DESCRIPTION

Package: mlr3torch
Title: Deep Learning with 'mlr3'
Version: 0.1.2-9000
Authors@R:
    c(person(given = "Sebastian",
             family = "Fischer",
             role = c("cre", "aut"),
             email = "sebf.fischer@gmail.com",
             comment = c(ORCID = "0000-0002-9609-3197")),
      person(given = "Bernd",
             family = "Bischl",
             role = "ctb",
             email = "bernd_bischl@gmx.net",
             comment = c(ORCID = "0000-0001-6002-6980")),
      person(given = "Lukas",
             family = "Burk",
             role = "ctb",
             email = "github@quantenbrot.de",
             comment = c(ORCID = "0000-0001-7528-3795")),
      person(given = "Martin",
             family = "Binder",
             role = "aut",
             email = "mlr.developer@mb706.com"),
      person(given = "Florian",
             family = "Pfisterer",
             role = "ctb",
             email = "pfistererf@googlemail.com",
             comment = c(ORCID = "0000-0001-8867-762X")),
      person(given = "Carson",
             family = "Zhang",
             role = "ctb",
             email = "carsonzhang4@gmail.com")
             )
Description: Deep Learning library that extends the mlr3 framework by building
  upon the 'torch' package. It allows to conveniently build, train,
  and evaluate deep learning models without having to worry about low level
  details. Custom architectures can be created using the graph language
  defined in 'mlr3pipelines'.
License: LGPL (>= 3)
BugReports: https://github.com/mlr-org/mlr3torch/issues
URL: https://mlr3torch.mlr-org.com/, https://github.com/mlr-org/mlr3torch/
Depends:
    mlr3 (>= 0.20.0),
    mlr3pipelines (>= 0.6.0),
    torch (>= 0.13.0),
    R (>= 3.5.0)
Imports:
    backports,
    checkmate (>= 2.2.0),
    data.table,
    lgr,
    methods,
    mlr3misc (>= 0.14.0),
    paradox (>= 1.0.0),
    R6,
	withr
Suggests:
    callr,
    future,
    ggplot2,
    igraph,
    jsonlite,
    knitr,
    mlr3tuning (>= 1.0.0),
    progress,
    rmarkdown,
    rpart,
    viridis,
    visNetwork,
    testthat (>= 3.0.0),
    tfevents,
    torchvision (>= 0.6.0),
    waldo
Config/testthat/edition: 3
NeedsCompilation: no
ByteCompile: yes
Encoding: UTF-8
Roxygen: list(markdown = TRUE, r6 = TRUE)
RoxygenNote: 7.3.2
Collate:
    'CallbackSet.R'
    'zzz.R'
    'TorchCallback.R'
    'CallbackSetCheckpoint.R'
    'CallbackSetEarlyStopping.R'
    'CallbackSetHistory.R'
    'CallbackSetProgress.R'
    'CallbackSetTB.R'
    'ContextTorch.R'
    'DataBackendLazy.R'
    'utils.R'
    'DataDescriptor.R'
    'LearnerTorch.R'
    'LearnerTorchFeatureless.R'
    'LearnerTorchImage.R'
    'LearnerTorchMLP.R'
    'task_dataset.R'
    'shape.R'
    'PipeOpTorchIngress.R'
    'LearnerTorchModel.R'
    'LearnerTorchTabResNet.R'
    'LearnerTorchVision.R'
    'ModelDescriptor.R'
    'PipeOpModule.R'
    'PipeOpTorch.R'
    'PipeOpTaskPreprocTorch.R'
    'PipeOpTorchActivation.R'
    'PipeOpTorchAdaptiveAvgPool.R'
    'PipeOpTorchAvgPool.R'
    'PipeOpTorchBatchNorm.R'
    'PipeOpTorchBlock.R'
    'PipeOpTorchCallbacks.R'
    'PipeOpTorchConv.R'
    'PipeOpTorchConvTranspose.R'
    'PipeOpTorchDropout.R'
    'PipeOpTorchHead.R'
    'PipeOpTorchLayerNorm.R'
    'PipeOpTorchLinear.R'
    'TorchLoss.R'
    'PipeOpTorchLoss.R'
    'PipeOpTorchMaxPool.R'
    'PipeOpTorchMerge.R'
    'PipeOpTorchModel.R'
    'PipeOpTorchOptimizer.R'
    'PipeOpTorchReshape.R'
    'PipeOpTorchSoftmax.R'
    'TaskClassif_lazy_iris.R'
    'TaskClassif_mnist.R'
    'TaskClassif_tiny_imagenet.R'
    'TorchDescriptor.R'
    'TorchOptimizer.R'
    'bibentries.R'
    'cache.R'
    'lazy_tensor.R'
    'learner_torch_methods.R'
    'materialize.R'
    'merge_graphs.R'
    'nn.R'
    'nn_graph.R'
    'paramset_torchlearner.R'
    'preprocess.R'
    'rd_info.R'
    'with_torch_settings.R'