http to https for pytorch.org in the tutorial documentation

Ref pytorch/pytorch#13951

advanced_source/README.txt

@@ -3,16 +3,16 @@ Advanced Tutorials
 
 1. neural_style_tutorial.py
 	Neural Transfer with PyTorch
-	http://pytorch.org/tutorials/advanced/neural_style_tutorial.html
+	https://pytorch.org/tutorials/advanced/neural_style_tutorial.html
 
 2. numpy_extensions_tutorial.py
 	Creating Extensions Using numpy and scipy
-	http://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html
+	https://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html
 
 3. c_extension.rst
 	Custom C Extensions for PyTorch
-	http://pytorch.org/tutorials/advanced/c_extension.html
+	https://pytorch.org/tutorials/advanced/c_extension.html
 
 4. super_resolution_with_caffe2.py
 	Transfering a Model from PyTorch to Caffe2 and Mobile using ONNX
-	http://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html
+	https://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html

advanced_source/cpp_export.rst

@@ -2,7 +2,7 @@ Loading a PyTorch Model in C++
 ==============================
 
 .. attention:: This tutorial requires PyTorch 1.0 (preview) or later.
-   For installation information visit http://pytorch.org/get-started.
+   For installation information visit https://pytorch.org/get-started.
 
 As its name suggests, the primary interface to PyTorch is the Python
 programming language. While Python is a suitable and preferred language for

advanced_source/cpp_extension.rst

@@ -11,7 +11,7 @@ you developed as part of your research.
 
 The easiest way of integrating such a custom operation in PyTorch is to write it
 in Python by extending :class:`Function` and :class:`Module` as outlined `here
-<http://pytorch.org/docs/master/notes/extending.html>`_. This gives you the full
+<https://pytorch.org/docs/master/notes/extending.html>`_. This gives you the full
 power of automatic differentiation (spares you from writing derivative
 functions) as well as the usual expressiveness of Python. However, there may be
 times when your operation is better implemented in C++. For example, your code

advanced_source/neural_style_tutorial.py

@@ -90,8 +90,8 @@
 #
 # .. Note::
 #     Here are links to download the images required to run the tutorial:
-#     `picasso.jpg <http://pytorch.org/tutorials/_static/img/neural-style/picasso.jpg>`__ and
-#     `dancing.jpg <http://pytorch.org/tutorials/_static/img/neural-style/dancing.jpg>`__.
+#     `picasso.jpg <https://pytorch.org/tutorials/_static/img/neural-style/picasso.jpg>`__ and
+#     `dancing.jpg <https://pytorch.org/tutorials/_static/img/neural-style/dancing.jpg>`__.
 #     Download these two images and add them to a directory
 #     with name ``images`` in your current working directory.
 

advanced_source/super_resolution_with_caffe2.py

@@ -103,7 +103,7 @@ def _initialize_weights(self):
 # or a random tensor as long as it is the right size.
 #
 # To learn more details about PyTorch's export interface, check out the
-# `torch.onnx documentation <http://pytorch.org/docs/master/onnx.html>`__.
+# `torch.onnx documentation <https://pytorch.org/docs/master/onnx.html>`__.
 #
 
 # Input to the model

beginner_source/README.txt

@@ -3,20 +3,20 @@ Beginner Tutorials
 
 1. blitz/* and deep_learning_60min_blitz.rst
 	Deep Learning with PyTorch: A 60 Minute Blitz
-	http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html
+	https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html
 
 2. former_torches/* and former_torchies_tutorial.rst
 	PyTorch for Former Torch Users
-	http://pytorch.org/tutorials/beginner/former_torchies_tutorial.html
+	https://pytorch.org/tutorials/beginner/former_torchies_tutorial.html
 
 3. examples_*/* and pytorch_with_examples.rst
 	Learning PyTorch with Examples
-	http://pytorch.org/tutorials/beginner/pytorch_with_examples.html
+	https://pytorch.org/tutorials/beginner/pytorch_with_examples.html
 
 4. transfer_learning_tutorial.py
 	Transfer Learning Tutorial
-	http://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
+	https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
 
 5. nlp/* and deep_learning_nlp_tutorial.rst
 	Deep Learning for NLP with Pytorch
-	http://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html
+	https://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html

beginner_source/blitz/README.txt

@@ -3,22 +3,22 @@ Deep Learning with PyTorch: A 60 Minute Blitz
 
 1. tensor_tutorial.py
 	What is PyTorch?
-	http://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html
+	https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html
 
 2. autograd_tutorial.py
 	Autograd: Automatic Differentiation
-	http://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html
+	https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html
 
 3. neural_networks_tutorial.py
 	Neural Networks
-	http://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#
+	https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#
 
 4. autograd_tutorial.py
 	Automatic Differentiation 
 	https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html
 
 5. cifar10_tutorial.py
 	Training a Classifier
-	http://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
+	https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
 
 

beginner_source/blitz/autograd_tutorial.py

@@ -140,4 +140,4 @@
 # **Read Later:**
 #
 # Documentation of ``autograd`` and ``Function`` is at
-# http://pytorch.org/docs/autograd
+# https://pytorch.org/docs/autograd

beginner_source/blitz/cifar10_tutorial.py

@@ -331,4 +331,4 @@ def forward(self, x):
 # .. _More examples: https://github.com/pytorch/examples
 # .. _More tutorials: https://github.com/pytorch/tutorials
 # .. _Discuss PyTorch on the Forums: https://discuss.pytorch.org/
-# .. _Chat with other users on Slack: http://pytorch.slack.com/messages/beginner/
+# .. _Chat with other users on Slack: https://pytorch.slack.com/messages/beginner/

beginner_source/blitz/data_parallel_tutorial.py

@@ -251,5 +251,5 @@ def forward(self, input):
 # collects and merges the results before returning it to you.
 #
 # For more information, please check out
-# http://pytorch.org/tutorials/beginner/former\_torchies/parallelism\_tutorial.html.
+# https://pytorch.org/tutorials/beginner/former\_torchies/parallelism\_tutorial.html.
 #

beginner_source/blitz/neural_networks_tutorial.py

@@ -148,7 +148,7 @@ def num_flat_features(self, x):
 # value that estimates how far away the output is from the target.
 #
 # There are several different
-# `loss functions <http://pytorch.org/docs/nn.html#loss-functions>`_ under the
+# `loss functions <https://pytorch.org/docs/nn.html#loss-functions>`_ under the
 # nn package .
 # A simple loss is: ``nn.MSELoss`` which computes the mean-squared error
 # between the input and the target.
@@ -214,7 +214,7 @@ def num_flat_features(self, x):
 #
 #   The neural network package contains various modules and loss functions
 #   that form the building blocks of deep neural networks. A full list with
-#   documentation is `here <http://pytorch.org/docs/nn>`_.
+#   documentation is `here <https://pytorch.org/docs/nn>`_.
 #
 # **The only thing left to learn is:**
 #

beginner_source/blitz/tensor_tutorial.py

@@ -124,7 +124,7 @@
 #   100+ Tensor operations, including transposing, indexing, slicing,
 #   mathematical operations, linear algebra, random numbers, etc.,
 #   are described
-#   `here <http://pytorch.org/docs/torch>`_.
+#   `here <https://pytorch.org/docs/torch>`_.
 #
 # NumPy Bridge
 # ------------

beginner_source/chatbot_tutorial.py

@@ -14,7 +14,7 @@
 # Corpus <https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html>`__.
 #
 # .. attention:: This example requires PyTorch 1.0 (preview) or later.
-#    For installation information visit http://pytorch.org/get-started.
+#    For installation information visit https://pytorch.org/get-started.
 #
 # Conversational models are a hot topic in artificial intelligence
 # research. Chatbots can be found in a variety of settings, including

beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py

@@ -19,7 +19,7 @@
 # training.
 #
 # .. attention:: This example requires PyTorch 1.0 (preview) or later.
-#    For installation information visit http://pytorch.org/get-started.
+#    For installation information visit https://pytorch.org/get-started.
 #
 # What is the Hybrid Frontend?
 # ----------------------------

beginner_source/former_torchies/README.txt

@@ -3,16 +3,16 @@
 
 1. tensor_tutorial.py
 	Tensors
-	http://pytorch.org/tutorials/beginner/former_torchies/tensor_tutorial.html
+	https://pytorch.org/tutorials/beginner/former_torchies/tensor_tutorial.html
 
 2. autograd.py
 	Autograd
-	http://pytorch.org/tutorials/beginner/former_torchies/autograd_tutorial.html
+	https://pytorch.org/tutorials/beginner/former_torchies/autograd_tutorial.html
 
 3. nn_tutorial.py
 	nn package
-	http://pytorch.org/tutorials/beginner/former_torchies/nn_tutorial.html
+	https://pytorch.org/tutorials/beginner/former_torchies/nn_tutorial.html
 
 4. parallelism_tutorial.py
 	Multi-GPU examples
-	http://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html
+	https://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html

beginner_source/former_torchies/parallelism_tutorial.py

@@ -40,7 +40,7 @@ def forward(self, x):
 # The code does not need to be changed in CPU-mode.
 #
 # The documentation for DataParallel can be found
-# `here <http://pytorch.org/docs/nn.html#dataparallel>`_.
+# `here <https://pytorch.org/docs/nn.html#dataparallel>`_.
 #
 # **Primitives on which DataParallel is implemented upon:**
 #
@@ -125,4 +125,4 @@ def forward(self, x):
 # .. _More examples: https://github.com/pytorch/examples
 # .. _More tutorials: https://github.com/pytorch/tutorials
 # .. _Discuss PyTorch on the Forums: https://discuss.pytorch.org/
-# .. _Chat with other users on Slack: http://pytorch.slack.com/messages/beginner/
+# .. _Chat with other users on Slack: https://pytorch.slack.com/messages/beginner/

beginner_source/hybrid_frontend/README.txt

@@ -3,8 +3,8 @@
 
 1. learning_hybrid_frontend_through_example_tutorial.py
 	Learning Hybrid Frontend Through Example
-	http://pytorch.org/tutorials/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.html
+	https://pytorch.org/tutorials/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.html
 
 2. introduction_to_hybrid_frontend_tutorial.py
 	Introduction to Hybrid Frontend
-	http://pytorch.org/tutorials/beginner/hybrid_frontend/introduction_to_hybrid_frontend_tutorial.html
+	https://pytorch.org/tutorials/beginner/hybrid_frontend/introduction_to_hybrid_frontend_tutorial.html

beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py

@@ -5,7 +5,7 @@
 **Author:** `Nathan Inkawhich <https://github.com/inkawhich>`_
 
 This tutorial requires PyTorch 1.0 (preview) or later. For installation 
-information visit http://pytorch.org/get-started.
+information visit https://pytorch.org/get-started.
 
 This document is meant to highlight the syntax of the Hybrid Frontend
 through a non-code intensive example. The Hybrid Frontend is one of the

beginner_source/nlp/README.txt

@@ -3,20 +3,20 @@ Deep Learning for NLP with Pytorch
 
 1. pytorch_tutorial.py
 	Introduction to PyTorch
-	http://pytorch.org/tutorials/beginner/nlp/pytorch_tutorial.html
+	https://pytorch.org/tutorials/beginner/nlp/pytorch_tutorial.html
 
 2. deep_learning_tutorial.py
 	Deep Learning with PyTorch
-	http://pytorch.org/tutorials/beginner/nlp/deep_learning_tutorial.html
+	https://pytorch.org/tutorials/beginner/nlp/deep_learning_tutorial.html
 
 3. word_embeddings_tutorial.py
 	Word Embeddings: Encoding Lexical Semantics
-	http://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html
+	https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html
 
 4. sequence_models_tutorial.py
 	Sequence Models and Long-Short Term Memory Networks
-	http://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html
+	https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html
 
 5. advanced_tutorial.py
 	Advanced: Making Dynamic Decisions and the Bi-LSTM CRF
-	http://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html
+	https://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html

beginner_source/nlp/pytorch_tutorial.py

@@ -102,7 +102,7 @@
 
 
 ######################################################################
-# See `the documentation <http://pytorch.org/docs/torch.html>`__ for a
+# See `the documentation <https://pytorch.org/docs/torch.html>`__ for a
 # complete list of the massive number of operations available to you. They
 # expand beyond just mathematical operations.
 #

beginner_source/transfer_learning_tutorial.py

@@ -289,7 +289,7 @@ def visualize_model(model, num_images=6):
 # gradients are not computed in ``backward()``.
 #
 # You can read more about this in the documentation
-# `here <http://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward>`__.
+# `here <https://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward>`__.
 #
 
 model_conv = torchvision.models.resnet18(pretrained=True)

index.rst

@@ -18,7 +18,7 @@ Some considerations:
 * If you would like the tutorials section improved, please open a github issue
   `here <https://github.com/pytorch/tutorials>`_ with your feedback.
 
-Lastly, some of the tutorials are marked as requiring the *Preview release*. These are tutorials that use the new functionality from the PyTorch 1.0 Preview. Please visit the `Get Started <http://pytorch.org/get-started>`_ section of the PyTorch website for instructions on how to install the latest Preview build before trying these tutorials.
+Lastly, some of the tutorials are marked as requiring the *Preview release*. These are tutorials that use the new functionality from the PyTorch 1.0 Preview. Please visit the `Get Started <https://pytorch.org/get-started>`_ section of the PyTorch website for instructions on how to install the latest Preview build before trying these tutorials.
 
 Getting Started
 ------------------

intermediate_source/README.txt

@@ -3,24 +3,24 @@ Intermediate tutorials
 
 1. char_rnn_classification_tutorial.py
 	Classifying Names with a Character-Level RNN
-	http://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html
+	https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html
 
 2. char_rnn_generation_tutorial.py
 	Generating Names with a Character-Level RNN
-	http://pytorch.org/tutorials/intermediate/char_rnn_generation_tutorial.html
+	https://pytorch.org/tutorials/intermediate/char_rnn_generation_tutorial.html
 
 3. seq2seq_translation_tutorial.py
 	Translation with a Sequence to Sequence Network and Attention
-	http://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
+	https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
 
 4. reinforcement_q_learning.py
 	Reinforcement Learning (DQN) Tutorial
-	http://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
+	https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
 
 5. dist_tuto.rst
 	Writing Distributed Applications with PyTorch
-	http://pytorch.org/tutorials/intermediate/dist_tuto.html
+	https://pytorch.org/tutorials/intermediate/dist_tuto.html
 
 6. spatial_transformer_tutorial
 	Spatial Transformer Networks Tutorial
-	http://pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html
+	https://pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html

intermediate_source/char_rnn_classification_tutorial.py

@@ -32,7 +32,7 @@
 I assume you have at least installed PyTorch, know Python, and
 understand Tensors:
 
--  http://pytorch.org/ For installation instructions
+-  https://pytorch.org/ For installation instructions
 -  :doc:`/beginner/deep_learning_60min_blitz` to get started with PyTorch in general
 -  :doc:`/beginner/pytorch_with_examples` for a wide and deep overview
 -  :doc:`/beginner/former_torchies_tutorial` if you are former Lua Torch user
@@ -171,7 +171,7 @@ def lineToTensor(line):
 # as regular feed-forward layers.
 #
 # This RNN module (mostly copied from `the PyTorch for Torch users
-# tutorial <http://pytorch.org/tutorials/beginner/former_torchies/
+# tutorial <https://pytorch.org/tutorials/beginner/former_torchies/
 # nn_tutorial.html#example-2-recurrent-net>`__)
 # is just 2 linear layers which operate on an input and hidden state, with
 # a LogSoftmax layer after the output.

intermediate_source/char_rnn_generation_tutorial.py

@@ -42,7 +42,7 @@
 I assume you have at least installed PyTorch, know Python, and
 understand Tensors:
 
--  http://pytorch.org/ For installation instructions
+-  https://pytorch.org/ For installation instructions
 -  :doc:`/beginner/deep_learning_60min_blitz` to get started with PyTorch in general
 -  :doc:`/beginner/pytorch_with_examples` for a wide and deep overview
 -  :doc:`/beginner/former_torchies_tutorial` if you are former Lua Torch user

intermediate_source/dist_tuto.rst

@@ -255,7 +255,7 @@ GitHub repository <https://github.com/seba-1511/dist_tuto.pth/>`__.
 Now that we understand how the distributed module works, let us write
 something useful with it. Our goal will be to replicate the
 functionality of
-`DistributedDataParallel <http://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__.
+`DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__.
 Of course, this will be a didactic example and in a real-world
 situtation you should use the official, well-tested and well-optimized
 version linked above.
@@ -380,7 +380,7 @@ could train any model on a large computer cluster.
 lot more tricks <http://seba-1511.github.io/dist_blog>`__ required to
 implement a production-level implementation of synchronous SGD. Again,
 use what `has been tested and
-optimized <http://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__.
+optimized <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__.
 
 Our Own Ring-Allreduce
 ~~~~~~~~~~~~~~~~~~~~~~
@@ -424,7 +424,7 @@ an exercise left to the reader, there is still one difference between
 our version and the one in DeepSpeech: their implementation divide the
 gradient tensor into *chunks*, so as to optimally utilize the
 communication bandwidth. (Hint:
-`torch.chunk <http://pytorch.org/docs/stable/torch.html#torch.chunk>`__)
+`torch.chunk <https://pytorch.org/docs/stable/torch.html#torch.chunk>`__)
 
 Advanced Topics
 ---------------
@@ -447,7 +447,7 @@ there are currently three backends implemented in PyTorch: TCP, MPI, and
 Gloo. They each have different specifications and tradeoffs, depending
 on the desired use-case. A comparative table of supported functions can
 be found
-`here <http://pytorch.org/docs/stable/distributed.html#module-torch.distributed>`__. Note that a fourth backend, NCCL, has been added since the creation of this tutorial.  See `this section <https://pytorch.org/docs/stable/distributed.html#multi-gpu-collective-functions>`__ of the ``torch.distributed`` docs for more information about its use and value.
+`here <https://pytorch.org/docs/stable/distributed.html#module-torch.distributed>`__. Note that a fourth backend, NCCL, has been added since the creation of this tutorial.  See `this section <https://pytorch.org/docs/stable/distributed.html#multi-gpu-collective-functions>`__ of the ``torch.distributed`` docs for more information about its use and value.
 
 **TCP Backend**
 
@@ -552,7 +552,7 @@ Those methods allow you to define how this coordination is done.
 Depending on your hardware setup, one of these methods should be
 naturally more suitable than the others. In addition to the following
 sections, you should also have a look at the `official
-documentation <http://pytorch.org/docs/stable/distributed.html#initialization>`__.
+documentation <https://pytorch.org/docs/stable/distributed.html#initialization>`__.
 
 Before diving into the initialization methods, let's have a quick look
 at what happens behind ``init_process_group`` from the C/C++
@@ -673,7 +673,7 @@ multiple jobs to be scheduled on the same cluster.
 I'd like to thank the PyTorch developers for doing such a good job on
 their implementation, documentation, and tests. When the code was
 unclear, I could always count on the
-`docs <http://pytorch.org/docs/stable/distributed.html>`__ or the
+`docs <https://pytorch.org/docs/stable/distributed.html>`__ or the
 `tests <https://github.com/pytorch/pytorch/blob/master/test/test_distributed.py>`__
 to find an answer. In particular, I'd like to thank Soumith Chintala,
 Adam Paszke, and Natalia Gimelshein for providing insightful comments