pytorch-distributed | Ape-X DQN & DDPG with pytorch & tensorboard | Reinforcement Learning library

by jingweiz Python Version: Current License: MIT

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kandi X-RAY | pytorch-distributed Summary

pytorch-distributed is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Pytorch applications. pytorch-distributed has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However pytorch-distributed build file is not available. You can download it from GitHub.

Ape-X DQN & DDPG with pytorch & tensorboard

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pytorch-distributed has a low active ecosystem.

It has 99 star(s) with 14 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 176 days. 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 MIT 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.

pytorch-distributed has no build file. You will be need to create the build yourself to build the component from source.

pytorch-distributed saves you 594 person hours of effort in developing the same functionality from scratch.

It has 1384 lines of code, 92 functions and 29 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.

Dqn actor
Feed the given experience
Return a new Experiment instance
Run a single action
Return an experience instance
Capture the image
Wrapper function for DAG
Apply the forward critic
Update the target model with the given model
Find the node with the given value
Internal function to get the value of a node
Resets the game
Reset experiment state
Returns the action for the given input
Apply the critic
Append data to the buffer
Propagate a node to the sum
Calculate action
Create a forward actor
Get the action for the given input
Run dqn learner
Run DDL actor
Run the forward action

Get all kandi verified functions for this library.

pytorch-distributed Key Features

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pytorch-distributed Examples and Code Snippets

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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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