# Linux

### Deploy Supervisely agent on Linux

Supervisely agent can work both with and without GPU support. If you don't have a GPU, you can deploy the agent on any machine with Linux OS and you can skip the GPU installation steps. This tutorial explains how to deploy the Supervisely agent on Linux OS.

### Table of Contents

* [Prerequisites](https://github.com/supervisely/docs/blob/master/getting-started/connect-your-computer/unix-based/gpu-agent-linux-installation.md#prerequisites)
* [How to install](https://github.com/supervisely/docs/blob/master/getting-started/connect-your-computer/unix-based/gpu-agent-linux-installation.md#how-to-install)
* [Step 1. Install Docker](https://github.com/supervisely/docs/blob/master/getting-started/connect-your-computer/unix-based/gpu-agent-linux-installation.md#step-1-install-docker)
* [Step 2. Install CUDA Toolkit](https://github.com/supervisely/docs/blob/master/getting-started/connect-your-computer/unix-based/gpu-agent-linux-installation.md#step-2-install-cuda-toolkit)
* [Step 3. Install NVIDIA Driver](https://github.com/supervisely/docs/blob/master/getting-started/connect-your-computer/unix-based/gpu-agent-linux-installation.md#step-3-install-nvidia-driver)
* [Step 4. Install NVIDIA Container Toolkit](https://github.com/supervisely/docs/blob/master/getting-started/connect-your-computer/unix-based/gpu-agent-linux-installation.md#step-4-install-nvidia-container-toolkit)
* [Step 5. Deploy Supervisely Agent](https://github.com/supervisely/docs/blob/master/getting-started/connect-your-computer/unix-based/gpu-agent-linux-installation.md#step-5-deploy-supervisely-agent)
* [Troubleshooting](https://github.com/supervisely/docs/blob/master/getting-started/connect-your-computer/unix-based/gpu-agent-linux-installation.md#troubleshooting)

### Prerequisites

* Linux OS (Kernel 3.10 or higher)
* [Docker](https://docs.docker.com/engine/install/ubuntu/) (Version 19.3 or higher)
* [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) (Version 9.0 or higher)
* [NVIDIA Driver](https://developer.nvidia.com/cuda-downloads) (Version 452.39 or higher)
* [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)

#### How to install

#### Step 1. Install Docker

First, set up the Docker apt repository:

```bash
# Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the repository to Apt sources:
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
```

Then install the Docker packages:

```bash
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
```

Finally, verify that the Docker Engine installation is successful by running the hello-world image:

```bash
 sudo docker run hello-world
```

Check out the official [Docker documentation](https://docs.docker.com/engine/install/ubuntu/) for more information.

#### Step 2. Install CUDA Toolkit

Install the CUDA Toolkit using the following commands:

```bash
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda-repo-ubuntu2204-12-4-local_12.4.1-550.54.15-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204-12-4-local_12.4.1-550.54.15-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2204-12-4-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-4
```

Check out the official [CUDA Toolkit documentation](https://developer.nvidia.com/cuda-downloads) for more information and the latest version.

### Step 3. Install NVIDIA Driver

Install the NVIDIA driver using the following commands:

```bash
sudo apt-get install -y cuda-drivers-550
```

To verify the installation, run the following command:

```bash
nvidia-smi
```

It's recommended to restart your system after installing the NVIDIA driver with the following command:

```bash
sudo reboot
```

The output should display the NVIDIA driver version, CUDA version, and GPU information.

![nvidia-smi](https://github.com/supervisely/developer-portal/assets/118521851/0816dc4f-8ac7-4a80-b4c0-09652a7f21d9)

If you can see this information, the installation was successful. Otherwise, please check the [Troubleshooting](https://github.com/supervisely/docs/blob/master/getting-started/connect-your-computer/unix-based/gpu-agent-linux-installation.md#troubleshooting) section.

Check out the official [NVIDIA Driver documentation](https://developer.nvidia.com/cuda-downloads) for more information and the latest version.

### Step 4. Install NVIDIA Container Toolkit

The NVIDIA drivers must be also available in the Docker containers so the agent can utilize the GPU. To do this, install the NVIDIA Container Toolkit using the following commands:

```bash
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
```

Now, configure the Docker daemon to use the NVIDIA runtime:

```bash
sudo nvidia-ctk runtime configure --runtime=docker
```

Finally, restart the Docker daemon:

```bash
sudo systemctl restart docker
```

Now, we'll need to ensure that the NVIDIA Container Toolkit is installed and working correctly and that the NVIDIA runtime is available inside the Docker containers. To do this, run the following command:

```bash
sudo docker run --rm --runtime=nvidia --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi
```

The output should display the NVIDIA driver version, CUDA version, and GPU information.

![nvidia-smi in Docker](https://github.com/supervisely/developer-portal/assets/118521851/d117b3f3-2d59-4fa7-a735-37edc8f49804)

If you can't see this information, please check the [Troubleshooting](https://github.com/supervisely/docs/blob/master/getting-started/connect-your-computer/unix-based/gpu-agent-linux-installation.md#troubleshooting) section.

### Step 5. Deploy Supervisely Agent

Now it's time to deploy the Supervisely agent.

Open Supervisely instance, go to the Cluster page and press the `Add` button. Select the `Supervisely agent` option.

![Add Agent](https://github.com/supervisely/developer-portal/assets/118521851/4b9942ba-8a5c-4909-a6c1-c1a81defefe6)

Copy the command and run it in the terminal on the machine where you want to deploy the agent.

![Command](https://github.com/supervisely/developer-portal/assets/118521851/b3206a8a-cae8-4930-9cb0-f0214ba04324)

That's it! Now your agent is deployed and running.

#### TroubleShooting

If the `nvidia-smi` command does not display the GPU information from OS or the Docker container, the NVIDIA drivers were not successfully installed. In this case, you can try to uninstall the NVIDIA drivers and CUDA Toolkit:

```bash
sudo apt-get purge nvidia*
sudo apt-get purge cuda*
sudo apt-get autoremove
sudo apt-get autoclean
```

After that restart your system, and try to install the components again.