Merge remote-tracking branch 'upstream/rc1'

Author: ericnakagawa

.gitignore

@@ -2,11 +2,13 @@
 beginner
 intermediate
 advanced
+pytorch_basics
 
 #data things
 beginner_source/hymenoptera_data
 beginner_source/blitz/data
 beginner_source/faces
+beginner_source/hybrid_frontend/data
 intermediate_source/data/
 advanced_source/images/
 *data.zip
@@ -91,8 +93,7 @@ target/
 .python-version
 
 # celery beat schedule file
-celerybeat-schedule
-
+celerybeat-schedule 
 # dotenv
 .env
 
@@ -105,3 +106,8 @@ ENV/
 
 # Rope project settings
 .ropeproject
+
+# Mac things
+.DS_Store
+cleanup.sh
+*.swp

.jenkins/build.sh

@@ -0,0 +1,67 @@
+sudo apt-get update
+sudo apt-get install -y --no-install-recommends unzip p7zip-full sox libsox-dev libsox-fmt-all
+
+# 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
+# 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==1.7.9 pandas pillow=4.1.1
+# PyTorch Theme
+rm -rf src
+pip install -e git+git://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
+pip install sphinx-gallery tqdm matplotlib ipython
+
+git clone https://github.com/pytorch/vision --quiet
+pushd vision
+pip install . --no-deps  # We don't want it to install the stock PyTorch version from pip
+popd
+
+git clone https://github.com/pytorch/audio --quiet
+pushd audio
+python setup.py install
+popd
+
+# Download dataset for beginner_source/dcgan_faces_tutorial.py
+curl https://s3.amazonaws.com/pytorch-tutorial-assets/img_align_celeba.zip --output img_align_celeba.zip
+sudo mkdir -p /home/ubuntu/facebook/datasets/celeba
+sudo chmod -R 0777 /home/ubuntu/facebook/datasets/celeba
+unzip img_align_celeba.zip -d /home/ubuntu/facebook/datasets/celeba > null
+
+# Download dataset for beginner_source/hybrid_frontend/introduction_to_hybrid_frontend_tutorial.py
+mkdir data/
+curl https://s3.amazonaws.com/pytorch-tutorial-assets/iris.data --output data/iris.data
+
+# Download dataset for beginner_source/chatbot_tutorial.py
+curl https://s3.amazonaws.com/pytorch-tutorial-assets/cornell_movie_dialogs_corpus.zip --output cornell_movie_dialogs_corpus.zip
+mkdir -p beginner_source/data
+unzip cornell_movie_dialogs_corpus.zip -d beginner_source/data/ > null
+
+# Download dataset for beginner_source/audio_classifier_tutorial.py
+curl https://s3.amazonaws.com/pytorch-tutorial-assets/UrbanSound8K.tar.gz --output UrbanSound8K.tar.gz
+tar -xzf UrbanSound8K.tar.gz -C ./beginner_source
+
+# Download model for beginner_source/fgsm_tutorial.py
+curl https://s3.amazonaws.com/pytorch-tutorial-assets/lenet_mnist_model.pth --output ./beginner_source/lenet_mnist_model.pth
+
+# We will fix the hybrid frontend tutorials when the API is stable
+rm beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py
+rm beginner_source/hybrid_frontend/introduction_to_hybrid_frontend_tutorial.py
+
+make docs
+
+rm -rf vision
+rm -rf audio

Makefile

@@ -18,7 +18,7 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -v
 
 download:
 	# transfer learning tutorial data
@@ -32,6 +32,9 @@ download:
 	# data loader tutorial
 	wget -N https://download.pytorch.org/tutorial/faces.zip
 	unzip -o faces.zip -d beginner_source
+
+	wget -N https://download.pytorch.org/models/tutorials/4000_checkpoint.tar
+	cp 4000_checkpoint.tar beginner_source
 
 	# neural style images
 	rm -rf advanced_source/images/

README.md

@@ -21,6 +21,6 @@ In case you prefer to write your tutorial in jupyter, you can use [this script](
 
 ## Building
 
-- Start with installing torch and torchvision. Install other requirements using `pip install -r requirements.txt`
-- Then you can build using `make docs`. This will download the data, execute the tutorials and build the documentation to `docs/` directory. However, this will take about 30-60 min based on your system. 
-- You can skip the execution by running `make html-noplot` to build html documentation to `_build/html`. This way, you can quickly preview your tutorial.
+- Start with installing torch, torchvision, and your GPUs latest drivers. Install other requirements using `pip install -r requirements.txt`
+- Then you can build using `make docs`. This will download the data, execute the tutorials and build the documentation to `docs/` directory. This will take about 60-120 min for systems with GPUs. If you do not have a GPU installed on your system, then see next step.
+- You can skip the computationally intensive graph generation by running `make html-noplot` to build basic html documentation to `_build/html`. This way, you can quickly preview your tutorial.