---
title: Training with AMD GPUs on HPC Systems
description: A comprehensive guide for using Axolotl on distributed systems with AMD GPUs
---

This guide provides step-by-step instructions for installing and configuring Axolotl on a High-Performance Computing (HPC) environment equipped with AMD GPUs.

## Setup

### 1. Install Python

We recommend using Miniforge, a minimal conda-based Python distribution:

```bash
curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh
```

### 2. Configure Python Environment
Add Python to your PATH and ensure it's available at login:

```bash
echo 'export PATH=~/miniforge3/bin:$PATH' >> ~/.bashrc
echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' >> ~/.bash_profile
```

### 3. Load AMD GPU Software

Load the ROCm module:

```bash
module load rocm/5.7.1
```

Note: The specific module name and version may vary depending on your HPC system. Consult your system documentation for the correct module name.

### 4. Install PyTorch

Install PyTorch with ROCm support:

```bash
pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7 --force-reinstall
```

### 5. Install Flash Attention

Clone and install the Flash Attention repository:

```bash
git clone --recursive https://github.com/ROCmSoftwarePlatform/flash-attention.git
export GPU_ARCHS="gfx90a"
cd flash-attention
export PYTHON_SITE_PACKAGES=$(python -c 'import site; print(site.getsitepackages()[0])')
patch "${PYTHON_SITE_PACKAGES}/torch/utils/hipify/hipify_python.py" hipify_patch.patch
pip install .
```

### 6. Install Axolotl

Clone and install Axolotl:

```bash
git clone https://github.com/axolotl-ai-cloud/axolotl
cd axolotl
pip install packaging ninja
pip install -e .
```

### 7. Apply xformers Workaround

xformers appears to be incompatible with ROCm. Apply the following workarounds:
 - Edit $HOME/packages/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py modifying the code to always return `False` for SwiGLU availability from xformers.
 - Edit $HOME/miniforge3/lib/python3.10/site-packages/xformers/ops/swiglu_op.py replacing the "SwiGLU" function with a pass statement.

### 8. Prepare Job Submission Script

Create a script for job submission using your HPC's particular software (e.g. Slurm, PBS). Include necessary environment setup and the command to run Axolotl training. If the compute node(s) do(es) not have internet access, it is recommended to include

```bash
export TRANSFORMERS_OFFLINE=1
export HF_DATASETS_OFFLINE=1
```

### 9. Download Base Model

Download a base model using the Hugging Face CLI:

```bash
huggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
```

### 10. Create Axolotl Configuration

Create an Axolotl configuration file (YAML format) tailored to your specific training requirements and dataset. Use FSDP for multi-node training.

Note: Deepspeed did not work at the time of testing. However, if anyone managed to get it working, please let us know.

### 11. Preprocess Data

Run preprocessing on the login node:

```bash
CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess /path/to/your/config.yaml
```

### 12. Train

You are now ready to submit your previously prepared job script. 🚂
