Merge branch 'master' into feat/dynamo_export_onnx

Author: GdoongMathew

.github/ISSUE_TEMPLATE/1_bug_report.yaml

@@ -46,12 +46,22 @@ body:
     attributes:
       value: "**Note: The rest of this form is optional, but filling it out may help us to provide better support.**"
 
+  - type: input
+    attributes:
+      label: Reproduced in studio
+      description: >
+        Create a new Lightning Studio with code that reproduces the issue and share the link.
+        Also include all the relevant files and data required to reproduce shared issue.
+        In case the code does not crash, please add assert statements to show what is the real and expected output.
+        A simple guide on how to create such a studio can be found [here](https://www.youtube.com/watch?v=YcW-2Zt_bFg&ab_channel=LightningAI).
+      placeholder: https://lightning.ai/live-session/...
+    validations:
+      required: false
   - type: textarea
     attributes:
       label: How to reproduce the bug
       description: >
-        Provide steps and example code here.
-        You can also paste a link to Google Colab (see our [Colab bug report template](https://colab.research.google.com/github/Lightning-AI/lightning/blob/master/examples/pytorch/bug_report/bug_report_model.ipynb)) or adapt this minimal [snippet](https://github.com/Lightning-AI/lightning/blob/master/examples/pytorch/bug_report/bug_report_model.py).
+        In the special case when the issue can't be reproduced in a studio, provide steps and example code here.
       placeholder: |
         ```python
         # Sample code to reproduce the problem

.github/dependabot.yml

@@ -19,8 +19,6 @@ updates:
       separator: "-"
     # Allow up to 5 open pull requests for pip dependencies
     open-pull-requests-limit: 10
-    reviewers:
-      - "Lightning-AI/teams/core-lightning"
 
   # Enable version updates for GitHub Actions
   - package-ecosystem: "github-actions"
@@ -37,5 +35,3 @@ updates:
       separator: "-"
     # Allow up to 5 open pull requests for GitHub Actions
     open-pull-requests-limit: 10
-    reviewers:
-      - "Lightning-AI/teams/core-lightning"

.github/workflows/_legacy-checkpoints.yml

@@ -113,7 +113,7 @@ jobs:
 
       - run: pip install -r requirements/ci.txt
       - name: Upload checkpoints to S3
-        if: secrets.AWS_REGION != ''
+        if: ${{ secrets[AWS_REGION] != '' }}
         working-directory: ${{ env.LEGACY_FOLDER }}
         env:
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY }}

.github/workflows/ci-checkpoints.yml …ub/workflows/ci-checkpoints.yml.disabled.github/workflows/ci-checkpoints.yml renamed to .github/workflows/ci-checkpoints.yml.disabled .github/workflows/ci-checkpoints.yml renamed to .github/workflows/ci-checkpoints.yml.disabled

@@ -94,6 +94,11 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
+        # adding dome more images as Thunder mainly using python 3.10,
+        # and we need to support integrations as for example LitGPT
+        python_version: ["3.10"]
+        pytorch_version: ["2.6.0", "2.7.0"]
+        cuda_version: ["12.6.3"]
         include:
           # These are the base images for PL release docker images.
           # Make sure the matrix here matches the one above.

.github/workflows/docker-build.yml

@@ -13,7 +13,7 @@ on:
       - "requirements/ci.txt"
       - ".github/actions/pkg-check/*"
       - ".github/actions/pkg-publish/*"
-      - ".github/workflows/_legacy-checkpoints.yml.yml"
+      - ".github/workflows/_legacy-checkpoints.yml"
       - ".github/workflows/_build-packages.yml"
       - ".github/workflows/release-pypi.yml"
 
@@ -179,12 +179,12 @@ jobs:
         with:
           pkg-folder: dist/${{ steps.folder.outputs.pkg }}
           pypi-token: ${{ secrets[format('PYPI_TOKEN_{0}', matrix.name)] }}
-
-  legacy-checkpoints:
-    needs: [build-packages]
-    uses: ./.github/workflows/_legacy-checkpoints.yml
-    with:
-      push_to_s3: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
-      upload_local: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
-      create_pr: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
-    secrets: inherit
+# FIXME: this is not working suddenly, Unrecognized named-value: 'secrets'
+#  legacy-checkpoints:
+#    needs: [build-packages]
+#    uses: ./.github/workflows/_legacy-checkpoints.yml
+#    with:
+#      push_to_s3: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
+#      upload_local: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
+#      create_pr: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
+#    secrets: inherit

.github/workflows/release-pkg.yml

@@ -198,7 +198,7 @@ node_modules/
 **/events.out.tfevents.*
 examples/**/*.png
 
-# instalation artifacts
+# installation artifacts
 requirements/base.txt
 
 # CI

.gitignore

@@ -276,7 +276,7 @@ Next steps
 
 .. displayitem::
     :header: Pipeline Parallelism
-    :description: Coming sooon
+    :description: Coming soon
     :col_css: col-md-4
     :height: 160
     :tag: advanced

_notebooks

@@ -262,7 +262,7 @@ Avoid graph breaks
 When ``torch.compile`` looks at the code in your model's ``forward()`` or ``*_step()`` method, it will try to compile as much of the code as possible.
 If there are regions in the code that it doesn't understand, it will introduce a so-called "graph break" that essentially splits the code in optimized and unoptimized parts.
 Graph breaks aren't a deal breaker, since the optimized parts should still run faster.
-But if you want to get the most out of ``torch.compile``, you might want to invest rewriting the problematic section of the code that produce the breaks.
+But if you want to get the most out of ``torch.compile``, you might want to invest rewriting the problematic section of the code that produces the breaks.
 
 You can check whether your model produces graph breaks by calling ``torch.compile`` with ``fullgraph=True``:
 
@@ -332,7 +332,7 @@ Enabling CUDA Graphs often results in a significant speedup, but sometimes also
 
 **Shape padding:** The specific shape/size of the tensors involved in the computation of your model (input, activations, weights, gradients, etc.) can have an impact on the performance.
 With shape padding enabled, ``torch.compile`` can extend the tensors by padding to a size that gives a better memory alignment.
-Naturally, the tradoff here is that it will consume a bit more memory.
+Naturally, the tradeoff here is that it will consume a bit more memory.
 
 .. code-block:: python