docs: include external pages (#17826)

* pull docs

* local

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* ...

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* replace

* strategies

* 1.0.0

* skip

* links

* more

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

(cherry picked from commit 6b0ec10)

Author: Borda

.actions/assistant.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import glob
+import logging
 import os
 import pathlib
 import re
@@ -421,6 +422,42 @@ def copy_replace_imports(
             source_dir, source_imports, target_imports, target_dir=target_dir, lightning_by=lightning_by
         )
 
+    @staticmethod
+    def pull_docs_files(
+        gh_user_repo: str,
+        target_dir: str = "docs/source-pytorch/XXX",
+        checkout: str = "tags/1.0.0",
+        source_dir: str = "docs/source",
+    ) -> None:
+        """Pull docs pages from external source and append to local docs."""
+        import zipfile
+
+        zip_url = f"https://github.com/{gh_user_repo}/archive/refs/{checkout}.zip"
+
+        with tempfile.TemporaryDirectory() as tmp:
+            zip_file = os.path.join(tmp, "repo.zip")
+            urllib.request.urlretrieve(zip_url, zip_file)
+
+            with zipfile.ZipFile(zip_file, "r") as zip_ref:
+                zip_ref.extractall(tmp)
+
+            zip_dirs = [d for d in glob.glob(os.path.join(tmp, "*")) if os.path.isdir(d)]
+            # check that the extracted archive has only repo folder
+            assert len(zip_dirs) == 1
+            repo_dir = zip_dirs[0]
+
+            ls_pages = glob.glob(os.path.join(repo_dir, source_dir, "*.rst"))
+            ls_pages += glob.glob(os.path.join(repo_dir, source_dir, "**", "*.rst"))
+            for rst in ls_pages:
+                rel_rst = rst.replace(os.path.join(repo_dir, source_dir) + os.path.sep, "")
+                rel_dir = os.path.dirname(rel_rst)
+                os.makedirs(os.path.join(_PROJECT_ROOT, target_dir, rel_dir), exist_ok=True)
+                new_rst = os.path.join(_PROJECT_ROOT, target_dir, rel_rst)
+                if os.path.isfile(new_rst):
+                    logging.warning(f"Page {new_rst} already exists in the local tree so it will be skipped.")
+                    continue
+                shutil.copy(rst, new_rst)
+
 
 if __name__ == "__main__":
     import jsonargparse

docs/source-pytorch/accelerators/hpu_basic.rst

@@ -58,11 +58,11 @@ Cutting-edge and third-party Strategies
 
 Cutting-edge Lightning strategies are being developed by third-parties outside of Lightning.
 
-If you want to try some of the latest and greatest features for model-parallel training, check out the :doc:`Colossal-AI Strategy <./third_party/colossalai>` integration.
+If you want to try some of the latest and greatest features for model-parallel training, check out the :doc:`Colossal-AI Strategy <../integrations/strategies/colossalai>` integration.
 
-Another integration is :doc:`Bagua Strategy <./third_party/bagua>`, deep learning training acceleration framework for PyTorch, with advanced distributed training algorithms and system optimizations.
+Another integration is :doc:`Bagua Strategy <../integrations/strategies/bagua>`, deep learning training acceleration framework for PyTorch, with advanced distributed training algorithms and system optimizations.
 
-For training on unreliable mixed GPUs across the internet check out the :doc:`Hivemind Strategy <./third_party/hivemind>` integration.
+For training on unreliable mixed GPUs across the internet check out the :doc:`Hivemind Strategy <../integrations/strategies/hivemind>` integration.
 
 ----
 

docs/source-pytorch/accelerators/hpu_intermediate.rst

@@ -18,7 +18,7 @@
    Save memory with half-precision <precision>
    ../advanced/model_parallel
    Train on single or multiple GPUs <../accelerators/gpu>
-   Train on single or multiple HPUs <../accelerators/hpu>
+   Train on single or multiple HPUs <../integrations/hpu/index>
    Train on single or multiple IPUs <../accelerators/ipu>
    Train on single or multiple TPUs <../accelerators/tpu>
    Train on MPS <../accelerators/mps>
@@ -150,7 +150,7 @@ How-to Guides
 .. displayitem::
     :header: Train on single or multiple HPUs
     :description: Train models faster with HPU accelerators
-    :button_link: ../accelerators/hpu.html
+    :button_link: ../integrations/hpu/index.html
     :col_css: col-md-4
     :height: 180
 

docs/source-pytorch/advanced/model_parallel.rst

@@ -123,7 +123,7 @@ Customize and extend Lightning for things like custom hardware or distributed st
    :header: Train on single or multiple HPUs
    :description: Train models faster with HPUs.
    :col_css: col-md-12
-   :button_link: accelerators/hpu.html
+   :button_link: integrations/hpu/index.html
    :height: 100
 
 .. displayitem::