Initial Query

nicholas@server:~/project$ sudo docker start myproject [sudo] password for nicholas: Error response from daemon: could not select device driver "" with capabilities: [[gpu]] Error: failed to start containers: myproject

LLM Generation

Query

nicholas@server:~/project$ sudo docker start myproject [sudo] password for nicholas: Error response from daemon: could not select device driver "" with capabilities: [[gpu]] Error: failed to start containers: myproject

Output

Okay, this error message `could not select device driver "" with capabilities: [[gpu]]` strongly indicates that Docker is trying to fulfill a request for GPU resources for the `myproject` container, but it cannot find or use the necessary driver integration – typically the **NVIDIA Container Toolkit**. Here's a breakdown of the likely causes and how to troubleshoot them: 1. **NVIDIA Container Toolkit Not Installed or Configured:** * This is the most common cause. Docker itself doesn't natively manage GPUs. It relies on the NVIDIA Container Toolkit (previously nvidia-docker2) to expose the host's NVIDIA GPUs to containers. * **Verification:** Check if the toolkit is installed. On Debian/Ubuntu systems: 

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        dpkg -l | grep nvidia-container-toolkit
On CentOS/RHEL/Fedora: 
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        rpm -qa | grep nvidia-container-toolkit
* **Solution:** If it's not installed, follow the official NVIDIA Container Toolkit installation guide for your distribution: [https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) 2. **Docker Daemon Not Configured to Use NVIDIA Runtime:** * Even if the toolkit is installed, the Docker daemon needs to be configured to use it as the default runtime or at least know about the `nvidia` runtime. * **Verification:** Check the Docker daemon configuration file, typically `/etc/docker/daemon.json`. It should contain something like this to make `nvidia` the default runtime for GPU requests: 
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        {
            "runtimes": {
                "nvidia": {
                    "path": "nvidia-container-runtime",
                    "runtimeArgs": []
                }
            },
            "default-runtime": "nvidia" 
            // Or ensure the container was started with --runtime=nvidia if this isn't the default
        }
*Note: Setting `default-runtime` to `nvidia` might not always be necessary or desired, but the `runtimes` block defining `nvidia` usually is.* The installation script for the toolkit *should* configure this, but it's worth checking. * **Solution:** Edit `/etc/docker/daemon.json` (create it if it doesn't exist) to include the necessary configuration. Then, **restart the Docker daemon**: 
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        sudo systemctl daemon-reload
        sudo systemctl restart docker
3. **Host NVIDIA Drivers Not Installed or Not Working:** * The NVIDIA Container Toolkit relies on the host system having properly installed and functioning NVIDIA drivers. * **Verification:** Run `nvidia-smi` on the host machine (outside Docker). 
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        nvidia-smi
This command should successfully output information about your GPU(s) and the driver version. * **Solution:** If `nvidia-smi` fails or shows errors, you need to install or reinstall the NVIDIA drivers for your specific GPU and Linux distribution. Make sure to install drivers compatible with the CUDA version your container might require. 4. **Incorrect Container Creation:** * While `docker start` reuses the original creation settings, it's possible the container wasn't initially created correctly to request GPU access (though the error message suggests it *did* request it). The standard way to request GPUs during creation is using the `--gpus` flag (e.g., `--gpus all`). * **Verification:** You can inspect the container's configuration: 
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        sudo docker inspect myproject | jq '.[0].HostConfig.DeviceRequests'
(You might need to install `jq`: `sudo apt install jq` or `sudo yum install jq`). This should show the GPU request. * **Solution:** If the container wasn't created with the GPU request, you'll need to remove it (`sudo docker rm myproject`) and recreate it using the correct `docker run` command including the `--gpus` flag. 5. **Restart Docker Service:** * Sometimes, simply restarting the Docker daemon can resolve transient issues. * **Solution:** 
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        sudo systemctl restart docker
**Troubleshooting Steps:** 1. Run `nvidia-smi` on the host. Does it work? If not, fix host drivers first. 2. Check if `nvidia-container-toolkit` is installed using `dpkg` or `rpm`. If not, install it. 3. Check `/etc/docker/daemon.json` for the `nvidia` runtime configuration. If missing or incorrect, fix it. 4. Restart the Docker daemon: `sudo systemctl restart docker`. 5. Try starting your container again: `sudo docker start myproject`. 6. If it still fails, try running a simple test container: `sudo docker run --rm --gpus all nvidia/cuda:11.0.3-base-ubuntu20.04 nvidia-smi` (adjust the image tag if needed). If this *test* container fails with the same error, the issue is definitely with the Docker/NVIDIA runtime setup (Steps 1-4). If the test container *works*, the problem might be specific to how `myproject` was configured or built. Start with these steps, focusing on the NVIDIA Container Toolkit installation and Docker daemon configuration, as these are the most frequent culprits for this specific error.

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string nvidia-docker2: True

Substring Evaluation

Testing if the previous output contains the string nvidia-container-toolkit: True

Final Answer: True