quantized.pytorch | replicate results in Scalable Methods

by eladhoffer Python Version: Current License: MIT

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

kandi X-RAY | quantized.pytorch Summary

quantized.pytorch is a Python library. quantized.pytorch 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.

Code to replicate results in Scalable Methods for 8-bit Training of Neural Networks. e.g: running an 8-bit quantized resnet18 from the paper on ImageNet.

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Quality

Security

License

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Support

quantized.pytorch has a low active ecosystem.

It has 186 star(s) with 51 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 2 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of quantized.pytorch is current.

Quality

quantized.pytorch has 0 bugs and 0 code smells.

Security

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

quantized.pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

quantized.pytorch 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

quantized.pytorch 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.

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

It has 2438 lines of code, 167 functions and 18 files.

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

Top functions reviewed by kandi - BETA

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

Forward forward computation
Create a BoundedWeighNorm function
Compute weight for given module
Normalize x
Get the transformation for a given image
Random crop
Resize a crop
Perform inception preproccess
Forward computation
2d convolution layer
Removes the weight norm of a module
Remove parameter from module
Validate training mode
Calculate the accuracy of a model
Get a dataset by name
Weight norm operator
Compute gradients
Perform a forward computation
Birelu
Train a model

Get all kandi verified functions for this library.

quantized.pytorch Key Features

No Key Features are available at this moment for quantized.pytorch.

quantized.pytorch Examples and Code Snippets

No Code Snippets are available at this moment for quantized.pytorch.

Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install quantized.pytorch

You can download it from GitHub.
You can use quantized.pytorch 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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