Numpyro svi. I am trying to use a small Bayesian Network on simple synthetic data, with no success so far. io import output_notebook import numpy as np import scipy. When I run inference or predictive sampling on a test set, I get a broadcasting shape mismatch error: ValueError: Incompatible shapes for broadcasting: shapes=[(train_size,), (test_size,)] From Feb 13, 2025 · If we use batchnorm layer of Flax Linen in numpyro, the mutable node needs to continuously iterate updated batch_stats. These simple effect handlers can be composed together or new ones added to enable implementation of custom inference utilities and Oct 11, 2021 · I’m learning how to use the NumPyro SVI API. , 2019] is a probabilistic programming library that combines the flexibility of numpy with the probabilistic modelling capabilities of pyro, making it an excellent choice for researchers and data scientists. Stochastic Variational Inference with NumPyro Previously we learned how to write probabilistic models and perform MCMC with numpyro the fundamental ideas of variational inference (VI) In this lesson we will explore the tools offered by numpyro to obtain approximate variational posteriors. My understanding is that all parameters are initialized to their mean, and if they don’t have a mean, the “init_to_median” strategy will be used. import numpyro from jax import numpy as See docstrings for SVI and MCMCKernel to see example code of this in context. In order to take advantage of algorithms that require refitting models several times, ArviZ uses SamplingWrapper to convert the API of the sampling backend to a common set of functions. interval (0. vmap` to compute ELBOs over the num_particles-many particles in parallel. scan for spe Dec 1, 2024 · There are a couple of similar posts here about this, more so in this thread but I don’t think I have a clear understanding of the solution / proper use of init_to_value. The only input argument to Flax model is layers. This example also serves as an introduction to Flax and Haiku modules in NumPyro. I have a dataset of ~billion data points, which I have broken up into batches of ~0. Hence, functions like Leave Future Out Cross Validation can be used in ArviZ independently of the sampling Oct 5, 2024 · In this second notebook, we continue working on the NumPyro implementation of the hierarchical forecasting models presented in Pyro’s forecasting documentation: Forecasting III: hierarchical models. 0 from collections import namedtuple from functools import partial import warnings import tqdm import jax from jax import jit, lax, random from jax. It supports arbitrary dependency structure for the model and guide. This would a) speed up SVI and b) reduce non-converging chains. style. I expected some variance on the losses, however I was surprised by how large Oct 24, 2023 · Hello community, I am new to Bayesian models and to pyro (and numpyro). constraints. The only params I can see is auto_loc & auto_key, whic… Apr 13, 2021 · (Thanks to the developers for their hard work, I’m very happily considering converting Stan code to numpyro for a few projects!) I’m interested in reproducing the results in Yao, Vehtari, Simpson & Gelman, “Yes, but Did It Work?: Evaluating Variational Inference” [ICML 2018] and have a short colab here that shows what’s going on (feel free to add comments inline): Google Colab As Parameters: batch_shape – The batch shape for the distribution. Stochastic Variational Inference (SVI) We offer a brief overview of the three most commonly used ELBO implementations in NumPyro: Trace_ELBO is our basic ELBO implementation. If you have a distribution at hand, you can transform via from numpyro. distributions as dist import seaborn as sns import xarray as xr from flax import linen as nn from jax import random from numpyro. Certainly due to numerical instabilities. Returns Jan 8, 2024 · In this notebook we present an alternative implementation of the cohort-revenue-retention model presented in the blog post Cohort Revenue & Retention Analysis: A Bayesian Approach where we show how to replace the BART retention component with a general neural network implemented with Flax. numpy as jnp import jax. :rtype: dict """raiseNotImplementedError This is currently the only ELBO estimator in NumPyro that uses analytic KL divergences when those are available. I’ve followed the Pyro tutorial (I couldn’t find a NumPyro one) and have been trying to fit a simple linear regression example using SVI. It would be good to stop the loop, when the change in loss value becomes very small. Any suggestions? Nov 9, 2021 · Hello, In a complex example performing a SVI on a model, I have some doubts on the ELBO computation for a MVN guide. May 10, 2022 · Hello, I am training a Hierarchical Bayesian model in numpyro using SVI. distributions as dist from jax import random from numpyro. AutoNormal works fine, but when I use AutoMultivariateNormal the SVI fitting throws an er… Feb 19, 2024 · AutoNormal, AutoDelta, and AutoGuideList raise an exception in SVI when the subsample size varies across different log_density evaluation. import os import arviz as az import matplotlib. Since JAX and thus NumPyro explicitly handles the random state, it’s not super trivial to do a random initialization in the guide function as the guide has to reflect the parameters of the model. For example, I would expect a May 9, 2022 · Hi all, I’ve read a few posts on the forum about how to use GPU for MCMC: Transfer SVI, NUTS and MCMC to GPU (Cuda), How to move MCMC run on GPU to CPU and Training on single GPU, but there are a few questions I still have on how to get the most out of numpyro. util import Predictive def model (x=None): n = x. event_shape – The event shape for the distribution. Now I want to try using variational inference to get (hopefully) a faster approximate answer. But if I run two batches in parallel, the process runs out of memory, on a machine with 128 GB RAM. plate. optim). GradientTransformation so that it can be used with numpyro. For each object, I sample a discrete variable c and eight continuous variables – s , h and six parameters theta_i which determine an analytical function (defined by the method dst). autoguide import AutoNormal Usage:: svi = SVI (model, guide, optimizer, loss=Trace_ELBO ()) svi_result = svi. … Oct 1, 2024 · In this notebooks we port the Pyro forecasting example Forecasting I: univariate, heavy tailed to NumPyro. real. I aim to initialize all sites in my model with the output of SVI MAP result (a dictionary which the keys are the site names and the values are the result of the optimization). Now, I am interested in getting Confidence Intervals, so I have started experimenting with AutoNormal and AutoMultivariateNormal Mar 7, 2025 · Works well for distributed Bayesian computing. distributions. conc_g, options. Jul 25, 2023 · Hi, i’m wondering what would be the most straightforward / high-level / accessible way to implement custom objectives for SVI in numpyro (reference example in pyro) I’m trying to implement various experimental l1, l2 regularizers for SVI-based GLM. random import PRNGKey import numpyro import numpyro. NumPyro is a lightweight probabilistic programming library that provides a NumPy backend for Pyro. environ: %config InlineBackend. Effect Handlers This provides a small set of effect handlers in NumPyro that are modeled after Pyro’s poutine module. However, I have a problem with the shape of the obs variable when I draw the prior/posterior distribution from the model. MSOA_female_nested_as_at_mcmc_svi TraceGraph_ELBO ¶ class TraceGraph_ELBO(num_particles=1) [source] ¶ Bases: numpyro. sample ( "mu_product_sigma", … SVI class SVI(model, guide, optim, loss, loss_and_grads=None, num_samples=0, num_steps=0, **kwargs) [source] Bases: pyro. If the latent space is non-euclidean (due to e. The gradient estimator is constructed along the lines of reference [1] specialized to the case of the ELBO. args – arguments to the model / guide (these can possibly vary during the course of fitting). log_density log_density(model, model_args: tuple, model_kwargs: dict, params: dict) [source] (EXPERIMENTAL INTERFACE) Computes log of joint density for the model given latent values params. Jul 25, 2025 · Hi everyone, Sorry if this is naive — I’m a relatively new user of NumPyro and Pyro. I am simply putting some pieces together from the following resources (strongly recommended to read): References: Blackjax docs: Use with Numpyro models Blackjax Sampling Book: Pathfinder Numpyro Issue #1485 PyMC Experimental - Pathfinder Pathfinder: Parallel quasi-Newton Stochastic Variational Inference (SVI) Automatic Guide Generation Reparameterizers Funsor-based NumPyro Optimizers Diagnostics Runtime Utilities Inference Utilities Visualization Utilities Effect Handlers Contributed Code Change Log Introductory Tutorials Bayesian Regression Using NumPyro Bayesian Hierarchical Linear Regression Example Feb 7, 2022 · Hello, I’m trying to setup a demo notebook on Google Collab (K80) but I face the problem of slow SVI. numpy as jnp from numpyro. :param str name: name of site. Parameters: svi_state – current state of SVI. handlers import replay, seed, substitute, trace from numpyro. Docs and Examples | Forum For an example of how `param` statements can be used in inference algorithms, refer to :class:`~numpyro. I have seen people use numpyro. The relevant part of the model is given bellow. Setup We’re going to assume we’ve already defined our model in Pyro (for more details on how this is done see Apr 3, 2025 · I have a model – below joint_model – that I’m able to run with NUTS. Adagrad(step_size=. autoguide as autoguide from numpyro. We do this by working through two examples: a very simple parameter recovery model and a Bayesian Neural Network. My model and guide are defined in this post, and I’m using the Stochastic Variation Inference with Auto Guide Generation of an Emission Spectrum Using NumPyro Last update: Febrary 3rd (2025) Hajime Kawahara for v2. numpy as jnp import numpyro. Right now i get nan ELBO loss when using AutoDAIS… but i do not run into this issue with AutoNormal or AutoLowRankMultivariateNormal. In this tutorial we show how advanced users can modify and/or augment the variational objectives (alternatively: loss functions) and the training step implementation provided by Pyro to support special use cases. Generally speaking Jan 3, 2024 · Are there any examples using standard pytorch dataloaders to train a numpyro svi model in batches? In the example below, there are roughly 2. It has also many helpful utility functions to generate features and for model evaluation. However, there are Jan 5, 2024 · Prepare Notebook import arviz as az import jax. Where possible, conditional dependency information as recorded in the Dec 4, 2023 · In this notebook we describe how to use blackjax ’s pathfinder implementation to do inference with a numpyro model. When I’m using svi. ) with NumPyro), and read through all the introductory tutorials for Pyro (more than once for the SVI tutorials) and numpyro. g Apr 25, 2023 · I am trying to use numpyro to fit a large model with ~1500 latent parameters with a dense covariance matrix. Oct 31, 2022 · I am doing SVI and was wondering if there is a way to choose different learning rates for different parameters? Jan 10, 2020 · Hi there, great initial work wrapping numpyro and pyro into a more user friendly interface! I'm having an issue with a few simple models where the numpyro backend gives me the following error: Cann Testing Different MCMC Samplers - A quick overview of the different samplers in numpyro and their strengths / weaknesses Nested Sampling - An exploration of nested sampling in NumPyro Stochastic Variational Inference - An intro and guide to SVI in NumPyro SVI Part 1 - Introduction & Explanation SVI Part 2 - Examples & Comparison with MCMC Dec 13, 2022 · I am implementing a Bayesian NN and I want to solve it with SVI. Cauchy ()) Apr 30, 2021 · Hi @murphyk, I’m not so familiar with how to extract the unscaled parameters from NumPyro’s MCMC results (@fehiepsi?), but you can take a look at the constraint registry where it looks like interval constraints like [0,50] are transformed using a sigmoid function. It seems to happen because an assumption in Numpyro’s SVI class is violated by having a model containing Jun 24, 2021 · Is it possible to model a custom dependency structure in the posterior guide between random variables with separate definitions in the model? Here is an example of what I’d like to accomplish, but I’m not sure how to do that with the naming constraints in the guide. :param vectorize_particles: Whether to use `jax. So I wanted to know whether there is a difference in Nov 10, 2023 · numpyro. The simple example I’ve been working with is using SVI to recover a two-variable gaussian distribution. stable_update (svi_state, *args, **kwargs) [source]¶ Similar to update () but returns the current state if the the loss or the new state contains invalid values. Jan 27, 2022 · So I was playing around with SVI API and problems from Statistical Rethinking 2ed. transforms import biject_to In this notebook we provide a brief introduction to Stochastic Variational Inference (SVI) with NumPyro. predictions_constant optax_to_numpyro(transformation) → _NumPyroOptim [source] This function produces a numpyro. AutoNormal(quadratic_model, init_loc_fn Note that the onus of using this to initialize the optimizer is on the user inference algorithm, since there is no global parameter store in NumPyro. I’m looking at SteinVI performance on ResNet-size BNNs (11-25k params), but I don’t have results to share yet. - The main difference between the two methods is that the TSB method uses the demand probability instead of the demand periods. And one thing - extracting posterior sample data - made me quite puzzled for a while. For example, consider the simple model: def… Note that the onus of using this to initialize the optimizer is on the user inference algorithm, since there is no global parameter store in NumPyro. Continue training the model. random as random from jax. numpy as jnp import optax import numpyro import nu Mar 15, 2024 · svi_result = svi. optim as optim from numpyro. infer import SVI Aug 12, 2025 · So the init values (which are shape N=1000) give a mismatch with the subsample_size=100… Any idea how I can set init values and use subsampling? Or am I approaching this totally wrong? fehiepsi August 15, 2025, 1:59am 2 Getting Started with NumPyro Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. _NumPyroOptim instance from an optax. Ideally the Apr 4, 2023 · I’m having issues running Numpyro’s SVI in parallel. mcmc. The doc on SVI describes int Jan 25, 2021 · The following code: import numpyro as npy from jax import lax, random import jax. numpy as jnp import numpyro from numpyro. the latter is an approximation and does not e. For example, we can add a method svi. autoguide import AutoLaplaceApproximation if "SVG" in os. To try and help NUTS along I have broken the problem into several stages: Use SVI with AutoDelta to find a decent MAP solution (works well) Use this MAP solution as an initial location for AutoMultivariateNormal to estimate the dense covariance matrix Use this covariance matrix as the Jan 15, 2025 · numpyro. losses = [] for … Feb 21, 2022 · Hi All, What is the efficient and correct approach to save the SVI results in a file for: Load the file to draw samples from guide/model as the post-processing step. ELBO A TraceGraph implementation of ELBO-based SVI. This means evaluating the model at each epoch to get the training loss and validation loss (using in and out-of-sample data, respectively), and get a plot similar to the one below: I would like SVI Part I: An Introduction to Stochastic Variational Inference in Pyro Pyro has been designed with particular attention paid to supporting stochastic variational inference as a general purpose inference algorithm. Model Description: I have a dataset of L objects. Jan 9, 2021 · Hi numpyro team! I am interested in doing mini-batch training with both local and global variables. optim import Adam svi = SVI(model, guide, Adam(1e-3), loss=Trace_ELBO()) 🔹 Using mini-batch gradient-based inference allows Bayesian models to scale efficiently on large datasets. AutoMultivariateNormal(model_spl, init_loc_fn=numpyro. update(svi_state, data) with the same number of steps, there is approximately no difference with the pyro one. In the future it may be interesting to compare the performance of RxInfer with that of this more traditional approach. Along the way we defined models and guides (i. numpy as jnp import optax import numpyro from numpyro import handlers from numpyro. stats from jax import pure_callback, random import jax. infer import Predictive, SVI, Trace_ELBO, TraceMeanField_ELBO from numpyro. a positivity constraint on one of the sample sites) an appropriate bijective transformation is automatically used under the hood to map between the unconstrained Jul 15, 2021 · However, when I try to use SVI with the following code, it gets nowhere near the result of MCMC: quadratic_guide = autoguide. autoguide import Aug 6, 2021 · Hi! I have a time series model which uses scan, and I would like to do leave-one-out cross validation with it, first by running arviz’s loo-function and then by refitting the model for the few observations where the pareto-k values are too high. Docs and Examples | Forum I also presented a few stripped-down examples of NumPyro’s SVI features, including some simple cases of its automatic guide generation and a few ways you can build more complicated guides of your own. 0 In this guide, we perform retrieval of an emission spectrum using stochastic variational inference (SVI) with automatic guide generation. variational distributions), setup variational objectives (in particular ELBOs), and constructed optimizers (pyro. elbo. Several ways appear to be possible to accomplish random Aug 5, 2025 · What is NumPyro? NumPyro is a lightweight probabilistic programming library that provides a NumPy backend for Pyro. example_libraries import optimizers import jax. Dec 3, 2020 · In summary, you can use numpyro. infer. get_params` method from :class:`~numpyro. There is also this blog post comparing MCMC sampling methods on GPU, and although the model is built in pymc, it uses numpyro from functools import namedtuple import os import warnings import jax from jax import random, value_and_grad from numpyro. In the first, I introduce the reader to the basics: the broad ideas of SVI and the maths that underpin them, as well as some step-by-step examples of what SVI tools are available in NumPyro and how to use them. optim. distributions import constraints from numpyro. So most of these improvements might center on HMC/NUTS, but not variational inference. :return: A dict mapping sample site name to an array of quantile values. constraints import biject_to from numpyro. ,Bayesian DataAnalysis:Sec. Note that unlike [1, 2], this implementation uses a Dirichlet prior directly rather than approximating it with a softmax-normal distribution. funsor import config_enumerate, infer_discrete import numpyro. I’m working on a hierarchical negative binomial model in NumPyro with multiple categorical random effects. Sep 3, 2025 · Hi all! 👋 I wanted to share some material I presented at PyData Berlin 2025, introducing variational inference with NumPyro. The unified interface for VI in numpyro is located in numpyro. ELBO) – an instance of a Parameters: posterior numpyro. model_kwargs SVI class SVI(model, guide, optim, loss, loss_and_grads=None, num_samples=0, num_steps=0, **kwargs) [source] Bases: pyro. Jan 27, 2021 · For some models when using SVI a proper random initialization is necessary as also explained in Pyro’s docs for Gaussian Mixture Models. Let’s see how we go about doing variational inference in Pyro. But, when using SVI with any kind of automatic guide, it fails …. Jun 29, 2021 · Hi, I just started to experiment with svi to get some increase in inference speed. module import flax_module from numpyro. pyplot as plt import scipy. random. factor() in other posts on this forum, so I know this should be a workable approach, but I am not Automatic Guide Generation We provide a brief overview of the automatically generated guides available in NumPyro: AutoNormal and AutoDiagonalNormal are our basic mean-field guides. stable_update(svi_state, data) params = svi. TraceGraph_ELBO offers variance reduction strategies for models with discrete latent variables. I seem to get a good parameter fit, however I was a bit surprised by the variance on the losses: I’m fairly new to using SVI. disable_jit in the notebook to debug. TraceGraph_ELBO ¶ class TraceGraph_ELBO(num_particles=1) [source] ¶ Bases: numpyro. NumPyro is designed to be lightweight and focuses on providing a flexible substrate Numpyro’s support for stochastic variational inference (SVI) offers an alternative to MCMC-based inference for certain scenarios. I’m trying to simply redo a simple linear regression model implemented by a colleague - The TSB method is similar to the Croston’s method: constructs two different time series out of the original one and then forecast each of them separately, so that the final forecast is generated by combining the forecasts of the two time series. 5 million data points. Bernoulli(probs=p), obs=results) On simulated synthetic data, I was able to recover the true values of event times quite well, when using methods such as NUTS, Laplace approximation and MAP. Dec 18, 2020 · Hello - I’m quite new to pyro/numpyro and have read through most of the numpyro port of Statistical Rethinking (GitHub - fehiepsi/rethinking-numpyro: Statistical Rethinking (2nd ed. optimizers. autoguide import AutoContinuous from numpyro. import numpy as np import jax import jax. This allows faster inference, as we can use NumPyro ’s NUTS sampler or any of the stochastic Nov 23, 2024 · In this post we use NumPyro to undertake a simple linear regression problem (based on McElreath’s book Statistical Rethinking). Ideally using Pytorch style with DataLoader to avoid copying all data to GPU and lax. One of the useful outputs when training a neural net is the train vs. fori_loop in _fit method, then use the context managers numpyro. Aug 29, 2024 · Dear Numpyro, I am looking to solve a hierarchical modelling problem using SVI whereby conditional independence in the model is used to train the model in a distributed way. 1) svi = SVI(model, guide, optimizer, Trace_ELBO()) result Apr 2, 2024 · SVI tends to break, when the (local) optimum is reached. I dont have this problem with MCMC using pymc, but I do have this problem using MCMC with numpyro. distributions import Getting Started with NumPyro Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. NumPyro is under active development, so beware of brittleness, bugs, and changes to the API as the design evolves. Predictive returns the same values independent of the model when running it on SVI results. PRNGKey (0), 2000, data) # upon inspection of svi_result the user decides that the model has not converged # continue from the end of the previous svi run rather than beginning again from iteration 0 svi_result = svi. See the docs for more information on the various SVI implementations and SVI tutorials I, II, and III for background on SVI. SVI. However, when I inspect out the value of a parameter during this initialization phase, it is not as is expected. For the MCMC I used arviz interface and I was able to do all my plots after training from the netcdf file. optim import Adam guide = autoguide. Where possible, conditional dependency information as recorded in the Stochastic Variational Inference (SVI) We offer a brief overview of the three most commonly used ELBO implementations in NumPyro: Trace_ELBO is our basic ELBO implementation. It seems that it will take 6h or so. Clearly something is off. Gamma(options. distributions import biject_to constrained_samples = my LetusexploreNumPyrousingasimpleexample. I’ve written custom guides in the past and I had to add my own constraints e. Constraint) – NumPyro constraint, defaults to constraints. e. I think it is better to support a high-level api for the above job. example_libraries. What I get for the prior_predictions['obs'] has the shape (100,10,1) which is Nov 10, 2021 · The question is related to the optimizer used in SVI: Imagiine that I have a numpyro model that is valid, and I would like to use a MVN variational optimisation import numpyro. My experiments with SVI have taken much less time (5-60 mins), but the posterior looks very different. init_to Refitting NumPyro models with ArviZ # ArviZ is backend agnostic and therefore does not sample directly. Did anything go wrong? Any suggestion will be useful. distributions as dist import numpyro. WewillusetheeightschoolsexamplefromGelmanetal. Since the data is high-dimensional, I am not doing a Bayesian inference but rather SVI. I want to add the subsampling option to the numpyro. 5 svi updates per second which is much slower than I’d hope. stats as stats import jax. :param init_value: initial value specified by the user or a lazy callable that accepts a JAX random PRNGKey and returns an array. This designates the dependent dimensions of a sample from the May 17, 2020 · It seems that Jax is the lightest auto-differentiation library out there. svi. constraint (numpyro. One illustrative case is Neal’s funnel. figure_formats = ["svg"] az. subsample), the model input will be constant. May 6, 2022 · In pyro, it is possible to use a model containing parameters, a null guide, and SVI to compute MLE for a given model and data, per this tutorial. I’m struggling to save the guide (as this is the object used in the inference process). abstract_infer. As per Sep 9, 2024 · I am trying to figure out an issue with the choice of autoguide for my numpyro model fitted with svi. Bayesian Regression Using NumPyro In this tutorial, we will explore how to do bayesian regression in NumPyro, using a simple example adapted from Statistical Rethinking [1]. get_params(svi_state Dec 15, 2022 · I am using the following simple example to see how to use SVI for a Bayesian NN. rate_g)) gamma_l = numpyro Jul 27, 2023 · Hi. I have tried three different approaches: An autoguide using AutoMultivariateNormal An autoguide using AutoNormal / AutoDiagonalNormal A manually defined Bayesian Neural Networks with numpyro Deep Neural Networks are non-linear function approximators and the state of the art in pattern recognition for unstructured data (audio, images, text, video) But they do have limitations Very deep models require lots of data to train Selecting an architecture requires a lot of experimentation They can be easily fooled They are poor at representing uncertainty Example: Bayesian Neural Network We demonstrate how to use NUTS to do inference on a simple (small) Bayesian neural network with two hidden layers. I made some further progress by replacing the Adam optimizer with Adagrad and things are working quite sensibly now! For posterity (hopefully), I modified laplace_approx_svi() to: def laplace_approx_svi(model, *args): guide = AutoLaplaceApproximation(model) optimizer = numpyro. constraints as constraints import numpyro. But to investigate I would like to proceed to a “by hand” verification. distributions as dist from numpyro. Automatic Guide Generation We provide a brief overview of the automatically generated guides available in NumPyro: AutoNormal and AutoDiagonalNormal are our basic mean-field guides. model_args – args provided to the model. I've set XLA_PYTHON_CLIENT_ALLOCATOR=platform as advised in jax docs to debug this further. This is fixed for a distribution instance and is inferred from the shape of the distribution parameters. My first approach was to try use pickle to save the guide, but it does not support saving some JAX components. Mar 24, 2022 · Hello, My understanding is that training a model using SVI is very similar to training a neural network. As a quick overview, an SVI job consistent of four components: A standard NumPyro model that can generate a posterior and defines latent variables in the form of numpyro. :param num_particles: The number of particles/samples used to form the ELBO (gradient) estimators. We provide the key mathematical concepts, but we focus on the code implementation. validation learning curves. TracePosterior Parameters model – the model (callable containing Pyro primitives) guide – the guide (callable containing Pyro primitives) optim (PyroOptim) – a wrapper a for a PyTorch optimizer loss (pyro. I am trying to reproduce this simple example with a simple BNN from this tutorial. Maybe its the way I specify the likelihood contribution from censored data Getting Started with NumPyro ¶ Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. When I tried the same approach in numpyro, I get an error: TypeError: unsupported operand type(s) for *: ‘dict’ and ‘DeviceArray’. Apr 12, 2022 · Hi, I am trying to get the log-likelihood for my model: def model (data): x = numpyro. That’s the BNN model D_Y=1 D_H = n_neurons D_X = 1 w_ = () b_ = () # sample first layer (we put Feb 11, 2025 · Here’s a code example where I train a model, and then pass the params/guide from that model to predictive but pass the wrong model function and Predictive functions just fine (but in some cases returns the wrong answer). Here is the code from bokeh. Inference works fine when using NUTS. The forecasting module in Pyro is fantastic as it provides an easy interface to develop custom forecasting models. SVI`. Generally speaking Example: Stochastic Volatility Example: ProdLDA with Flax and Haiku Variationally Inferred Parameterization Automatic rendering of NumPyro models Bad posterior geometry and how to deal with it Truncated and folded distributions Bayesian Censoring Data Modeling I'm having issues with numpyro, specifically SVI using GPU memory when running the same model multiple times. Slides Scaling Probabilistic Models with Variational Inference Notebook: PyData Berlin 2025: Introduction to Stochastic Variational Inference with NumPyro - Dr. SVI is particularly useful when dealing with large datasets or when obtaining exact posterior samples is computationally prohibitive. I run the optimization for a certain number of steps, and save the final parameters to a dictionary and check if the parameters have converged to my liking. event_dim (int) – (optional) number of rightmost dimensions unrelated to batching. Here is a comparison of the median estimates comparing numpyro to SVI with AutoLowRankMultivariateNormal. 5,2003 Apr 3, 2024 · I’m estimating a fairly straightforward multinomial logistic regression model with random (hierarchical) coefficients. I try a AutoMultivariateNormal with Adam optimizer and loss=Trace_ELBO(num_particles=1) with 5_000 steps. ELBO) – an instance of a Introduction to NumPyro # NumPyro [Bingham et al. Docs and Examples | Forum Aug 20, 2023 · Hi all, I try to run the bayesian hierarchical linear regression tutorial as Bayesian Hierarchical Linear Regression — NumPyro documentation and got the same results with tutorial. I have tried reducing learning rate but that hasn’t changed anything. autoguide import AutoDiagonalNormal, AutoNormal from numpyro. infer import SVI, Trace_ELBO from numpyro. Juan Camilo Orduz In particular, I show a case with a little example: New Flax NNX integration Custom Optax stable_update(svi_state, *args, **kwargs) [source] ¶ Similar to update() but returns the current state if the the loss or the new state contains invalid values. Initially, I was interested in getting only point estimates, so I was using AutoDelta. I was running SVI for my model with NumPyRO to compare its speed-up vs PyRO version. I’ve looked around the forum for advice, but I still have some simple questions: I’d like to use an AutoGuide (probably MVN or a normalizing flow). In particular, we would like to explore the following: Write a simple model using the sample NumPyro primitive. We use NumPyro and compare the NUTS and SVI results. Here is an example reproducing the issue (run on master). sample ("mu", dist. It seems like when the counts get low, the model loses the ability to recover the true parameters. However, I looked the code and have barriers to understand the propagation process of this node valu… Example: Neural Transport This example illustrates how to use a trained AutoBNAFNormal autoguide to transform a posterior to a Gaussian-like one. a positivity constraint on one of the sample sites) an appropriate bijective transformation is automatically used under the hood to map between the unconstrained Dec 26, 2024 · Hello, I am trying to perform linear regression in the probabilistic framework. sample("gamma_g", dist. use("arviz-darkgrid Mar 17, 2022 · Hello, I am trying to save the model artifacts of my SVI session so I can run an inference process later on with a different script. , 2019, Phan et al. For a tutorial on effect handlers more generally, readers are encouraged to read Poutine: A Guide to Programming with Effect Handlers in Pyro. util. PRNGKey (1), 2000, data Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. This function is fit to observed data points, one fit per object. This introductory notebook is meant for practitioners. The transform will be used to get better mixing rate for NUTS sampler. plotting import figure, show from bokeh. infer import SVI, Predictive, Trace_ELBO from numpyro. For such a purpose, we use Continuous Ranked Probability Score (CRPS). What we settled on was passing in posterior samples and writing a wrapper around Predictive to force people to pass in the right arguments. contrib. We rely on JAX for automatic differentiation and JIT compilation to GPU / CPU. MCMC Fitted MCMC object from NumPyro prior: dict Prior samples from a NumPyro model posterior_predictive dict Posterior predictive samples for the posterior predictions: dict Out of sample predictions constant_data: dict Dictionary containing constant data variables mapped to their values. It is a lightweight wrapper that recreates the (init_fn, update_fn, get_params_fn) interface defined by jax. pyplot as plt import jax. I come from the land of MCMC where we abide by “just use NUTS”, but there seem to be so many choices for SVI. But I’m running into this issue >>> guide = AutoNormal (j… Blog of Hugh McDougall (BE / BSc)Doing SVI in NumPyro NumPyro has ready-made tools for doing SVI without much more complexity than an MCMC run. Thanks. run(jax. sample("obs", dist. Is there some way to speed up the optimization? Thanks SVI isn’t a small topic to cover, so I’ve split this entry into two parts. run (which mimics mcmc. :param list quantiles: A list of requested quantiles between 0 and 1. PRNGKey(seed), num_steps, data) there is like a 5 times speed-up vs pyro version; however when I’m using svi. not sure what kind of discrepancy you’re talking about but the GaussianHMM integrates out the gaussian latent variables exactly, where as the numpyro code does not (instead it does mean field variational inference). infer. lax. numpy as jnp import matplotlib. run (random. - pyro-ppl/numpyro Sep 20, 2023 · I am making a comparison between Dan Foreman Mackey’s “Astronomer’s Guide to NumPyro”, attempting to demonstrate how SVI can be used to approach his first example of “linear relationships with outliers” as an alternative to MCMC. The structure is the same as in the getting started guide, except that we use SVI instead of HMC-NUTS; i Apr 22, 2025 · the issue was because this was not differentiable! the fix was following guidance from Best practice to handle division by zero in auto-differentiation · Issue #5039 · jax-ml/jax · GitHub Oct 30, 2020 · To me, this is unfortunate and in most of the cases (given the fact that we already support subsampling via numpyro. This Python library has a focus on stochastic variational inference (SVI). TraceMeanField_ELBO is like Trace_ELBO but computes part of the ELBO analytically if doing so is possible. Best ! Below is full codes for SVI import torch import pandas as pd import Jul 2, 2023 · I’m trying to fit a censored log likelihood poisson model but with low count data and Im running into issues using SVI. kwargs – keyword arguments to the model / guide (these can possibly vary during the May 1, 2025 · When I want to conduct inference on my model using SVI, I use the “init_to_mean” strategy. The model and training procedure is in the minimal example below. Below is a ‘academic example’ but it can help to setup the code I need, so I think if we can manage for this simple example, I guess I can transpose for my import numpyro import numpyro. run) which do all the job, kind of Apr 21, 2022 · Hi all, Link to the google-colab: I am trying to use the numpyro sampling on top of the model defined by flax. This designates independent (possibly non-identical) dimensions of a sample from the distribution. Next I defined the numpyro model as follows: def BayesianMLP(x, y = None): # Wrap Flax obeject to numpyro net = random_flax Feb 21, 2022 · Hi all, I have a model which I successfully trained with SVI and the results are good. Docs and Examples | Forum The parameters can be obtained using :meth:`~numpyro. shape [0] if x is not None else 1 mu = numpyro. samples A numpyro “guide” that defines surrogate sample distributions in in Feb 10, 2025 · Thanks for the response, we’re working on using numpyro at our company and this behavior really confused us. So, NumPyro might be ideal for traditional bayesian statistics, whereas Pyro Nov 24, 2022 · I’m seeking advice on improving runtime performance of the below numpyro model. Docs and Examples | Forum Apr 6, 2021 · Appears I’m too quick to post. control_flow_prims_disabled together with jax. Dill, JSON, or CloudPickle do not seem to work either Jun 23, 2022 · I am trying to get a similar posterior to my NUTS model, which converges well but takes about a day on GPU to run. Why this happens, I don't know. The purpose of this translation is to dig deeper into the some forecasting SVI Part IV: Tips and Tricks The three SVI tutorials leading up to this one (Part I, Part II, & Part III) go through the various steps involved in using Pyro to do variational inference. distributions as dist pos = constraints. (In the future I’d like to do LFO-CV too) For LOO-CV I have to compute the log-likelihood of the left-out datapoint y_i, given the model fitted Jun 16, 2023 · When introducing a BatchNorm layer in a flax linen module and using SVI inference, I get assertion errors when attempting to undertake posterior prediction. Jun 27, 2022 · Is there something wrong with my code or is SVI + discrete HMM combination not supported in numpyro? My ultimate goal of exploring numpyro was to speed up runtime through using jax’s scan() as its documentation makes me think it would be more efficient than writing a python for-loop over each time point in a pyro HMM. Best i can think of rn is interfering somewhere into: svi_state, loss = svi. Defined the feed-forward Flax model FlaxMLP and trained it using optax. 5. numpy as jnp from jax import random import numpyro import numpyro. - pyro-ppl/numpyro Jan 16, 2025 · Hi there, I’m trying to use an AutoMultivariateNormal guide for and SVI fit of a model with discrete latent variables in numpyro. The problem I am facing is the following: There is no noise in the data generated Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. The effect of all this machinery is to May 18, 2022 · SVI on the GaussianHMM gets close to the true parameters very quickly, but the numpyro implementation does not. Uniform (low=-2, high=2), sample_shape= (10,)) img Stochastic Variational Inference (SVI) We offer a brief overview of the three most commonly used ELBO implementations in NumPyro: Trace_ELBO is our basic ELBO implementation. First I loaded the Boston Housing Data. Nov 17, 2024 · I’m trying to utilize SVI with the AutoDelta guide. def model(x, options): gamma_g = numpyro. Getting Started with NumPyro Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. SVI The arguments of the SVI object are # SPDX-License-Identifier: Apache-2. Jun 7, 2024 · In this blog post, we experiment with a hierarchical exponential smoothing forecasting model, extending the ideas from the univariate case presented in the blog post “Notes on Exponential Smoothing with NumPyro”. pyplot as plt import numpy as np import numpyro import numpyro. Parameters: model – Python callable containing NumPyro primitives. NumPyro is designed to be lightweight and focuses on providing a flexible substrate that users can update (svi_state, *args, **kwargs) [source]¶ Take a single step of SVI (possibly on a batch / minibatch of data), using the optimizer. PyTorch, conversely, builds computational graphs that might be a little bloated for HMC/NUTS efficiency but potentially offer improved SVI functionality. In this introductory tutorial, we’ll explore the basics of numpyro and how to get started with probabilistic May 5, 2022 · Hi all, I’m looking to try out SVI my model. In this second part, we extend the model described in the first part From Pyro to NumPyro: Forecasting Hierarchical Models - Part I by adding all stations to the model. But when I try to use SVI to solve the same problem, I got total different results. From my inspection of the init_to_value function Variationally Inferred Parameterization Author: Madhav Kanda Occasionally, the Hamiltonian Monte Carlo (HMC) sampler encounters challenges in effectively sampling from the posterior distribution. fully propagate from collections import defaultdict import os import matplotlib. If I run SVI for a single batch, the whole thing uses < 3GB memory. infer_util import log_density, transform_fn SVIState Jun 10, 2025 · When running the SVI algorithm, keep in mind that Numpyro takes some time to compile all the code and data, so there’s some overhead as the data size and model complexity increases. Example: Stochastic Variational Inference (SVI) In NumPyro from numpyro. g. kwargs – keyword arguments to the model / guide (these can possibly vary during the course of fitting). If not, when I re-run the optimization (with a reduced learning rate), and pass in the last saved parameters as initial parameters for this new optimization stage, the init_loss printed Sep 6, 2023 · I am playing with some basic implementations of SVI in NumPyro, and having issues getting my guide’s to work for all but the most basic of dummy distributions. infer import SVI, Trace_ELBO from numpyro. PRNGKey(0), 10000, X=X ,K=k) The estimates for the parameters are fine, but I will need an estimate of the credible intervals (or the posterior distribution for the parameters) and I can’t figure out how to do it with SVI. sample ("x", dist. References: Nov 5, 2024 · I have evidence that SteinVI is generally preferable over SVI for small BNNs (1-layered 50-100 hidden dim). However, what leaves confused is how I can obtain the samples for each parameter. run(random. As part of the model definition I have a variable mu_product_sigma defined by mu_product_sigma = numpyro. fori_loop instead of jax. What’s going on here? import jax import jax. In these situations, the conventional centered parameterization may prove inadequate, leading us to employ non-centered parameterization. zrpwycp nprfdqht oiyr pytc ghlkgy gwhwh kqwzm inum dqqdgu flcti