{"id":2510,"date":"2022-04-19T06:59:25","date_gmt":"2022-04-19T06:59:25","guid":{"rendered":"https:\/\/mdr.foobrdigital.com\/?p=2510"},"modified":"2022-04-19T06:59:25","modified_gmt":"2022-04-19T06:59:25","slug":"pytorch-packages","status":"publish","type":"post","link":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/2022\/04\/19\/pytorch-packages\/","title":{"rendered":"PyTorch Packages"},"content":{"rendered":"\n

PyTorch is an optimized tensor library for deep learning using CPUs and GPUs. PyTorch has a rich set of packages which are used to perform deep learning concepts. These packages help us in optimization, conversion, and loss calculation, etc. Let’s get a brief knowledge of these packages.<\/p>\n\n\n\n

S.No<\/th>Name<\/th>Description<\/th><\/tr>
1.<\/td>Torch<\/td>The torch package includes data structure for multi-dimensional tensors and mathematical operation over these are defined.<\/td><\/tr>
2.<\/td>torch.Tensor<\/td>This package is a multi-dimensional matrix which contains an element of a single data type.<\/td><\/tr>
3.<\/td>Tensor Attributes<\/td><\/td><\/tr>
a) torch.dtype<\/td>It is an object which represents the datatype of thetorch.Tensor.<\/td><\/tr>
b) torch.device<\/td>It is an object that represents the device on which torch.Tensor will be allocated.<\/td><\/tr>
c) torch.layout<\/td>It is an object which represents a memory layout of a toch.Tensor.<\/td><\/tr>
4.<\/td>Type Info<\/td>The numerical properties of a torch.dtype will be accessed through either the torch.iinfo or the torch.finfo.<\/td><\/tr>
1) torch.finfo<\/td>It is an object which represents the numerical properties of a floating-point torch.dtype.<\/td><\/tr>
2) torch.iinfo<\/td>It is an object which represents the numerical properties of an integer torch.dtype.<\/td><\/tr>
5.<\/td>torch.sparse<\/td>Torch supports sparse tensors in COO (rdinate) format, which will efficiently store and process tensors for which the majority of elements are zero.<\/td><\/tr>
6.<\/td>torch.cuda<\/td>Torch supports for CUDA tensor types which implement the same function as CPU tensors, but for computation they utilize GPUs.<\/td><\/tr>
7.<\/td>torch.Storage<\/td>A torch.Storage is a contiguous, one-dimensional array of a single data type.<\/td><\/tr>
8.<\/td>torch.nn<\/td>This package provides us many more classes and modules to implement and train the neural network.<\/td><\/tr>
9.<\/td>torch.nn.functional<\/td>This package has functional classes which are similar to torch.nn.<\/td><\/tr>
10.<\/td>torch.optim<\/td>This package is used to implement various optimization algorithm.<\/td><\/tr>
11.<\/td>torch.autogard<\/td>This package provides classes and functions to implement automatic differentiation of arbitrary scalar value functions.<\/td><\/tr>
12.<\/td>torch.distributed<\/td>This package supports three backends and each one is with different capabilities.<\/td><\/tr>
13.<\/td>torch.distribution<\/td>This package allows us to construct the stochastic computation graphs, and stochastic gradient estimators for optimization<\/td><\/tr>
14.<\/td>torch.hub<\/td>It is a pre-trained model repository which is designed to facilitate research reproducibility.<\/td><\/tr>
15.<\/td>torch.multiprocessing<\/td>It is a wrapper around the native multiprocessing module.<\/td><\/tr>
16.<\/td>torch.utils.bottleneck<\/td>It is a tool which can be used as an initial step for debugging bottlenecks in our program.<\/td><\/tr>
17.<\/td>torch.utils.checkpoint<\/td>It is used to create checkpoint in our source program.<\/td><\/tr>
18.<\/td>torch.tils.cpp_extension<\/td>It is used to create the extension of C++, CUDA, and other languages.<\/td><\/tr>
19.<\/td>torch.utils.data<\/td>This package is mainly used for creating the dataset.<\/td><\/tr>
20.<\/td>torch.utils.dlpack<\/td>It will use to decode the Dlpack into tensor.<\/td><\/tr>
21.<\/td>torch.onnx<\/td>The ONNX<\/strong> exporter is a trace-based exporter, which means that it operates by executing your model once and exporting the operators which were actually run during this run<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"

PyTorch is an optimized tensor library for deep learning using CPUs and GPUs. PyTorch has a rich set of packages which are used to perform deep learning concepts. These packages help us in optimization, conversion, and loss calculation, etc. Let’s get a brief knowledge of these packages. S.No Name Description 1. Torch The torch package […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[374],"tags":[],"_links":{"self":[{"href":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/wp-json\/wp\/v2\/posts\/2510"}],"collection":[{"href":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/wp-json\/wp\/v2\/comments?post=2510"}],"version-history":[{"count":0,"href":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/wp-json\/wp\/v2\/posts\/2510\/revisions"}],"wp:attachment":[{"href":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/wp-json\/wp\/v2\/media?parent=2510"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/wp-json\/wp\/v2\/categories?post=2510"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/wp-json\/wp\/v2\/tags?post=2510"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}