Want to contribute to TensorFlow? Here’s a quick guide on how to get started. We welcome contributions from the community. Thank you for your help in making TensorFlow better!
Check out this video for more information:
TensorFlow is an open source project that welcomes contributions from the community. We appreciate the valuable time and effort you spend developing code and improvements.
To help you be as productive as possible, this guide outlines the tools and conventions we use when developing TensorFlow. Following these guidelines should help reduce the time it takes to get your changes accepted and improve the overall experience for all TensorFlow contributors.
What is TensorFlow?
TensorFlow is an open source software library for numerical computation using data flow graphs. In other words, TensorFlow allows you to create algorithms that you can use to efficiently carry out linear algebra operations on Tensors (multidimensional arrays). These algorithms can be used for a variety of tasks, such as image recognition, speech recognition, and machine translation.
The TensorFlow Contributor Guide
If you’re interested in contributing to the TensorFlow project, we’d love to have you on board! Here’s a quick overview of the process:
1. First, take a look at the Contributor Guide to get an overview of the process and what we’re looking for in contributions.
2. Next, choose an area that you’re interested in contributing to and find an issue to work on. Once you’ve identified an issue, leave a comment letting us know that you’re working on it so that we can coordinate with you.
3. Finally, submit a Pull Request (PR) with your changes. We’ll review your PR and provide feedback; once the PR is approved, your changes will be merged into the codebase!
Setting up your Development Environment
TensorFlow is an open-source project, and we welcome contributions of all kinds.
Before you start working on a contribution, be sure to set up your development environment. You will need to install Bazel and the TensorFlow source code. Follow the instructions in the INSTALL file.
Once you have your environment set up, you can begin working on a contribution. We suggest starting with one of the issues labeled “good first issue”. These are issues that we think are especially well suited for new contributors.
When you have made a change that you would like to contribute, submit a Pull Request (PR). One of the TensorFlow maintainers will review your PR and provide feedback. Once the PR is approved, it will be merged into the TensorFlow codebase.
Making your first contribution
In order to make your first contribution to TensorFlow, you must first sign up for a GitHub account. Once you have done so, you canfork the main TensorFlow repository. Forking allows you to create a copy of the repository in your own account so that you can make changes without affecting the original project.
Once you have forked the repository, you can make changes to the codebase and then submit a pull request. A pull request is basically a request for the main repository maintainers to review your changes and merge them into the project. Before submitting a pull request, be sure to check that your changes adhere to the style guide and that all tests are passing.
Making your first contribution to TensorFlow is a great way to get involved in the project and help shape its future!
Contributing to TensorFlow Core
TensorFlow Core is the central part of the TensorFlow project. It implements the algorithms and mathematical operations that are used by the other parts of TensorFlow to conduct machine learning and deep learning.
If you are a developer and you want to contribute to TensorFlow Core, there are many ways to do so. The easiest way is to submit a Pull Request (PR) on GitHub. For more information on how to do this, see the Contributing section in the README. If you have any questions about contributing to TensorFlow Core, please feel free to reach out to us on the TensorFlow discuss mailing list.
Contributing to TensorFlow Addons
TensorFlow Addons is a project for high quality, community-maintained code. The goal is to provide prefix pipelines that can be used with TensorFlow models out of the box and with minimal configuration.
We welcome all contributions, whether they be new additions to the library, improvements to existing code, or documentation updates. If you’re interested in contributing, please take a look at our contribution guidelines and feel free to reach out to us with any questions.
Contributing to TensorFlow Hub
TensorFlow Hub is an online repository of reusable machine learning components. As a contributor, you can publish your own components to the repository, so that other users can discover and use them in their own TensorFlow programs.
In order to contribute to TensorFlow Hub, you will need to have a GitHub account and be familiar with basic Git commands. You can find more information about how to create a GitHub account and use Git in the GitHub documentation.
Once you have a GitHub account and are familiar with Git, you can fork the TensorFlow Hub repository and make your own changes. Once you have made your changes, you can submit a pull request to the TensorFlow Hub team for review. If your pull request is approved, your changes will be merged into the main TensorFlow Hub repository and will be available for everyone to use.
Contributing to TensorFlow Models
This section covers the process for contributing to TensorFlow models.
Before you start, take a look at the CONTRIBUTING.md file in the TensorFlow Models repository. This file contains important information about the contribution process, including where to find the contribution guide and style guide.
In order to contribute a model to TensorFlow, you will need to submit a Pull Request (PR). The first step is to fork the TensorFlow Models repository. Once you have forked the repository, you can make your changes and submit a PR.
When submitting a PR, please make sure to:
-Include tests for your changes
-Update the documentation as needed
-Follow the code style guidelines in CONTRIBUTING.md
Once your PR has been reviewed and approved, it will be merged into the main TensorFlow Models repository.
In this final section, we’ll cover how to wrap up your code contributions to TensorFlow. We’ll go over a few tips on how to make your code more readable and maintainable, as well as how to submit your changes for review.
As you’ve been working on your changes, you may have noticed that the TensorFlow codebase uses a particular style for Python code (we also have a style guide for C++). We recommend that you follow the style guide when making changes to the codebase, as it will make it easier for other developers to read and understand your code. In particular, we recommend using autopep8 to format your Python code so that it adheres to the style guide.
When you’re ready to submit your changes for review, you’ll need to send a “patch” via GitHub. You can do this by clicking on the “Create a new file” button in your fork of the TensorFlow repository, making your changes, and then selecting “Create a pull request.” (You can also create a pull request from the command line with git.) Your pull request will be automatically added to our build system so that it can be run on our continuous integration servers.
Once your pull request has been submitted, one of our reviewers will take a look at it and provide feedback. If they have any suggestions on how to improve your code, please make sure to address them before merging your changes into the main TensorFlow repository.
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