Principle:Axolotl ai cloud Axolotl Distributed Environment Setup
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Training, Infrastructure |
| Last Updated | 2026-02-06 23:00 GMT |
Overview
An environment configuration pattern that sets up distributed training backends (FSDP, DeepSpeed) via environment variables and runtime configuration before training begins.
Description
Distributed Environment Setup configures the runtime environment for multi-GPU and multi-node training. Modern distributed training frameworks (FSDP, DeepSpeed) rely heavily on environment variables to coordinate between processes. This step bridges the gap between Axolotl's declarative YAML config and the environment-variable-based configuration expected by PyTorch Distributed, HuggingFace Accelerate, and DeepSpeed.
The setup handles three major backends:
- FSDP (Fully Sharded Data Parallel): Shards model parameters, gradients, and optimizer states across GPUs
- DeepSpeed: Microsoft's training optimization library with ZeRO stages 1/2/3
- Tensor Parallelism / Context Parallelism: Advanced parallelism for very large models
Usage
Use distributed environment setup when:
- Training across multiple GPUs (multi-GPU or multi-node)
- Using FSDP for memory-efficient distributed training
- Using DeepSpeed ZeRO for optimizer state sharding
- Combining multiple parallelism strategies (HSDP+TP)
Theoretical Basis
FSDP shards model parameters across GPUs:
# Pseudo-code for FSDP operation
# Before: Full model on each GPU (N * model_size memory)
# After: Each GPU holds 1/N of parameters
for each_training_step:
all_gather(parameters) # Temporarily reconstruct full params
forward_pass()
backward_pass()
reduce_scatter(gradients) # Distribute gradients
optimizer_step() # Update local shard only
DeepSpeed ZeRO progressively shards different training components:
- Stage 1: Shard optimizer states only
- Stage 2: Shard optimizer states + gradients
- Stage 3: Shard optimizer states + gradients + parameters