Merge branch 'main' into 099_gpu_quant

Author: svekars

.gitignore

@@ -3,7 +3,7 @@ beginner
 intermediate
 advanced
 pytorch_basics
-recipes
+/recipes
 prototype
 
 #data things

.jenkins/build.sh

@@ -57,6 +57,8 @@ if [[ "${JOB_TYPE}" == "worker" ]]; then
   # IMPORTANT NOTE: We assume that each tutorial has a UNIQUE filename.
   FILES_TO_RUN=$(python .jenkins/get_files_to_run.py)
   echo "FILES_TO_RUN: " ${FILES_TO_RUN}
+  # Files to run must be accessible to subprocessed (at least to `download_data.py`)
+  export FILES_TO_RUN
 
   # Step 3: Run `make docs` to generate HTML files and static files for these tutorials
   make docs

beginner_source/basics/optimization_tutorial.py

@@ -163,7 +163,7 @@ def train_loop(dataloader, model, loss_fn, optimizer):
         optimizer.zero_grad()
 
         if batch % 100 == 0:
-            loss, current = loss.item(), (batch + 1) * len(X)
+            loss, current = loss.item(), batch * batch_size + len(X)
             print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
 
 

recipes_source/recipes/defining_a_neural_network.py

@@ -17,7 +17,7 @@
 
 In this recipe, we will use ``torch.nn`` to define a neural network
 intended for the `MNIST
-dataset <https://pytorch.org/docs/stable/torchvision/datasets.html#mnist>`__.
+dataset <hhttps://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST>`__.
 
 Setup
 -----

recipes_source/recipes/tensorboard_with_pytorch.py

@@ -121,44 +121,6 @@ def train_model(iter):
 #
 
 
-######################################################################
-# Share TensorBoard dashboards
-# ----------------------------
-#
-# `TensorBoard.dev <https://tensorboard.dev/>`_ lets you upload and share 
-# your ML experiment results with anyone. Use ``TensorBoard.dev`` to host,
-# track, and share your TensorBoard dashboards.
-#
-# Install the latest version of TensorBoard to use the ``uploader``.
-#
-# .. code-block:: sh
-#
-#    pip install tensorboard --upgrade
-#
-# Use a simple command to upload and share your TensorBoard.
-#
-# .. code-block:: sh
-#
-#    tensorboard dev upload --logdir runs \
-#    --name "My latest experiment" \ # optional
-#    --description "Simple comparison of several hyperparameters" # optional
-#
-# For help, run ``$ tensorboard dev --help``.
-#
-# **Note:** Uploaded TensorBoards are public and visible to everyone.
-# Do not upload sensitive data.
-#
-# View your TensorBoard live at URL provided in your terminal.
-# For example: `https://tensorboard.dev/experiment/AdYd1TgeTlaLWXx6I8JUbA <https://tensorboard.dev/experiment/AdYd1TgeTlaLWXx6I8JUbA>`_
-#
-#
-# .. image:: ../../_static/img/thumbnails/tensorboard_dev.png
-#    :scale: 40 %
-# 
-# 
-# .. note::
-#    ``TensorBoard.dev`` currently supports scalars, graphs, histograms, distributions, ``hparams``, and text dashboards.
-
 ########################################################################
 # Learn More
 # ----------------------------

recipes_source/torch_logs.py

@@ -8,7 +8,7 @@
 
 ######################################################################
 #
-# This tutorial introduces the ``TORCH_LOGS`` environment variable, as well ass the Python API, and
+# This tutorial introduces the ``TORCH_LOGS`` environment variable, as well as the Python API, and
 # demonstrates how to apply it to observe the phases  of ``torch.compile``.
 #
 # .. note::