pytorch-distributed | A quickstart and benchmark for pytorch distributed training | Machine Learning library
kandi X-RAY | pytorch-distributed Summary
kandi X-RAY | pytorch-distributed Summary
A quickstart and benchmark for pytorch distributed training.
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Top functions reviewed by kandi - BETA
- Main worker function
- Train the model
- Validate the model
- Preload the next input
- Compute accuracy
- Get the next input
- Reduce a tensor by reducing its mean value
- Adjust learning rate
- Update the statistics
- Save a checkpoint
- Display a batch
pytorch-distributed Key Features
pytorch-distributed Examples and Code Snippets
Lines of Code : 88
License : No License
Copy(torch)
Documents/Distributed-Pytorch-Boilerplate/src master ✔ 43m ⍉
▶ python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=2 --use_env main.py
World size: 2 ; Rank: 0 ; LocalRank: 0 dist.init_process_group(
backend,
init_method=f"file://{sync_file}",
rank=node_rank,
world_size=world_size,
)
python train.py --learning-rate {lr} --node-rank {node_rank} --world-size {world_size}
lr:
- 0.001
- 0.005
node_rank:
- [0 import pytorch_lightning as ptl
from ray_lightning import RayAccelerator
# Create your PyTorch Lightning model here.
ptl_model = MNISTClassifier(...)
accelerator = RayAccelerator(num_workers=4, cpus_per_worker=1, use_gpu=True)
# If using GPUs, set Community Discussions
Trending Discussions on pytorch-distributed
QUESTION
I've been reading through some documentation and example code with the end goal of writing scripts for distributed computing (running PyTorch), but the concepts confuse me.
Let's assume that we have a single node with 4 GPUs, and we want to run our script on those 4 GPUs (i.e. one process per GPU). In such a scenario, what are the rank world size and rank? I often find the explanation for world size: Total number of processes involved in the job, so I assume that that is four in our example, but what about rank?
To explain it further, another example with multiple nodes and multiple GPUs could be useful, too.
...ANSWER
Answered 2019-Oct-07 at 18:35When I was learning torch.distributed, I was also confused by those terms. The followings are based on my own understanding and the API documents, please correct me if I'm wrong.
I think group should be understood correctly first. It can be thought as "group of processes" or "world", and one job is corresponding to one group usually. world_size is the number of processes in this group, which is also the number of processes participating in the job. rank is a unique id for each process in the group.
So in your example, world_size is 4 and rank for the processes is [0,1,2,3].
Sometimes, we could also have local_rank argument, it means the GPU id inside one process. For example, rank=1 and local_rank=1, it means the second GPU in the second process.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install pytorch-distributed
You can use pytorch-distributed like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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