# Enabling NVIDIA® GPUs for accelerating deep learning model training

## Prerequisites
Before using NVIDIA® GPUs, ensure that you meet the following prerequisites:
- System requirements
  - Ubuntu 22.04 LTS
- Hardware requirements
  - NVIDIA® GPUs that meet the specifications outlined by TensorFlow in this [link](https://www.tensorflow.org/install/pip#hardware_requirements).


## Step-by-step instructions
### Option 1 (**Recommended**)
- Step 1. Install NVIDIA® GPU drivers

  Install the [NVIDIA® GPU drivers](https://www.nvidia.com/en-us/drivers/unix/) with a minimum version of 525.60.13.

  For example, you can download and install the driver of 535.274.02 with the following commands.

  ```bash
  wget https://download.nvidia.com/XFree86/Linux-x86_64/535.274.02/NVIDIA-Linux-x86_64-535.274.02.run

  sudo sh NVIDIA-Linux-x86_64-535.274.02.run
  ```

  Next, verify the installation using:
  ```bash
  nvidia-smi
  ```


- Step 2. Install CUDA Toolkit 12.5

  Time Series Studio requires the [CUDA Toolkit 12.5](https://developer.nvidia.com/cuda-12-5-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=runfile_local). You can install and config the CUDA Toolkit as follows.

  ```bash
  wget https://developer.download.nvidia.com/compute/cuda/12.5.0/local_installers/cuda_12.5.0_555.42.02_linux.run
  sudo sh cuda_12.5.0_555.42.02_linux.run
  ```

  Next, you must add the following lines to your `~/.bashrc` file.

  ```bash
  export PATH=/usr/local/cuda-12.5/bin:$PATH
  export LD_LIBRARY_PATH=/usr/local/cuda-12.5/lib64:$LD_LIBRARY_PATH
  ```

  After you have added them to the `~/.bashrc` file, execute `source ~/.bashrc` and `sudo ldconfig`.


- Step 3. Install cuDNN 9.3

  Time Series Studio also requires [cuDNN 9.3](https://developer.nvidia.com/cudnn-9-3-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_network). You can install the cuDNN as follows.

  ```bash
  wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
  sudo dpkg -i cuda-keyring_1.1-1_all.deb
  sudo apt-get update
  sudo apt-get -y install cudnn
  sudo apt-get -y install cudnn-cuda-12
  ```

After installing the CUDA Toolkit and cuDNN, you can use the GPU to train integrated deep learning models in eIQ Time Series Studio. If either component is installed incorrectly. For example, if the version is incompatible, the GPU does not function properly during training.

### Option 2
- Step 1. Install NVIDIA® GPU drivers

  Install the [NVIDIA® GPU drivers](https://www.nvidia.com/en-us/drivers/unix/) with a minimum version of 525.60.13.

  For example, you can download and install the driver of 535.274.02 with the following commands.

  ```bash
  wget https://download.nvidia.com/XFree86/Linux-x86_64/535.274.02/NVIDIA-Linux-x86_64-535.274.02.run

  sudo sh NVIDIA-Linux-x86_64-535.274.02.run
  ```

  Next, verify the installation using:
  ```bash
  nvidia-smi
  ```


- Step 2. Install Python 3.10.8

    If you already have Python 3.10.8 installed, you can skip this step.

    If Python 3.10.8 is not installed, use a Python version management tool such as pyenv.

    Example:
    ```bash
    pyenv install 3.10.8
    # set Python 3.10.8 globally
    pyenv global 3.10.8
    ```


- Step 3. Create a virtual environment with [venv](https://docs.python.org/3/library/venv.html)

    Navigate to your preferred directory and create a virtual environment named `tss` using Python 3.10.8:
    ```bash
    python -m venv tss
    ```

    Activate the environment:
    ```bash
    source tss/bin/activate
    ```


- Step 4. Install TensorFlow

  Upgrade pip to the latest version:
  ```bash
  pip install --upgrade pip
  ```

  Then, install TensorFlow with CUDA support:
  ```bash
  pip install tensorflow[and-cuda]==2.18.1
  ```


- Step 5. Configure environment variables

    After installing TensorFlow, locate the `nvidia` folder under your virtual environment. For example, `lib/python3.10/site-packages/nvidia`.

    Add the following lines to your `~/.bashrc` file, replacing `/your_path/` with the actual path:

    ```bash
    export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$(find /your_path/tss/lib/python3.10/site-packages/nvidia -type f -name "*.so*" -exec dirname {} \; | sort -u | paste -sd: -)"

    export XLA_FLAGS="--xla_gpu_cuda_data_dir=/your_path/tss/lib/python3.10/site-packages/nvidia/cuda_nvcc"
    ```

    Reload the configuration:
    ```bash
    source ~/.bashrc
    ```