Merge branch 'rc1' into rc1

MatthewInkawhich · web-flow · commit ae949d605409 · 2018-09-18T17:07:22.000-04:00
diff --git a/.jenkins/build.sh b/.jenkins/build.sh
@@ -3,19 +3,25 @@ sudo apt-get install -y --no-install-recommends unzip p7zip-full sox libsox-dev
 
 # Install a nightly build of pytorch
 
-# GPU, requires CUDA version 9.0 and python version 3.6
-pip install cython torch_nightly -f https://download.pytorch.org/whl/nightly/cu90/torch_nightly-2018.8.28.dev1-cp36-cp36m-linux_x86_64.whl
+# GPU, requires CUDA version 8.0
+pip install cython torch_nightly -f https://download.pytorch.org/whl/nightly/cu80/torch_nightly.html
+
+# GPU, requires CUDA version 9.0
+# pip install cython torch_nightly -f https://download.pytorch.org/whl/nightly/cu90/torch_nightly.html
+
+# GPU, requires CUDA version 9.2
+# pip install cython torch_nightly -f https://download.pytorch.org/whl/nightly/cu92/torch_nightly.html
 
 # CPU
-# pip install cython torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly-2018.8.24.dev1-cp36-cp36m-linux_x86_64.whl
+# pip install cython torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
 
 ## Install doc dependencies
 
 export PATH=/opt/conda/bin:$PATH
 # pillow >= 4.2 will throw error when trying to write mode RGBA as JPEG,
 # this is a workaround to the issue.
 conda install -y sphinx pandas pillow=4.1.1
-pip install sphinx-gallery sphinx_rtd_theme tqdm matplotlib ipython
+pip install sphinx-gallery pytorch_sphinx_theme tqdm matplotlib ipython
 
 git clone https://github.com/pytorch/vision --quiet
 pushd vision
diff --git a/_static/img/hybrid.png b/_static/img/hybrid.png
diff --git a/advanced_source/cpp_export.rst b/advanced_source/cpp_export.rst
@@ -1,6 +1,9 @@
-Loading a PyTorch model in C++
+Loading a PyTorch Model in C++
 ==============================
 
+This tutorial requires PyTorch 1.0 (preview) or later. For installation information 
+visit http://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
 many scenarios requiring dynamism and ease of iteration, there are equally many
diff --git a/beginner_source/chatbot_tutorial.py b/beginner_source/chatbot_tutorial.py
@@ -7,6 +7,9 @@
 
 
 ######################################################################
+# This example requires PyTorch 1.0 (preview) or later. 
+# For installation information visit http://pytorch.org/get-started.
+# 
 # In this tutorial, we explore a fun and interesting use-case of recurrent
 # sequence-to-sequence models. We will train a simple chatbot using movie
 # scripts from the `Cornell Movie-Dialogs
diff --git a/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py b/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py
@@ -7,17 +7,17 @@
 
 
 ######################################################################
+# This example requires PyTorch 1.0 (preview) or later. For installation 
+# information visit http://pytorch.org/get-started.
+#
 # This tutorial will walk through the process of transitioning a
 # sequence-to-sequence model to Torch Script using PyTorch’s Hybrid
 # Frontend. The model that we will convert is the chatbot model from the
-# `Chatbot tutorial <>`__. While the `introductory hybrid frontend
-# tutorials <>`__ are useful for gaining an understanding of the
-# work-flow, purpose, and basic syntax of the feature, this document
-# covers a more challenging model and a more practical use-case. You can
-# either treat this tutorial as a “Part 2” to the `Chatbot tutorial <>`__
+# `Chatbot tutorial <https://pytorch.org/tutorials/beginner/chatbot_tutorial.html>`__. 
+# You can either treat this tutorial as a “Part 2” to the Chatbot tutorial
 # and deploy your own pretrained model, or you can start with this
 # document and use a pretrained model that we host. In the latter case,
-# you can reference the original `Chatbot tutorial <>`__ for details
+# you can reference the original Chatbot tutorial for details
 # regarding data preprocessing, model theory and definition, and model
 # training.
 #
@@ -117,10 +117,9 @@
 import unicodedata
 import numpy as np
 
-
-USE_CUDA = torch.cuda.is_available()
 device = torch.device("cpu")
 
+
 MAX_LENGTH = 10  # Maximum sentence length
 
 # Default word tokens
@@ -728,10 +727,9 @@ def evaluateExample(sentence, encoder, decoder, searcher, voc):
 dropout = 0.1
 batch_size = 64
 
+# If you're loading your own model
 # Set checkpoint to load from
 checkpoint_iter = 4000
-
-# If you're loading your own model
 # loadFilename = os.path.join(save_dir, model_name, corpus_name,
 #                             '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size),
 #                             '{}_checkpoint.tar'.format(checkpoint_iter))
diff --git a/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py b/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py
@@ -4,6 +4,9 @@
 ===============================================
 **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.
+
 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
 new shiny features of Pytorch 1.0 and provides an avenue for developers
diff --git a/index.rst b/index.rst
@@ -43,9 +43,9 @@ Getting Started
    :description: :doc:`beginner/transfer_learning_tutorial`
 
 .. customgalleryitem::
-   :figure: /_static/img/hybrid_frontend/220px-KnnClassification.png
+   :figure: /_static/img/hybrid.png
    :tooltip: Experiment with some of the key features of the PyTorch hybrid frontend
-   :description: :doc:`beginner/hybrid_frontend_tutorial`
+   :description: :doc:`beginner/deploy_seq2seq_hybrid_frontend_tutorial`
 
 .. customgalleryitem::
    :figure: /_static/img/thumbnails/floppy.png
@@ -214,7 +214,7 @@ Production Usage
    beginner/data_loading_tutorial
    beginner/pytorch_with_examples
    beginner/transfer_learning_tutorial
-   beginner/hybrid_frontend_tutorial
+   beginner/deploy_seq2seq_hybrid_frontend_tutorial   
    beginner/saving_loading_models
 
 .. toctree::
