Pyspelling: Python intermediate tutorials A-M (#2287)

* Pyspelling: Python intermediate tutorials A-M

Author: Svetlana Karslioglu

.pyspelling.yml

@@ -3,6 +3,21 @@ matrix:
 - name: python
   sources:
     - beginner_source/*.py
+    - intermediate_source/autograd_saved_tensors_hooks_tutorial.py
+    - intermediate_source/ax_multiobjective_nas_tutorial.py
+    - intermediate_source/char_rnn_classification_tutorial.py
+    - intermediate_source/char_rnn_generation_tutorial.py
+    - intermediate_source/custom_function_conv_bn_tutorial.py
+    - intermediate_source/ensembling.py
+    #- intermediate_source/flask_rest_api_tutorial.py
+    - intermediate_source/forward_ad_usage.py
+    - intermediate_source/fx_conv_bn_fuser.py
+    - intermediate_source/fx_profiling_tutorial.py
+    - intermediate_source/jacobians_hessians.py
+    - intermediate_source/mario_rl_tutorial.py
+    - intermediate_source/mnist_train_nas.py
+    - intermediate_source/memory_format_tutorial.py
+    - intermediate_source/model_parallel_tutorial.py
   dictionary:
     wordlists:
       - en-wordlist.txt

en-wordlist.txt

@@ -2,6 +2,7 @@ APIs
 Args
 Autograd
 BCE
+BN
 BOS
 Bahdanau
 BatchNorm
@@ -10,76 +11,112 @@ CIFAR
 CLS
 CNNDM
 CNNs
+CPUs
 CUDA
 Chatbots
 Colab
 Conv
 ConvNet
+ConvNets
 DCGAN
 DCGANs
+DDQN
+DNN
 DataLoaders
 DeiT
+DenseNet
 EOS
+FC
 FGSM
 FLAVA
+FX
+FX's
 FloydHub
 FloydHub's
 GAN
 GANs
 GPUs
 GRU
 GRUs
+GeForce
 Goodfellow
 Goodfellow’s
 GreedySearchDecoder
+HVP
 Hugging Face
 IMDB
 ImageNet
 Initializations
 Iteratively
 JSON
+JVP
+Jacobian
+Kiuk
+Kubernetes
 Kuei
 LSTM
+LSTMs
 LeNet
 LeakyReLU
 LeakyReLUs
+Lua
 Luong
 MLP
+MLPs
 MNIST
 Mypy
+NAS
+NCHW
+NES
 NLP
 NaN
 NeurIPS
 NumPy
 Numericalization
 Numpy's
+OpenAI
+Plotly
+Prec
 Profiler
 PyTorch's
 RGB
+RL
 RNN
 RNNs
+RTX
 Radford
 ReLU
+ResNet
 SST2
+Sequentials
 Sigmoid
 SoTA
+TPU
 TensorBoard
 TextVQA
 Tokenization
 TorchMultimodal
 TorchScript
+TorchX
+Tunable
 Unescape
 VQA
 Wikitext
+Xeon
 accuracies
 activations
 adversarially
 al
+autodiff
+autograd
 backend
+backends
 backprop
+backpropagate
 backpropagated
 backpropagates
 backpropagation
+batchnorm
 batchnorm's
 benchmarking
 boolean
@@ -89,12 +126,15 @@ chatbot's
 checkpointing
 composable
 concat
+config
 contrastive
 conv
 convolutional
 cpu
 csv
+cuDNN
 datafile
+dataframe
 dataloader
 dataloaders
 datapipes
@@ -105,26 +145,43 @@ deserialize
 deserialized
 dir
 downsample
+downsamples
 embeddings
 encodings
+ensembling
 eq
 et
 evaluateInput
+extensibility
 fastai
 fbgemm
 feedforward
 finetune
 finetuning
+fp
+functorch
+fuser
+grayscale
+hardcode
 helpdesk
 helpdesks
+hessian
+hessians
+hvp
 hyperparameter
 hyperparameters
 imagenet
+initializations
+inlined
+interpretable
 io
 iterable
 iteratively
+jacobian
+jacobians
 jit
 jpg
+kwargs
 labelled
 learnable
 loadFilename
@@ -139,6 +196,7 @@ modularity
 modularized
 multimodal
 multimodality
+multiobjective
 multithreaded
 namespace
 natively
@@ -153,56 +211,78 @@ overfitting
 parallelizable
 parallelization
 perceptibility
+pipelining
+pointwise
+precomputing
 prepend
 preprocess
 preprocessing
+prespecified
 pretrained
 prewritten
+primals
 profiler
 profilers
 pytorch
 quantized
 quantizing
+queryable
 randint
 readably
 reinitializes
 relu
 reproducibility
 rescale
+resnet
+restride
 rewinded
+romanized
+runnable
 runtime
 runtime
 runtimes
+scalable
 softmax
 src
 stacktrace
 stateful
 storages
 strided
 subclasses
+subclassing
 subdirectories
 submodule
+subreddit
 summarization
 tanh
 th
 thresholding
+timestep
+timesteps
 tokenization
 tokenize
 tokenizer
 torchaudio
 torchdata
+torchscriptable
 torchtext
 torchtext's
 torchvision
+torchviz
 traceback
 tradeoff
+tradeoffs
 uncomment
 uncommented
+unfused
 unimodal
 unnormalized
 unpickling
 utils
+vectorization
+vectorize
 vectorized
+vhp
 voc
 walkthrough
 warmstart

intermediate_source/autograd_saved_tensors_hooks_tutorial.py

@@ -1,6 +1,6 @@
 """
 Hooks for autograd saved tensors
-=======================
+================================
 
 """
 
@@ -13,8 +13,7 @@
 # packing/unpacking process.
 #
 # This tutorial assumes you are familiar with how backpropagation works in
-# theory. If not, read this first:
-# https://colab.research.google.com/drive/1aWNdmYt7RcHMbUk-Xz2Cv5-cGFSWPXe0#scrollTo=AHcEJ6nXUb7W
+# theory. If not, read `this <https://colab.research.google.com/drive/1aWNdmYt7RcHMbUk-Xz2Cv5-cGFSWPXe0#scrollTo=AHcEJ6nXUb7W>`_ first.
 #
 
 
@@ -107,7 +106,7 @@ def f(x):
 
 ######################################################################
 # In the example above, executing without grad would only have kept ``x``
-# and ``y`` in the scope, But the graph additionnally stores ``f(x)`` and
+# and ``y`` in the scope, But the graph additionally stores ``f(x)`` and
 # ``f(f(x))``. Hence, running a forward pass during training will be more
 # costly in memory usage than during evaluation (more precisely, when
 # autograd is not required).
@@ -182,7 +181,7 @@ def unpack_hook(x):
 
 
 ######################################################################
-# The ``pack_hook`` function will be called everytime an operation saves
+# The ``pack_hook`` function will be called every time an operation saves
 # a tensor for backward.
 # The output of ``pack_hook`` is then stored in the computation graph
 # instead of the original tensor.
@@ -218,8 +217,9 @@ def unpack_hook(x):
 #
 
 ######################################################################
-# **Returning and int**
-
+# Returning an ``int``
+# ^^^^^^^^^^^^^^^^^^^^
+#
 # Returning the index of a Python list
 # Relatively harmless but with debatable usefulness
 
@@ -240,8 +240,9 @@ def unpack(x):
 assert(x.grad.equal(2 * x))
 
 ######################################################################
-# **Returning a tuple**
-
+# Returning a tuple
+# ^^^^^^^^^^^^^^^^^
+#
 # Returning some tensor and a function how to unpack it
 # Quite unlikely to be useful in its current form
 
@@ -262,9 +263,10 @@ def unpack(packed):
 assert(torch.allclose(x.grad, 2 * x))
 
 ######################################################################
-# **Returning a str**
-
-# Returning the __repr__ of the tensor
+# Returning a ``str``
+# ^^^^^^^^^^^^^^^^^^^
+#
+# Returning the ``__repr__ of`` the tensor
 # Probably never do this
 
 x = torch.randn(5, requires_grad=True)
@@ -337,7 +339,7 @@ def forward(self, x):
 
 
 ######################################################################
-# In practice, on a A100 GPU, for a resnet-152 with batch size 256, this
+# In practice, on a A100 GPU, for a ResNet-152 with batch size 256, this
 # corresponds to a GPU memory usage reduction from 48GB to 5GB, at the
 # cost of a 6x slowdown.
 #