Using ROCm with Ollama

What is ROCm?

What is Ollama?

Preparing Your System for ROCm

1. Check System Requirements

• Make sure your system has a SUPPORTED AMD GPU. According to the System Requirements, the following GPUs are some examples:
  
  • AMD Radeon RX 7900 XTX
  • AMD Radeon PRO W6800
  • AMD Instinct MI250
• Ensure you have at least 16GB of system memory and a sufficient amount of GPU memory to support your deep learning models.

2. Install ROCm

• Follow the instructions on the ROCm Linux installation guide for your specific Linux distribution. Generally, you can use the package manager (like
  1 apt
  for Ubuntu) to install ROCm quickly.
• Example commands for Ubuntu:
  1 2 3 4 bash sudo apt update sudo apt install linux-headers-$(uname -r) linux-modules-extra-$(uname -r) sudo apt install rocm
• After installation, you'll need to reboot your system.

3. Verify the Installation

• To check that ROCm is installed properly, use the
  1 rocminfo
  command in your terminal. This will display information regarding your AMD GPU and validate the installation. The expected output should list your GPU along with its respective details.
• Example:
  1 2 bash rocminfo

Setting Up Ollama with ROCm

1. Install Ollama

• If you haven’t installed Ollama yet, you can do it effortlessly. Use the one-liner install script:
  1 2 bash curl -fsSL https://ollama.com/install.sh | sh
• This will set everything up, including dependencies required for running your models.

2. Configuring For ROCm

• After setting up Ollama, you need to enable GPU support:
  • Ensure that your user is part of the
    1 render
    group:
    1 2 bash sudo usermod -aG render $USER
  • Depending on your setup, you may need to tweak configuration files to ensure that Ollama detects your ROCm setup correctly. Look for options related to ROCR_VISIBLE_DEVICES in Ollama’s documentation.

3. Preparing Models for Ollama

• You'll want to ensure that the models you're working with are compatible with ROCm. For training purposes, it’s essential to adopt libraries and frameworks that have robust support for ROCm, such as PyTorch and TensorFlow. However, keep an eye out for forks of these libraries specifically optimized for ROCm, as standard installations may not always guarantee compatibility.
• For deep learning models, you can use the command line to pull models Ollama offers. Check out their GitHub page or Ollama Docs for specifics.

Tuning Performance with ROCm in Ollama

Tips For Tuning

• Environment Variables: Ensure you set the right environment variables to optimize your ROCm performance. Variables like
  1 HSA_OVERRIDE_GFX_VERSION
  can be crucial, especially with GPUs such as the RX 6700XT. You may need to use:
  1 2 bash export HSA_OVERRIDE_GFX_VERSION=10.3.0
  • Diverse Algorithms: With ROCm running on AMD GPUs, it may be necessary to experiment with different training algorithms and library configurations to harness full processing power. This may be essential for tasks that can dynamically adjust their workload based on available GPU resources.

Troubleshooting Common Issues

• No GPU Detected: If you see messages indicating that your system doesn’t recognize your GPU, ensure that:
  • Your installations are complete & there are no missing drivers.
  • Your user is part of the right groups (e.g.,
    1 render
    ).
  • iGPU settings in BIOS are correctly configured (disabled if not required).
• Performance Issues: If you're experiencing slow performance, fine-tuning guides for ROCm may offer specific settings related to BIOS configurations, CPU C-states, and general optimizations relevant to AMD systems. Always refer to the System Optimization document for the latest tuning tips.

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