pytorch-distributed | Distributed and parallel extensions for PyTorch | Machine Learning library

by montefiore-ai Python Version: Current License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | pytorch-distributed Summary

pytorch-distributed is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. pytorch-distributed has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Distributed and parallel extensions for PyTorch.

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pytorch-distributed has a low active ecosystem.

It has 4 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

pytorch-distributed has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch-distributed is current.

Quality

pytorch-distributed has 0 bugs and 0 code smells.

Security

pytorch-distributed has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pytorch-distributed code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch-distributed is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

pytorch-distributed releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

pytorch-distributed saves you 199 person hours of effort in developing the same functionality from scratch.

It has 490 lines of code, 54 functions and 14 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed pytorch-distributed and discovered the below as its top functions. This is intended to give you an instant insight into pytorch-distributed implemented functionality, and help decide if they suit your requirements.

The main worker function
Fetch all the transformations
Create a data loader for a training set
Fetch a training set
Performs the master procedure
Fetch validation set
Create a data loader for validation
Validate arguments
Performs the worker update
Pull the parameters from the master
Commit parameters to the master
Start the worker
True if the master is the master
Perform the GEM procedure
Return the next worker
Run the optimizer
Parse command line arguments
Allocate master resources
Train the model
The main routine
Download the training set
Run an optimizer on a model
Return True if the master is the master

Get all kandi verified functions for this library.

pytorch-distributed Key Features

No Key Features are available at this moment for pytorch-distributed.

pytorch-distributed Examples and Code Snippets

No Code Snippets are available at this moment for pytorch-distributed.

Community Discussions

Trending Discussions on pytorch-distributed

In distributed computing, what are world size and rank?

QUESTION

In distributed computing, what are world size and rank?

Asked 2019-Nov-05 at 02:54

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 18:35

When 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.

Source https://stackoverflow.com/questions/58271635

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pytorch-distributed

You can download it from GitHub.
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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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