Which Deep Learning Framework Should I Choose: TensorFlow, PyTorch, or JAX?

Beginners
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Hey everyone, I’m trying to decide on a deep learning framework to dive into, and I could really use your advice! I’m torn between TensorFlow and PyTorch, and I’ve also heard about JAX as another option. Here’s where I’m at:

  • TensorFlow: I know it’s super popular in the industry and has a lot of production-ready tools, but I’ve heard setting it up can be a pain, especially since they dropped native GPU support on Windows. Has anyone run into issues with this, or found a smooth way to get it working?
  • PyTorch: It seems to have great GPU support on Windows, and I’ve noticed it’s gaining a lot of traction lately, especially in research. Is it easier to set up and use compared to TensorFlow? How does it hold up for industry projects?
  • JAX: I recently came across JAX and it sounds intriguing, especially for its performance and flexibility. Is it worth learning for someone like me, or is it more suited for advanced users? How does it compare to TensorFlow and PyTorch for practical projects?

A bit about me: I have a solid background in machine learning and I’m comfortable with Python. I’ve worked on deep learning projects using high-level APIs like Keras, but now I want to dive deeper and work without high-level APIs to better understand the framework’s inner workings, tweak the available knobs, and have more control over my models. I’m looking for something that’s approachable yet versatile enough to support personal projects, research, or industry applications as I grow.

Additional Questions:

  • What are the key strengths and weaknesses of these frameworks based on your experience?
  • Are there any specific use cases (like computer vision, NLP, or reinforcement learning) where one framework shines over the others?
  • How steep is the learning curve for each, especially for someone moving from high-level APIs to lower-level framework features?
  • Are there any other frameworks or tools I should consider?

Thanks in advance for any insights! I’m excited to hear about your experiences and recommendations.

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I don’t have deep knowledge of ML, but if I had to recommend one, it would be PyTorch. PyTorch models can be converted to TensorRT format via ONNX, making them suitable for everything from research purposes to execution on edge devices. Even considering the hellishly large library size and nightmarish installation experience due to CUDA, I think it’s an excellent library.

The main libraries on Hugging Face are PyTorch wrappers (inheriting from torch.nn.Module). This means that if you learn PyTorch, you can use most of the model weights and code available on Hugging Face as learning materials. This is a significant advantage.:grinning_face:

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Hey, thank you for your response. I really appreciate all the sources that you’ve linked. I will definitely check them out. :grinning_face_with_smiling_eyes:
Initially i was considering starting out with TensorFlow but after coming across all the installation issues people have been facing on windows and on the other hand PyTorch being a less of a hassle, I think i will go with PyTorch.

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