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Flyheap / PyTorch

Find a collection of PyTorch-based projects, models, and resources that empower you to harness the full potential of deep learning in your applications.
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PyTorch Workflow #2

Closed Flyheap closed 1 year ago

Flyheap commented 1 year ago

Key Objectives:

Streamlined Development: This issue seeks to identify and address any bottlenecks or complexities in the PyTorch development workflow. Enhancements may include optimizing the process of setting up PyTorch environments, simplifying data loading and preprocessing, and facilitating model training and evaluation.

Best Practices and Guidelines: We will work on establishing and documenting best practices and guidelines for PyTorch workflows. This will cover aspects such as project structuring, code organization, and model versioning to ensure consistent and efficient development practices.

Integration with Ecosystem: PyTorch is often used in conjunction with various tools and libraries. We aim to enhance the integration and compatibility of PyTorch with these external components, such as data management frameworks, deployment platforms, and model serving solutions.

Automation and Tooling: The issue will explore opportunities to introduce automation and tooling that can simplify common PyTorch tasks, such as hyperparameter tuning, model visualization, and distributed training.

Lakshya-GG commented 1 year ago

can you assign me this issue @Flyheap

Flyheap commented 1 year ago

ISSUE ASSIGNED @Lakshya-GG