numpyro | Probabilistic programming with NumPy | Machine Learning library

by pyro-ppl Python Version: 0.15.0 License: Apache-2.0

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | numpyro Summary

numpyro is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. numpyro has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install numpyro' or download it from GitHub, PyPI.

NumPyro is a lightweight probabilistic programming library that provides a NumPy backend for Pyro. We rely on JAX for automatic differentiation and JIT compilation to GPU / CPU. NumPyro is under active development, so beware of brittleness, bugs, and changes to the API as the design evolves.

Support

Quality

Security

License

Reuse

Support

numpyro has a medium active ecosystem.

It has 1787 star(s) with 190 fork(s). There are 47 watchers for this library.

There were 1 major release(s) in the last 6 months.

There are 40 open issues and 610 have been closed. On average issues are closed in 64 days. There are 13 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of numpyro is 0.15.0

Quality

numpyro has 0 bugs and 0 code smells.

Security

numpyro has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

numpyro code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

numpyro is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

numpyro releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

numpyro saves you 6928 person hours of effort in developing the same functionality from scratch.

It has 31506 lines of code, 2530 functions and 150 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed numpyro and discovered the below as its top functions. This is intended to give you an instant insight into numpyro implemented functionality, and help decide if they suit your requirements.

Construct an HMC .
Build a warmup adapter .
Construct a proxy function for a given model .
Initialize a model using rng_key .
Generate a function that can be used to create a model .
Scan an enum function .
Get model relations .
Perform a scan .
Find initial parameters for a given model .
Benchmark MCMC .

Get all kandi verified functions for this library.

numpyro Key Features

No Key Features are available at this moment for numpyro.

numpyro Examples and Code Snippets

Evaluation of NumPyro autoguides on Stan programs,Experiments

Python

Lines of Code : 18

License : Permissive (Apache-2.0)

Copy

make eval

$ python eval.py --help
usage: eval.py [-h] --backend BACKEND --mode MODE [--test]
               [--posteriors POSTERIORS [POSTERIORS ...]] [--guide GUIDE]
               [--steps STEPS] [--samples SAMPLES]

Run autoguide accuracy experim

Spatio-temporal analysis of B.1.1.7 infection dynamics

Jupyter Notebook

Lines of Code : 4

License : No License

Copy

$ pip install -r requirements.txt

pip install -r requirements-no-deps.txt --no-deps

$ brew install spatialindex

$ conda install -c conda-forge libspatialindex

Twinify,Using Twinify,How to Run Twinify

Python

Lines of Code : 3

License : Permissive (Apache-2.0)

Copy

twinify input_data_path model_path output_path_prefix

twinify my_data.csv my_model.txt my_twin --seed=123 --num_synthetic=1000 --visualize=store

twinify my_data.csv my_numpyro_model.py my_twin --seed=123 --num_synthetic=1000 --visualize=store

Community Discussions

Trending Discussions on numpyro

Vectorise nested vmap

A JAX custom VJP function for multiple input variable does not work for NumPyro/HMC-NUTS

QUESTION

Vectorise nested vmap

Asked 2021-Nov-05 at 01:37

Here's some data I have:

...

ANSWER

Answered 2021-Nov-05 at 01:37

I believe Vectorization guidelnes for jax is quite similar to your question; to replicate the logic of nested for-loops with vmap requires nested vmaps.

The cleanest approach using jax.vmap is probably something like this:

Source https://stackoverflow.com/questions/69846536

QUESTION

A JAX custom VJP function for multiple input variable does not work for NumPyro/HMC-NUTS

Asked 2021-Apr-07 at 16:08

I am trying to use a custom VJP (vector-Jacobian product) function as a model for a HMC-NUTS in numpyro. I was able to make a single variable function that works for HMC-NUTS as follows:

...

ANSWER

Answered 2021-Feb-11 at 03:08

def model(x,y):
sigma = numpyro.sample('sigma', dist.Exponential(1.))
x0 = numpyro.sample('x0', dist.Uniform(-1.,1.))
A = numpyro.sample('A', dist.Exponential(1.))
hv=vmap(h,(0,None),0)
mu=hv(x-x0,A)
numpyro.sample('y', dist.Normal(mu, sigma), obs=y)

Source https://stackoverflow.com/questions/65684271

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install numpyro

Limited Windows Support: Note that NumPyro is untested on Windows, and might require building jaxlib from source. See this JAX issue for more details. Alternatively, you can install Windows Subsystem for Linux and use NumPyro on it as on a Linux system. See also CUDA on Windows Subsystem for Linux and this forum post if you want to use GPUs on Windows.

Support

Unlike in Pyro, numpyro.sample('x', dist.Normal(0, 1)) does not work. Why?. You are most likely using a numpyro.sample statement outside an inference context. JAX does not have a global random state, and as such, distribution samplers need an explicit random number generator key (PRNGKey) to generate samples from. NumPyro's inference algorithms use the seed handler to thread in a random number generator key, behind the scenes. Can I use the same Pyro model for doing inference in NumPyro?.

Find more information at: