Deep learning frameworks are extremely transitory. If you compare the deep learning frameworks people use today with what it was eight years ago, you will find the landscape is completely different. There were Theano, Caffe2, and MXNet, which all went obsolete. Today's most popular frameworks, like TensorFlow and PyTorch, were just released to the public.
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21 sats \ 0 replies \ @TheWildHustle 11 Apr 2024
Design an Easy-to-Use Deep Learning Framework
Write End-to-End Workflows: Writing comprehensive tutorials or guides on how to use the software helps identify potential UX issues during the development phase. For instance, when implementing KerasTuner, writing an entire workflow revealed that the objective argument needed improvement.
Minimize Cognitive Load: Avoid forcing users to learn new concepts or APIs unless absolutely necessary. Stick to well-known libraries like NumPy and reuse existing knowledge wherever possible. For example, PyTorch uses standard Python syntax for its operations, making it easier for users to understand and adapt.
Prioritize Interaction Over Documentation: Users prefer figuring things out through hands-on experimentation rather than reading lengthy documentation. Ensure that your software follows consistent naming conventions and provides helpful error messages to facilitate this process.
Additional Principles:
Keep It Simple: Complexity often leads to confusion and frustration among users. Aim for simplicity in both design and implementation.
Be Consistent: Maintain consistency across different parts of the software, such as naming conventions, error handling, and input/output formats. This makes it easier for users to learn and remember how to use the software.
Provide Extensive Examples: Including numerous examples showcasing various use cases helps users understand the capabilities of the software and encourages exploration.
Offer Flexibility: While keeping the core functionality simple, provide options for advanced users who require additional customization or control over their models.
Embrace Open Source Contribution: Encourage community involvement by providing clear contribution guidelines and fostering a supportive environment for developers to share ideas and improve the software together.
Focus on Performance: Optimizing performance ensures that users can train large models efficiently, reducing waiting times and improving overall satisfaction with the software.
Stay Up-to-Date: Continuously monitor advancements in the field and incorporate relevant improvements into the software to maintain competitiveness and meet evolving user needs.
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