Скачать или смотреть How to Efficiently Add the pytorch Library to Google App Engine

Learn how to add the `pytorch` and `torchvision` libraries to your Google App Engine project without hitting the file limit. Follow our step-by-step guide to make it seamless.
---
This video is based on the question https://stackoverflow.com/q/65404654/ asked by the user 'Mezo' ( https://stackoverflow.com/u/13850137/ ) and on the answer https://stackoverflow.com/a/65404730/ provided by the user 'Mezo' ( https://stackoverflow.com/u/13850137/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to add pytorch library to Google App Engine

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
The Challenge of Adding PyTorch to Google App Engine

Are you struggling to add the powerful pytorch library to your Google App Engine (GAE) project? Many developers face a common challenge when it comes to deploying machine learning applications on cloud platforms. Specifically, when trying to include libraries like torch and torchvision, you might encounter limitations due to Google App Engine's restrictions on file sizes and counts.

In this guide, we’ll present a clear and simple solution to integrate the pytorch library into your GAE deployment seamlessly. Let's cut through the confusion and get your machine learning models up and running in the cloud.

Understanding the Problem

When planning to use pytorch, you may usually install it locally with the following command:

[[See Video to Reveal this Text or Code Snippet]]

However, on Google App Engine, your libraries must be declared in the requirements.txt file. Without careful management of file limits, you can erode your project's chances of deployment, facing frustrating errors such as:

[[See Video to Reveal this Text or Code Snippet]]

You might also have tried other workarounds by manually downloading libraries into a lib folder, which also led to complications. Fear not! There’s a straightforward method to successfully include pytorch without exceeding file constraints.

Solution: Edit Your requirements.txt

To get pytorch and torchvision accepted by your Google App Engine environment, simply follow these steps to modify your requirements.txt file correctly:

Open your requirements.txt file: This file is crucial as it tells GAE which libraries to install in your deployment.

Add the following lines:

[[See Video to Reveal this Text or Code Snippet]]

Save your changes: Ensure that your updates are saved and ready for the next deployment.

Explanation of the Additions

-f https://download.pytorch.org/whl/torc... This line specifies the additional repository where GAE can find the pytorch libraries. It directs the system to the proper source for installation.

Library Versioning: By defining the specific versions of torch and torchvision, you ensure compatibility and reliability in your machine learning environment.

Final Steps

After editing the requirements.txt file, it’s time to redeploy your application to the Google App Engine using the following command:

[[See Video to Reveal this Text or Code Snippet]]

This command will leverage the updated requirements.txt and include the required pytorch libraries without exceeding the file limit.

Conclusion

Adding pytorch to Google App Engine can be simple once you know how to modify your requirements.txt correctly. By specifying the source of the libraries and their versions, you can successfully deploy your machine learning models and applications in the cloud.

Remember to keep your requirements.txt organized and to stay within the limits established by GAE to avoid any potential deployment issues.

Start implementing these steps today and harness the full potential of machine learning in your web applications!