Merge branch 'main' into patch-1

Author: svekars

.devcontainer/requirements.txt

@@ -24,7 +24,7 @@ ipython
 # to run examples
 pandas
 scikit-image
-pillow==10.0.1
+pillow==10.2.0
 wget
 
 # for codespaces env

.jenkins/build.sh

@@ -24,9 +24,9 @@ pip install --progress-bar off -r $DIR/../requirements.txt
 
 #Install PyTorch Nightly for test.
 # Nightly - pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html
-# Install 2.1 for testing
+# Install 2.2 for testing - uncomment to install nightly binaries (update the version as needed).
 # pip uninstall -y torch torchvision torchaudio torchtext torchdata
-# pip3 install torch torchvision torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu121
+# pip3 install torch==2.2.0 torchvision torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu121
 # pip3 install torchdata torchtext --index-url https://download.pytorch.org/whl/test/cpu
 
 # Install two language tokenizers for Translation with TorchText tutorial

.jenkins/validate_tutorials_built.py

@@ -29,6 +29,7 @@
     "intermediate_source/_torch_export_nightly_tutorial",  # does not work on release
     "advanced_source/super_resolution_with_onnxruntime",
     "advanced_source/ddp_pipeline",  # requires 4 gpus
+    "advanced_source/usb_semisup_learn", # in the current form takes 140+ minutes to build - can be enabled when the build time is reduced
     "prototype_source/fx_graph_mode_ptq_dynamic",
     "prototype_source/vmap_recipe",
     "prototype_source/torchscript_freezing",

.pyspelling.yml

@@ -45,6 +45,9 @@ matrix:
         - open: '\.\. (code-block|math)::.*$\n*'
           content: '(?P<first>(^(?P<indent>[ ]+).*$\n))(?P<other>(^([ \t]+.*|[ \t]*)$\n)*)'
           close: '(^(?![ \t]+.*$))'
+        # Ignore references like "[1] Author: Title"
+        - open: '\[\d\]'
+          close: '\n'
   - pyspelling.filters.markdown:
   - pyspelling.filters.html:
       ignores:
@@ -97,7 +100,7 @@ matrix:
         content: '''''*'
         close: '$'
       # Ignore reStructuredText block directives
-      - open: '\.\. (code-block|math)::.*$\n*'
+      - open: '\.\. (code-block|math|table)::.*$\n*'
         content: '(?P<first>(^(?P<indent>[ ]+).*$\n))(?P<other>(^([ \t]+.*|[ \t]*)$\n)*)'
         close: '(^(?![ \t]+.*$))'
       - open: '\.\. (raw)::.*$\n*'

CONTRIBUTING.md

@@ -86,6 +86,9 @@ download:
 	wget -nv -N https://www.manythings.org/anki/deu-eng.zip -P $(DATADIR)
 	unzip -o $(DATADIR)/deu-eng.zip -d beginner_source/data/
 
+	# Download PennFudanPed dataset for intermediate_source/torchvision_tutorial.py
+	wget https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip -P $(DATADIR)
+	unzip -o $(DATADIR)/PennFudanPed.zip -d intermediate_source/data/
 
 docs:
 	make download
@@ -103,3 +106,5 @@ html-noplot:
 clean-cache:
 	make clean
 	rm -rf advanced beginner intermediate recipes
+	# remove additional python files downloaded for torchvision_tutorial.py
+	rm -rf intermediate_source/engine.py intermediate_source/utils.py intermediate_source/transforms.py intermediate_source/coco_eval.py intermediate_source/coco_utils.py

Makefile

@@ -0,0 +1,19 @@
+/* Survey banner .css */
+
+.survey-banner {
+  margin-top: 10px;
+  background-color: #f3f4f7;
+  padding-top: 15px;
+  padding-left: 10px;
+  padding-bottom: 1px;
+}
+
+@media screen and (max-width: 600px) {
+  .survey-banner {
+    padding-top: 5px;
+    padding-left: 5px;
+    padding-bottom: -1px;
+    font-size: 12px;
+    margin-bottom: 5px;
+  }
+}

README.md

@@ -1,6 +1,5 @@
 {% extends "!layout.html" %}
 
-
 {%- block content %}
 {{ super() }}
 <script>
@@ -17,7 +16,9 @@
 {{ super() }}
 <script>
 
-  
+// Helper function to make it easier to call dataLayer.push() 
+function gtag(){window.dataLayer.push(arguments);}
+
 //add microsoft link
 
 if(window.location.href.indexOf("/beginner/basics/")!= -1)

_static/css/custom2.css

@@ -58,26 +58,33 @@
 # Imports and setup
 # -----------------
 #
-
-import torchrl
+#  .. code-block:: bash
+#
+#      %%bash
+#      pip3 install torchrl mujoco glfw
 
 # sphinx_gallery_start_ignore
 import warnings
-from typing import Tuple
-
 warnings.filterwarnings("ignore")
+import multiprocessing
+# TorchRL prefers spawn method, that restricts creation of  ``~torchrl.envs.ParallelEnv`` inside
+# `__main__` method call, but for the easy of reading the code switch to fork
+# which is also a default spawn method in Google's Colaboratory
+try:
+    multiprocessing.set_start_method("fork")
+except RuntimeError:
+    assert multiprocessing.get_start_method() == "fork"
 # sphinx_gallery_end_ignore
 
-import torch.cuda
-import tqdm
 
-import torch.multiprocessing
+import torchrl
+import torch
+import tqdm
+from typing import Tuple
 
 ###############################################################################
 # We will execute the policy on CUDA if available
-device = (
-    torch.device("cpu") if torch.cuda.device_count() == 0 else torch.device("cuda:0")
-)
+device =  torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 collector_device = torch.device("cpu")  # Change the device to ``cuda`` to use CUDA
 
 ###############################################################################
@@ -1221,6 +1228,6 @@ def ceil_div(x, y):
 # 
 # To iterate further on this loss module we might consider:
 # 
-# - Using `@dispatch` (see `[Feature] Distpatch IQL loss module <https://github.com/pytorch/rl/pull/1230>`_.
+# - Using `@dispatch` (see `[Feature] Distpatch IQL loss module <https://github.com/pytorch/rl/pull/1230>`_.)
 # - Allowing flexible TensorDict keys.
 # 

_static/img/distributed/device_mesh.png

@@ -439,7 +439,7 @@ def evaluate(eval_model, data_source):
 
 ######################################################################
 # Evaluate the model with the test dataset
-# -------------------------------------
+# ----------------------------------------
 #
 # Apply the best model to check the result with the test dataset.
 

_static/img/hta/comm_across_ranks.png

@@ -56,7 +56,7 @@
 import matplotlib.pyplot as plt
 
 import torchvision.transforms as transforms
-import torchvision.models as models
+from torchvision.models import vgg19, VGG19_Weights
 
 import copy
 
@@ -87,7 +87,7 @@
 # to 255 tensor images. 
 #
 #
-# .. Note::
+# .. note::
 #     Here are links to download the images required to run the tutorial:
 #     `picasso.jpg <https://pytorch.org/tutorials/_static/img/neural-style/picasso.jpg>`__ and
 #     `dancing.jpg <https://pytorch.org/tutorials/_static/img/neural-style/dancing.jpg>`__.
@@ -183,7 +183,7 @@ def forward(self, input):
         return input
 
 ######################################################################
-# .. Note::
+# .. note::
 #    **Important detail**: although this module is named ``ContentLoss``, it
 #    is not a true PyTorch Loss function. If you want to define your content
 #    loss as a PyTorch Loss function, you have to create a PyTorch autograd function 
@@ -262,7 +262,7 @@ def forward(self, input):
 # network to evaluation mode using ``.eval()``.
 # 
 
-cnn = models.vgg19(pretrained=True).features.eval()
+cnn = vgg19(weights=VGG19_Weights.DEFAULT).features.eval()
 
 
 
@@ -372,7 +372,7 @@ def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
 input_img = content_img.clone()
 # if you want to use white noise by using the following code:
 #
-# ::
+# .. code-block:: python
 #
 #    input_img = torch.randn(content_img.data.size())