CIPs
jax
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CIPs | jax | |
---|---|---|
157 | 82 | |
459 | 27,842 | |
0.7% | 3.6% | |
9.1 | 10.0 | |
1 day ago | 6 days ago | |
JavaScript | Python | |
Creative Commons Attribution 4.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
CIPs
- Ledger vs Trezor vs Others addr derivations
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Catalyst Weekly #72: Fund10 Updates (tech stack), Summon Testnet, Voltaire Musings, CIPs & more
CPS-???? | Governance Security [New Problem]: This problem statement describes security issues to be considered by protocol governance CIPs.
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Lace 1.0 is now live on Cardano mainnet!
The Lace team is already working with the community on how to find the best way to develop the CIP-72 - DApp Registration & Discovery. If you’re interested in taking part, contribute here
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Cardano Dapp store
All mobile wallets have some kind of dApp browser, but there are no hard specs for how they should be run that is implemented at the moment. You might be interested in CIP72 if you're interested in standards for this topic more generally: https://github.com/cardano-foundation/CIPs/pull/355
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Catalyst Weekly #71: F10 updates, Ariob demo day, LATAM CIP-1694 Workshops, Cardano Talent hub & more
CIP-???? | Extend token metadata for translations [New Proposal]: This proposal defines an addition to CIP-25 | Media NFT Metadata Standard to support more languages than English.
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Catalyst Weekly #70: Voltaire musings, Film festival winners, Fund10 updates, Workshops & more
If you wish to add your perspectives to CIP 1694, make sure to share them here. Add your voice as the era of Voltaire rolls out!
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Catalyst Weekly #68: Circle updates, Cardano4Climate, #letstalkcardano, CIPs and more!
Last week, a community-led roundtable took place on the topic of CIP-1694. You can catch the recap and outcome in last week’s newsletter or this Cardano Forum recap here. Worth a read.
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Account Abstraction
Conditions inside a native script (but there is a CIP for changing this here)
- What’s upcoming for the next CIP’s any details and timelines? Thank you.
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Cardano Express and React skeletons with CIP-0008 Signing spec
Thank you for the information! That is very important. Actually I wrote a specification https://github.com/cardano-foundation/CIPs/pull/442 where I address exactly that issue.
jax
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The Elements of Differentiable Programming
The dual numbers exist just as surely as the real numbers and have been used well over 100 years
https://en.m.wikipedia.org/wiki/Dual_number
Pytorch has had them for many years.
https://pytorch.org/docs/stable/generated/torch.autograd.for...
JAX implements them and uses them exactly as stated in this thread.
https://github.com/google/jax/discussions/10157#discussionco...
As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.
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Julia GPU-based ODE solver 20x-100x faster than those in Jax and PyTorch
On your last point, as long as you jit the topmost level, it doesn't matter whether or not you have inner jitted functions. The end result should be the same.
Source: https://github.com/google/jax/discussions/5199#discussioncom...
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Apple releases MLX for Apple Silicon
The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.
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MLPerf training tests put Nvidia ahead, Intel close, and Google well behind
I'm still not totally sure what the issue is. Jax uses program transformations to compile programs to run on a variety of hardware, for example, using XLA for TPUs. It can also run cuda ops for Nvidia gpus without issue: https://jax.readthedocs.io/en/latest/installation.html
There is also support for custom cpp and cuda ops if that's what is needed: https://jax.readthedocs.io/en/latest/Custom_Operation_for_GP...
I haven't worked with float4, but can imagine that new numerical types would require some special handling. But I assume that's the case for any ml environment.
But really you probably mean fixed point 4bit integer types? Looks like that has had at least some work done in Jax: https://github.com/google/jax/issues/8566
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MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
>
Are they even comparing apples to apples to claim that they see these improvements over NumPy?
> While the code complexity and length are roughly the same, the MatX version shows a 2100x over the Numpy version, and over 4x faster than the CuPy version on the same GPU.
NumPy doesn't use GPU by default unless you use something like Jax [1] to compile NumPy code to run on GPUs. I think more honest comparison will mainly compare MatX running on same CPU like NumPy as focus the GPU comparison against CuPy.
[1] https://github.com/google/jax
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Actually that never changed. The README has always had an example of differentiating through native Python control flow:
https://github.com/google/jax/commit/948a8db0adf233f333f3e5f...
The constraints on control flow expressions come from jax.jit (because Python control flow can't be staged out) and jax.vmap (because we can't take multiple branches of Python control flow, which we might need to do for different batch elements). But autodiff of Python-native control flow works fine!
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Julia and Mojo (Modular) Mandelbrot Benchmark
For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...
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Functional Programming 1
2. https://github.com/fantasyland/fantasy-land (A bit heavy on jargon)
Note there is a python version of Ramda available on pypi and there’s a lot of FP tidbits inside JAX:
3. https://pypi.org/project/ramda/ (Worth making your own version if you want to learn, though)
4. For nested data, JAX tree_util is epic: https://jax.readthedocs.io/en/latest/jax.tree_util.html and also their curry implementation is funny: https://github.com/google/jax/blob/4ac2bdc2b1d71ec0010412a32...
Anyway don’t put FP on a pedestal, main thing is to focus on the core principles of avoiding external mutation and making helper functions. Doesn’t always work because some languages like Rust don’t have legit support for currying (afaik in 2023 August), but in those cases you can hack it with builder methods to an extent.
Finally, if you want to understand the middle of the midwit meme, check out this wiki article and connect the free monoid to the Kleene star (0 or more copies of your pattern) and Kleene plus (1 or more copies of your pattern). Those are also in regex so it can help you remember the regex symbols. https://en.wikipedia.org/wiki/Free_monoid?wprov=sfti1
The simplest example might be {0}^* in which case
0: “” // because we use *
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Best Way to Learn JAX
Hello! I'm trying to learn JAX over the next couple of weeks. Ideally, I want to be comfortable with using it for projects after about 3 weeks to a month, although I understand that may not be realistic. I currently have experience with PyTorch and TensorFlow. How should I go about learning JAX? Is there a specific YouTube tutorial or online course I should use, or should I just use the tutorial on https://jax.readthedocs.io/? Any information, advice, or experience you can share would be much appreciated!
- Codon: Python Compiler
What are some alternatives?
nami - Nami Wallet is a browser based wallet extension to interact with the Cardano blockchain. Support requests: https://iohk.zendesk.com/hc/en-us/requests/new
Numba - NumPy aware dynamic Python compiler using LLVM
essential-cardano - Repository for the Essential Cardano list
functorch - functorch is JAX-like composable function transforms for PyTorch.
plutus-use-cases - Plutus Use Cases
julia - The Julia Programming Language
cardano-ledger - The ledger implementation and specifications of the Cardano blockchain.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
dex-lang - Research language for array processing in the Haskell/ML family
Cython - The most widely used Python to C compiler
bip-0039-recovery - Recover your BIP-0039 phrase if you only have 23/24 words
jax-windows-builder - A community supported Windows build for jax.