How to use this image

All examples in this guide use the public image. If you've mirrored the repository for your own use (for example, to your Docker Hub namespace), update your commands to reference the mirrored image instead of the public one.

For example:

• Public image: dhi.io/dcgm-exporter:<tag>
• Mirrored image: <your-namespace>/dhi-dcgm-exporter:<tag>

For the examples, you must first use docker login dhi.io to authenticate to the registry to pull the images.

What's included in this dcgm-exporter image

DCGM Exporter is a Prometheus exporter for NVIDIA GPU metrics, built on top of the NVIDIA Data Center GPU Manager (DCGM) C library. It exposes GPU telemetry (including SM clock frequency, memory usage, temperature, power draw, and hardware profiling counters) as a Prometheus-compatible /metrics endpoint on port 9400. The exporter uses CGo to call into libdcgm.so at runtime, and the set of exported metrics is controlled by a counter CSV file (/etc/dcgm-exporter/default-counters.csv by default, overridable via DCGM_EXPORTER_COLLECTORS).

The dcgm-exporter binary is built from source and is Apache-2.0. The bundled DCGM libraries (libdcgm.so.4 and its modules, including libdcgmmoduleprofiling.so) are NVIDIA prebuilt binaries distributed under the NVIDIA Data Center GPU Manager license, redistributed here with NVIDIA's permission. The libnvperf_dcgm_host.so library, which NVIDIA requires for profiling counters (DCGM_FI_PROF_* fields) on Ampere (A100) and older GPU architectures (Volta, Turing), is intentionally excluded. Profiling counters on Hopper (H100) and newer GPUs use open hardware performance counters and work without that library. On Ampere and older GPUs, DCGM_FI_PROF_* fields return zero. Basic device metrics (DCGM_FI_DEV_*) work on all supported GPU generations.

Run the dcgm-exporter container

The exporter requires the NVIDIA container runtime on the host. Pass --gpus all to expose GPU devices to the container.

docker run -d --gpus all --rm \
  -p 9400:9400 \
  dhi.io/dcgm-exporter:<tag>

Once running, metrics are available at http://localhost:9400/metrics.

To display help information for the binary:

docker run --rm --gpus all dhi.io/dcgm-exporter:<tag> --help

Container user

This image runs as root (uid 0). Most DHI images default to a nonroot user, but dcgm-exporter forks /sbin/ldconfig at startup to refresh the dynamic linker cache for libdcgm.so, and that path requires uid 0. This matches upstream NVIDIA's image behavior.

Enable profiling metrics with SYS_ADMIN

Basic DCGM_FI_DEV_* metrics work without elevated privileges. Profiling metrics (DCGM_FI_PROF_*) require the SYS_ADMIN capability, because DCGM uses perf event interfaces to read hardware performance counters. Pass --cap-add SYS_ADMIN at runtime to enable them; the capability is not granted by default.

To enable profiling metrics in Docker:

docker run -d --gpus all --rm \
  --cap-add SYS_ADMIN \
  -p 9400:9400 \
  dhi.io/dcgm-exporter:<tag>

To enable profiling metrics in Kubernetes, add the capability to the container's security context:

securityContext:
  capabilities:
    add:
      - SYS_ADMIN

Docker Compose with Prometheus

The following Compose example starts dcgm-exporter alongside Prometheus on a GPU node.

services:
  dcgm-exporter:
    image: dhi.io/dcgm-exporter:<tag>
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
    ports:
      - "9400:9400"
    cap_add:
      - SYS_ADMIN  # required for DCGM_FI_PROF_* profiling metrics

  prometheus:
    image: dhi.io/prometheus:<tag>
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml:ro

Add a scrape job to your prometheus.yml:

scrape_configs:
  - job_name: dcgm
    scrape_interval: 15s
    static_configs:
      - targets: ["dcgm-exporter:9400"]

Deploy on Kubernetes with Helm

The upstream Helm chart is the recommended way to run dcgm-exporter in Kubernetes. To use the Docker Hardened image, override the image reference at install time:

helm repo add gpu-helm-charts https://nvidia.github.io/dcgm-exporter/helm-charts
helm repo update

helm install dcgm-exporter gpu-helm-charts/dcgm-exporter \
  --set image.repository=dhi.io/dcgm-exporter \
  --set image.tag=<VERSION>

For detailed configuration options, see the upstream Helm chart documentation⁠ and the NVIDIA GPU telemetry guide⁠.

Non-hardened images vs. Docker Hardened Images

Image variants

Docker Hardened Images come in different variants depending on their intended use. Image variants are identified by their tag.

• Runtime variants are designed to run your application in production. These images are intended to be used either directly or as the FROM image in the final stage of a multi-stage build. These images typically:

  • Run as the nonroot user

  • Do not include a shell or a package manager

  • Contain only the minimal set of libraries needed to run the app

  Note: Unlike most DHI runtime images, this image runs as root (uid 0). See Container user⁠ for the reason.

• Build-time variants typically include dev in the variant name and are intended for use in the first stage of a multi-stage Dockerfile. These images typically:

  • Run as the root user
  • Include a shell and package manager
  • Are used to build or compile applications

• FIPS variants include fips in the variant name and tag. They come in both runtime and build-time variants. These variants use cryptographic modules that have been validated under FIPS 140, a U.S. government standard for secure cryptographic operations. For example, usage of MD5 fails in FIPS variants.

To view the image variants and get more information about them, select the Tags tab for this repository, and then select a tag.

Migrate to a Docker Hardened Image

To migrate your application to a Docker Hardened Image, you must update your Dockerfile. At minimum, you must update the base image in your existing Dockerfile to a Docker Hardened Image. This and a few other common changes are listed in the following table of migration notes.

The following steps outline the general migration process.

1. Find hardened images for your app.

   A hardened image may have several variants. Inspect the image tags and find the image variant that meets your needs.

2. Update the base image in your Dockerfile.

   Update the base image in your application's Dockerfile to the hardened image you found in the previous step. For framework images, this is typically going to be an image tagged as dev because it has the tools needed to install packages and dependencies.

3. For multi-stage Dockerfiles, update the runtime image in your Dockerfile.

   To ensure that your final image is as minimal as possible, you should use a multi-stage build. All stages in your Dockerfile should use a hardened image. While intermediary stages will typically use images tagged as dev, your final runtime stage should use a non-dev image variant.

4. Install additional packages

   Docker Hardened Images contain minimal packages in order to reduce the potential attack surface. You may need to install additional packages in your Dockerfile. Inspect the image variants to identify which packages are already installed.

   Only images tagged as dev typically have package managers. You should use a multi-stage Dockerfile to install the packages. Install the packages in the build stage that uses a dev image. Then, if needed, copy any necessary artifacts to the runtime stage that uses a non-dev image.

   For Alpine-based images, you can use apk to install packages. For Debian-based images, you can use apt-get to install packages.

Troubleshooting migration

The following are common issues that you may encounter during migration.

General debugging

The hardened images intended for runtime don't contain a shell nor any tools for debugging. The recommended method for debugging applications built with Docker Hardened Images is to use Docker Debug⁠ to attach to these containers. Docker Debug provides a shell, common debugging tools, and lets you install other tools in an ephemeral, writable layer that only exists during the debugging session.

Permissions

Most DHI runtime images run as the nonroot user, but this image runs as root (uid 0); see Container user⁠. Ensure that necessary files and directories are accessible to the user the container runs as. You may need to copy files to different directories or change permissions so your application can access them.

Privileged ports

Non-dev hardened images run as a nonroot user by default. As a result, applications in these images can't bind to privileged ports (below 1024) when running in Kubernetes or in Docker Engine versions older than 20.10. To avoid issues, configure your application to listen on port 1025 or higher inside the container, even if you map it to a lower port on the host. For example, docker run -p 80:8080 my-image will work because the port inside the container is 8080, and docker run -p 80:81 my-image won't work because the port inside the container is 81.

No shell

By default, image variants intended for runtime don't contain a shell. Use dev images in build stages to run shell commands and then copy any necessary artifacts into the runtime stage. In addition, use Docker Debug to debug containers with no shell.

Entry point

Docker Hardened Images may have different entry points than images such as Docker Official Images. Use docker inspect to inspect entry points for Docker Hardened Images and update your Dockerfile if necessary.