Merge branch 'main' into add-matplotlib

Author: carljparker

.github/scripts/docathon-label-sync.py

@@ -0,0 +1,43 @@
+import os
+from github import Github
+import sys
+import re
+
+def main():
+    token = os.environ.get('GITHUB_TOKEN')
+
+    repo_owner = "pytorch"
+    repo_name = "tutorials"
+    pull_request_number = int(sys.argv[1])
+
+    g = Github(token)
+    repo = g.get_repo(f'{repo_owner}/{repo_name}')
+    pull_request = repo.get_pull(pull_request_number)
+    pull_request_body = pull_request.body
+
+    # get issue number from the PR body
+    if not re.search(r'#\d{1,5}', pull_request_body):
+        print("The pull request does not mention an issue.")
+        return
+    issue_number = int(re.findall(r'#(\d{1,5})', pull_request_body)[0])
+    issue = repo.get_issue(issue_number)
+    issue_labels = issue.labels
+    docathon_label_present = any(label.name == 'docathon-h1-2023' for label in issue_labels)
+
+    # if the issue has a docathon label, add all labels from the issue to the PR.
+    if not docathon_label_present:
+        print("The 'docathon-h1-2023' label is not present in the issue.")
+        return    
+    pull_request_labels = pull_request.get_labels()
+    issue_label_names = [label.name for label in issue_labels]
+    labels_to_add = [label for label in issue_label_names if label not in pull_request_labels]
+    if not labels_to_add:
+        print("The pull request already has the same labels.")
+        return
+    pull_request.set_labels(*labels_to_add)
+    print("Labels added to the pull request!")
+        
+
+           
+if __name__  == "__main__":
+    main()

.github/workflows/docathon-label-sync.yml

@@ -0,0 +1,27 @@
+name: Docathon Labels Sync
+
+on:
+  pull_request_target:
+    types: [opened, synchronize, edited]
+
+jobs:
+  check-labels:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check if PR mentions an issue and get labels
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.x
+      - name: Install dependencies
+        run: |
+          pip install requests
+          pip install PyGithub
+      - name: Run Python script
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: python ./.github/scripts/docathon-label-sync.py ${{ github.event.pull_request.number }}

advanced_source/super_resolution_with_onnxruntime.py

@@ -16,10 +16,7 @@
 and `ONNX Runtime <https://github.com/microsoft/onnxruntime>`__.
 You can get binary builds of ONNX and ONNX Runtime with
 ``pip install onnx onnxruntime``.
-Note that ONNX Runtime is compatible with Python versions 3.5 to 3.7.
-
-``NOTE``: This tutorial needs PyTorch master branch which can be installed by following
-the instructions `here <https://github.com/pytorch/pytorch#from-source>`__
+ONNX Runtime recommends using the latest stable runtime for PyTorch.
 
 """
 

beginner_source/README.txt

@@ -23,4 +23,4 @@ Beginner Tutorials
 
 6. transformer_translation.py
 	Language Translation with Transformers
-	https://pytorch.org/tutorials/beginner/transformer_tutorial.html
+	https://pytorch.org/tutorials/beginner/translation_transformer.html

beginner_source/hyperparameter_tuning_tutorial.py

@@ -201,7 +201,7 @@ def forward(self, x):
 #
 # The checkpoint saving is optional, however, it is necessary if we wanted to use advanced
 # schedulers like
-# `Population Based Training <https://docs.ray.io/en/master/tune/tutorials/tune-advanced-tutorial.html>`_.
+# `Population Based Training <https://docs.ray.io/en/latest/tune/examples/pbt_guide.html>`_.
 # Also, by saving the checkpoint we can later load the trained models and validate them
 # on a test set. Lastly, saving checkpoints is useful for fault tolerance, and it allows
 # us to interrupt training and continue training later.

beginner_source/transformer_tutorial.py

@@ -37,7 +37,7 @@
 # ``nn.TransformerEncoder`` consists of multiple layers of
 # `nn.TransformerEncoderLayer <https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoderLayer.html>`__.
 # Along with the input sequence, a square attention mask is required because the
-# self-attention layers in ``nn.TransformerEncoder`` are only allowed to attend
+# self-attention layers in ``nn.TransformerDecoder`` are only allowed to attend
 # the earlier positions in the sequence. For the language modeling task, any
 # tokens on the future positions should be masked. To produce a probability
 # distribution over output words, the output of the ``nn.TransformerEncoder``

intermediate_source/char_rnn_classification_tutorial.py

@@ -2,7 +2,7 @@
 """
 NLP From Scratch: Classifying Names with a Character-Level RNN
 **************************************************************
-**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_
+**Author**: `Sean Robertson <https://github.com/spro>`_
 
 We will be building and training a basic character-level RNN to classify
 words. This tutorial, along with the following two, show how to do

intermediate_source/char_rnn_generation_tutorial.py

@@ -2,7 +2,7 @@
 """
 NLP From Scratch: Generating Names with a Character-Level RNN
 *************************************************************
-**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_
+**Author**: `Sean Robertson <https://github.com/spro>`_
 
 This is our second of three tutorials on "NLP From Scratch".
 In the `first tutorial </intermediate/char_rnn_classification_tutorial>`

intermediate_source/dynamic_quantization_bert_tutorial.rst

@@ -68,7 +68,7 @@ built-in F1 score calculation helper function.
 .. code:: shell
 
    pip install sklearn
-   pip install transformers
+   pip install transformers==4.29.2
 
 
 Because we will be using the beta parts of the PyTorch, it is

intermediate_source/seq2seq_translation_tutorial.py

@@ -2,7 +2,7 @@
 """
 NLP From Scratch: Translation with a Sequence to Sequence Network and Attention
 *******************************************************************************
-**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_
+**Author**: `Sean Robertson <https://github.com/spro>`_
 
 This is the third and final tutorial on doing "NLP From Scratch", where we
 write our own classes and functions to preprocess the data to do our NLP