Push latest changes from master into release/1.6

.gitignore

@@ -4,6 +4,7 @@ intermediate
 advanced
 pytorch_basics
 recipes
+prototype
 
 #data things
 _data/
@@ -117,3 +118,6 @@ ENV/
 .DS_Store
 cleanup.sh
 *.swp
+
+# PyTorch things
+*.pt

.jenkins/build.sh

@@ -86,6 +86,16 @@ if [[ "${JOB_BASE_NAME}" == *worker_* ]]; then
       FILES_TO_RUN+=($(basename $filename .py))
     fi
     count=$((count+1))
+   done
+   for filename in $(find prototype_source/ -name '*.py' -not -path '*/data/*'); do
+    if [ $(($count % $NUM_WORKERS)) != $WORKER_ID ]; then
+      echo "Removing runnable code from "$filename
+      python $DIR/remove_runnable_code.py $filename $filename
+    else
+      echo "Keeping "$filename
+      FILES_TO_RUN+=($(basename $filename .py))
+    fi
+    count=$((count+1))
   done
   echo "FILES_TO_RUN: " ${FILES_TO_RUN[@]}
 
@@ -94,13 +104,13 @@ if [[ "${JOB_BASE_NAME}" == *worker_* ]]; then
 
   # Step 4: If any of the generated files are not related the tutorial files we want to run,
   # then we remove them
-  for filename in $(find docs/beginner docs/intermediate docs/advanced docs/recipes -name '*.html'); do
+  for filename in $(find docs/beginner docs/intermediate docs/advanced docs/recipes docs/prototype -name '*.html'); do
     file_basename=$(basename $filename .html)
     if [[ ! " ${FILES_TO_RUN[@]} " =~ " ${file_basename} " ]]; then
       rm $filename
     fi
   done
-  for filename in $(find docs/beginner docs/intermediate docs/advanced docs/recipes -name '*.rst'); do
+  for filename in $(find docs/beginner docs/intermediate docs/advanced docs/recipes docs/prototype -name '*.rst'); do
     file_basename=$(basename $filename .rst)
     if [[ ! " ${FILES_TO_RUN[@]} " =~ " ${file_basename} " ]]; then
       rm $filename
@@ -124,7 +134,7 @@ if [[ "${JOB_BASE_NAME}" == *worker_* ]]; then
       rm $filename
     fi
   done
-  for filename in $(find docs/.doctrees/beginner docs/.doctrees/intermediate docs/.doctrees/advanced docs/.doctrees/recipes -name '*.doctree'); do
+  for filename in $(find docs/.doctrees/beginner docs/.doctrees/intermediate docs/.doctrees/advanced docs/.doctrees/recipes docs/.doctrees/prototype -name '*.doctree'); do
     file_basename=$(basename $filename .doctree)
     if [[ ! " ${FILES_TO_RUN[@]} " =~ " ${file_basename} " ]]; then
       rm $filename

README.md

@@ -14,8 +14,8 @@ We use sphinx-gallery's [notebook styled examples](https://sphinx-gallery.github
 Here's how to create a new tutorial or recipe:
 1. Create a notebook styled python file. If you want it executed while inserted into documentation, save the file with suffix `tutorial` so that file name is `your_tutorial.py`.
 2. Put it in one of the beginner_source, intermediate_source, advanced_source based on the level. If it is a recipe, add to recipes_source.
-2. For Tutorials, include it in the TOC tree at index.rst
-3. For Tutorials, create a thumbnail in the [index.rst file](https://github.com/pytorch/tutorials/blob/master/index.rst) using a command like `.. customcarditem:: beginner/your_tutorial.html`. For Recipes, create a thumbnail in the [recipes_index.rst](https://github.com/pytorch/tutorials/blob/master/recipes_source/recipes_index.rst)
+2. For Tutorials (except if it is a prototype feature), include it in the TOC tree at index.rst
+3. For Tutorials (except if it is a prototype feature), create a thumbnail in the [index.rst file](https://github.com/pytorch/tutorials/blob/master/index.rst) using a command like `.. customcarditem:: beginner/your_tutorial.html`. For Recipes, create a thumbnail in the [recipes_index.rst](https://github.com/pytorch/tutorials/blob/master/recipes_source/recipes_index.rst)
 
 In case you prefer to write your tutorial in jupyter, you can use [this script](https://gist.github.com/chsasank/7218ca16f8d022e02a9c0deb94a310fe) to convert the notebook to python file. After conversion and addition to the project, please make sure the sections headings etc are in logical order.
 

advanced_source/dynamic_quantization_tutorial.py

@@ -1,5 +1,5 @@
 """
-(experimental) Dynamic Quantization on an LSTM Word Language Model
+(beta) Dynamic Quantization on an LSTM Word Language Model
 ==================================================================
 
 **Author**: `James Reed <https://github.com/jamesr66a>`_
@@ -13,7 +13,7 @@
 to int, which can result in smaller model size and faster inference with only a small
 hit to accuracy.
 
-In this tutorial, we'll apply the easiest form of quantization - 
+In this tutorial, we'll apply the easiest form of quantization -
 `dynamic quantization <https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic>`_ -
 to an LSTM-based next word-prediction model, closely following the
 `word language model <https://github.com/pytorch/examples/tree/master/word_language_model>`_

advanced_source/neural_style_tutorial.py

@@ -83,7 +83,7 @@
 # An important detail to note is that neural networks from the
 # torch library are trained with tensor values ranging from 0 to 1. If you
 # try to feed the networks with 0 to 255 tensor images, then the activated
-# feature maps will be unable sense the intended content and style.
+# feature maps will be unable to sense the intended content and style.
 # However, pre-trained networks from the Caffe library are trained with 0
 # to 255 tensor images. 
 #

advanced_source/static_quantization_tutorial.py

@@ -1,5 +1,5 @@
 """
-(experimental) Static Quantization with Eager Mode in PyTorch
+(beta) Static Quantization with Eager Mode in PyTorch
 =========================================================
 
 **Author**: `Raghuraman Krishnamoorthi <https://github.com/raghuramank100>`_