This revision is from 2024/06/21 19:26. You can Restore it.

1. BIOS Settings: Check your motherboard BIOS settings to ensure all PCIe slots are enabled and set to their maximum bandwidth.
2. OS Support: Use an operating system that supports multiple GPUs, such as a recent version of Linux (Ubuntu, for example)
3. Install Drivers: Install the latest drivers for your GPUs. For NVIDIA GPUs, download and install the latest drivers from the NVIDIA website.
4. CUDA Toolkit: Install the CUDA toolkit compatible with your GPU drivers. Follow the installation instructions on the NVIDIA CUDA Toolkit website.
5. cuDNN: Install the cuDNN library compatible with your CUDA version. Download it from the NVIDIA cuDNN page and follow the installation instructions.
6. Frameworks: Install the machine learning frameworks that support multi-GPU setups. For LLMs, popular frameworks include TensorFlow and PyTorch.

Use nvidia-smi to monitor GPU usage and ensure all GPUs are being utilized.

nvidia-smi
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Some codes...

python3 -m venv myenv
source myenv/bin/activate
pip install tensorflow
pip install torch
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Tensorflow: tf.distribute.MirroredStrategy