mars-project / mars-project/mars
[BUG] Distributed training failed on Ray cluster
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- Dominant language
- Python
- Stars
- 2.7k
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Description
Describe the bug
Mars integrates some deep learning frameworks(PyTorch, TensorFlow), these frameworks usually need to set some environments for distributed training, TF_CONFIG for TensorFlow, MASTER_ADDR for PyTorch. We use ctx.get_worker_addresses() to collect all worker addresses, it works well for Oscar backend, while for Ray, the addresses start with ray:// which is invalid for them, we need a method to get worker's host IP not address to address the issue.
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 tracing the use of ctx.get_worker_addresses() in the distributed-training path and compare its behavior on the Oscar and Ray backends. Check how TF_CONFIG and MASTER_ADDR consume the collected values. Done means Ray workers provide usable host IPs rather than ray:// addresses, while existing Oscar behavior remains intact.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch, tensorflow
- Domain
- distributed-systems, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100