Merge branch 'master' of https://github.com/lara-hdr/tutorials into lahaidar/ort_tutorial

Author: Lara

.jenkins/build.sh

@@ -16,29 +16,34 @@ rm -rf src
 pip install -r $DIR/../requirements.txt
 
 export PATH=/opt/conda/bin:$PATH
-conda install -y sphinx==1.8.2 pandas
+pip install sphinx==1.8.2 pandas
+
+# install awscli
+# pip uninstall awscli
+# pip install awscli==1.16.35
+
 # PyTorch Theme
 rm -rf src
 pip install -e git+git://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
 # pillow >= 4.2 will throw error when trying to write mode RGBA as JPEG,
 # this is a workaround to the issue.
-pip install sphinx-gallery tqdm matplotlib ipython pillow==4.1.1
+pip install sphinx-gallery==0.3.1 tqdm matplotlib ipython pillow==4.1.1
 
 # Install torchaudio from source
-git clone https://github.com/pytorch/audio --quiet
-pushd audio
-python setup.py install
-popd
+# git clone https://github.com/pytorch/audio --quiet
+# pushd audio
+# python setup.py install
+# popd
 
 aws configure set default.s3.multipart_threshold 5120MB
 
 # 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
+  python $DIR/remove_runnable_code.py beginner_source/aws_distributed_training_tutorial.py beginner_source/aws_distributed_training_tutorial.py || true
   # 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
+  python $DIR/remove_runnable_code.py beginner_source/audio_classifier_tutorial.py beginner_source/audio_classifier_tutorial.py || true
 
   # 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.
@@ -180,4 +185,4 @@ else
 fi
 
 rm -rf vision
-rm -rf audio
+# rm -rf audio

advanced_source/numpy_extensions_tutorial.py

@@ -35,13 +35,14 @@
 
 
 class BadFFTFunction(Function):
-
-    def forward(self, input):
+    @staticmethod
+    def forward(ctx, input):
         numpy_input = input.detach().numpy()
         result = abs(rfft2(numpy_input))
         return input.new(result)
 
-    def backward(self, grad_output):
+    @staticmethod
+    def backward(ctx, grad_output):
         numpy_go = grad_output.numpy()
         result = irfft2(numpy_go)
         return grad_output.new(result)
@@ -51,7 +52,7 @@ def backward(self, grad_output):
 
 
 def incorrect_fft(input):
-    return BadFFTFunction()(input)
+    return BadFFTFunction.apply(input)
 
 ###############################################################
 # **Example usage of the created layer:**

beginner_source/PyTorch Cheat.md

@@ -26,13 +26,13 @@ from torch.jit import script, trace       # hybrid frontend decorator and tracin
 ```
 See [autograd](https://pytorch.org/docs/stable/autograd.html), [nn](https://pytorch.org/docs/stable/nn.html), [functional](https://pytorch.org/docs/stable/nn.html#torch-nn-functional) and [optim](https://pytorch.org/docs/stable/optim.html)
 
-### Hybrid frontend
+### Torchscript and JIT
 
 ```
 torch.jit.trace()         # takes your module or function and an example data input, and traces the computational steps that the data encounters as it progresses through the model
 @script                   # decorator used to indicate data-dependent control flow within the code being traced
 ```
-See [hybrid frontend](https://pytorch.org/docs/stable/hybridfrontend)
+See [Torchscript](https://pytorch.org/docs/stable/jit.html)
 
 ### ONNX
 

beginner_source/blitz/autograd_tutorial.py

@@ -185,5 +185,5 @@
 ###############################################################
 # **Read Later:**
 #
-# Documentation of ``autograd`` and ``Function`` is at
-# https://pytorch.org/docs/autograd
+# Document about ``autograd.Function`` is at
+# https://pytorch.org/docs/stable/autograd.html#function

beginner_source/blitz/cifar10_tutorial.py

@@ -235,7 +235,7 @@ def forward(self, x):
     100 * correct / total))
 
 ########################################################################
-# That looks waaay better than chance, which is 10% accuracy (randomly picking
+# That looks way better than chance, which is 10% accuracy (randomly picking
 # a class out of 10 classes).
 # Seems like the network learnt something.
 #
@@ -298,7 +298,7 @@ def forward(self, x):
 #         inputs, labels = data[0].to(device), data[1].to(device)
 #
 # Why dont I notice MASSIVE speedup compared to CPU? Because your network
-# is realllly small.
+# is really small.
 #
 # **Exercise:** Try increasing the width of your network (argument 2 of
 # the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` –

beginner_source/dcgan_faces_tutorial.py

@@ -53,7 +53,7 @@
 # :math:`D(x)` is the discriminator network which outputs the (scalar)
 # probability that :math:`x` came from training data rather than the
 # generator. Here, since we are dealing with images the input to
-# :math:`D(x)` is an image of HWC size 3x64x64. Intuitively, :math:`D(x)`
+# :math:`D(x)` is an image of CHW size 3x64x64. Intuitively, :math:`D(x)`
 # should be HIGH when :math:`x` comes from training data and LOW when
 # :math:`x` comes from the generator. :math:`D(x)` can also be thought of
 # as a traditional binary classifier.

beginner_source/ptcheat.rst

@@ -29,7 +29,7 @@ See `autograd <https://pytorch.org/docs/stable/autograd.html>`__,
 `functional <https://pytorch.org/docs/stable/nn.html#torch-nn-functional>`__
 and `optim <https://pytorch.org/docs/stable/optim.html>`__
 
-Hybrid frontend
+Torchscript and JIT
 ---------------
 
 .. code-block:: python
@@ -41,7 +41,7 @@ Hybrid frontend
     @script                   # decorator used to indicate data-dependent 
                               # control flow within the code being traced
 
-See `hybrid frontend <https://pytorch.org/docs/stable/hybridfrontend>`__
+See `Torchscript <https://pytorch.org/docs/stable/jit.html>`__
 
 ONNX
 ----

requirements.txt

@@ -1,7 +1,7 @@
 # Refer to ./jenkins/build.sh for tutorial build instructions 
 
 sphinx
-sphinx-gallery
+sphinx-gallery==0.3.1
 tqdm
 numpy
 matplotlib