b10-transfer 0.3.11

Distributed PyTorch file transfer for Baseten - Environment-aware, lock-free file transfer management

https://www.notion.so/ml-infra/mega-base-cache-24291d247273805b8e20fe26677b7b0f

B10 Transfer

PyTorch file transfer for Baseten deployments.

Usage

import b10_transfer

# Inside model.load() function
def load()
    # Load cache before torch.compile()
    cache_loaded = b10_transfer.load_compile_cache()

    # ...

    # Your model compilation
    model = torch.compile(model)
    # Warm up the model with dummy prompts, and arguments that would be typically used in your requests (e.g resolutions)
    dummy_input = "What is the capital of France?"
    model(dummy_input)

    # ...

    # Save cache after compilation
    if not cache_loaded:
        b10_transfer.save_compile_cache()

Configuration

Configure via environment variables:

# Cache directories
export TORCH_CACHE_DIR="/tmp/torchinductor_root"      # Default
export B10FS_CACHE_DIR="/cache/model/compile_cache"   # Default  
export LOCAL_WORK_DIR="/app"                          # Default

# Cache limits
export MAX_CACHE_SIZE_MB="1024"                       # 1GB default

How It Works

Environment-Specific Caching

The library automatically creates unique cache keys based on your environment:

torch-2.1.0_cuda-12.1_cc-8.6_triton-2.1.0 → cache_a1b2c3d4e5f6.latest.tar.gz
torch-2.0.1_cuda-11.8_cc-7.5_triton-2.0.1 → cache_x9y8z7w6v5u4.latest.tar.gz
torch-2.1.0_cpu_triton-none                → cache_m1n2o3p4q5r6.latest.tar.gz

Components used:

• PyTorch version (e.g., torch-2.1.0)
• CUDA version (e.g., cuda-12.1 or cpu)
• GPU compute capability (e.g., cc-8.6 for A100)
• Triton version (e.g., triton-2.1.0 or triton-none)

Cache Workflow

1. Load Phase (startup): Generate environment key, check for matching cache in B10FS, extract to local directory
2. Save Phase (after compilation): Create archive, atomic copy to B10FS with environment-specific filename

Lock-Free Race Prevention

Uses journal pattern with atomic filesystem operations for parallel-safe cache saves.

API Reference

Functions

• load_compile_cache() -> bool: Load cache from B10FS for current environment
• save_compile_cache() -> bool: Save cache to B10FS with environment-specific filename
• clear_local_cache() -> bool: Clear local cache directory
• get_cache_info() -> Dict[str, Any]: Get cache status information for current environment
• list_available_caches() -> Dict[str, Any]: List all cache files with environment details

Exceptions

• CacheError: Base exception for cache operations
• CacheValidationError: Path validation or compatibility check failed

Performance Impact

Debugging

Enable debug logging:

import logging
logging.getLogger('b10_transfer').setLevel(logging.DEBUG)