---
layout: docs
page_title: 'Device Plugins: Nvidia'
description: The Nvidia Device Plugin detects and makes Nvidia devices available to tasks.
---

# Nvidia GPU Device Plugin

Name: `nvidia-gpu`

The Nvidia device plugin is used to expose Nvidia GPUs to Nomad.

~> **Note**: The Nvidia device plugin setup has changed in Nomad 1.2. You must
add a [`plugin`] block to your clients configuration and install the
[external Nvidia device plugin][nvidia_plugin_download] into their
[`plugin_dir`] prior to upgrading. See plugin options below for an example.
Note the job specification remains the same.

## Fingerprinted Attributes

<table>
  <thead>
    <tr>
      <th>Attribute</th>
      <th>Unit</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>
        <tt>memory</tt>
      </td>
      <td>MiB</td>
    </tr>
    <tr>
      <td>
        <tt>power</tt>
      </td>
      <td>W (Watt)</td>
    </tr>
    <tr>
      <td>
        <tt>bar1</tt>
      </td>
      <td>MiB</td>
    </tr>
    <tr>
      <td>
        <tt>driver_version</tt>
      </td>
      <td>string</td>
    </tr>
    <tr>
      <td>
        <tt>cores_clock</tt>
      </td>
      <td>MHz</td>
    </tr>
    <tr>
      <td>
        <tt>memory_clock</tt>
      </td>
      <td>MHz</td>
    </tr>
    <tr>
      <td>
        <tt>pci_bandwidth</tt>
      </td>
      <td>MB/s</td>
    </tr>
    <tr>
      <td>
        <tt>display_state</tt>
      </td>
      <td>string</td>
    </tr>
    <tr>
      <td>
        <tt>persistence_mode</tt>
      </td>
      <td>string</td>
    </tr>
  </tbody>
</table>

## Runtime Environment

The `nvidia-gpu` device plugin exposes the following environment variables:

- `NVIDIA_VISIBLE_DEVICES` - List of Nvidia GPU IDs available to the task.

### Additional Task Configurations

Additional environment variables can be set by the task to influence the runtime
environment. See [Nvidia's
documentation](https://github.com/NVIDIA/nvidia-container-runtime#environment-variables-oci-spec).

## Installation Requirements

In order to use the `nvidia-gpu` the following prerequisites must be met:

1. GNU/Linux x86_64 with kernel version > 3.10
2. NVIDIA GPU with Architecture > Fermi (2.1)
3. NVIDIA drivers >= 340.29 with binary `nvidia-smi`

### Docker Driver Requirements

The Nvidia driver plugin currently only supports the older v1.0 version of the
Docker driver provided by Nvidia. In order to use the Nvidia driver plugin with
the Docker driver, please follow the installation instructions for
[`nvidia-container-runtime`](https://github.com/nvidia/nvidia-container-runtime#installation).

## Plugin Configuration

```hcl
plugin "nvidia-gpu" {
  config {
    enabled            = true
    ignored_gpu_ids    = ["GPU-fef8089b", "GPU-ac81e44d"]
    fingerprint_period = "1m"
  }
}
```

The `nvidia-gpu` device plugin supports the following configuration in the agent
config:

- `enabled` `(bool: true)` - Control whether the plugin should be enabled and running.

- `ignored_gpu_ids` `(array<string>: [])` - Specifies the set of GPU UUIDs that
  should be ignored when fingerprinting.

- `fingerprint_period` `(string: "1m")` - The period in which to fingerprint for
  device changes.

## Restrictions

The Nvidia integration only works with drivers who natively integrate with
Nvidia's [container runtime
library](https://github.com/NVIDIA/libnvidia-container).

Nomad has tested support with the [`docker` driver][docker-driver] and plans to
bring support to the built-in [`exec`][exec-driver] and [`java`][java-driver]
drivers. Support for [`lxc`][lxc-driver] should be possible by installing the
[Nvidia hook](https://github.com/lxc/lxc/blob/master/hooks/nvidia) but is not
tested or documented by Nomad.

## Examples

Inspect a node with a GPU:

```shell-session
$ nomad node status 4d46e59f
ID            = 4d46e59f
Name          = nomad
Class         = <none>
DC            = dc1
Drain         = false
Eligibility   = eligible
Status        = ready
Uptime        = 19m43s
Driver Status = docker,mock_driver,raw_exec

Node Events
Time                  Subsystem  Message
2019-01-23T18:25:18Z  Cluster    Node registered

Allocated Resources
CPU          Memory      Disk
0/15576 MHz  0 B/55 GiB  0 B/28 GiB

Allocation Resource Utilization
CPU          Memory
0/15576 MHz  0 B/55 GiB

Host Resource Utilization
CPU             Memory          Disk
2674/15576 MHz  1.5 GiB/55 GiB  3.0 GiB/31 GiB

Device Resource Utilization
nvidia/gpu/Tesla K80[GPU-e1f6f4f1-1ea5-7b9d-5f03-338a9dc32416]  0 / 11441 MiB

Allocations
No allocations placed
```

Display detailed statistics on a node with a GPU:

```shell-session
$ nomad node status -stats 4d46e59f
ID            = 4d46e59f
Name          = nomad
Class         = <none>
DC            = dc1
Drain         = false
Eligibility   = eligible
Status        = ready
Uptime        = 19m59s
Driver Status = docker,mock_driver,raw_exec

Node Events
Time                  Subsystem  Message
2019-01-23T18:25:18Z  Cluster    Node registered

Allocated Resources
CPU          Memory      Disk
0/15576 MHz  0 B/55 GiB  0 B/28 GiB

Allocation Resource Utilization
CPU          Memory
0/15576 MHz  0 B/55 GiB

Host Resource Utilization
CPU             Memory          Disk
2673/15576 MHz  1.5 GiB/55 GiB  3.0 GiB/31 GiB

Device Resource Utilization
nvidia/gpu/Tesla K80[GPU-e1f6f4f1-1ea5-7b9d-5f03-338a9dc32416]  0 / 11441 MiB

// ...TRUNCATED...

Device Stats
Device              = nvidia/gpu/Tesla K80[GPU-e1f6f4f1-1ea5-7b9d-5f03-338a9dc32416]
BAR1 buffer state   = 2 / 16384 MiB
Decoder utilization = 0 %
ECC L1 errors       = 0
ECC L2 errors       = 0
ECC memory errors   = 0
Encoder utilization = 0 %
GPU utilization     = 0 %
Memory state        = 0 / 11441 MiB
Memory utilization  = 0 %
Power usage         = 37 / 149 W
Temperature         = 34 C

Allocations
No allocations placed
```

Run the following example job to see that that the GPU was mounted in the
container:

```hcl
job "gpu-test" {
  datacenters = ["dc1"]
  type = "batch"

  group "smi" {
    task "smi" {
      driver = "docker"

      config {
        image = "nvidia/cuda:9.0-base"
        command = "nvidia-smi"
      }

      resources {
        device "nvidia/gpu" {
          count = 1

          # Add an affinity for a particular model
          affinity {
            attribute = "${device.model}"
            value     = "Tesla K80"
            weight    = 50
          }
        }
      }
    }
  }
}
```

```shell-session
$ nomad run example.nomad
==> Monitoring evaluation "21bd7584"
    Evaluation triggered by job "gpu-test"
    Allocation "d250baed" created: node "4d46e59f", group "smi"
    Evaluation status changed: "pending" -> "complete"
==> Evaluation "21bd7584" finished with status "complete"

$ nomad alloc status d250baed
ID                  = d250baed
Eval ID             = 21bd7584
Name                = gpu-test.smi[0]
Node ID             = 4d46e59f
Job ID              = example
Job Version         = 0
Client Status       = complete
Client Description  = All tasks have completed
Desired Status      = run
Desired Description = <none>
Created             = 7s ago
Modified            = 2s ago

Task "smi" is "dead"
Task Resources
CPU        Memory       Disk     Addresses
0/100 MHz  0 B/300 MiB  300 MiB

Device Stats
nvidia/gpu/Tesla K80[GPU-e1f6f4f1-1ea5-7b9d-5f03-338a9dc32416]  0 / 11441 MiB

Task Events:
Started At     = 2019-01-23T18:25:32Z
Finished At    = 2019-01-23T18:25:34Z
Total Restarts = 0
Last Restart   = N/A

Recent Events:
Time                  Type        Description
2019-01-23T18:25:34Z  Terminated  Exit Code: 0
2019-01-23T18:25:32Z  Started     Task started by client
2019-01-23T18:25:29Z  Task Setup  Building Task Directory
2019-01-23T18:25:29Z  Received    Task received by client

$ nomad alloc logs d250baed
Wed Jan 23 18:25:32 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.48                 Driver Version: 410.48                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K80           On   | 00004477:00:00.0 Off |                    0 |
| N/A   33C    P8    37W / 149W |      0MiB / 11441MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
```

[docker-driver]: /docs/drivers/docker 'Nomad docker Driver'
[exec-driver]: /docs/drivers/exec 'Nomad exec Driver'
[java-driver]: /docs/drivers/java 'Nomad java Driver'
[lxc-driver]: /plugins/drivers/community/lxc 'Nomad lxc Driver'
[`plugin`]: /docs/configuration/plugin
[`plugin_dir`]: /docs/configuration#plugin_dir
[nvidia_plugin_download]: https://releases.hashicorp.com/nomad-device-nvidia/
