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

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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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Hey, great question — I was in the same spot not long ago.

I’m currently using TensorFlow since I’m working on a real-time deep learning project that’s meant to run on an edge device. So far, it’s been super helpful, especially because of the strong support for TensorFlow Lite, which makes deploying models on mobile or microcontrollers much smoother.

Yes, the GPU setup on Windows can be tricky, but I personally use Google Colab or Linux (WSL) for training, and then optimize/export the model for deployment. That way, I avoid most of the setup pain.

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Hey, Thank you for the response. I have also heard that WSL can be used to set TF up but i haven’t come across any reliable guide/resource to set up the same. Would you mind sharing the guide that you used to set up the same? Thanks :grinning_face:

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I used google collab, so, i dont know how to set up WSL