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Hugging Face
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Distributed Training Abstraction

4 posts · Jul 2022 – Sep 2026

What this trail covers

Hugging Face offers multiple levels of abstraction for distributed training, from native PyTorch DDP to the flexible 🤗 Accelerate library and the high-level 🤗 Trainer API. This post details how to leverage these tools for efficient distributed training, including single-GPU, multi-GPU, and multi-node setups, and introduces the concept of distributed training and its importance for scaling model training. This post specifically details how to train a language model using Megatron-LM, a framework for distributed training, and introduces techniques for optimizing inference speed. The focus is on achieving high throughput and enabling the execution of very large models by offloading parameters to the CPU.

All Hugging Face trails
  1. Training a coding model to paint watercolours with TRL and OpenEnvThis post details the implementation of a Reinforcement Learning (RL) pipeline for training a coding model to generate watercolour paintings using TRL and OpenEnv. It describes the custom RL environment, which includes the p5.brush library for painting, a system prompt restricting the model to 10 specific methods, a headless Chromium renderer, and a gate to prevent cheating. The reward function is a weighted combination of a 'gate' score, a 'length' score, a pairwise judge comparing the generated image against references, and a score from the HPSv3 preference model. Three reward mixes were trained and compared: 'judge-led', 'hps-led', and 'hps-only'. The post also provides the full pipeline running on Hugging Face infrastructure, including training on Jobs, environments and scorer models on Spaces, and pairwise judging through Inference Providers.
  2. How 🤗 Accelerate runs very large models thanks to PyTorchThis post details how 🤗 Accelerate, in conjunction with PyTorch's FSDP (Fully Sharded Data Parallel), enables the execution of very large models that exceed GPU memory. It explains the mechanism of offloading parameters, gradients, and optimizer states to the CPU, and how PyTorch's CPU offloading capabilities are leveraged. The post provides practical insights into managing memory for large models and highlights the role of Accelerate in simplifying this complex process.
  3. Reachy Mini goes fully localThis post details the implementation of a fully local speech-to-speech pipeline for Reachy Mini, integrating various components for VAD, STT, LLM inference, and TTS. It provides specific instructions and configurations for running llama.cpp with Gemma 4 locally, setting up the `speech-to-speech` library, and connecting it to the Reachy Mini. It also discusses alternative LLM backends like vLLM and Hugging Face Inference Endpoints, highlighting trade-offs in latency, privacy, and cost.
  4. Shipping a Trillion Parameters With a Hub Bucket: Delta Weight Sync in TRLThis post introduces Delta Weight Sync for TRL, a technique that leverages weight update sparsity to significantly reduce communication overhead in distributed reinforcement learning, enabling the efficient training of trillion-parameter models by only synchronizing sparse weight deltas instead of full model weights.
  5. From PyTorch DDP to Accelerate to Trainer, mastery of distributed training with easeThis post details the progression from native PyTorch DDP to 🤗 Accelerate and 🤗 Trainer for distributed training. It explains the setup and cleanup functions for DDP, the role of `DistributedDataParallel`, and the advantages of Accelerate in simplifying code modifications for various distributed scenarios (single GPU, multi-GPU, TPU). It also highlights the boilerplate abstraction provided by the 🤗 Trainer API.
  7. How to train a Language Model with Megatron-LMThis post details how to train a language model using Megatron-LM, a framework for efficient large-scale transformer training. It covers the setup and configuration required for distributed training with Megatron-LM, including data parallelism, tensor parallelism, and pipeline parallelism.