DeepSpeed support for ignite.distributed
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- Python
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Description
🚀 Feature
Pytorch lightning recently added native support for MS DeepSpeed.
I believe it is also helpful for users if ignite incorporates the DeepSpeed pipeline for memory-efficient distributed training.
1. for idist.auto_model ..?
To initialize the DeepSpeed engine:
model_engine, optimizer, _, _ = deepspeed.initialize(args=cmd_args,
model=model,
model_parameters=params)
And for distributed environment setup, we need to replace torch.distributed.init_process_group(...) to deepspeed.init_distributed()
2. checkpoint handler
slightly different thing for checkpointing
model_engine.save_checkpoint(args.save_dir, ckpt_id, client_sd = client_sd)
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the idist.auto_model and distributed environment setup entry points, along with the checkpoint handler. Compare the requested DeepSpeed initialization, process-group setup, and save_checkpoint behavior with the existing interfaces. Done means the supported integration scope and checkpoint behavior are defined and covered by appropriate tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- distributed-systems, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100