Learn more about the project:

Nabla is now on GitHub Sponsors 🥳 We are building a fast, customizable & educative ML framework. Our roadmap: Automated ND-parallelism, reviving the Nabla Mojo API, and one more thing to bring AI training to non-coders... Help us build the future of ML:

Here is a glimpse into the newly added GPU support for Nabla 🤗: Automatic device placement, custom Mojo kernel integration, and huge speedups on modern @AMD and @nvidia hardware. Shout out to @Modular and @LambdaAPI for making this all possible! More: https://t.co/oWWcvHGEkr

Visualizing SGD doing its thing 🥾 Nabla + MAX + Matplotlib + NumPy: https://t.co/i0HU3i8qvW

I am starting a (notebook) series on training transformers with Nabla. Part 1 is a side-by-side (Nabla vs. JAX) toy implementation from scratch: https://t.co/RxSX9urQEi

Great work building on top of MAX and Mojo, bringing a cool new approach to AI training into the Modular ecosystem. Amazing work @tilli_fe!

Automatic Vectorization (vmap) in action: Write a program once, then use it for any batched input. If applied correctly, this can greatly reduce the number of for-loops and speed up a program. 🎓 Learn more about visualizing program transformations: https://t.co/qK2yPpkqly

JAX vs. Nabla: An initial speed comparison (on cpu) for training an MLP on a simple regression task. 🤗 The full Notebook: https://t.co/yuwuMNyHxj

I am reverse-engineering JAX from scratch in Python, but instead of using XLA, I am using NumPy @numpy_team and MAX @Modular for CPU/GPU acceleration. 🐍🫦 Working: Function transforms like vmap, grad, jit etc., some built-in nn/ modules, pip-install. https://t.co/YPS3CSm67d

Can you guess the output shape? 🤨

say hello to the modular forum to see many mojo-related discussions. https://t.co/MEwpY9xLMq

There are many rough edges and features that still need to be implemented (operator coverage, GPU support, etc.), but the core AD engine has proven effective in initial tests. Thank you for checking out Nabla!

Unlike previous attempts (e.g. Endia) that failed by attempting to rebuild the entire stack, Nabla was designed from the ground up as a direct wrapper around Mojo and MAX to provide the same performance guarantees as them. Code, examples and our roadmap:

Ok, but what even is Differentiable Programming? How does Automatic Differentiation work? https://t.co/08xBy83bGT

Introducing NABLA - Differentiable Programming in Mojo A Research Preview Nabla aims to bring to Mojo what parts of JAX/PyTorch brought to Python: a high-level API for general program transformations, including vmap, jit, vjp, jvp & grad. Learn more: