nbdistributed 0.1.0

Jupyter extension for interactive distributed PyTorch training

nbdistributed

A library for distributed PyTorch execution in Jupyter notebooks with seamless REPL-like behavior.

In Development Note:

This library is being built to help run my new course and as a result is constantly changing. For right now it is "stable enough" however as I find new features to use/need in the course I need to expand the framework.

As a result it is not open to contributions at this time.

Features

• Seamless Distributed Execution: Run PyTorch code across multiple GPUs directly from Jupyter notebooks
• REPL-like Behavior: See results immediately without explicit print statements
• Automatic GPU Management: Smart allocation of GPUs to worker processes
• Interactive Development: Real-time feedback and error reporting
• IDE Support: Namespace synchronization for code completion and type hints
• Robust Process Management: Graceful startup, monitoring, and shutdown

Installation

pip install nbdistributed

Quick Start

1. Import and initialize in your Jupyter notebook:

%load_ext nbdistributed
%dist_init -n 4  # Start 4 worker processes

2. Run code on all workers:

import torch
print(f"Rank {rank} running on {torch.cuda.get_device_name()}")

3. Run code on specific ranks:

%%rank[0,1]
print(f"Running on rank {rank}")

Architecture

The library consists of four main components:

1. Magic Commands (magic.py)

• Provides IPython magic commands for interaction
• Manages automatic distributed execution
• Handles namespace synchronization
• Key commands:
  • %dist_init: Initialize workers
  • %%distributed: Execute on all ranks
  • %%rank[n]: Execute on specific ranks
  • %sync: Synchronize workers
  • %dist_status: Show worker status
  • %dist_mode: Toggle automatic mode
  • %dist_shutdown: Clean shutdown

2. Worker Process (worker.py)

• Runs on each GPU/CPU
• Executes distributed PyTorch code
• Maintains isolated Python namespace
• Features:
  • REPL-like output capturing
  • Error handling and reporting
  • GPU device management
  • Namespace synchronization

3. Process Manager (process_manager.py)

• Manages worker lifecycle
• Handles GPU assignments
• Monitors process health
• Provides:
  • Clean process startup
  • Status monitoring
  • Graceful shutdown
  • GPU utilization tracking

4. Communication Manager (communication.py)

• Coordinates inter-process communication
• Uses ZMQ for efficient messaging
• Features:
  • Asynchronous message handling
  • Reliable message delivery
  • Timeout management
  • Worker targeting

Usage Examples

Basic Distributed Training

%dist_init -n 2  # Start 2 workers

import torch
import torch.distributed as dist

# Create tensor on each GPU
x = torch.randn(100, 100).cuda()

# All-reduce across GPUs
dist.all_reduce(x)
print(f"Rank {rank}: {x.mean():.3f}")  # Same value on all ranks

Selective Execution

%%rank[0]
# Only runs on rank 0
model = torch.nn.Linear(10, 10).cuda()
print("Model created on rank 0")
# In another cell:
# Broadcast model parameters to all ranks
for param in model.parameters():
    dist.broadcast(param.data, src=0)
print(f"Rank {rank} received model")

GPU Information

%dist_status
# Shows:
# - Process status
# - GPU assignments
# - Memory usage
# - Device names

Advanced Features

1. GPU Assignment

Specify exact GPU-to-rank mapping:

%dist_init -n 4 -g "0,1,2,3"  # Assign specific GPUs

2. Namespace Synchronization

The library automatically syncs worker namespaces to enable IDE features:

• Code completion
• Type hints
• Variable inspection

3. Error Handling

Errors are caught and reported with:

• Full traceback
• Rank information
• GPU context

4. Process Recovery

The library provides robust error recovery:

%dist_reset    # Complete environment reset
%dist_init     # Start fresh