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HIPS/autograd

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Efficiently computes derivatives of NumPy code.

21 mentions on Hacker News by 18 people: 2 stories, 19 comments · first 2015-05-12 · latest 2023-10-02

Mentions per year 21 mentions · busiest year 2018 (6)
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  1. Actually, that's never been a constraint for JAX autodiff. JAX grew out of the original Autograd ( https://github.com/hips/autograd ), so differentiating through Python control flow always worked. It's jax.jit and jax.vmap which place…
    comment by mattjjatgoogle · 2023-10-02 · in JAX – NumPy on the CPU, GPU, and TPU
  2. …There's no explanatory material to go with it though. [0] https://github.com/mattjj/autodidact [1] https://github.com/hips/autograd [2] https://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slid... [3]…
    comment by mattjjatgoogle · 2023-02-11 · in Autodidax: JAX core from scratch
  3. …except for special cases that involved exponents and logs that needed a bit of delicate care. [1] https://github.com/HIPS/autograd
    comment by PartiallyTyped · 2022-12-27 · in Numba: A High Performance Python Compiler
  4. …for tracing function execution in Python. This technique is used in the [autograd library]( https://github.com/HIPS/autograd ), which is a precursor to [Jax]( https://github.com/google/jax ), a competitor to Tensorflow and pytorch that's…
    comment by slewis · 2022-02-10 · in Tracing Functions in Python
  5. …one point but it was scrapped essentially because of these difficulties I assume you mean autograd? https://github.com/HIPS/autograd
    comment by cl3misch · 2021-12-25 · in Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia
  6. …the lax module lets you program XLA HLO pretty directly [2].) Disclaimer: I work on JAX! [1] https://github.com/hips/autograd [2] https://www.tensorflow.org/xla/operation_semantics
    comment by mattjjatgoogle · 2020-09-21 · in Swift for TensorFlow – A system for deep learning and differentiable computing
  7. …of thumb, and standard practices have yet to be defined. [1] https://github.com/google/jax [2] https://github.com/HIPS/autograd
    comment by aaron-santos · 2020-02-16 · in Demystifying Differentiable Programming (2018)
  8. …and TF, but feels cleaner and more expressive. It has a very nice autodiff implementation (based on https://github.com/HIPS/autograd ) and performance is comparable to TF in my experience.
    comment by ibab · 2019-10-13 · in Deep Learning with PyTorch: A 60 Minute Blitz [video]
  9. This looks neat . In python is this also a project that does the same thing ? https://github.com/HIPS/autograd
    comment by zitterbewegung · 2019-07-19 · in A Differentiable Programming System to Bridge ML and Scientific Computing
  10. ▲ 4
    Autograd: Automatic differentiation of Python and numpy code
    story by zitterbewegung · 2019-07-19 · 0 comments
  11. …of `lambda x: exp(x)` or similar–it needs a lookup table for elementary functions, for example: https://github.com/HIPS/autograd/blob/304552b1ba42e42bce97f0... However, AD does support differentiating through program flow control including…
    comment by cgearhart · 2018-12-09 · in JAX: Numpy with Gradients, GPUs and TPUs
  12. …right now, but it’s early days. Try it out, we’re really excited to see what you build with it! 0: https://github.com/hips/autograd
    comment by alexbw · 2018-12-08 · in JAX: Numpy with Gradients, GPUs and TPUs
  13. …this from working with the autograd package, except no one's taken the time to implement it ( https://github.com/HIPS/autograd/issues/47 ). I believe it's more complicated than most posters there realize, especially in the context of…
    comment by ffriend · 2018-08-10 · in Julia 1.0
  14. …this from working with the autograd package, except no one's taken the time to implement it ( https://github.com/HIPS/autograd/issues/47 ). That said, for many tasks with wide vector data, a DL framework is going to do ok, e.g. PyTorch. >…
    comment by marmaduke · 2018-08-10 · in Julia 1.0
  15. …whether to use autograd or numba, I can't JIT the autograded function. Seriously, read this issue: https://github.com/HIPS/autograd/issues/47
    comment by Certhas · 2018-08-10 · in Julia 1.0
  16. …tools like autograd [1] can differentiate somewhat arbitrary Python and numpy instructions. [1] https://github.com/HIPS/autograd
    comment by davidwihl · 2018-05-03 · in Differentiable Programming: A Semantics Perspective
  17. I love the discussion thread on GitHub: https://github.com/HIPS/autograd/pull/175#issuecomment-30652... As someone just starting to study machine learning it's inspiring and exciting seeing this kind of thing.
    comment by davedx · 2017-06-14 · in A new trick for calculating Jacobian vector products
  18. ▲ 4
    HIPS/autograd: Efficiently computes derivatives of numpy code
    story by Labo333 · 2016-04-27 · 0 comments
  19. …been initialized so that it will end up matching a given image (i.e. blowing a fancy smoke ring:) https://github.com/HIPS/autograd/blob/master/examples/fluids... and here's a free-form wing shape in the middle of being optimized to…
    comment by duvenaud · 2016-02-03 · in Gradient-Based Hyperparameter Optimization Through Reversible Learning
  20. …what happens as these tools mature and especially if they find applications in other fields. [1] https://github.com/HIPS/autograd
    comment by davmre · 2016-01-02 · in Automatic Differentiation (2009)
  21. Have you taken a look at this ? https://github.com/HIPS/autograd and paper http://arxiv.org/pdf/1502.03492.pdf They reverse the computation of the gradient to calculate the derivative wrt the hyper parameters.
    comment by Fede_V · 2015-05-12 · in Keras: Fast Deep Learning Prototyping for Python