{"Package":"torch","Type":"Package","Title":"Tensors and Neural Networks with 'GPU' Acceleration","Version":"0.2.1","Authors@R":"c(\nperson(\"Daniel\", \"Falbel\", email = \"daniel@rstudio.com\", role = c(\"aut\", \"cre\", \"cph\")),\nperson(\"Javier\", \"Luraschi\", email = \"jluraschi@gmail.com\", role = c(\"aut\")),\nperson(\"Dmitriy\", \"Selivanov\", role = c(\"ctb\")),\nperson(\"Athos\", \"Damiani\", role = c(\"ctb\")),\nperson(family = \"RStudio\", role = c(\"cph\"))\n)","Description":"Provides functionality to define and train neural networks similar to\n'PyTorch' by Paszke et al (2019) but written entirely in R\nusing the 'libtorch' library. Also supports low-level tensor operations and\n'GPU' acceleration.","License":"MIT + file LICENSE","URL":"https://torch.mlverse.org/docs, https://github.com/mlverse/torch","BugReports":"https://github.com/mlverse/torch/issues","Encoding":"UTF-8","LazyData":"true","SystemRequirements":"C++11, LibTorch (https://pytorch.org/)","LinkingTo":"Rcpp","Imports":"Rcpp, R6, withr, rlang, methods, utils, stats, bit64,\nmagrittr, tools, coro, callr, cli","RoxygenNote":"7.1.1","Suggests":"testthat (>= 3.0.0), covr, knitr, rmarkdown, glue,\npalmerpenguins","VignetteBuilder":"knitr","Collate":"'R7.R' 'RcppExports.R' 'tensor.R' 'autograd.R' 'backends.R'\n'codegen-utils.R' 'conditions.R' 'creation-ops.R' 'cuda.R'\n'device.R' 'dimname_list.R' 'distributions-constraints.R'\n'utils.R' 'distributions-exp-family.R' 'distributions.R'\n'dtype.R' 'gen-method.R' 'gen-namespace-docs.R'\n'gen-namespace-examples.R' 'gen-namespace.R' 'generator.R'\n'help.R' 'indexing.R' 'install.R' 'lantern_load.R'\n'lantern_sync.R' 'layout.R' 'memory_format.R' 'utils-data.R'\n'nn.R' 'nn-activation.R' 'nn-batchnorm.R' 'nn-conv.R'\n'nn-distance.R' 'nn-dropout.R' 'nn-init.R' 'nn-linear.R'\n'nn-loss.R' 'nn-pooling.R' 'nn-rnn.R' 'nn-sparse.R'\n'nn-utils-clip-grad.R' 'nn-utils-rnn.R' 'nn-utils.R'\n'nn_adaptive.R' 'nnf-activation.R' 'nnf-batchnorm.R'\n'nnf-conv.R' 'nnf-distance.R' 'nnf-dropout.R' 'nnf-embedding.R'\n'nnf-fold.R' 'nnf-instancenorm.R' 'nnf-linear.R' 'nnf-loss.R'\n'nnf-normalization.R' 'nnf-padding.R' 'nnf-pixelshuffle.R'\n'nnf-pooling.R' 'nnf-upsampling.R' 'nnf-vision.R' 'operators.R'\n'optim.R' 'optim-adadelta.R' 'optim-adagrad.R' 'optim-adam.R'\n'optim-asgd.R' 'optim-lbfgs.R' 'optim-lr_scheduler.R'\n'optim-rmsprop.R' 'optim-rprop.R' 'optim-sgd.R' 'package.R'\n'qscheme.R' 'quantization.R' 'reduction.R' 'save.R' 'scalar.R'\n'stack.R' 'storage.R' 'tensor_list.R' 'tensor_options.R'\n'threads.R' 'trace.R' 'type-info.R' 'utils-data-collate.R'\n'utils-data-dataloader.R' 'utils-data-enum.R'\n'utils-data-fetcher.R' 'utils-data-sampler.R' 'utils-pipe.R'\n'variable_list.R' 'with-indices.R' 'wrapers.R'","NeedsCompilation":"yes","Packaged":"2021-01-05 12:32:10 UTC; dfalbel","Author":"Daniel Falbel [aut, cre, cph],\nJavier Luraschi [aut],\nDmitriy Selivanov [ctb],\nAthos Damiani [ctb],\nRStudio [cph]","Maintainer":"Daniel Falbel ","Repository":"CRAN","Date/Publication":"2021-01-05 20:00:07 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/torch_0.2.1.tar.gz","jsonAuthors":[{"name":"Daniel Falbel","email":"daniel@rstudio.com","maintainer":true},{"name":"Javier Luraschi","email":"jluraschi@gmail.com","maintainer":false},{"name":"Dmitriy Selivanov","email":null,"maintainer":false},{"name":"Athos Damiani","email":null,"maintainer":false},{"name":" RStudio","email":null,"maintainer":false}],"readme":"\n\n\n# torch

\n\n[![Lifecycle:\nexperimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)\n![R build\nstatus](https://github.com/mlverse/torch/workflows/Test/badge.svg)\n[![CRAN\nstatus](https://www.r-pkg.org/badges/version/torch)](https://CRAN.R-project.org/package=torch)\n[![](https://cranlogs.r-pkg.org/badges/torch)](https://cran.r-project.org/package=torch)\n\n## Installation\n\ntorch can be installed from CRAN with:\n\n``` r\ninstall.packages(\"torch\")\n```\n\nYou can also install the development version with:\n\n``` r\nremotes::install_github(\"mlverse/torch\")\n```\n\nAt the first package load additional software will be installed.\n\n## Installation with Docker\n\nIf you would like to install with Docker, please read following\ndocument.\n\n - [The way of installation with\n Docker](https://github.com/mlverse/torch/blob/master/docker/build_env_guide.md)\n\n## Examples\n\nYou can create torch tensors from R objects with the `torch_tensor`\nfunction and convert them back to R objects with `as_array`.\n\n``` r\nlibrary(torch)\nx <- array(runif(8), dim = c(2, 2, 2))\ny <- torch_tensor(x, dtype = torch_float64())\ny\n#> torch_tensor\n#> (1,.,.) = \n#> 0.9192 0.4962\n#> 0.3191 0.3114\n#> \n#> (2,.,.) = \n#> 0.7482 0.7318\n#> 0.6661 0.8189\n#> [ CPUDoubleType{2,2,2} ]\nidentical(x, as_array(y))\n#> [1] TRUE\n```\n\n### Simple Autograd Example\n\nIn the following snippet we let torch, using the autograd feature,\ncalculate the derivatives:\n\n``` r\nx <- torch_tensor(1, requires_grad = TRUE)\nw <- torch_tensor(2, requires_grad = TRUE)\nb <- torch_tensor(3, requires_grad = TRUE)\ny <- w * x + b\ny$backward()\nx$grad\n#> torch_tensor\n#> 2\n#> [ CPUFloatType{1} ]\nw$grad\n#> torch_tensor\n#> 1\n#> [ CPUFloatType{1} ]\nb$grad\n#> torch_tensor\n#> 1\n#> [ CPUFloatType{1} ]\n```\n\n## Contributing\n\nNo matter your current skills it’s possible to contribute to `torch`\ndevelopment. See the [contributing\nguide](https://github.com/mlverse/torch/blob/master/.github/CONTRIBUTING.md)\nfor more information.","jobInfo":{"package":"torch","version":"0.2.1","parsingStatus":"success","parserVersion":1,"parsedAt":"2021-04-28T15:30:22+0000"}}

Description

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