_pkgdown.yml

destination: docs
template:
  params:
    bootswatch: united
    ganalytics: UA-178883486-1
    
development:
  mode: auto

navbar:
 structure:
  left:  [home, getting-started, articles, examples, reference, news]
  right: [github]
 components:
  getting-started:
    text: Getting started
    menu:
      - text: Beginers guide
      - text: Warm-up
        href: articles/getting-started/warmup.html
      - text: Tensors
        href: articles/getting-started/tensors.html  
      - text: Tensors and autograd
        href: articles/getting-started/tensors-and-autograd.html
      - text: Defining new autograd functions
        href: articles/getting-started/new-autograd-functions.html
      - text: 'nn: neural networks with torch'
        href: articles/getting-started/nn.html
      - text: 'optim: optimizers in torch'
        href: articles/getting-started/optim.html
      - text: Custom nn modules
        href: articles/getting-started/custom-nn.html
      - text: Control flow & Weight sharing
        href: articles/getting-started/control-flow-and-weight-sharing.html
      - text: Torch Mechanics
      - text: What's torch?
        href: articles/getting-started/what-is-torch.html
      - text: 'Autograd: automatic differentiation'
        href: articles/getting-started/autograd.html
      - text: Neural networks
        href: articles/getting-started/neural-networks.html
  articles:
    text: Articles
    menu:
     - text: Installation
       href: articles/installation.html
     - text: Tensors
     - text: Creating tensors
       href: articles/tensor-creation.html
     - text: Indexing
       href: articles/indexing.html
     - text: Tensor class
       href: articles/tensor/index.html
     - text: Serialization
       href: articles/serialization.html
     - text: Datasets
     - text: Loading Data
       href: articles/loading-data.html
     - text: Autograd
     - text: Using autograd
       href: articles/using-autograd.html
     - text: Extending autograd
       href: articles/extending-autograd.html
  examples:
    text: Examples
    menu:
      - text: basic-autograd
        href: articles/examples/basic-autograd.html
      - text: basic-nn-module
        href: articles/examples/basic-nn-module.html
      - text: dataset
        href: articles/examples/dataset.html    
   
reference:  
  - title: Tensor creation utilities
    contents: 
      - torch_empty
      - torch_arange
      - torch_eye
      - torch_full
      - torch_linspace
      - torch_logspace
      - torch_ones
      - torch_rand
      - torch_randint
      - torch_randn
      - torch_randperm
      - torch_zeros
      - matches("torch_.*_like")
      - as_array
  - title: Tensor attributes
    contents:
      - has_concept("tensor-attributes")
      - is_torch_layout
      - is_torch_memory_format
      - is_torch_qscheme
      - is_undefined_tensor
  - title: Serialization
    contents:
      - has_concept("serialization")
  - title: Mathematical operations on tensors
    contents:
      - starts_with("torch_")
      - -torch_empty
      - -torch_arange
      - -torch_eye
      - -torch_full
      - -torch_linspace
      - -torch_logspace
      - -torch_ones
      - -torch_rand
      - -torch_randint
      - -torch_randn
      - -torch_randperm
      - -torch_zeros
      - -matches("torch_.*_like")
      - -has_concept("tensor-attributes")
      - -has_concept("serialization")
  - title: Neural network modules
    contents:
      - starts_with("nn_")
      - is_nn_module
      - is_nn_parameter
      - is_nn_buffer
  - title: Neural networks functional module
    contents:
      - starts_with("nnf_")
  - title: Optimizers
    contents:
      - starts_with("optim_")
      - is_optimizer
  - title: Learning rate schedulers
    contents:
      - starts_with("lr_")
  - title: Datasets
    contents: 
      - starts_with("dataset")
      - starts_with("dataloader")
      - starts_with("enumerate")
      - tensor_dataset
      - is_dataloader
  - title: Autograd
    contents:
      - starts_with("autograd_")
      - with_no_grad
      - with_enable_grad
      - AutogradContext
  - title: Cuda utilities
    contents:
      - starts_with("cuda_")
  - title: Installation
    contents:
      - install_torch