![]() In this series, you will learn about Accelerating Deep Learning Models with PyTorch 2.0. This blog series aims to understand and test the capabilities of PyTorch 2.0 via its beta release. The stable release of PyTorch 2.0 is planned for March 2023. On December 2, 2022, the team announced the launch of PyTorch 2.0, a next-generation release that will make training deep neural networks much faster and support dynamic shapes. Thus, to leverage these resources and deliver high-performance eager execution, the team moved substantial parts of PyTorch internals to C++. However, over all these years, hardware accelerators like GPUs have become 15x and 2x faster in compute and memory access, respectively. With continuous innovation from the PyTorch team, PyTorch has moved from version 1.0 to the most recent version, 1.13. It has provided some of the best abstractions for distributed training, data loading, and automatic differentiation. Since the launch of PyTorch in 2017, it has strived for high performance and eager execution. The success of PyTorch is attributed to its simplicity, first-class Python integration, and imperative style of programming. Over the last few years, PyTorch has evolved as a popular and widely used framework for training deep neural networks (DNNs). Evaluating Convolutional Neural Networks.Parsing Command Line Arguments and Running a Model.Accelerating Convolutional Neural Networks.Configuring Your Development Environment.The names are conveniently returned as expected when using Predictive, e.g. Then, I can just reference model.parameter_names to get those names later. Setattr(m, name, PyroSample(prior=dist.Laplace(0., 3.) Pyro.nn.module.to_pyro_module_(self.shape) ('shape_fc1L:final', nn.Linear(in_features=h1,out_features=out_features))įor name, param in _parameters(): ('shape_fc0', nn.Linear(in_features=in_features,out_features=h1)), Self.mu_func_call = self.mu_func(in_features, h1 = h1, out_features = out_features) Using idea from here from collections import OrderedDict I guess you can do theta = nn.Sequential( Your code def _init_(self, in_features, h1 = 2, out_features = 1): I would recommend playing a bit with some PyTorch modules like Sequential to see how naming works in PyTorch. If your nn.Module has two submodules mu and theta, then the name of parameters will be "mu.linear.weight". It is just the same as the way you use m.named_parameters(recurse=False) in your code. With a = A(), I think you can do a.named_parameters() to get names of parameters of theta. In general though, is there a way to get all possible options to use in return_sites? I looked at poutine but could not get that to work.ĭef _init_(self, in_features, h1 = 2, out_features = 1): ![]() But I can’t figure out how theta gets named and how to access that distribution. With mu for example, if I use self.linear = PyroModule(.), I can use Predictive(model, guide, num_samples, return_sites = ("linear.weight")). How do the sites get named for theta? I’d like to look at the distributions of those parameters using Predictive. Obs = pyro.sample("obs", GammaHurdle(concentration = shape, rate = shape / mu, theta = theta), obs=y) Setattr(m, name, PyroSample(prior=dist.Laplace(0, 2) Pyro.nn.module.to_pyro_module_(self.theta)įor name, value in list(m.named_parameters(recurse=False)): Relevant code snippet (some lines removed to make it more clear): def _init_(self, in_features, h1 = 2, out_features = 1): I’ve now updated theta to be modeled as a two layer nn.Sequential. I’m continuing on the model I’ve described here, adding complexity bit by bit.
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