Merge remote-tracking branch 'upstream/master' into mp

Author: mrshenli

.circleci/config.yml

@@ -6,11 +6,24 @@ install_official_git_client: &install_official_git_client
     sudo apt-get -qq update
     sudo apt-get -qq install openssh-client git
 
+# This system setup script is meant to run before the CI-related scripts, e.g.,
+# installing Git client, checking out code, setting up CI env, and
+# building/testing.
+setup_linux_system_environment: &setup_linux_system_environment
+  name: Set Up System Environment
+  no_output_timeout: "1h"
+  command: |
+    set -e
+     # Set up CircleCI GPG keys for apt, if needed
+    curl -L https://packagecloud.io/circleci/trusty/gpgkey | sudo apt-key add -
+
 pytorch_tutorial_build_defaults: &pytorch_tutorial_build_defaults
   machine:
     image: default
   steps:
   - checkout
+  - run:
+      <<: *setup_linux_system_environment
   - run:
       name: Set Up CI Environment
       no_output_timeout: "1h"
@@ -29,7 +42,8 @@ pytorch_tutorial_build_defaults: &pytorch_tutorial_build_defaults
           linux-image-generic \
           moreutils \
           nvidia-docker2 \
-          expect-dev
+          expect-dev \
+          docker-ce=18.06.2~ce~3-0~ubuntu
 
         sudo pkill -SIGHUP dockerd
 
@@ -51,15 +65,8 @@ pytorch_tutorial_build_defaults: &pytorch_tutorial_build_defaults
       command: |
         set -e
 
-        # Get latest PyTorch docker image version
-        curl -O https://raw.githubusercontent.com/pytorch/pytorch/master/.circleci/config.yml
-        while read line; do
-          if [[ "$line" == *PyTorchDockerVersion* ]]; then
-            export pyTorchDockerImageTag=${line:23}  # len("# PyTorchDockerVersion:") == 23
-            echo "PyTorchDockerImageTag: "${pyTorchDockerImageTag}
-            break
-          fi
-        done < config.yml
+        export pyTorchDockerImageTag=291
+        echo "PyTorchDockerImageTag: "${pyTorchDockerImageTag}
 
         cat >/home/circleci/project/ci_build_script.sh <<EOL
         # =================== The following code will be executed inside Docker container ===================
@@ -107,14 +114,14 @@ pytorch_tutorial_build_defaults: &pytorch_tutorial_build_defaults
 
 pytorch_tutorial_build_worker_defaults: &pytorch_tutorial_build_worker_defaults
   environment:
-    DOCKER_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda8-cudnn7-py3"
-    CUDA_VERSION: "8"
+    DOCKER_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda9-cudnn7-py3"
+    CUDA_VERSION: "9"
   resource_class: gpu.medium
   <<: *pytorch_tutorial_build_defaults
 
 pytorch_tutorial_build_manager_defaults: &pytorch_tutorial_build_manager_defaults
   environment:
-    DOCKER_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda8-cudnn7-py3"
+    DOCKER_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda9-cudnn7-py3"
   resource_class: medium
   <<: *pytorch_tutorial_build_defaults
 

.jenkins/build.sh

@@ -16,20 +16,6 @@ rm -rf src
 pip install -r $DIR/../requirements.txt
 pip uninstall -y torchvision || true
 
-# Install a nightly build of pytorch
-
-# 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.html
-
 export PATH=/opt/conda/bin:$PATH
 conda install -y sphinx==1.8.2 pandas
 # PyTorch Theme
@@ -53,11 +39,24 @@ popd
 
 aws configure set default.s3.multipart_threshold 5120MB
 
+if [[ $(pip show torch) ]]; then
+  # Clean up previous PyTorch installations
+  pip uninstall -y torch || true
+  pip uninstall -y torch || true
+fi
+
+# Install a nightly build of pytorch
+
+# GPU, requires CUDA version 9.0
+pip install cython torch_nightly -f https://download.pytorch.org/whl/nightly/cu90/torch_nightly.html
+
 # Decide whether to parallelize tutorial builds, based on $JOB_BASE_NAME
 export NUM_WORKERS=20
 if [[ "${JOB_BASE_NAME}" == *worker_* ]]; then
   # Step 1: Remove runnable code from tutorials that are not supposed to be run
   python $DIR/remove_runnable_code.py beginner_source/aws_distributed_training_tutorial.py beginner_source/aws_distributed_training_tutorial.py
+  # TODO: Fix bugs in these tutorials to make them runnable again
+  python $DIR/remove_runnable_code.py beginner_source/audio_classifier_tutorial.py beginner_source/audio_classifier_tutorial.py
 
   # Step 2: Keep certain tutorials based on file count, and remove runnable code in all other tutorials
   # IMPORTANT NOTE: We assume that each tutorial has a UNIQUE filename.

_templates/layout.html

@@ -12,5 +12,73 @@
   ga('send', 'pageview');
 
 </script>
+
+<script async src="https://www.googletagmanager.com/gtag/js?id=UA-117752657-2"></script>
+
+<script>
+
+  window.dataLayer = window.dataLayer || [];
+
+  function gtag(){dataLayer.push(arguments);}
+
+  gtag('js', new Date());
+  gtag('config', 'UA-117752657-2');
+
+</script>
+
+<script>
+  !function(f,b,e,v,n,t,s)
+  {if(f.fbq)return;n=f.fbq=function(){n.callMethod?
+  n.callMethod.apply(n,arguments):n.queue.push(arguments)};
+  if(!f._fbq)f._fbq=n;n.push=n;n.loaded=!0;n.version='2.0';
+  n.queue=[];t=b.createElement(e);t.async=!0;
+  t.src=v;s=b.getElementsByTagName(e)[0];
+  s.parentNode.insertBefore(t,s)}(window,document,'script',
+  'https://connect.facebook.net/en_US/fbevents.js');
+  fbq('init', '243028289693773');
+  fbq('track', 'PageView');
+
+  $("[data-behavior='call-to-action-event']").on('click', function(){
+    fbq('trackCustom', "Download", {
+      tutorialTitle: $('h1:first').text(),
+      downloadLink: this.href,
+      tutorialLink: window.location.href,
+      downloadTitle: $(this).attr("data-response")
+    });
+    ga('send', {
+      hitType: 'event',
+      eventCategory: 'Download',
+      eventAction: 'click',
+      eventLabel: $(this).attr("data-response")
+    });
+   });
+
+   $("[data-behavior='was-this-helpful-event']").on('click', function(){
+    $(".helpful-question").hide();
+    $(".was-helpful-thank-you").show();
+    fbq('trackCustom', "Was this Helpful?", {
+      tutorialLink: window.location.href,
+      tutorialTitle: $('h1:first').text(),
+      helpful: $(this).attr("data-response")
+    });
+    ga('send', {
+      hitType: 'event',
+      eventCategory: 'Was this Helpful?',
+      eventAction: 'click',
+      eventLabel: $(this).attr("data-response")
+    });
+   });
+
+   if (location.pathname == "/") {
+     $(".helpful-container").hide();
+     $(".hr-bottom").hide();
+   }
+</script>
+
+<noscript>
+  <img height="1" width="1"
+  src="https://www.facebook.com/tr?id=243028289693773&ev=PageView
+  &noscript=1"/>
+</noscript>
 <img height="1" width="1" style="border-style:none;" alt="" src="https://www.googleadservices.com/pagead/conversion/795629140/?label=txkmCPmdtosBENSssfsC&amp;guid=ON&amp;script=0"/>
 {% endblock %}

advanced_source/numpy_extensions_tutorial.py

@@ -4,7 +4,7 @@
 =========================================
 **Author**: `Adam Paszke <https://github.com/apaszke>`_
 
-**Updated by**: `Adam Dziedzic` [https://github.com/adam-dziedzic](https://github.com/adam-dziedzic)
+**Updated by**: `Adam Dziedzic <https://github.com/adam-dziedzic>`_
 
 In this tutorial, we shall go through two tasks:
 

beginner_source/colab.rst

@@ -0,0 +1,70 @@
+Using Tutorial Data from Google Drive in Colab
+==============================================
+
+We've added a new feature to tutorials that allows users to open the
+notebook associated with a tutorial in Google Colab. You may need to
+copy data to your Google drive account to get the more complex tutorials
+to work.
+
+In this example, we'll demonstrate how to change the notebook in Colab
+to work with the Chatbot Tutorial. To do this, you'll first need to be
+logged into Google Drive. (For a full description of how to access data
+in Colab, you can view their example notebook
+`here <https://colab.research.google.com/notebooks/io.ipynb#scrollTo=XDg9OBaYqRMd>`__.)
+
+To get started open the `Chatbot
+Tutorial <https://pytorch.org/tutorials/beginner/chatbot_tutorial.html>`__
+in your browser.
+
+At the top of the page click **Run in Google Colab**.
+
+The file will open in Colab.
+
+If you choose, **Runtime** then **Run All**, you'll get an error as the
+file can't be found.
+
+To fix this, we'll copy the required file into our Google Drive account.
+
+1. Log into Google Drive.
+2. In Google Drive, make a folder named **data**, with a subfolder named
+   **cornell**.
+3. Visit the Cornell Movie Dialogs Corpus and download the ZIP file.
+4. Unzip the file on your local machine.
+5. Copy the file **movie\_lines.txt** to **data/cornell** folder you
+   created in Google Drive.
+
+Now we'll need to edit the file in\_ \_Colab to point to the file on
+Google Drive.
+
+In Colab, add the following to top of the code section over the line
+that begins *corpus\_name*:
+
+::
+
+    from google.colab import drive
+    drive.mount('/content/gdrive')
+
+Change the two lines that follow:
+
+1. Change the **corpus\_name** value to **"cornell"**.
+2. Change the line that begins with **corpus** to this:
+
+::
+
+    corpus = os.path.join("/content/gdrive/My Drive/data", corpus_name)
+
+We're now pointing to the file we uploaded to Drive.
+
+Now when you click on the **Run cell** button for the code section,
+you'll be prompted to authorize Google Drive and you'll get an
+authorization code. Paste the code into the prompt in Colab and you
+should be set.
+
+Rerun the notebook from **Runtime** / **Run All** menu command and
+you'll see it process. (Note that this tutorial takes a long time to
+run.)
+
+Hopefully this example will give you a good starting point for running
+some of the more complex tutorials in Colab. As we evolve our use of
+Colab on the PyTorch tutorials site, we'll look at ways to make this
+easier for users.

beginner_source/data_loading_tutorial.py

@@ -111,7 +111,7 @@ def show_landmarks(image, landmarks):
 # stored in the memory at once but read as required.
 #
 # Sample of our dataset will be a dict
-# ``{'image': image, 'landmarks': landmarks}``. Our datset will take an
+# ``{'image': image, 'landmarks': landmarks}``. Our dataset will take an
 # optional argument ``transform`` so that any required processing can be
 # applied on the sample. We will see the usefulness of ``transform`` in the
 # next section.

beginner_source/dcgan_faces_tutorial.py

@@ -74,7 +74,7 @@
 # :math:`D` will predict its outputs are fake (:math:`log(1-D(G(x)))`).
 # From the paper, the GAN loss function is
 # 
-# .. math:: \underset{G}{\text{min}} \underset{D}{\text{max}}V(D,G) = \mathbb{E}_{x\sim p_{data}(x)}\big[logD(x)\big] + \mathbb{E}_{z\sim p_{z}(z)}\big[log(1-D(G(x)))\big]
+# .. math:: \underset{G}{\text{min}} \underset{D}{\text{max}}V(D,G) = \mathbb{E}_{x\sim p_{data}(x)}\big[logD(x)\big] + \mathbb{E}_{z\sim p_{z}(z)}\big[log(1-D(G(z)))\big]
 # 
 # In theory, the solution to this minimax game is where
 # :math:`p_g = p_{data}`, and the discriminator guesses randomly if the

beginner_source/former_torchies/README.txt

@@ -9,9 +9,9 @@
 	Autograd
 	https://pytorch.org/tutorials/beginner/former_torchies/autograd_tutorial_old.html
 
-3. nn_tutorial.py
+3. nnft_tutorial.py
 	nn package
-	https://pytorch.org/tutorials/beginner/former_torchies/nn_tutorial.html
+	https://pytorch.org/tutorials/beginner/former_torchies/nnft_tutorial.html
 
 4. parallelism_tutorial.py
 	Multi-GPU examples

beginner_source/former_torchies/nn_tutorial.py renamed to beginner_source/former_torchies/nnft_tutorial.py

@@ -11,6 +11,7 @@
 One can wrap a Module in ``DataParallel`` and it will be parallelized
 over multiple GPUs in the batch dimension.
 
+
 DataParallel
 -------------
 """
@@ -41,7 +42,20 @@ def forward(self, x):
 #
 # The documentation for DataParallel can be found
 # `here <https://pytorch.org/docs/nn.html#dataparallel>`_.
-#
+# 
+# **Attributes of the wrapped module**
+# 
+# After wrapping a Module with ``DataParallel``, the attributes of the module
+# (e.g. custom methods) became inaccessible. This is because ``DataParallel``
+# defines a few new members, and allowing other attributes might lead to 
+# clashes in their names. For those who still want to access the attributes, 
+# a workaround is to use a subclass of ``DataParallel`` as below.
+
+class MyDataParallel(nn.DataParallel):
+    def __getattr__(self, name):
+        return getattr(self.module, name)
+    
+########################################################################
 # **Primitives on which DataParallel is implemented upon:**
 #
 #

beginner_source/former_torchies/parallelism_tutorial.py

@@ -19,15 +19,15 @@ In this tutorial, you will learn the following:
 
    /beginner/former_torchies/tensor_tutorial_old
    /beginner/former_torchies/autograd_tutorial_old
-   /beginner/former_torchies/nn_tutorial
+   /beginner/former_torchies/nnft_tutorial
    /beginner/former_torchies/parallelism_tutorial
 
 .. galleryitem:: /beginner/former_torchies/tensor_tutorial_old.py
     :figure: /_static/img/tensor_illustration_flat.png
 
 .. galleryitem:: /beginner/former_torchies/autograd_tutorial_old.py
 
-.. galleryitem:: /beginner/former_torchies/nn_tutorial.py
+.. galleryitem:: /beginner/former_torchies/nnft_tutorial.py
     :figure: /_static/img/torch-nn-vs-pytorch-nn.png
 
 .. galleryitem:: /beginner/former_torchies/parallelism_tutorial.py

beginner_source/former_torchies_tutorial.rst

@@ -93,7 +93,7 @@
 .. math::
 
     \text{Similarity}(\text{physicist}, \text{mathematician}) = \frac{q_\text{physicist} \cdot q_\text{mathematician}}
-   {\| q_\text{\physicist} \| \| q_\text{mathematician} \|} = \cos (\phi)
+   {\| q_\text{physicist} \| \| q_\text{mathematician} \|} = \cos (\phi)
 
 Where :math:`\phi` is the angle between the two vectors. That way,
 extremely similar words (words whose embeddings point in the same

beginner_source/nlp/word_embeddings_tutorial.py

@@ -6,7 +6,7 @@
 """
 ###############################################################################
 # We recommend running this tutorial as a notebook, not a script. To download the notebook (.ipynb) file,
-# click `here <https://pytorch.org/tutorials/beginner/nn_tutorial.html#sphx-glr-download-beginner-nn-tutorial-py>`_ .
+# click the link at the top of the page.
 #
 # PyTorch provides the elegantly designed modules and classes `torch.nn <https://pytorch.org/docs/stable/nn.html>`_ ,
 # `torch.optim <https://pytorch.org/docs/stable/optim.html>`_ ,

beginner_source/nn_tutorial.py

@@ -7,6 +7,8 @@ starting point, and provides a broad view into how to use PyTorch from the basic
 
 Some considerations:
 
+* We’ve added a new feature to tutorials that allows users to open the notebook associated with a tutorial in Google Colab. 
+  Visit `this page <https://pytorch.org/tutorials/beginner/colab.html>`_ for more information.
 * If you would like to do the tutorials interactively via IPython / Jupyter,
   each tutorial has a download link for a Jupyter Notebook and Python source code.
 * Additional high-quality examples are available, including image classification,