SpeedTorch | Library for faster pinned CPU - GPU transfer in Pytorch | GPU library

by Santosh-Gupta Python Version: 0.1.6 License: MIT

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

kandi X-RAY | SpeedTorch Summary

SpeedTorch is a Python library typically used in Hardware, GPU, Deep Learning, Pytorch, Tensorflow applications. SpeedTorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install SpeedTorch' or download it from GitHub, PyPI.

This library revovles around Cupy tensors pinned to CPU, which can achieve 3.1x faster CPU -> GPU transfer than regular Pytorch Pinned CPU tensors can, and 410x faster GPU -> CPU transfer. Speed depends on amount of data, and number of CPU cores on your system (see the How it Works section for more details). The library includes functions for embeddings training; it can host embeddings on CPU RAM while they are idle, sparing GPU RAM.

Support

Quality

Security

License

Reuse

Support

SpeedTorch has a low active ecosystem.

It has 620 star(s) with 37 fork(s). There are 26 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 2 have been closed. On average issues are closed in 2 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of SpeedTorch is 0.1.6

Quality

SpeedTorch has 0 bugs and 0 code smells.

Security

SpeedTorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

SpeedTorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SpeedTorch 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

SpeedTorch releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

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

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Init the memmap with uniform distribution
Initializes a numpy array
Init the memmap with normal distribution
Initializes the optimizer
Initialize a numpy npy npy npy npy npy npy npy npy
Reshapes the state of the optimizer
Returns a reshaped reshaped array
Called when the CUDA library is initialized
Initialize the memory map
Reshape the optimizer step
Reshapes the model for the optimizer step
Initialize the memmap with zeros
Reshaped forward pass
Reshaped optimizer step
Reshapes the state of the optimizer step
Reshaped optimizer
Get reshaped indices
Reshaped optimization step
Returns the reshaped indices
Reshapes the optimizer
Reshaped optimizer step
Reshapes the weight value of the model
Reshapes the weights of the optimizer step
Reshapes the tensorflow weight

Get all kandi verified functions for this library.

SpeedTorch Key Features

No Key Features are available at this moment for SpeedTorch.

SpeedTorch Examples and Code Snippets

No Code Snippets are available at this moment for SpeedTorch.

Community Discussions

Trending Discussions on SpeedTorch

In package uploaded to pypi not checking if package requirements already installed, though install of same code from github does

QUESTION

In package uploaded to pypi not checking if package requirements already installed, though install of same code from github does

Asked 2019-Sep-09 at 07:55

This is a followup to this question

Installing a pip package with cupy as a requirement puts install in never ending loop

Where somehow a pip package was not able to detect that cupy is already installed, and tried to re-install it.

The solution given was to use

...

ANSWER

Answered 2019-Sep-09 at 07:55

This is not issue specific to CuPy. You should not modify install_requires in setup.py if you want to distribute your package as a wheel. setup.py runs when building a wheel package, not when installing it. In other words, install_requires is determined depending on whether cupy is available when building a wheel package.

Source https://stackoverflow.com/questions/57837177

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

Vulnerabilities

No vulnerabilities reported

Install SpeedTorch

You can install using 'pip install SpeedTorch' or download it from GitHub, PyPI.
You can use SpeedTorch 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 .

Find more information at: