cattrs
equinox
cattrs | equinox | |
---|---|---|
7 | 31 | |
756 | 1,819 | |
0.8% | - | |
8.8 | 9.2 | |
17 days ago | 15 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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cattrs
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Writing Python like it’s Rust
I'd suggest you look at my cattrs (https://catt.rs) library as a good serde lookalike in Python (sum type support present and getting better), and to use attrs instead of dataclasses in general.
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Starlite updates March '22 | 2.0 is coming
Pydantic is by far not the only library of its kind, with prominent members of the same class being attrs, cattrs or even plain dataclasses for some use cases.
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Noob question on saving objects in YAML files
That being said, data serialization is a very common thing to do, so naturally there are tons of libraries that automate it for you. Personally, using dataclasses and cattrs is my goto way for doing such things.
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Taking JSON input for "posts", "tags" etc. How to escape '\' charecter or detect carefully?
I'm fond of attrs and cattrs myself, attrs make creating data classes a snap, writing all of the stupid code python requires to have a dataclass. Note the new built in dataclass is actually a limited copy of attrs. https://www.attrs.org/en/stable/ and https://github.com/python-attrs/cattrs
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apischema v0.17 - I've developed the fastest typed JSON (de)serialization library, and you can also build your GraphQL schema with it
This month, I've released version 0.17, and it's now blazing fast; there is in fact no more comparison with Pydantic, which more than 5x slower (up to 30x in serialization). It's also faster than alternatives like mashumaro or cattrs. (See the quick benchmark result in documentation, and the code)
- cattrs – an open source Python library for structuring and unstructuring data
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I use attrs instead of pydantic
```
Cattrs has some problems with generics [1] [2]. Dacite and marshmallow-dataclasses don't support generics well either, with some issues around Union types.
They do work well for simple python types but what I'd like to see is guarantee that the serialisation operation is completely reversible and if not raise warning/exception.
[1] https://github.com/Tinche/cattrs/issues/149
equinox
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Ask HN: What side projects landed you a job?
I wrote a JAX-based neural network library (Equinox [1]) and numerical differential equation solving library (Diffrax [2]).
At the time I was just exploring some new research ideas in numerics -- and frankly, procrastinating from writing up my PhD thesis!
But then one of the teams at Google starting using them, so they offered me a job to keep developing them for their needs. Plus I'd get to work in biotech, which was a big interest of mine. This was a clear dream job offer, so I accepted.
Since then both have grown steadily in popularity (~2.6k GitHub stars) and now see pretty widespread use! I've since started writing several other JAX libraries and we now have a bit of an ecosystem going.
[1] https://github.com/patrick-kidger/equinox
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[P] Optimistix, nonlinear optimisation in JAX+Equinox!
The elevator pitch is Optimistix is really fast, especially to compile. It plays nicely with Optax for first-order gradient-based methods, and takes a lot of design inspiration from Equinox, representing the state of all the solvers as standard JAX PyTrees.
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
If you like PyTorch then you might like Equinox, by the way. (https://github.com/patrick-kidger/equinox ; 1.4k GitHub stars now!)
- Equinox: Elegant easy-to-use neural networks in Jax
- Show HN: Equinox (1.3k stars), a JAX library for neural networks and sciML
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Pytrees
You're thinking of `jax.closure_convert`. :)
(Although technically that works by tracing and extracting all constants from the jaxpr, rather than introspecting the function's closure cells -- it sounds like your trick is the latter.)
When you discuss dynamic allocation, I'm guessing you're mainly referring to not being able to backprop through `jax.lax.while_loop`. If so, you might find `equinox.internal.while_loop` interesting, which is an unbounded while loop that you can backprop through! The secret sauce is to use a treeverse-style checkpointing scheme.
https://github.com/patrick-kidger/equinox/blob/f95a8ba13fb35...
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Writing Python like it’s Rust
I'm a big fan of using ABCs to declare interfaces -- so much so that I have an improved abc.ABCMeta that also handles abstract instance variables and abstract class variables: https://github.com/patrick-kidger/equinox/blob/main/equinox/_better_abstract.py
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[D] JAX vs PyTorch in 2023
For the daily research, I use Equinox (https://github.com/patrick-kidger/equinox) as a DL librarry in JAX.
- [Machinelearning] [D] État actuel de JAX vs Pytorch?
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Training Deep Networks with Data Parallelism in Jax
It sounds like you're concerned about how downstream libraries tend to wrap JAX transformations to handle their own thing? (E.g. `haiku.grad`.)
If so, then allow me to make my usual advert here for Equinox:
https://github.com/patrick-kidger/equinox
This actually works with JAX's native transformations. (There's no `equinox.vmap` for example.)
On higher-order functions more generally, Equinox offers a way to control these quite carefully, by making ubiquitous use of callables that are also pytrees. E.g. a neural network is both a callable in that it has a forward pass, and a pytree in that it records its parameters in its tree structure.
What are some alternatives?
marshmallow - A lightweight library for converting complex objects to and from simple Python datatypes.
flax - Flax is a neural network library for JAX that is designed for flexibility.
pydantic - Data validation using Python type hints
dm-haiku - JAX-based neural network library
Fast JSON schema for Python - Fast JSON schema validator for Python.
torchtyping - Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
serpy - ridiculously fast object serialization
treex - A Pytree Module system for Deep Learning in JAX
datamodel-code-generator - Pydantic model and dataclasses.dataclass generator for easy conversion of JSON, OpenAPI, JSON Schema, and YAML data sources.
extending-jax - Extending JAX with custom C++ and CUDA code
lupin is a Python JSON object mapper - Python document object mapper (load python object from JSON and vice-versa)
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/