Ddip ("Dee dip") --- Distributed Data "interactive" Parallel is a little iPython extension of line and cell magics to bring together fastai lesson notebooks [1] and PyTorch's Distributed Data Parallel [2]. It uses ipyparallel [3] to manage the DDP process group.

Platform tested: single host with multiple Nvidia CUDA GPUs, Ubuntu linux + PyTorch + Python 3, fastai v1 and fastai course-v3.

"Distributed training doesn’t work in a notebook..."

-- FastAI's tutorial on How to launch a distributed training

Ddip was conceived to address the above, with the following features:

1. Switch execution easily between PyTorch's multiprocess DDP group and local notebook namespace.

2. Takes 3 - 5 lines of iPython magics to port a Fastai course v3 notebook to train in DDP.

3. Reduce chance of GPU out of memory error by automatically emptying GPU cache memory after executing a cell in the GPU proc.

4. Extensible, to support future versions of fastai.

Summary of speedup observed in FastAI notebooks when trained with 3 GPUs.

Current version: 0.1.1

pip install git+https://github.com/philtrade/[email protected]#egg=Ddip

• %load_ext Ddip, to load the extension.
• %makedip ..., to start/stop/restart a DDP group, and an app, e.g. fastai_v1.
• %%dip {remote, local, everywhere} ..., where to execute the cell.
• %autodip {on,off}, to automatically prepend %%dip to subsequent cells.
• %dipush, and %dipull, to pass objects between the notebook and the DDP namespaces.

• Distributed Training in fastai Notebook using Ddip - a tutorial
• Example notebooks of Ddip iPython magics:
  • %makedip
  • %%dip %autodip
  • %dipush %dipull.
• More Notebooks

1. FastAI Course v3

2. On Distributed Training:

• Tutorial from PyTorch on Distributed Data Parallel
• Launching fastai to use DDP, FastAI
• Further readings: PyTorch Lightning -- Tips for faster training , On the performance of different training parallelism

3. On ipyparallel:

• The Official ipyparallel Documentation
• An Intro to ipyparallel, Activevision Game Science
• Using ipyparallel, Duke University, "Computational Statistics in Python"
• Interactive Distributed Deep Learning with Jupyter Notebooks, Lawrence Berkeley National Laboratory/Cray Inc.