numpyro | Probabilistic programming with NumPy | Machine Learning library

 by   pyro-ppl Python Version: 0.13.2 License: Apache-2.0

kandi X-RAY | numpyro Summary

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.

            kandi-support 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 3 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 0.13.2

            kandi-Quality Quality

              numpyro has 0 bugs and 0 code smells.

            kandi-Security 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.

            kandi-License 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.

            kandi-Reuse 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
            Pythondot img1Lines of Code : 18dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            make eval
            $ python --help
            usage: [-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 Notebookdot img2Lines of Code : 4dot img2no licencesLicense : No License
            copy iconCopy
            $ 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
            Pythondot img3Lines of Code : 3dot img3License : Permissive (Apache-2.0)
            copy iconCopy
            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_twin --seed=123 --num_synthetic=1000 --visualize=store

            Community Discussions


            Vectorise nested vmap
            Asked 2021-Nov-05 at 01:37

            Here's some data I have:



            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:



            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:



            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.))
            numpyro.sample('y', dist.Normal(mu, sigma), obs=y)


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


            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.


            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?.
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            pip install numpyro

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            gh repo clone pyro-ppl/numpyro

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