGC CLI in pipelines:

  ```bash
  ngc registry image pull nvidia/pytorch:24.01-py3 --format_type json
  ```
</Accordion>

<Accordion title="Multi-GPU Optimization">
  NGC containers are optimized for multi-GPU setups out of the box.
</Accordion>

## Essential Links

| Resource                | URL                                                                                               |
| ----------------------- | ------------------------------------------------------------------------------------------------- |
| NGC Catalog             | [catalog.ngc.nvidia.com](https://catalog.ngc.nvidia.com/)                                         |
| NGC Documentation       | [docs.nvidia.com/ngc](https://docs.nvidia.com/ngc/)                                               |
| NGC CLI Docs            | [docs.ngc.nvidia.com/cli](https://docs.ngc.nvidia.com/cli/)                                       |
| NGC User Guide          | [NGC User Guide](https://docs.nvidia.com/ngc/latest/ngc-user-guide.html)                          |
| Public Cloud Deployment | [NGC Deploy Guide](https://docs.nvidia.com/ngc/ngc-deploy-public-cloud/)                          |
| Private Registry Guide  | [Private Registry Guide](https://docs.nvidia.com/ngc/latest/ngc-private-registry-user-guide.html) |

## Summary

**NGC = GPU-optimized container registry + model hub + Helm charts**

```bash
# Get started in 3 commands:
pip install ngc-cli
ngc config set  # Enter your API key
docker pull nvcr.io/nvidia/pytorch:24.01-py3
```

You're ready to start building.