Скачать или смотреть How to install Axolotl or any Heavy Libraries on HPC systems

You can read the blog post here:
https://moeinmoradi.privatekernel.com...

In this video, I explain how to install and run Axolotl, an open source LLM fine tuning framework, in a High Performance Computing (HPC) environment. I focus on practical, reproducible installation methods and explain why containerization is the only reliable approach for complex AI stacks on shared systems.

The video walks through using Apptainer on HPC clusters where Docker is not available, including how to pull the official Axolotl container image and verify GPU support. I also briefly cover Docker installation for non HPC systems.

Based on real experience fine tuning Llama 3.2 3B on an HPC cluster, I explain why installing Axolotl via pip, conda, or uv is fragile, time consuming, and often fails due to CUDA, PyTorch, and flash attention build issues. You will see why containerized workflows are effectively mandatory for modern LLM training and fine tuning on clusters.

If you are working with GPUs on HPC, managing AI infrastructure, or deploying LLM workloads in shared or restricted environments, this video will help you avoid common installation pitfalls and save hours of debugging.

Topics covered:
Installing Axolotl on HPC using Apptainer
Docker vs Apptainer for AI workloads
Why pip based installation fails for complex CUDA stacks
Best practices for reproducible LLM training environments

#Axolotl #LLMFineTuning #HPC #Apptainer #Docker #GPUComputing #CUDA #PyTorch #Llama3 #AIInfrastructure #MLOps #AIEngineering #ClusterComputing #HighPerformanceComputing