Spellcheck intermediate 2 python

.pyspelling.yml

@@ -3,21 +3,7 @@ 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
+    - intermediate_source/*.py
   dictionary:
     wordlists:
       - en-wordlist.txt
@@ -30,9 +16,14 @@ matrix:
         # Exclude figure rST tags
         - open: '\.\.\s+(figure|literalinclude|math|image|grid)::'
           close: '\n'
+        # Exclude roles:
+        - open: ':(?:(class|py:mod|mod|func)):`'
+          content: '[^`]*'
+          close: '`'
         # Exclude raw directive
         - open: '\.\. (raw)::.*$\n*'
           close: '\n'
+        # Exclude
         # Exclude Python coding directives
         - open: '-\*- coding:'
           close: '\n'
@@ -46,7 +37,7 @@ matrix:
         - open: '(?s)^::\n\n  '
           close: '^\n'
         # Ignore reStructuredText block directives
-        - open: '\.\. (code-block)::.*$\n*'
+        - open: '\.\. (code-block|math)::.*$\n*'
           content: '(?P<first>(^(?P<indent>[ ]+).*$\n))(?P<other>(^([ \t]+.*|[ \t]*)$\n)*)'
           close: '(^(?![ \t]+.*$))'
   - pyspelling.filters.markdown:

en-wordlist.txt

@@ -1,4 +1,5 @@
 APIs
+ATen
 Args
 Autograd
 BCE
@@ -13,38 +14,50 @@ CNNDM
 CNNs
 CPUs
 CUDA
+CartPole
+Cayley
 Chatbots
 Colab
 Conv
 ConvNet
 ConvNets
 DCGAN
 DCGANs
+DDP
 DDQN
 DNN
+DQN
 DataLoaders
+DeepMind
 DeiT
 DenseNet
 EOS
+EPS
 FC
 FGSM
 FLAVA
 FX
 FX's
 FloydHub
 FloydHub's
+Frobenius
+GAE
 GAN
 GANs
+GLOO
+GPU's
 GPUs
 GRU
 GRUs
+GTC
 GeForce
 Goodfellow
 Goodfellow’s
 GreedySearchDecoder
 HVP
 Hugging Face
 IMDB
+IOT
 ImageNet
 Initializations
 Iteratively
@@ -56,25 +69,32 @@ Kubernetes
 Kuei
 LSTM
 LSTMs
+LeCun
 LeNet
 LeakyReLU
 LeakyReLUs
+Lipschitz
 Lua
 Luong
 MLP
 MLPs
 MNIST
+MacBook
 Mypy
 NAS
+NCCL
 NCHW
 NES
 NLP
+NTK
 NaN
+NanoGPT
 NeurIPS
 NumPy
 Numericalization
 Numpy's
 OpenAI
+PPO
 Plotly
 Prec
 Profiler
@@ -83,31 +103,48 @@ RGB
 RL
 RNN
 RNNs
+RPC
 RTX
 Radford
 ReLU
 ResNet
+SDPA
+SGD
+SPD
 SST2
+STN
 Sequentials
 Sigmoid
 SoTA
 TPU
 TensorBoard
 TextVQA
 Tokenization
+TorchDynamo
+TorchInductor
 TorchMultimodal
+TorchRL
+TorchRL's
 TorchScript
 TorchX
 Tunable
+UI
 Unescape
 VQA
+VS Code
 Wikitext
 Xeon
 accuracies
 activations
 adversarially
+affine
 al
+allocator
+allocator's
+allocators
+approximators
 autodiff
+autoencoder
 autograd
 backend
 backends
@@ -121,9 +158,13 @@ batchnorm's
 benchmarking
 boolean
 broadcasted
+bytecode
+cardinality
 chatbot
 chatbot's
 checkpointing
+colorbar
+compilable
 composable
 concat
 config
@@ -141,11 +182,17 @@ datapipes
 dataset
 datasets
 dataset’s
+deallocation
+decorrelated
 deserialize
 deserialized
+deterministically
+dimensionality
 dir
 downsample
 downsamples
+dropdown
+duration
 embeddings
 encodings
 ensembling
@@ -161,6 +208,7 @@ finetuning
 fp
 functorch
 fuser
+geomean
 grayscale
 hardcode
 helpdesk
@@ -174,16 +222,19 @@ imagenet
 initializations
 inlined
 interpretable
+invariance
 io
 iterable
 iteratively
 jacobian
 jacobians
 jit
 jpg
+judgements
 kwargs
 labelled
 learnable
+learnings
 loadFilename
 manualSeed
 matplotlib
@@ -194,29 +245,39 @@ misclassification
 misclassified
 modularity
 modularized
+multihead
 multimodal
 multimodality
 multiobjective
+multiprocessed
 multithreaded
 namespace
 natively
 ndarrays
 num
 numericalize
 numpy
+nvFuser
+nvFuser's
 optimizable
 optimizer's
 optimizers
 overfitting
 parallelizable
 parallelization
+parametrization
+parametrizations
+parametrized
+parametrizing
 perceptibility
 pipelining
 pointwise
+precompute
 precomputing
 prepend
 preprocess
 preprocessing
+prepruned
 prespecified
 pretrained
 prewritten
@@ -229,20 +290,28 @@ quantizing
 queryable
 randint
 readably
+recomputation
+regressor
+reimplement
+reimplementing
+reimplements
 reinitializes
 relu
 reproducibility
 rescale
 resnet
 restride
 rewinded
+rollout
 romanized
 runnable
 runtime
 runtime
 runtimes
 scalable
 softmax
+sparsify
+specificities
 src
 stacktrace
 stateful
@@ -252,6 +321,8 @@ subclasses
 subclassing
 subdirectories
 submodule
+submodules
+subnetworks
 subreddit
 summarization
 tanh
@@ -262,6 +333,8 @@ timesteps
 tokenization
 tokenize
 tokenizer
+tooltip
+topologies
 torchaudio
 torchdata
 torchscriptable
@@ -277,7 +350,11 @@ uncommented
 unfused
 unimodal
 unnormalized
+unoptimized
+unparametrized
 unpickling
+unpruned
+updation
 utils
 vectorization
 vectorize

intermediate_source/flask_rest_api_tutorial.py

@@ -318,10 +318,10 @@ def get_prediction(image_bytes):
 #
 # .. code-block:: python
 #
-#   import requests
+#    import requests
 #
-#   resp = requests.post("http://localhost:5000/predict",
-#                        files={"file": open('<PATH/TO/.jpg/FILE>/cat.jpg','rb')})
+#    resp = requests.post("http://localhost:5000/predict",
+#                         files={"file": open('<PATH/TO/.jpg/FILE>/cat.jpg','rb')})
 
 #######################################################################
 # Printing `resp.json()` will now show the following:

intermediate_source/neural_tangent_kernels.py

@@ -58,7 +58,7 @@ def forward(self, x):
 # we will need a function that accepts the parameters of the model and a single
 # input (as opposed to a batch of inputs!) and returns a single output.
 #
-# We'll use ``torch.func.functional_call``, which allows us to call an nn.Module
+# We'll use ``torch.func.functional_call``, which allows us to call an ``nn.Module``
 # using different parameters/buffers, to help accomplish the first step.
 #
 # Keep in mind that the model was originally written to accept a batch of input
@@ -200,21 +200,21 @@ def func_x2(params):
         output, vjp_fn = vjp(func_x1, params)
 
         def get_ntk_slice(vec):
-            # This computes vec @ J(x2).T
+            # This computes ``vec @ J(x2).T``
             # `vec` is some unit vector (a single slice of the Identity matrix)
             vjps = vjp_fn(vec)
-            # This computes J(X1) @ vjps
+            # This computes ``J(X1) @ vjps``
             _, jvps = jvp(func_x2, (params,), vjps)
             return jvps
 
         # Here's our identity matrix
         basis = torch.eye(output.numel(), dtype=output.dtype, device=output.device).view(output.numel(), -1)
         return vmap(get_ntk_slice)(basis)
 
-    # get_ntk(x1, x2) computes the NTK for a single data point x1, x2
-    # Since the x1, x2 inputs to empirical_ntk_ntk_vps are batched,
+    # ``get_ntk(x1, x2)`` computes the NTK for a single data point x1, x2
+    # Since the x1, x2 inputs to ``empirical_ntk_ntk_vps`` are batched,
     # we actually wish to compute the NTK between every pair of data points
-    # between {x1} and {x2}. That's what the vmaps here do.
+    # between {x1} and {x2}. That's what the ``vmaps`` here do.
     result = vmap(vmap(get_ntk, (None, 0)), (0, None))(x1, x2)
 
     if compute == 'full':