RCE via Property/Class Pollution in lightning-ai/pytorch-lightning

@@ -14,10 +14,8 @@
 import glob
 import logging
 import os
-import pathlib
 import re
 import shutil
-import tarfile
 import tempfile
 import urllib.request
 from distutils.version import LooseVersion
@@ -35,11 +33,6 @@
         "requirements/pytorch/strategies.txt",
         "requirements/pytorch/examples.txt",
     ),
-    "app": (
-        "requirements/app/app.txt",
-        "requirements/app/cloud.txt",
-        "requirements/app/ui.txt",
-    ),
     "fabric": (
         "requirements/fabric/base.txt",
         "requirements/fabric/strategies.txt",
@@ -216,30 +209,6 @@ def distribute_version(src_folder: str, ver_file: str = "version.info") -> None:
         shutil.copy2(ver_template, fpath)
 
 
-def _download_frontend(pkg_path: str, version: str = "v0.0.0"):
-    """Downloads an archive file for a specific release of the Lightning frontend and extracts it to the correct
-    directory."""
-
-    try:
-        frontend_dir = pathlib.Path(pkg_path, "ui")
-        download_dir = tempfile.mkdtemp()
-
-        shutil.rmtree(frontend_dir, ignore_errors=True)
-        # TODO: remove this once lightning-ui package is ready as a dependency
-        frontend_release_url = f"https://lightning-packages.s3.amazonaws.com/ui/{version}.tar.gz"
-        response = urllib.request.urlopen(frontend_release_url)
-
-        file = tarfile.open(fileobj=response, mode="r|gz")
-        file.extractall(path=download_dir)  # noqa: S202
-
-        shutil.move(download_dir, frontend_dir)
-        print("The Lightning UI has successfully been downloaded!")
-
-    # If installing from source without internet connection, we don't want to break the installation
-    except Exception:
-        print("The Lightning UI downloading has failed!")
-
-
 def _load_aggregate_requirements(req_dir: str = "requirements", freeze_requirements: bool = False) -> None:
     """Load all base requirements from all particular packages and prune duplicates.
 
@@ -466,7 +435,7 @@ def pull_docs_files(
                 raise RuntimeError(f"Requesting file '{zip_url}' does not exist or it is just unavailable.")
 
             with zipfile.ZipFile(zip_file, "r") as zip_ref:
-                zip_ref.extractall(tmp)  # noqa: S202
+                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

@@ -1,206 +0,0 @@
-# Python package
-# Create and test a Python package on multiple Python versions.
-# Add steps that analyze code, save the dist with the build record, publish to a PyPI-compatible index, and more:
-# https://docs.microsoft.com/azure/devops/pipelines/languages/python
-
-trigger:
-  tags:
-    include:
-      - "*"
-  branches:
-    include:
-      - "master"
-      - "release/*"
-      - "refs/tags/*"
-
-schedules:
-  - cron: "0 0 * * *" # At the end of every day
-    displayName: Daily midnight testing
-    branches:
-      include:
-        - "master"
-
-pr:
-  branches:
-    include:
-      - "master"
-      - "release/*"
-  paths:
-    include:
-      - ".actions/*"
-      - ".azure/app-cloud-e2e.yml"
-      - "src/lightning/__about__.py"
-      - "src/lightning/__init__.py"
-      - "src/lightning/__main__.py"
-      - "src/lightning/__setup__.py"
-      - "src/lightning/__version__.py"
-      - "src/lightning/app/**"
-      - "src/lightning_app/*"
-      - "examples/app/**"
-      - "requirements/app/**"
-      - "tests/integrations_app/**"
-      - "setup.py"
-    exclude:
-      - "!tests/integrations_app/flagship/**"
-      - "requirements/*/docs.txt"
-      - "*.md"
-      - "**/*.md"
-
-# variables are automatically exported as environment variables so this will override pip's default cache dir
-variables:
-  - name: pip_cache_dir
-    value: $(Pipeline.Workspace)/.pip
-  - name: local_id
-    value: $(Build.BuildId)
-  - name: video_artifact_dir
-    value: ./videos
-
-jobs:
-  - job: test_e2e
-    pool: "azure-cpus"
-    container:
-      # see all available tags: https://mcr.microsoft.com/en-us/product/playwright/python/tags
-      image: mcr.microsoft.com/playwright/python:v1.38.0-focal
-      options: "--shm-size=4gb"
-    strategy:
-      matrix:
-        "App: v0_app":
-          name: "v0_app"
-          dir: "public"
-        "App: boring_app":
-          name: "boring_app"
-          dir: "public"
-        "App: template_streamlit_ui":
-          name: "template_streamlit_ui"
-          dir: "public"
-        "App: template_react_ui":
-          name: "template_react_ui"
-          dir: "public"
-        # 'App: template_jupyterlab':  # TODO: clarify where these files lives
-        #   name: "template_jupyterlab"
-        "App: installation_commands_app":
-          name: "installation_commands_app"
-          dir: "public"
-        "App: drive":
-          name: "drive"
-          dir: "public"
-        "App: payload":
-          name: "payload"
-          dir: "public"
-        "App: commands_and_api":
-          name: "commands_and_api"
-          dir: "public"
-        "App: quick_start":
-          name: "quick_start"
-          dir: "public"
-        "App: idle_timeout":
-          name: "idle_timeout"
-          dir: "local"
-        "App: collect_failures":
-          name: "collect_failures"
-          dir: "local"
-        "App: custom_work_dependencies":
-          name: "custom_work_dependencies"
-          dir: "local"
-    timeoutInMinutes: "15"
-    cancelTimeoutInMinutes: "1"
-    # values: https://docs.microsoft.com/en-us/azure/devops/pipelines/process/phases?view=azure-devops&tabs=yaml#workspace
-    workspace:
-      clean: all
-    variables:
-      FREEZE_REQUIREMENTS: "1"
-      HEADLESS: "1"
-      PACKAGE_LIGHTNING: "1"
-      CLOUD: "1"
-      VIDEO_LOCATION: $(video_artifact_dir)
-      PR_NUMBER: $(local_id)
-      TEST_APP_NAME: $(name)
-      TEST_APP_FOLDER: $(dir)
-      HAR_LOCATION: "./artifacts/hars"
-      SLOW_MO: "50"
-      LIGHTNING_DEBUG: "1"
-    steps:
-      - script: echo '##vso[task.setvariable variable=local_id]$(System.PullRequest.PullRequestNumber)'
-        displayName: "Set id for this PR"
-        condition: eq(variables['Build.Reason'], 'PullRequest')
-
-      - bash: |
-          whoami
-          mkdir -p "$(video_artifact_dir)/$(name)"
-          printf "local id: $(local_id)\n"
-          python --version
-          pip --version
-          echo "allow fail: ${{ in(variables['name'], 'quick_start', 'template_react_ui') }}"
-        displayName: "Info"
-
-      # TODO: we are testing it as `lightning`, so add also version for `lightning_app`
-      - bash: |
-          pip install -e .[app-dev] \
-            -f https://download.pytorch.org/whl/cpu/torch_stable.html
-        displayName: "Install Lightning & dependencies"
-
-      - bash: python -m playwright install # --with-deps
-        displayName: "Install Playwright system dependencies"
-
-      # The magic happens here it doesn't need to install the quick start dependencies.
-      # This test is very important to test the main user story of lightning app.
-      # It also e2e tests running on cloud without installing dependencies.
-      - bash: |
-          git clone https://github.com/Lightning-AI/lightning-quick-start examples/app/quick-start
-        # without succeeded this could run even if the job has already failed
-        condition: and(succeeded(), eq(variables['name'], 'quick_start'))
-        displayName: "Clone Quick start Repo"
-      - bash: |
-          git clone https://github.com/Lightning-AI/lightning-template-react examples/app/template_react_ui
-        # without succeeded this could run even if the job has already failed
-        condition: and(succeeded(), eq(variables['name'], 'template_react_ui'))
-        displayName: "Clone Template React UI Repo"
-
-      # Replace imports to use `lightning` instead of `lightning_app` since we install lightning only ATM
-      - bash: |
-          pip install -q -r .actions/requirements.txt
-          python .actions/assistant.py copy_replace_imports \
-            --source_dir="./examples" --source_import="lightning_app" --target_import="lightning.app"
-        displayName: "Adjust examples"
-
-      - bash: pip --version && pip list
-        displayName: "List pip dependency"
-
-      - bash: |
-          ls -l examples/app/$(TEST_APP_NAME)
-          echo ${TEST_FILE}
-          python -m pytest ${TEST_FILE}::test_$(TEST_APP_NAME)_example_cloud \
-            --timeout=360 --capture=no -v --color=yes
-        env:
-          TEST_FILE: tests/integrations_app/$(TEST_APP_FOLDER)/test_$(TEST_APP_NAME).py
-          #LAI_USER: $(LAI_USER)  # for STAGING
-          #LAI_PASS: $(LAI_PASS)  # for STAGING
-          LIGHTNING_USER_ID: $(LIGHTNING_USER_ID_PROD)
-          LIGHTNING_API_KEY: $(LIGHTNING_API_KEY_PROD)
-          LIGHTNING_USERNAME: $(LIGHTNING_USERNAME_PROD)
-          LIGHTNING_CLOUD_URL: $(LIGHTNING_CLOUD_URL_PROD)
-        # Todo: investigate why these apps are failing
-        continueOnError: ${{ in(variables['name'], 'quick_start', 'template_react_ui') }}
-        displayName: "Run the tests"
-
-      - task: PublishPipelineArtifact@1
-        condition: failed()
-        inputs:
-          path: "$(video_artifact_dir)/$(name)"
-          artifactName: $(name)
-          publishLocation: "pipeline"
-        displayName: "Publish videos"
-
-      - bash: |
-          time python -c "from lightning.app import testing; testing.delete_cloud_lightning_apps()"
-        condition: always()
-        continueOnError: "true"
-        timeoutInMinutes: "3"
-        env:
-          #LAI_USER: $(LAI_USER)  # for STAGING
-          #LAI_PASS: $(LAI_PASS)  # for STAGING
-          LIGHTNING_USER_ID: $(LIGHTNING_USER_ID_PROD)
-          LIGHTNING_API_KEY: $(LIGHTNING_API_KEY_PROD)
-          LIGHTNING_USERNAME: $(LIGHTNING_USERNAME_PROD)
-          LIGHTNING_CLOUD_URL: $(LIGHTNING_CLOUD_URL_PROD)
-        displayName: "Clean Previous Apps"

@@ -9,7 +9,6 @@
     </style>
   </head>
   <body>
-    <a href="app/"><button>App</button></a>
     <a href="fabric/"><button>Fabric</button></a>
     <a href="pytorch/"><button>PyTorch</button></a>
   </body>

@@ -5,7 +5,7 @@ if ! [ $READTHEDOCS_VERSION == "latest" -o $READTHEDOCS_VERSION == "stable" ];
 then
     export FAST_DOCS_DEV=1 ;
     root=$(pwd) ;
-    for pkg in 'app' 'fabric' 'pytorch' ;
+    for pkg in 'fabric' 'pytorch' ;
     do
       cd $root/docs/source-$pkg ;
       make html --jobs $(nproc) ;

@@ -1,35 +0,0 @@
-########################
-lightning.app.components
-########################
-
-.. contents::
-    :depth: 1
-    :local:
-    :backlinks: top
-
-.. currentmodule:: lightning.app.components
-
-
-Built-in Components
-___________________
-
-.. autosummary::
-    :toctree: generated/
-    :nosignatures:
-    :template: classtemplate.rst
-
-    ~database.client.DatabaseClient
-    ~database.server.Database
-    ~python.popen.PopenPythonScript
-    ~python.tracer.TracerPythonScript
-    ~training.LightningTrainerScript
-    ~serve.gradio_server.ServeGradio
-    ~serve.serve.ModelInferenceAPI
-    ~serve.python_server.PythonServer
-    ~serve.streamlit.ServeStreamlit
-    ~multi_node.base.MultiNode
-    ~multi_node.fabric.FabricMultiNode
-    ~multi_node.pytorch_spawn.PyTorchSpawnMultiNode
-    ~multi_node.trainer.LightningTrainerMultiNode
-    ~serve.auto_scaler.AutoScaler
-    ~serve.auto_scaler.ColdStartProxy

@@ -1,26 +0,0 @@
-:orphan:
-
-##################
-lightning.app.core
-##################
-
-.. contents::
-    :depth: 1
-    :local:
-    :backlinks: top
-
-.. currentmodule:: lightning.app.core
-
-Core APIs
-___________________
-
-.. autosummary::
-    :toctree: api/
-    :nosignatures:
-    :template: classtemplate.rst
-
-    LightningApp
-    LightningFlow
-    LightningWork
-
-Learn more about :ref:`Lightning Core <core_api>`.

@@ -1,25 +0,0 @@
-######################
-lightning.app.frontend
-######################
-
-.. contents::
-    :depth: 1
-    :local:
-    :backlinks: top
-
-.. currentmodule:: lightning.app.frontend
-
-Lightning FrontEnds
-___________________
-
-.. autosummary::
-    :toctree: generated/
-    :nosignatures:
-    :template: classtemplate.rst
-
-    ~frontend.Frontend
-    ~web.StaticWebFrontend
-    ~stream_lit.StreamlitFrontend
-    ~panel.PanelFrontend
-
-Learn more about :ref:`Frontend's <ui_and_frontends>`.

@@ -1,21 +0,0 @@
-#####################
-lightning.app.runners
-#####################
-
-.. contents::
-    :depth: 1
-    :local:
-    :backlinks: top
-
-.. currentmodule:: lightning.app.runners
-
-Lightning Core
-______________
-
-.. autosummary::
-    :toctree: generated/
-    :nosignatures:
-    :template: classtemplate.rst
-
-    ~cloud.CloudRuntime
-    ~multiprocess.MultiProcessRuntime

@@ -1,71 +0,0 @@
-#####################
-lightning.app.storage
-#####################
-
-Lightning Core
-______________
-
-.. contents::
-    :depth: 1
-    :local:
-    :backlinks: top
-
-.. currentmodule:: lightning.app.storage
-
-.. autosummary::
-    :toctree: generated/
-    :nosignatures:
-    :template: classtemplate.rst
-
-    ~path.Path
-    ~drive.Drive
-    ~payload.Payload
-    ~mount.Mount
-
-----
-
-************************
-Learn more about Storage
-************************
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Learn about the differences between Drive vs Path.
-   :description: Learn about their differences.
-   :col_css: col-md-4
-   :button_link: ../glossary/storage/differences.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: The Drive Object.
-   :description: Put, List and Get Files From a Shared Drive Disk.
-   :col_css: col-md-4
-   :button_link:  ../glossary/storage/drive.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: The Path Object.
-   :description: Transfer Files From One Component to Another by Reference.
-   :col_css: col-md-4
-   :button_link:  ../glossary/storage/path.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: The Mount Object.
-   :description: Mount an AWS S3 Bucket When Running on the Cloud.
-   :col_css: col-md-4
-   :button_link:  ../workflows/mount_aws_s3_bucket.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,259 +0,0 @@
-:orphan:
-
-.. _basics:
-
-######
-Basics
-######
-
-In this guide, we'll cover the basic terminology associated with the Lightning framework.
-
-----
-
-**************
-Lightning App
-**************
-
-The :class:`~lightning.app.core.app.LightningApp` runs a tree of one or more components that interact to create end-to-end applications. There are two kinds of components: :class:`~lightning.app.core.flow.LightningFlow` and :class:`~lightning.app.core.work.LightningWork`. This modular design enables you to reuse components created by other users.
-
-----
-
-Lightning Work
-^^^^^^^^^^^^^^
-
-The :class:`~lightning.app.core.work.LightningWork` component is a building block optimized for long-running jobs or integrating third-party services. LightningWork can be used for training large models, downloading a dataset, or any long-lasting operation.
-
-----
-
-Lightning Flow
-^^^^^^^^^^^^^^
-
-The :class:`~lightning.app.core.flow.LightningFlow` component coordinates long-running tasks :class:`~lightning.app.core.work.LightningWork` and runs its children :class:`~lightning.app.core.flow.LightningFlow` components.
-
-----
-
-Lightning App Tree
-^^^^^^^^^^^^^^^^^^
-
-Components can be nested to form component trees where the LightningFlows are its branches and LightningWorks are its leaves.
-
-Here's a basic application with four flows and two works:
-
-.. literalinclude:: code_samples/quickstart/app_comp.py
-
-And here's its associated tree structure:
-
-.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/tree.gif
-    :alt: Basic App Components
-    :width: 100 %
-
-A Lightning App runs all flows into a single process. Its flows coordinate the execution of the works each running in their own independent processes.
-
-----
-
-Lightning Distributed Event Loop
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Drawing inspiration from modern web frameworks like `React.js <https://reactjs.org/>`_, the Lightning app runs all flows in an **event loop** (forever), which is triggered every 0.1 seconds after collecting any works' state change.
-
-.. figure::  https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning_loop.gif
-
-When running an app in the cloud, the :class:`~lightning.app.core.work.LightningWork` run on different machines. Lightning communicates any :class:`~lightning.app.core.work.LightningWork` state changes to the **event loop** which re-executes the flow with the newly-collected works' state.
-
-----
-
-Lightning App State
-^^^^^^^^^^^^^^^^^^^
-
-By design, each component is stateful and its state is composed of all its attributes. The **Lightning App State** is the collection of all its components state.
-
-With this mechanism, any component can **react** to any other component **state changes**, simply by relying on its attributes within the flow.
-
-For example, here we define two flow components, **RootFlow** and **ChildFlow**, where the child flow prints and increments a counter indefinitely and gets reflected in **RootFlow** state.
-
-You can easily check the state of your entire app:
-
-.. literalinclude:: code_samples/quickstart/app_01.py
-
-Here's the entire tree structure associated with your app:
-
-.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/parent_child.png
-    :alt: Parent Child Components
-    :width: 100 %
-
-And here's the output you get when running the above application using **Lightning CLI**:
-
-.. code-block:: console
-
-    $ lightning_app run app docs/source/code_samples/quickstart/app_01.py
-      INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
-      State: {'works': {'w_1': {'vars': {'counter': 1}}, 'w_2': {'vars': {'counter': 0}}}}
-
-      State: {'works': {'w_1': {'vars': {'counter': 3}}, 'w_2': {'vars': {'counter': 1}}}}
-
-      State: {'works': {'w_1': {'vars': {'counter': 4}}, 'w_2': {'vars': {'counter': 1}}}}
-
-      State: {'works': {'w_1': {'vars': {'counter': 5}}, 'w_2': {'vars': {'counter': 2}}}}
-
-      State: {'works': {'w_1': {'vars': {'counter': 6}}, 'w_2': {'vars': {'counter': 2}}}}
-
-      State: {'works': {'w_1': {'vars': {'counter': 7}}, 'w_2': {'vars': {'counter': 3}}}}
-      ...
-
-This app will count forever because the **lightning event loop** indefinitely calls the root flow run method.
-
-----
-
-*******************************
-Controlling the Execution Flow
-*******************************
-
-
-LightningWork: To Cache or Not to Cache Calls
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-With Lightning, you can control how to run your components.
-
-By default, the :class:`~lightning.app.core.flow.LightningFlow` is executed infinitely by the **Lightning Infinite Loop** and the :class:`~lightning.app.core.work.LightningWork` does not run in **parallel**,
-meaning the **Lightning Infinite Loop** (a.k.a the flow) waits until that long-running work is completed to continue.
-
-Similar to `React.js Components and Props <https://reactjs.org/docs/components-and-props.html>`_, the :class:`~lightning.app.core.work.LightningWork`
-component accepts arbitrary inputs (the "props") to its **run** method and by default runs **once** for each unique input provided.
-
-Here's an example of this behavior:
-
-.. literalinclude:: code_samples/basics/0.py
-    :language: python
-    :emphasize-lines: 10, 19
-
-And you should see the following by running the code above:
-
-.. code-block:: console
-
-    $ python example.py
-      INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
-      # After you have clicked `run` on the UI.
-      I received the following props: args: () kwargs: {'value': 1}
-      I received the following props: args: () kwargs: {'value': 10}
-
-As you can see, the intermediate run didn't execute as already cached.
-
-To disable this behavior, set ``cache_calls=False`` to make any LightningWork run infinitely.
-
-.. literalinclude:: code_samples/basics/1.py
-    :diff: code_samples/basics/0.py
-
-.. code-block:: console
-
-    $ python example.py
-      INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
-      # After you have clicked `run` on the UI.
-      I received the following props: args: () kwargs: {'value': 1}
-      I received the following props: args: () kwargs: {'value': 1}
-      I received the following props: args: () kwargs: {'value': 1}
-      I received the following props: args: () kwargs: {'value': 1}
-      I received the following props: args: () kwargs: {'value': 1}
-      I received the following props: args: () kwargs: {'value': 10}
-
-
-.. note:: Passing a sequence of different props to the work run method queues their execution. We recommend avoiding this behavior as it can be hard to debug. Instead, wait for the previous run to execute.
-
-----
-
-LightningWork: Parallel vs Non Parallel
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The LightningWork component is made for long-running jobs.
-
-As an example, let's create a long-running **LightningWork** component that will take 1 hour to do its "work".
-
-.. literalinclude:: code_samples/quickstart/app_02.py
-    :language: python
-    :emphasize-lines: 15
-
-Here's the output you get when running the above application using **Lightning CLI**:
-
-.. code-block:: console
-
-    $ lightning_app run app docs/source/code_samples/quickstart/app_02.py
-      INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
-      # After you have clicked `run` on the UI.
-      0.0 0.0
-      ...
-      0.0003 0.0003
-      ...
-      1.0 1.0
-      ...
-      1 hour later!
-      1.0 1.0
-      1 hour later!
-      1.0 1.0
-      1 hour later!
-      ...
-
-The child work runs only once, hence why the progress counter stops increasing once the work is completed.
-
-This is useful for monitoring the progress of a long-running operation, like training a big model.
-
-.. note ::
-    The Lightning Infinite Loop runs multiple cycles per second.
-    It is good practice to keep the loop running fast, so that your application stays responsive,
-    especially when it contains user-interface components.
-
-----
-
-****************
-Multiple works
-****************
-
-In practical use cases, you might want to execute multiple long-running works in parallel.
-
-To enable this behavior, set ``parallel=True`` in the ``__init__`` method of
-your :class:`~lightning.app.core.work.LightningWork`.
-
-Here's an example of the interaction between parallel and non-parallel behaviors:
-
-.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/blocking_non_blocking.gif
-    :alt: mnist GPU bar
-    :width: 100 %
-
-Below, we reuse the **HourLongWork** work defined in the previous example, but modify the **RootFlow**
-to run two **HourLongWork** works in a parallel way.
-
-.. literalinclude:: code_samples/quickstart/app/app_0.py
-    :emphasize-lines: 21
-
-Above, both ``child_work_1`` and ``child_work_2`` are long-running works that are executed
-asynchronously in parallel.
-
-When running the above app, we see the following logs:
-
-.. code-block:: console
-
-    $ lightning_app run app docs/source/code_samples/quickstart/app/app_0.py
-      INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
-      # After you have clicked `run` on the UI.
-      0.0, 0.0
-      ...
-      0.0003, 0.0003
-      ...
-      1.0, 1.0
-      ...
-      1 hour later `child_work_1` started!
-      1 hour later `child_work_2` started!
-      0.0, 0.0
-      ...
-      0.0003, 0.0003
-      ...
-      1.0, 1.0
-      1 hour later `child_work_1` started!
-      1 hour later `child_work_2` started!
-      ...
-
-----
-
-***********
-Next Steps
-***********
-
-To keep learning about Lightning, build a :ref:`ui_and_frontends`.

@@ -1,19 +0,0 @@
-from lightning.app import LightningWork
-
-
-class ExampleWork(LightningWork):
-    def run(self, *args, **kwargs):
-        print(f"I received the following props: args: {args} kwargs: {kwargs}")
-
-
-work = ExampleWork()
-work.run(value=1)
-
-# Providing the same value. This won't run as already cached.
-work.run(value=1)
-work.run(value=1)
-work.run(value=1)
-work.run(value=1)
-
-# Changing the provided value. This isn't cached and will run again.
-work.run(value=10)

@@ -1,22 +0,0 @@
-from lightning.app import LightningWork
-
-
-class ExampleWork(LightningWork):
-    def __init__(self):
-        super().__init__(cache_calls=False)
-
-    def run(self, *args, **kwargs):
-        print(f"I received the following props: args: {args} kwargs: {kwargs}")
-
-
-work = ExampleWork()
-work.run(value=1)
-
-# Providing the same value. This won't run as already cached.
-work.run(value=1)
-work.run(value=1)
-work.run(value=1)
-work.run(value=1)
-
-# Changing the provided value. This isn't cached and will run again.
-work.run(value=10)

@@ -1,17 +0,0 @@
-from lightning.app import LightningFlow, LightningApp, CloudCompute
-from lightning.app.components import TracerPythonScript
-
-
-class RootFlow(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        self.runner = TracerPythonScript(
-            "train.py",
-            cloud_compute=CloudCompute("gpu"),
-        )
-
-    def run(self):
-        self.runner.run()
-
-
-app = LightningApp(RootFlow())

@@ -1,3 +0,0 @@
-torch
-torchvision
-pytorch_lightning

@@ -1,46 +0,0 @@
-import os
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.utils.data import DataLoader, random_split
-from torchvision import transforms as T
-from torchvision.datasets import MNIST
-
-import lightning.pytorch as pl
-
-
-class LitAutoEncoder(pl.LightningModule):
-    def __init__(self):
-        super().__init__()
-        self.encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3))
-        self.decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28))
-
-    def forward(self, x):
-        # in lightning,
-        # forward defines the prediction/inference actions
-        embedding = self.encoder(x)
-        return embedding
-
-    def training_step(self, batch, batch_idx):
-        # training_step defines the train loop.
-        # It is independent of forward
-        x, _ = batch
-        x = x.view(x.size(0), -1)
-        z = self.encoder(x)
-        x_hat = self.decoder(z)
-        loss = F.mse_loss(x_hat, x)
-        self.log("train_loss", loss)
-        return loss
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
-        return optimizer
-
-
-dataset = MNIST(os.getcwd(), download=True, transform=T.ToTensor())
-train, val = random_split(dataset, [55000, 5000])
-
-autoencoder = LitAutoEncoder()
-trainer = pl.Trainer(accelerator="auto")
-trainer.fit(autoencoder, DataLoader(train), DataLoader(val))

@@ -1,27 +0,0 @@
-from lightning.app import LightningWork, LightningFlow, LightningApp
-from lightning.app.utilities.app_helpers import pretty_state
-
-
-class Work(LightningWork):
-    def __init__(self):
-        super().__init__(cache_calls=False)
-        # Attributes are registered automatically in the state.
-        self.counter = 0
-
-    def run(self):
-        # Incrementing an attribute gets reflected in the `Flow` state.
-        self.counter += 1
-
-
-class Flow(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        self.w = Work()
-
-    def run(self):
-        if self.w.has_started:
-            print(f"State: {pretty_state(self.state)} \n")
-        self.w.run()
-
-
-app = LightningApp(Flow())

@@ -1,32 +0,0 @@
-from time import sleep
-
-from lightning.app import LightningWork, LightningFlow, LightningApp
-
-
-# This work takes an hour to run
-class HourLongWork(LightningWork):
-    def __init__(self, parallel: bool = False):
-        super().__init__(parallel=parallel)
-        self.progress = 0.0
-
-    def run(self):
-        self.progress = 0.0
-        for _ in range(3600):
-            self.progress += 1.0 / 3600  # Reporting my progress to the Flow.
-            sleep(1)
-
-
-class RootFlow(LightningFlow):
-    def __init__(self, child_work: LightningWork):
-        super().__init__()
-        self.child_work = child_work
-
-    def run(self):
-        # prints the progress from the child work
-        print(round(self.child_work.progress, 4))
-        self.child_work.run()
-        if self.child_work.counter == 1.0:
-            print("1 hour later!")
-
-
-app = LightningApp(RootFlow(HourLongWork()))

@@ -1,31 +0,0 @@
-from time import sleep
-
-from lightning.app import LightningWork, LightningFlow, LightningApp
-
-
-class HourLongWork(LightningWork):
-    def __init__(self):
-        super().__init__(cache_calls=False)
-        self.progress = 0.0
-
-    def run(self):
-        self.progress = 0.0
-        for _ in range(3600):
-            self.progress += 1.0 / 3600
-            sleep(1)
-
-
-class RootFlow(LightningFlow):
-    def __init__(self, child_work: LightningWork):
-        super().__init__()
-        self.child_work = child_work
-
-    def run(self):
-        # prints the progress from the child work
-        print(round(self.child_work.progress, 4))
-        self.child_work.run()
-        if self.child_work.counter == 1.0:
-            print("1 hour later!")
-
-
-app = LightningApp(RootFlow(HourLongWork()))

@@ -1,21 +0,0 @@
-from lightning.app import LightningWork, LightningFlow, LightningApp
-from docs.quickstart.app_02 import HourLongWork
-
-
-class RootFlow(LightningFlow):
-    def __init__(self, child_work_1: LightningWork, child_work_2: LightningWork):
-        super().__init__()
-        self.child_work_1 = child_work_1
-        self.child_work_2 = child_work_2
-
-    def run(self):
-        print(round(self.child_work_1.progress, 4), round(self.child_work_2.progress, 4))
-        self.child_work_1.run()
-        self.child_work_2.run()
-        if self.child_work_1.progress == 1.0:
-            print("1 hour later `child_work_1` started!")
-        if self.child_work_2.progress == 1.0:
-            print("1 hour later `child_work_2` started!")
-
-
-app = LightningApp(RootFlow(HourLongWork(parallel=True), HourLongWork(parallel=True)))

@@ -1,92 +0,0 @@
-import flash
-from flash.core.data.utils import download_data
-from flash.image import ImageClassificationData, ImageClassifier
-
-from lightning.app import LightningWork, LightningFlow, LightningApp, CloudCompute
-from lightning.pytorch.callbacks import ModelCheckpoint
-
-
-# Step 1: Create a training LightningWork component that gets a backbone as input
-# and saves the best model and its score
-class ImageClassifierTrainWork(LightningWork):
-    def __init__(self, max_epochs: int, backbone: str, cloud_compute: CloudCompute):
-        # parallel is set to True to run asynchronously
-        super().__init__(parallel=True, cloud_compute=cloud_compute)
-        # Number of epochs to run
-        self.max_epochs = max_epochs
-        # The model backbone to train on
-        self.backbone = backbone
-        self.best_model_path = None
-        self.best_model_score = None
-
-    def run(self, train_folder):
-        # Create a datamodule from the given dataset
-        datamodule = ImageClassificationData.from_folders(
-            train_folder=train_folder,
-            batch_size=1,
-            val_split=0.5,
-        )
-        # Create an image classfier task with the given backbone
-        model = ImageClassifier(datamodule.num_classes, backbone=self.backbone)
-        # Start a Lightning trainer, with 1 training batch and 4 validation batches
-        trainer = flash.Trainer(
-            max_epochs=self.max_epochs,
-            limit_train_batches=1,
-            limit_val_batches=4,
-            callbacks=[ModelCheckpoint(monitor="val_cross_entropy")],
-        )
-        # Train the model
-        trainer.fit(model, datamodule=datamodule)
-        # Save the model path
-        self.best_model_path = trainer.checkpoint_callback.best_model_path
-        # Save the model score
-        self.best_model_score = trainer.checkpoint_callback.best_model_score.item()
-
-
-# Step 2: Create a serving LightningWork component that gets a model input and serves it
-class ImageClassifierServeWork(LightningWork):
-    def run(self, best_model_path: str):
-        # Load the model from the model path
-        model = ImageClassifier.load_from_checkpoint(best_model_path)
-        model.serve(output="labels")
-
-
-# Step 3: Create a root LightningFlow component that gets number of epochs and a path to
-# a dataset as inputs, initialize 2 training components and serves the best model
-class RootFlow(LightningFlow):
-    def __init__(self, max_epochs: int, data_dir: str):
-        super().__init__()
-        self.data_dir = data_dir
-        # Init an image classifier with resnet18 backbone
-        self.train_work_1 = ImageClassifierTrainWork(
-            max_epochs,
-            "resnet18",
-        )
-        # Init an image classifier with resnet26 backbone
-        self.train_work_2 = ImageClassifierTrainWork(
-            max_epochs,
-            "resnet26",
-        )
-        # Init the serving component
-        self.server_work = ImageClassifierServeWork()
-
-    def run(self):
-        # running both `train_work_1` and `train_work_2` in parallel and asynchronously.
-        self.train_work_1.run(self.data_dir)
-        self.train_work_2.run(self.data_dir)
-
-        # run serve_work only when both `best_model_score` are available.
-        if self.train_work_1.best_model_score and self.train_work_2.best_model_score:
-            # serve only the best model between `train_work_1` and `train_work_2`.
-            self.server_work.run(
-                self.train_work_1.best_model_path
-                if self.train_work_1.best_model_score < self.train_work_2.best_model_score
-                else self.train_work_2.best_model_path
-            )
-
-
-# Step 4: download a dataset to your local directory under `/data`
-download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", "./data")
-
-# Initialize your Lightning app with 5 epochs
-app = LightningApp(RootFlow(5, "./data/hymenoptera_data"))

@@ -1,26 +0,0 @@
-from lightning.app import LightningFlow, LightningApp
-from lightning.app.testing import EmptyFlow, EmptyWork
-
-
-class FlowB(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        self.flow_d = EmptyFlow()
-        self.work_b = EmptyWork()
-
-    def run(self):
-        ...
-
-
-class FlowA(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        self.flow_b = FlowB()
-        self.flow_c = EmptyFlow()
-        self.work_a = EmptyWork()
-
-    def run(self):
-        ...
-
-
-app = LightningApp(FlowA())

@@ -1,15 +0,0 @@
-from lightning.app import LightningFlow, LightningApp
-
-
-# Step 1: Subclass LightningFlow component to define the app flow.
-class HelloWorld(LightningFlow):
-    # Step 2: Add the app logic to the LightningFlow run method to
-    # ``print("Hello World!")`.
-    # The LightningApp executes the run method of the main LightningFlow
-    # within an infinite loop.
-    def run(self):
-        print("Hello World!")
-
-
-# Step 3: Initialize a LightningApp with the LightningFlow you defined (in step 1)
-app = LightningApp(HelloWorld())

@@ -1,57 +0,0 @@
-import os
-
-from lightning.app import LightningFlow, LightningApp
-from lightning.app.frontend import StaticWebFrontend, StreamlitFrontend
-from lightning.app.utilities.state import AppState
-
-
-# Step 1: Define your LightningFlow component with the app UI
-class UIStreamLit(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        self.should_print = False
-
-    # Step 2: Override `configure_layout` to define the layout of the UI
-    # In this example, we are using `StreamlitFrontend`
-    def configure_layout(self):
-        return StreamlitFrontend(render_fn=render_fn)
-
-
-# Step 3: Implement the StreamLit render method
-def render_fn(state: AppState):
-    import streamlit as st
-    from streamlit_autorefresh import st_autorefresh
-
-    st_autorefresh(interval=2000, limit=None, key="refresh")
-
-    state.should_print = st.select_slider(
-        "Should the Application print 'Hello World !' to the terminal:",
-        [False, True],
-    )
-
-
-# Step 4: Implement a Static Web Frontend. This could be react, vue, etc.
-class UIStatic(LightningFlow):
-    # Step 5:
-    def configure_layout(self):
-        return StaticWebFrontend(os.path.join(os.path.dirname(__file__), "ui"))
-
-
-# Step 6: Implement the root flow.
-class HelloWorld(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        self.static_ui = UIStatic()
-        self.streamlit_ui = UIStreamLit()
-
-    def run(self):
-        print("Hello World!" if self.streamlit_ui.should_print else "")
-
-    def configure_layout(self):
-        return [
-            {"name": "StreamLit", "content": self.streamlit_ui},
-            {"name": "Static", "content": self.static_ui},
-        ]
-
-
-app = LightningApp(HelloWorld())

@@ -1 +0,0 @@
-<div>Hello from component UIStatic</div>

@@ -1,412 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file does only contain a selection of the most common options. For a
-# full list see the documentation:
-# http://www.sphinx-doc.org/en/master/config
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-
-import glob
-import inspect
-import os
-import shutil
-import sys
-
-import lai_sphinx_theme
-from lightning_utilities.docs import fetch_external_assets
-
-import lightning
-
-_PATH_HERE = os.path.abspath(os.path.dirname(__file__))
-_PATH_ROOT = os.path.realpath(os.path.join(_PATH_HERE, "..", ".."))
-sys.path.insert(0, os.path.abspath(_PATH_ROOT))
-
-_SPHINX_MOCK_REQUIREMENTS = int(os.environ.get("SPHINX_MOCK_REQUIREMENTS", True))
-_FAST_DOCS_DEV = int(os.environ.get("FAST_DOCS_DEV", True))
-_FETCH_S3_ASSETS = int(os.getenv("DOCS_FETCH_ASSETS", not _FAST_DOCS_DEV))
-
-# -- Project information -----------------------------------------------------
-
-# this name shall match the project name in Github as it is used for linking to code
-project = "lightning"
-copyright = lightning.__copyright__
-author = lightning.__author__
-
-# The short X.Y version
-version = lightning.__version__
-# The full version, including alpha/beta/rc tags
-release = lightning.__version__
-
-# -- Project documents -------------------------------------------------------
-
-if _FETCH_S3_ASSETS:
-    fetch_external_assets(
-        docs_folder=_PATH_HERE,
-        assets_folder="_static/fetched-s3-assets",
-        retrieve_pattern=r"https?://[-a-zA-Z0-9_]+\.s3\.[-a-zA-Z0-9()_\\+.\\/=]+"
-    )
-
-# -- General configuration ---------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-
-needs_sphinx = "5.3"
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    "sphinx.ext.autodoc",
-    "sphinx.ext.doctest",
-    "sphinx.ext.intersphinx",
-    "sphinx_toolbox.collapse",
-    "sphinx.ext.todo",
-    "sphinx.ext.coverage",
-    # "sphinx.ext.linkcode",
-    "sphinx.ext.autosummary",
-    "sphinx.ext.napoleon",
-    # 'sphinxcontrib.mockautodoc',  # raises error: directive 'automodule' is already registered ...
-    # 'sphinxcontrib.fulltoc',  # breaks pytorch-theme with unexpected kw argument 'titles_only'
-    "sphinxcontrib.video",
-    "myst_parser",
-    "sphinx.ext.autosectionlabel",
-    "nbsphinx",
-    "sphinx_autodoc_typehints",
-    "sphinx_copybutton",
-    "sphinx_paramlinks",
-    "sphinx_togglebutton",
-    "sphinx.ext.githubpages",
-    "lai_sphinx_theme.extensions.lightning",
-    'sphinx.ext.mathjax',
-]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ["_templates"]
-
-# myst-parser, forcing to parse all html pages with mathjax
-# https://github.com/executablebooks/MyST-Parser/issues/394
-myst_update_mathjax = False
-# https://myst-parser.readthedocs.io/en/latest/syntax/optional.html?highlight=anchor#auto-generated-header-anchors
-myst_heading_anchors = 3
-
-# https://berkeley-stat159-f17.github.io/stat159-f17/lectures/14-sphinx..html#conf.py-(cont.)
-# https://stackoverflow.com/questions/38526888/embed-ipython-notebook-in-sphinx-document
-# I execute the notebooks manually in advance. If notebooks test the code,
-# they should be run at build time.
-nbsphinx_execute = "never"
-nbsphinx_allow_errors = True
-nbsphinx_requirejs_path = ""
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-#
-# source_suffix = ['.rst', '.md']
-# source_suffix = ['.rst', '.md', '.ipynb']
-source_suffix = {
-    ".rst": "restructuredtext",
-    ".txt": "markdown",
-    ".md": "markdown",
-    ".ipynb": "nbsphinx",
-}
-
-# The master toctree document.
-master_doc = "index"
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = 'en'
-
-# List of patterns, relative to source-app directory, that match files and
-# directories to ignore when looking for source-app files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = [
-    "PULL_REQUEST_TEMPLATE.md",
-    "**/README.md/*",
-    "readme.md",
-    "_templates",
-    "code_samples/convert_pl_to_app/requirements.txt",
-    "**/_static/*"
-]
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = None
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = "lai_sphinx_theme"
-html_theme_path = [os.environ.get('LIT_SPHINX_PATH', lai_sphinx_theme.get_html_theme_path())]
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-
-html_theme_options = {
-    "pytorch_project": lightning.__homepage__,
-    "analytics_id": "G-D3Q2ESCTZR",
-    "canonical_url": lightning.__homepage__,
-    "collapse_navigation": False,
-    "display_version": True,
-    "logo_only": False,
-}
-
-html_favicon = "_static/images/icon.svg"
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_templates", "_static"]
-
-# Custom sidebar templates, must be a dictionary that maps document names
-# to template names.
-#
-# The default sidebars (for documents that don't match any pattern) are
-# defined by theme itself.  Builtin themes are using these templates by
-# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
-# 'searchbox.html']``.
-#
-# html_sidebars = {}
-
-# -- Options for HTMLHelp output ---------------------------------------------
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = project + "-doc"
-
-# -- Options for LaTeX output ------------------------------------------------
-
-latex_elements = {
-    # The paper size ('letterpaper' or 'a4paper').
-    # 'papersize': 'letterpaper',
-    # The font size ('10pt', '11pt' or '12pt').
-    # 'pointsize': '10pt',
-    # Additional stuff for the LaTeX preamble.
-    # 'preamble': '',
-    # Latex figure (float) alignment
-    "figure_align": "htbp",
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source-app start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (master_doc, project + ".tex", project + " Documentation", author, "manual"),
-]
-
-# MathJax configuration
-mathjax3_config = {
-    'tex': {
-        'packages': {'[+]': ['ams', 'newcommand', 'configMacros']}
-    },
-}
-
-# -- Options for manual page output ------------------------------------------
-
-# One entry per manual page. List of tuples
-# (source-app start file, name, description, authors, manual section).
-man_pages = [(master_doc, project, project + " Documentation", [author], 1)]
-
-# -- Options for Texinfo output ----------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source-app start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-    (
-        master_doc,
-        project,
-        project + " Documentation",
-        author,
-        project,
-        lightning.__docs__,
-        "Miscellaneous",
-    ),
-]
-
-# -- Options for Epub output -------------------------------------------------
-
-# Bibliographic Dublin Core info.
-epub_title = project
-
-# The unique identifier of the text. This can be a ISBN number
-# or the project homepage.
-#
-# epub_identifier = ''
-
-# A unique identification for the text.
-#
-# epub_uid = ''
-
-# A list of files that should not be packed into the epub file.
-epub_exclude_files = ["search.html"]
-
-# -- Extension configuration -------------------------------------------------
-
-# -- Options for intersphinx extension ---------------------------------------
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {
-    "python": ("https://docs.python.org/3", None),
-    "torch": ("https://pytorch.org/docs/stable/", None),
-    "numpy": ("https://numpy.org/doc/stable/", None),
-}
-
-nitpicky = True
-
-
-nitpick_ignore = [
-    ("py:class", "typing.Self"),
-    # missing in generated API
-    ("py:exc", "MisconfigurationException"),
-    # TODO: generated list of all existing ATM, need to be fixed
-    ('py:exc', 'ApiException'),
-    ('py:class', 'BaseModel'),
-    ('py:exc', 'LightningPlatformException'),
-    ('py:class', 'forwarded'),
-    ('py:class', 'lightning.app.api.http_methods.Delete'),
-    ('py:class', 'lightning.app.api.http_methods.Get'),
-    ('py:class', 'lightning.app.api.http_methods.HttpMethod'),
-    ('py:class', 'lightning.app.api.http_methods.Post'),
-    ('py:class', 'lightning.app.api.http_methods.Put'),
-    ('py:class', 'lightning.app.components.python.TracerPythonScript'),
-    ('py:func', 'lightning.app.pdb.set_trace'),
-    ('py:class', 'lightning.app.runners.runtime.Runtime'),
-    ('py:class', 'lightning.app.source_code.local.LocalSourceCodeDir'),
-    ('py:class', 'lightning.app.storage.payload._BasePayload'),
-    ('py:class', 'lightning.app.structures.Dict'),
-    ('py:class', 'lightning.app.structures.List'),
-    ('py:class', 'lightning.app.testing.testing.LightningTestApp'),
-    ('py:class', 'lightning.app.utilities.app_status.WorkStatus'),
-    ('py:class', 'lightning.app.utilities.frontend.AppInfo'),
-    ('py:class', 'lightning.app.utilities.packaging.app_config.AppConfig'),
-    ('py:class', 'lightning.app.utilities.packaging.build_config.BuildConfig'),
-    ('py:class', 'lightning.app.utilities.packaging.cloud_compute.CloudCompute'),
-    ('py:class', 'lightning.app.utilities.proxies.WorkRunExecutor'),
-    ('py:class', 'lightning.app.utilities.tracer.Tracer'),
-    ('py:class', 'lightning_cloud.openapi.models.cloudspace_id_runs_body.CloudspaceIdRunsBody'),
-    ('py:class', 'lightning_cloud.openapi.models.externalv1_lightningapp_instance.Externalv1LightningappInstance'),
-    ('py:class', 'lightning_cloud.openapi.models.v1_cloud_space.V1CloudSpace'),
-    ('py:class', 'lightning_cloud.openapi.models.v1_env_var.V1EnvVar'),
-    ('py:class', 'lightning_cloud.openapi.models.v1_flowserver.V1Flowserver'),
-    ('py:class', 'lightning_cloud.openapi.models.v1_lightning_auth.V1LightningAuth'),
-    ('py:class', 'lightning_cloud.openapi.models.v1_lightning_run.V1LightningRun'),
-    ('py:class', 'lightning_cloud.openapi.models.v1_lightningwork_drives.V1LightningworkDrives'),
-    ('py:class', 'lightning_cloud.openapi.models.v1_membership.V1Membership'),
-    ('py:class', 'lightning_cloud.openapi.models.v1_network_config.V1NetworkConfig'),
-    ('py:class', 'lightning_cloud.openapi.models.v1_queue_server_type.V1QueueServerType'),
-    ('py:class', 'lightning_cloud.openapi.models.v1_work.V1Work'),
-    ('py:class', 'pydantic.main.BaseModel'),
-    ('py:meth', 'transfer'),
-]
-
-# -- Options for todo extension ----------------------------------------------
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = True
-
-
-def setup(app):
-    # this is for hiding doctest decoration,
-    # see: http://z4r.github.io/python/2011/12/02/hides-the-prompts-and-output/
-    app.add_js_file("copybutton.js")
-    app.add_css_file("main.css")
-
-# copy all examples to local folder
-path_examples = os.path.join(_PATH_HERE, "..", "examples")
-if not os.path.isdir(path_examples):
-    os.mkdir(path_examples)
-for path_app_example in glob.glob(os.path.join(_PATH_ROOT, "examples", "app_*")):
-    path_app_example2 = os.path.join(path_examples, os.path.basename(path_app_example))
-    if not os.path.isdir(path_app_example2):
-        shutil.copytree(path_app_example, path_app_example2, dirs_exist_ok=True)
-
-
-# Ignoring Third-party packages
-# https://stackoverflow.com/questions/15889621/sphinx-how-to-exclude-imports-in-automodule
-def _package_list_from_file(file):
-    list_pkgs = []
-    with open(file) as fp:
-        lines = fp.readlines()
-    for ln in lines:
-        found = [ln.index(ch) for ch in list(",=<>#") if ch in ln]
-        pkg = ln[: min(found)] if found else ln
-        if pkg.rstrip():
-            list_pkgs.append(pkg.rstrip())
-    return list_pkgs
-
-
-# define mapping from PyPI names to python imports
-PACKAGE_MAPPING = {
-    "PyYAML": "yaml",
-}
-MOCK_PACKAGES = []
-if _SPHINX_MOCK_REQUIREMENTS:
-    # mock also base packages when we are on RTD since we don't install them there
-    MOCK_PACKAGES += _package_list_from_file(os.path.join(_PATH_ROOT, "requirements.txt"))
-MOCK_PACKAGES = [PACKAGE_MAPPING.get(pkg, pkg) for pkg in MOCK_PACKAGES]
-
-autodoc_mock_imports = MOCK_PACKAGES
-
-
-autosummary_generate = True
-
-autodoc_member_order = "groupwise"
-autoclass_content = "both"
-# the options are fixed and will be soon in release,
-#  see https://github.com/sphinx-doc/sphinx/issues/5459
-autodoc_default_options = {
-    "members": None,
-    "methods": None,
-    # 'attributes': None,
-    "special-members": "__call__",
-    "exclude-members": "_abc_impl",
-    "show-inheritance": True,
-    "private-members": True,
-    "noindex": True,
-}
-
-# Sphinx will add “permalinks” for each heading and description environment as paragraph signs that
-#  become visible when the mouse hovers over them.
-# This value determines the text for the permalink; it defaults to "¶". Set it to None or the empty
-#  string to disable permalinks.
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_permalinks
-# html_add_permalinks = "¶"
-# True to prefix each section label with the name of the document it is in, followed by a colon.
-#  For example, index:Introduction for a section called Introduction that appears in document index.rst.
-#  Useful for avoiding ambiguity when the same section heading appears in different documents.
-# http://www.sphinx-doc.org/en/master/usage/extensions/autosectionlabel.html
-autosectionlabel_prefix_document = True
-
-# only run doctests marked with a ".. doctest::" directive
-doctest_test_doctest_blocks = ""
-doctest_global_setup = """
-import importlib
-import os
-
-from lightning.app import LightningWork, LightningFlow, LightningApp, CloudCompute
-from lightning.fabric.loggers.tensorboard import _TENSORBOARD_AVAILABLE, _TENSORBOARDX_AVAILABLE
-"""
-coverage_skip_undoc_in_source = True
-
-# skip false positive linkcheck errors from anchors
-linkcheck_anchors = False
-
-# A timeout value, in seconds, for the linkcheck builder.
-linkcheck_timeout = 60
-
-# ignore all links in any CHANGELOG file
-linkcheck_exclude_documents = [r"^(.*\/)*CHANGELOG.*$"]
-
-
-# ignore the following relative links (false positive errors during linkcheck)
-linkcheck_ignore = [
-    "https://www.openai.com/index/clip/",
-]

@@ -1,7 +0,0 @@
-:orphan:
-
-#################
-Contribute an app
-#################
-
-Show off your work! Contribute and example to be highlighted in our documentation and App gallery.

@@ -1,27 +0,0 @@
-from lightning.app import LightningWork, LightningFlow, LightningApp
-from lightning.app.utilities.app_helpers import pretty_state
-
-
-class Work(LightningWork):
-    def __init__(self):
-        super().__init__(cache_calls=False)
-        # Attributes are registered automatically in the state.
-        self.counter = 0
-
-    def run(self):
-        # Incrementing an attribute gets reflected in the `Flow` state.
-        self.counter += 1
-
-
-class Flow(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        self.w = Work()
-
-    def run(self):
-        if self.w.has_started:
-            print(f"State: {pretty_state(self.state)} \n")
-        self.w.run()
-
-
-app = LightningApp(Flow())

@@ -1,160 +0,0 @@
-
-********************************
-Communication Between Components
-********************************
-
-When creating interactive Lightning Apps (App) with multiple components, you may need your components to share information with each other and rely on that information to control their execution, share progress in the UI, trigger a sequence of operations, etc.
-
-To accomplish that, Lightning components can communicate using the App State. The App State is composed of all attributes defined within each component's **__init__** method e.g anything attached to the component with **self.x = y**.
-
-All attributes of all **LightningWork (Work)** components are accessible in the **LightningFlow (Flow)** components in real-time.
-
-By design, the Flows communicate to all **Works** within the application. However, Works can't communicate with each other directly, they must use Flows as a proxy to communicate.
-
-Once a Work is running, any updates to the Work's state is automatically communicated to the Flow, as a delta (using `DeepDiff <https://github.com/seperman/deepdiff>`_). The state communication isn't bi-directional, communication is only done from Work to Flow.
-
-Internally, the App is alternatively collecting deltas sent from all the registered Works and/or UI, and running the root Flow run method of the App.
-
-----
-
-*************************************************
-Communication from LightningWork to LightningFlow
-*************************************************
-
-LightningFlow (Flow) can access their children's LightningWork (Work) state.
-
-When a running Work attribute gets updated inside its method (separate process locally or remote machine), the app re-executes Flow's run method once it receives the state update from the Work.
-
-Here's an example to better understand communication from Work to Flow.
-
-The ``WorkCounter`` increments a counter until 1 million and the ``Flow`` prints the work counter.
-
-As the Work is running its own process, its state changes are sent to the Flow which contains the latest value of the counter.
-
-.. code-block:: python
-
-    import lightning as L
-
-
-    class WorkCounter(L.LightningWork):
-        def __init__(self):
-            super().__init__(parallel=True)
-            self.counter = 0
-
-        def run(self):
-            for _ in range(int(10e6)):
-                self.counter += 1
-
-
-    class Flow(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.w = WorkCounter()
-
-        def run(self):
-            self.w.run()
-            print(self.w.counter)
-
-
-    app = L.LightningApp(Flow())
-
-
-A delta sent from the Work to the Flow looks like this:
-
-.. code-block:: python
-
-    {"values_changed": {"root['works']['w']['vars']['counter']": {"new_value": 425}}}
-
-Here is the associated illustration:
-
-.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/deltas.gif
-    :alt: Mechanism showing how delta are sent.
-    :width: 100 %
-
-Here's another example that is slightly different. Here we define a Flow and Work, where the Work increments a counter indefinitely and the Flow prints its state which contain the Work.
-
-You can easily check the state of your entire app as follows:
-
-.. literalinclude:: ../../core_api/lightning_app/app.py
-
-Run the app with:
-
-.. code-block:: bash
-
-    lightning run app docs/source/core_api/lightning_app/app.py
-
-And here's the output you get when running the App using the **Lightning CLI**:
-
-.. code-block:: console
-
-    INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
-    State: {'works': {'w': {'vars': {'counter': 1}}}}
-    State: {'works': {'w': {'vars': {'counter': 2}}}}
-    State: {'works': {'w': {'vars': {'counter': 3}}}}
-    State: {'works': {'w': {'vars': {'counter': 3}}}}
-    State: {'works': {'w': {'vars': {'counter': 4}}}}
-    ...
-
-----
-
-*************************************************
-Communication from LightningFlow to LightningWork
-*************************************************
-
-Communication from the LightningFlow (Flow) to the LightningWork (Work) while running **isn't supported yet**. If your application requires this feature, please open an issue on Github.
-
-Here's an example of what would happen if you try to have the Flow communicate with the Work:
-
-.. code-block:: python
-
-    import lightning as L
-    from time import sleep
-
-
-    class WorkCounter(L.LightningWork):
-        def __init__(self):
-            super().__init__(parallel=True)
-            self.counter = 0
-
-        def run(self):
-            while True:
-                sleep(1)
-                print(f"Work {self.counter}")
-
-
-    class Flow(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.w = WorkCounter()
-
-        def run(self):
-            self.w.run()
-            sleep(1)
-            print(f"Flow {self.w.counter}")
-            self.w.counter += 1
-
-
-    app = L.LightningApp(Flow())
-
-As you can see, there is a divergence between the values within the Work and the Flow.
-
-.. code-block:: console
-
-    Flow 0
-    Flow 1
-    Flow 2
-    Flow 3
-    Work 0
-    Flow 4
-    Work 0
-    Flow 5
-    Work 0
-    Flow 6
-    Work 0
-    Flow 7
-    Work 0
-    Flow 8
-    Work 0
-    Flow 9
-    Work 0
-    Flow 10

@@ -1,15 +0,0 @@
-:orphan:
-
-##########################################
-Communication between Lightning Components
-##########################################
-
-**Audience:** Users that want to create interactive applications.
-
-**Level:** Intermediate
-
-**Prerequisite**: Read the :doc:`Communication in Lightning Apps article <../../../workflows/access_app_state>`.
-
-----
-
-.. include:: ../../core_api/lightning_app/communication_content.rst

@@ -1,40 +0,0 @@
-:orphan:
-
-***************************
-Customize my Flow resources
-***************************
-
-In the cloud, you can simply configure which machine to run on by passing
-a :class:`~lightning.app.utilities.packaging.cloud_compute.CloudCompute` to your work ``__init__`` method:
-
-.. code-block:: python
-
-    import lightning as L
-
-    # Run on a small, shared CPU machine. This is the default for every LightningFlow.
-    app = L.LightningApp(L.Flow(), flow_cloud_compute=L.CloudCompute())
-
-
-Here is the full list of supported machine names:
-
-.. list-table:: Hardware by Accelerator Type
-   :widths: 25 25 25
-   :header-rows: 1
-
-   * - Name
-     - # of CPUs
-     - Memory
-   * - flow-lite
-     - 0.3
-     - 4 GB
-
-The up-to-date prices for these instances can be found `here <https://lightning.ai/pages/pricing>`_.
-
-----
-
-************
-CloudCompute
-************
-
-.. autoclass:: lightning.app.utilities.packaging.cloud_compute.CloudCompute
-    :noindex:

@@ -1,202 +0,0 @@
-***************************************
-What Dynamic LightningWork does for you
-***************************************
-
-Dynamic LightningWork (Work) changes the resources your application uses while the application is running (aka at runtime).
-
-For example, imagine you want to create a research notebook app for your team. You want every member to be able to create multiple `JupyterLab <https://jupyter.org/>`_ sessions on their hardware of choice.
-
-To allow every notebook to choose hardware, it needs to be set up in it's own :class:`~lightning.app.core.work.LightningWork`, but you can't know the number of notebooks user will need in advance. In this case you'll need to add ``LightningWorks`` dynamically at run time.
-
-----
-
-*****************
-Use Dynamic Works
-*****************
-
-Dynamic Works should be used anytime you want change the resources your application is using while it is running (aka at runtime).
-
-You're usually going to use the ``start`` and ``stop`` methods together.
-
-----
-
-Add a Dynamic Work
-^^^^^^^^^^^^^^^^^^
-
-There are a couple of ways you can add a dynamic Work:
-
-- Option 1: Attach your components in the **run** method using the Python functions.
-- Option 2: Use the Lightning built-in classes :class:`~lightning.app.structures.Dict` or :class:`~lightning.app.structures.List`.
-
-.. note:: Using the Lightning built-in classes is usually easier to read.
-
-----
-
-**OPTION 1:** Attach your components in the run method of a flow using the Python functions **hasattr**, **setattr**, and **getattr**:
-
-.. code-block:: python
-
-    class RootFlow(lapp.LightningFlow):
-
-        def run(self):
-
-            if not hasattr(self, "work"):
-                # The `Work` component is created and attached here.
-                setattr(self, "work", Work())
-            # Run the `Work` component.
-            getattr(self,  "work").run()
-
-**OPTION 2:** Use the built-in Lightning classes :class:`~lightning.app.structures.Dict` or :class:`~lightning.app.structures.List`
-
-.. code-block:: python
-
-    from lightning.app.structures import Dict
-
-    class RootFlow(lapp.LightningFlow):
-
-        def __init__(self):
-            super().__init__()
-            self.dict = Dict()
-
-        def run(self):
-            if "work" not in self.dict:
-                # The `Work` component is attached here.
-                self.dict["work"] = Work()
-            self.dict["work"].run()
-
-----
-
-Stop a Work
-^^^^^^^^^^^
-Stop a work when you are concerned about cost.
-
-To stop a work, use the work ``stop`` method:
-
-.. code-block:: python
-
-    class RootFlow(L.LightningFlow):
-
-        def __init__(self):
-            super().__init__()
-            self.work = Work()
-
-        def run(self):
-            self.work.stop()
-
-----
-
-*********************
-Dynamic Work Examples
-*********************
-
-..
-    The entire application can be found `here <https://github.com/Lightning-AI/lightning-template-jupyterlab>`_.
-
-----
-
-Dynamic Work with Jupyter Notebooks
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In this example, we are dynamically creating ``JupyterLabWork`` every time a user clicks the **Create Jupyter Notebook** button.
-
-In order to do that, we are iterating over the list of ``jupyter_config_requests`` infinitely.
-
-.. code-block:: python
-
-    import lightning as L
-
-
-    class JupyterLabManager(L.LightningFlow):
-
-        """This flow manages the users notebooks running within works.""""
-
-        def __init__(self):
-            super().__init__()
-            self.jupyter_works = L.structures.Dict()
-            self.jupyter_config_requests = []
-
-        def run(self):
-            for idx, jupyter_config in enumerate(self.jupyter_config_requests):
-
-                # The Jupyter Config has this form is:
-                # {"use_gpu": False/True, "token": None, "username": ..., "stop": False}
-
-                # Step 1: Check if JupyterWork already exists for this username
-                username = jupyter_config["username"]
-                if username not in self.jupyter_works:
-                    jupyter_config["ready"] = False
-
-                    # Set the hardware selected by the user: GPU or CPU.
-                    cloud_compute = L.CloudCompute("gpu" if jupyter_config["use_gpu"] else "cpu-small")
-
-                    # Step 2: Create new JupyterWork dynamically !
-                    self.jupyter_works[username] = JupyterLabWork(cloud_compute=cloud_compute)
-
-                # Step 3: Run the JupyterWork
-                self.jupyter_works[username].run()
-
-                # Step 4: Store the notebook token in the associated config.
-                # We are using this to know when the notebook is ready
-                # and display the stop button on the UI.
-                if self.jupyter_works[username].token:
-                    jupyter_config["token"] = self.jupyter_works[username].token
-
-                # Step 5: Stop the work if the user requested it.
-                if jupyter_config['stop']:
-                    self.jupyter_works[username].stop()
-                    self.jupyter_config_requests.pop(idx)
-
-        def configure_layout(self):
-            return L.app.frontend.StreamlitFrontend(render_fn=render_fn)
-
-----
-
-Dynamic Works with StreamLit UI
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Continuing from the Jupyter Notebook example, in the UI, we receive the **state** of the Jupyter Manager and the state can be modified directly from the UI.
-
-.. code-block:: python
-
-    import streamlit as st
-
-
-    def render_fn(state):
-
-        # Step 1: Enable users to select their notebooks and create them
-        column_1, column_2, column_3 = st.columns(3)
-        with column_1:
-            create_jupyter = st.button("Create Jupyter Notebook")
-        with column_2:
-            username = st.text_input('Enter your username', "tchaton")
-            assert username
-        with column_3:
-            use_gpu = st.checkbox('Use GPU')
-
-        # Step 2: If a user clicked the button, add an element to the list of configs
-        # Note: state.jupyter_config_requests = ... will sent the state update to the component.
-        if create_jupyter:
-            new_config = [{"use_gpu": use_gpu, "token": None, "username": username, "stop": False}]
-            state.jupyter_config_requests = state.jupyter_config_requests + new_config
-
-        # Step 3: List of running notebooks.
-        for idx, config in enumerate(state.jupyter_config_requests):
-            column_1, column_2, column_3 = st.columns(3)
-            with column_1:
-                if not idx:
-                    st.write(f"Idx")
-                st.write(f"{idx}")
-            with column_2:
-                if not idx:
-                    st.write(f"Use GPU")
-                st.write(config['use_gpu'])
-            with column_3:
-                if not idx:
-                    st.write(f"Stop")
-                if config["token"]:
-                    should_stop = st.button("Stop this notebook")
-
-                    # Step 4: Change stop if the user clicked the button
-                    if should_stop:
-                        config["stop"] = should_stop
-                        state.jupyter_config_requests = state.jupyter_config_requests

@@ -1,15 +0,0 @@
-:orphan:
-
-.. _dynamic_work:
-
-#####################
-Dynamic LightningWork
-#####################
-
-**Audience:** Users who want to create applications that adapt to user demands.
-
-**Level:** Advanced
-
-----
-
-.. include:: dynamic_work_content.rst

@@ -1,94 +0,0 @@
-#############
-Lightning App
-#############
-**Audience:** Users who want to know how an app works under the hood 🤯.
-
-**Lightning App:** We call workflows composed of multiple LightningWorks a **Lightning App**.
-
-----
-
-*******************
-Peek under the hood
-*******************
-
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: App Components Tree (Basic)
-   :description: Learn more component composition and nesting.
-   :col_css: col-md-4
-   :button_link: ../../glossary/app_tree.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: The event loop (Basic)
-   :description: Learn more about the event loop.
-   :col_css: col-md-4
-   :button_link: ../../glossary/event_loop.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Communication between Flow and Works
-   :description: Learn more about components communicate.
-   :col_css: col-md-4
-   :button_link: communication.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Customize Flow compute resources
-   :description: Learn more about Flow customizations.
-   :col_css: col-md-4
-   :button_link: compute_content.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Dynamically create, execute and stop Work
-   :description: Learn more about components creation.
-   :col_css: col-md-4
-   :button_link: dynamic_work.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Sharing My Components (Intermediate)
-   :description: Learn more component composition and nesting.
-   :col_css: col-md-4
-   :button_link: ../../glossary/sharing_components.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>
-
-----
-
-*****************
-Lightning App API
-*****************
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: LightningApp API
-   :description: Look into the Lightning API reference.
-   :col_css: col-md-4
-   :button_link: lightning_app.html
-   :height: 180
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,12 +0,0 @@
-:orphan:
-
-.. _lightning_app:
-
-############
-LightningApp
-############
-
-
-.. autoclass:: lightning.app.core.app.LightningApp
-    :exclude-members: _run, connect, get_component_by_name, maybe_apply_changes, set_state
-    :noindex:

@@ -1,8 +0,0 @@
-.. _lightning_flow:
-
-#############
-LightningFlow
-#############
-
-.. autoclass:: lightning.app.core.flow.LightningFlow
-    :exclude-members: _attach_backend, _exit, _is_state_attribute, set_state

@@ -1,94 +0,0 @@
-
-***************************
-Customize my Work resources
-***************************
-
-In the cloud, you can simply configure which machine to run on by passing
-a :class:`~lightning.app.utilities.packaging.cloud_compute.CloudCompute` to your work ``__init__`` method:
-
-.. code-block:: python
-
-    import lightning as L
-
-    # Run on a free, shared CPU machine. This is the default for every LightningWork.
-    MyCustomWork(cloud_compute=L.CloudCompute())
-
-    # Run on a dedicated, medium-size CPU machine (see specs below)
-    MyCustomWork(cloud_compute=L.CloudCompute("cpu-medium"))
-
-    # Run on cheap GPU machine with a single GPU (see specs below)
-    MyCustomWork(cloud_compute=L.CloudCompute("gpu"))
-
-    # Run on a fast multi-GPU machine (see specs below)
-    MyCustomWork(cloud_compute=L.CloudCompute("gpu-fast-multi"))
-
-.. warning::
-     Custom base images are not supported with the default CPU cloud compute. For example:
-
-     .. code-block:: py
-
-         class MyWork(LightningWork):
-             def __init__(self):
-              super().__init__(cloud_build_config=BuildConfig(image="my-custom-image")) # no cloud compute, for example default work
-
-
-Here is the full list of supported machine names:
-
-.. list-table:: Hardware by Accelerator Type
-   :widths: 25 25 25 25
-   :header-rows: 1
-
-   * - Name
-     - # of CPUs
-     - GPUs
-     - Memory
-   * - default
-     - 1
-     - 0
-     - 4 GB
-   * - cpu-small
-     - 2
-     - 0
-     - 8 GB
-   * - cpu-medium
-     - 8
-     - 0
-     - 32 GB
-   * - gpu
-     - 4
-     - 1 (T4, 16 GB)
-     - 16 GB
-   * - gpu-fast
-     - 8
-     - 1 (V100, 16 GB)
-     - 61 GB
-   * - gpu-fast-multi
-     - 32
-     - 4 (V100 16 GB)
-     - 244 GB
-
-The up-to-date prices for these instances can be found `here <https://lightning.ai/pages/pricing>`_.
-
-----
-
-**********************
-Stop my work when idle
-**********************
-
-By providing **idle_timeout=X Seconds**, the work is automatically stopped **X seconds** after doing nothing.
-
-.. code-block:: python
-
-    import lightning as L
-
-    # Run on a single CPU and turn down immediately when idle.
-    MyCustomWork(cloud_compute=L.CloudCompute("gpu", idle_timeout=0))
-
-----
-
-************
-CloudCompute
-************
-
-.. autoclass:: lightning.app.utilities.packaging.cloud_compute.CloudCompute
-    :noindex:

@@ -1,15 +0,0 @@
-:orphan:
-
-.. _cloud_compute:
-
-############################
-Customize your Cloud Compute
-############################
-
-**Audience:** Users who want to select the hardware to run in the cloud.
-
-**Level:** Intermediate
-
-----
-
-.. include:: compute_content.rst

@@ -1,74 +0,0 @@
-
-***************************************************
-What handling Lightning App exceptions does for you
-***************************************************
-
-Imagine you are creating a Lightning App (App) where your team can launch model training by providing their own Github Repo any time they want.
-
-As the App admin, you don't want the App to go down if their code has a bug and breaks.
-
-Instead, you would like the LightningWork (Work) to capture the exception and present the issue to users.
-
-----
-
-****************************
-Configure exception handling
-****************************
-
-The LightningWork (Work) accepts an argument **raise_exception** which is **True** by default. This aligns with Python default behaviors.
-
-However, for the user case stated in the previous section, we want to capture the Work exceptions. This is done by providing ``raise_exception=False`` to the work ``__init__`` method.
-
-.. code-block:: python
-
-    import lightning as L
-
-    MyCustomWork(raise_exception=False)  # <== HERE: The exception is captured.
-
-    # Default behavior
-    MyCustomWork(raise_exception=True)  # <== HERE: The exception is raised within the flow and terminates the app
-
-
-And you can customize this behavior by overriding the ``on_exception`` hook to the Work.
-
-.. code-block:: python
-
-    import lightning as L
-
-    class MyCustomWork(L.LightningWork):
-
-        def on_exception(self, exception: Exception):
-            # do something when an exception is triggered.
-
-----
-
-**************************
-Exception handling example
-**************************
-
-This is the pseudo-code for the application described above.
-
-.. code-block:: python
-
-    import lightning as L
-
-    class RootFlow(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.user_jobs = L.structures.Dict()
-            self.requested_jobs = []
-
-        def run(self):
-            for request in self.requested_jobs:
-                job_id = request["id"]
-                if job_id not in self.user_jobs:
-                    # Note: The `GithubRepoLauncher` doesn't exist yet.
-                    self.user_jobs[job_id] = GithubRepoLauncher(
-                        **request,
-                        raise_exception=False, # <== HERE: The exception is captured.
-                    )
-                self.user_jobs[job_id].run()
-
-                if self.user_jobs[job_id].status.stage == "failed" and "printed" not in request:
-                    print(self.user_jobs[job_id].status) # <== HERE: Print the user exception.
-                    request["printed"] = True

@@ -1,13 +0,0 @@
-:orphan:
-
-###############################
-Handle Lightning App exceptions
-###############################
-
-**Audience:** Users who want to make Lightning Apps more robust to potential issues.
-
-**Level:** Advanced
-
-----
-
-.. include:: handling_app_exception_content.rst

@@ -1,112 +0,0 @@
-##############
-Lightning Work
-##############
-
-**Audience:** Users who want to know how Lightning Work works under the hood 🤯.
-
-----
-
-*******************
-Peek under the hood
-*******************
-
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: To Cache or Not to Cache Calls
-   :description: Learn more about work execution and internal caching.
-   :col_css: col-md-4
-   :button_link: ../../workflows/run_work_once.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Parallel Work
-   :description: Learn more about work parallelization.
-   :col_css: col-md-4
-   :button_link: ../../workflows/run_work_in_parallel.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Sharing files between works
-   :description: Learn more about data transfer works.
-   :col_css: col-md-4
-   :button_link: ../../glossary/storage/storage.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Sharing Python Objects between works
-   :description: Learn more about sharing objects.
-   :col_css: col-md-4
-   :button_link: payload.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Checking Work Status
-   :description: Learn more about work status.
-   :col_css: col-md-4
-   :button_link: status.html
-   :height: 180
-   :tag: Advanced
-
-.. displayitem::
-   :header: Handling App Exceptions
-   :description: Learn more about exception capturing.
-   :col_css: col-md-4
-   :button_link: handling_app_exception.html
-   :height: 180
-   :tag: Advanced
-
-.. raw:: html
-
-        </div>
-    </div>
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Customize your Cloud Compute
-   :description: Learn more about changing hardware and requirements.
-   :col_css: col-md-4
-   :button_link: compute.html
-   :height: 180
-   :tag: Cloud
-
-.. raw:: html
-
-      </div>
-   </div>
-
-
-----
-
-******************
-Lightning Work API
-******************
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: LightningWork API
-   :description: Look into the Lightning API reference.
-   :col_css: col-md-4
-   :button_link: lightning_work.html
-   :height: 180
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,11 +0,0 @@
-:orphan:
-
-.. _lightning_work:
-
-#############
-LightningWork
-#############
-
-.. autoclass:: lightning.app.core.work.LightningWork
-    :exclude-members: _aggregate_status_timeout, _is_state_attribute, _is_state_attribute, set_state
-    :noindex:

@@ -1,75 +0,0 @@
-
-**************************************
-What transferring objects does for you
-**************************************
-
-Imagine your application is processing some data using `pandas DaFrame <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_ and you want to pass that data to another LightningWork (Work). This is what the **Payload API** is meant for.
-
-----
-
-*************************
-Use the Lightning Payload
-*************************
-
-The Payload enables non JSON-serializable attribute objects to be part of your Work's state and to be communicated to other Works.
-
-Here is an example:
-
-.. code-block:: python
-
-    import lightning as L
-    import pandas as pd
-
-
-    class SourceWork(L.LightningWork):
-        def __init__(self):
-            super().__init__()
-            self.df = None
-
-        def run(self):
-            # do some processing
-
-            df = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]})
-
-            # The object you care about needs to be wrapped into a Payload object.
-            self.df = L.storage.Payload(df)
-
-            # You can access the original object from the payload using its value property.
-            print("src", self.df.value)
-            # src  col1  col2
-            # 0     1     3
-            # 1     2     4
-
-Once the Payload object is attached to your Work's state, it can be passed to another work using the LightningFlow (Flow) as follows:
-
-.. code-block:: python
-
-    import lightning as L
-    import pandas as pd
-
-
-    class DestinationWork(L.LightningWork):
-        def run(self, df: L.storage.Payload):
-            # You can access the original object from the payload using its value property.
-            print("dst", df.value)
-            # dst  col1  col2
-            # 0     1     3
-            # 1     2     4
-
-
-    class Flow(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.src = SourceWork()
-            self.dst = DestinationWork()
-
-        def run(self):
-            self.src.run()
-            # The pandas DataFrame created by the ``SourceWork``
-            # is passed to the ``DestinationWork``.
-            # Internally, Lightning pickles and un-pickle the python object,
-            # so you receive a copy of the original object.
-            self.dst.run(df=self.src.df)
-
-
-    app = L.LightningApp(Flow())

@@ -1,15 +0,0 @@
-:orphan:
-
-######################################
-Sharing Objects between LightningWorks
-######################################
-
-**Audience:** Users who want to know how to transfer Python objects between their LightningWorks.
-
-**Level:** Advanced
-
-**Prerequisite**: Reach Level 16+, know about the `pandas DataFrames <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_ and read and read the :doc:`Access app state guide <../../workflows/access_app_state>`.
-
-----
-
-.. include:: payload_content.rst

@@ -1,197 +0,0 @@
-
-*************************************
-Everything about LightningWork Status
-*************************************
-
-Statuses indicate transition points in the life of a LightningWork (Work) and contain metadata.
-
-The different stages are:
-
-.. code-block:: python
-
-    class WorkStageStatus:
-        NOT_STARTED = "not_started"
-        STOPPED = "stopped"
-        PENDING = "pending"
-        RUNNING = "running"
-        SUCCEEDED = "succeeded"
-        FAILED = "failed"
-
-And a single status is as follows:
-
-.. code-block:: python
-
-    @dataclass
-    class WorkStatus:
-        stage: WorkStageStatus
-        timestamp: float
-        reason: Optional[str] = None
-        message: Optional[str] = None
-        count: int = 1
-
-
-On creation, the Work's status flags all evaluate to ``False`` (in particular ``has_started``) and when calling ``work.run`` in your Lightning Flow (Flow),
-the Work transitions from ``is_pending`` to ``is_running`` and then to ``has_succeeded`` if everything went well or ``has_failed`` otherwise.
-
-.. code-block:: python
-
-    from time import sleep
-    import lightning as L
-
-
-    class Work(L.LightningWork):
-        def run(self, value: int):
-            sleep(1)
-            if value == 0:
-                return
-            raise Exception(f"The provided value was {value}")
-
-
-    class Flow(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.work = Work(raise_exception=False)
-            self.counter = 0
-
-        def run(self):
-            if not self.work.has_started:
-                print("NOT STARTED")
-
-            elif self.work.is_pending:
-                print("PENDING")
-
-            elif self.work.is_running:
-                print("RUNNING")
-
-            elif self.work.has_succeeded:
-                print("SUCCESS")
-
-            elif self.work.has_failed:
-                print("FAILED")
-
-            elif self.work.has_stopped:
-                print("STOPPED")
-                self.stop()
-
-            print(self.work.status)
-            self.work.run(self.counter)
-            self.counter += 1
-
-
-    app = L.LightningApp(Flow())
-
-Run this app as follows:
-
-.. code-block:: bash
-
-    lightning run app test.py > app_log.txt
-
-And here is the expected output inside ``app_log.txt`` and as expected,
-we are observing the following transition ``has_started``, ``is_pending``, ``is_running``, ``has_succeeded``, ``is_running`` and ``has_failed``
-
-.. code-block:: console
-
-    NOT STARTED
-    WorkStatus(stage='not_started', timestamp=1653498225.18468, reason=None, message=None, count=1)
-    PENDING
-    WorkStatus(stage='pending', timestamp=1653498225.217413, reason=None, message=None, count=1)
-    PENDING
-    WorkStatus(stage='pending', timestamp=1653498225.217413, reason=None, message=None, count=1)
-    PENDING
-    ...
-    PENDING
-    WorkStatus(stage='pending', timestamp=1653498225.217413, reason=None, message=None, count=1)
-    PENDING
-    WorkStatus(stage='pending', timestamp=1653498225.217413, reason=None, message=None, count=1)
-    RUNNING
-    WorkStatus(stage='running', timestamp=1653498228.825194, reason=None, message=None, count=1)
-    ...
-    SUCCESS
-    WorkStatus(stage='succeeded', timestamp=1653498229.831793, reason=None, message=None, count=1)
-    SUCCESS
-    WorkStatus(stage='succeeded', timestamp=1653498229.831793, reason=None, message=None, count=1)
-    SUCCESS
-    WorkStatus(stage='succeeded', timestamp=1653498229.831793, reason=None, message=None, count=1)
-    RUNNING
-    WorkStatus(stage='running', timestamp=1653498229.846451, reason=None, message=None, count=1)
-    RUNNING
-    ...
-    WorkStatus(stage='running', timestamp=1653498229.846451, reason=None, message=None, count=1)
-    RUNNING
-    WorkStatus(stage='running', timestamp=1653498229.846451, reason=None, message=None, count=1)
-    FAILED
-    WorkStatus(stage='failed', timestamp=1653498230.852565, reason='user_exception', message='The provided value was 1', count=1)
-    FAILED
-    WorkStatus(stage='failed', timestamp=1653498230.852565, reason='user_exception', message='The provided value was 1', count=1)
-    FAILED
-    WorkStatus(stage='failed', timestamp=1653498230.852565, reason='user_exception', message='The provided value was 1', count=1)
-    FAILED
-    WorkStatus(stage='failed', timestamp=1653498230.852565, reason='user_exception', message='The provided value was 1', count=1)
-    ...
-
-In order to access all statuses:
-
-.. code-block:: python
-
-    from time import sleep
-    import lightning as L
-
-
-    class Work(L.LightningWork):
-        def run(self, value: int):
-            sleep(1)
-            if value == 0:
-                return
-            raise Exception(f"The provided value was {value}")
-
-
-    class Flow(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.work = Work(raise_exception=False)
-            self.counter = 0
-
-        def run(self):
-            print(self.statuses)
-            self.work.run(self.counter)
-            self.counter += 1
-
-
-    app = L.LightningApp(Flow())
-
-
-Run this app as follows:
-
-.. code-block:: bash
-
-    lightning run app test.py > app_log.txt
-
-And here is the expected output inside ``app_log.txt``:
-
-
-.. code-block:: console
-
-    # First execution with value = 0
-
-    []
-    [WorkStatus(stage='pending', timestamp=1653498622.252016, reason=None, message=None, count=1)]
-    ...
-    [WorkStatus(stage='pending', timestamp=1653498622.252016, reason=None, message=None, count=1)]
-    [WorkStatus(stage='pending', timestamp=1653498622.252016, reason=None, message=None, count=1)]
-    [WorkStatus(stage='pending', timestamp=1653498622.252016, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498626.185683, reason=None, message=None, count=1)]
-    [WorkStatus(stage='pending', timestamp=1653498622.252016, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498626.185683, reason=None, message=None, count=1)]
-    ...
-    [WorkStatus(stage='pending', timestamp=1653498622.252016, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498626.185683, reason=None, message=None, count=1)]
-    [WorkStatus(stage='pending', timestamp=1653498622.252016, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498626.185683, reason=None, message=None, count=1)]
-    [WorkStatus(stage='pending', timestamp=1653498622.252016, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498626.185683, reason=None, message=None, count=1), WorkStatus(stage='succeeded', timestamp=1653498627.191053, reason=None, message=None, count=1)]
-    [WorkStatus(stage='pending', timestamp=1653498622.252016, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498626.185683, reason=None, message=None, count=1), WorkStatus(stage='succeeded', timestamp=1653498627.191053, reason=None, message=None, count=1)]
-    [WorkStatus(stage='pending', timestamp=1653498622.252016, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498626.185683, reason=None, message=None, count=1), WorkStatus(stage='succeeded', timestamp=1653498627.191053, reason=None, message=None, count=1)]
-
-    # Second execution with value = 1
-
-    [WorkStatus(stage='pending', timestamp=1653498627.204636, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498627.205509, reason=None, message=None, count=1)]
-    [WorkStatus(stage='pending', timestamp=1653498627.204636, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498627.205509, reason=None, message=None, count=1)]
-    ...
-    [WorkStatus(stage='pending', timestamp=1653498627.204636, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498627.205509, reason=None, message=None, count=1)]
-    [WorkStatus(stage='pending', timestamp=1653498627.204636, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498627.205509, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498627.205509, reason=None, message=None, count=1), WorkStatus(stage='failed', timestamp=1653498628.210164, reason='user_exception', message='The provided value was 1', count=1)]
-    [WorkStatus(stage='pending', timestamp=1653498627.204636, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498627.205509, reason=None, message=None, count=1), WorkStatus(stage='running', timestamp=1653498627.205509, reason=None, message=None, count=1), WorkStatus(stage='failed', timestamp=1653498628.210164, reason='user_exception', message='The provided value was 1', count=1)]

@@ -1,13 +0,0 @@
-:orphan:
-
-####################
-LightningWork Status
-####################
-
-**Audience:** Users who want to understand ``LightningWork`` under the hood.
-
-**Level:** Advanced
-
-----
-
-.. include:: status_content.rst

@@ -1,40 +0,0 @@
-:orphan:
-
-.. _core_api:
-
-###############################
-Learn more about Lightning Core
-###############################
-
-.. raw:: html
-
-   <div class="display-card-container">
-      <div class="row">
-
-.. displayitem::
-   :header: Level-up with Lightning Apps
-   :description: From Basics to Advanced Skills
-   :col_css: col-md-6
-   :button_link: ../levels/basic/index.html
-   :height: 180
-
-.. displayitem::
-   :header: Understand Lightning App
-   :description: Detailed description
-   :col_css: col-md-6
-   :button_link: lightning_app/index.html
-   :height: 180
-
-.. displayitem::
-   :header: Understand Lightning Flow
-   :description: Detailed description
-   :col_css: col-md-6
-   :button_link: lightning_flow.html
-   :height: 180
-
-.. displayitem::
-   :header: Understand Lightning Work
-   :description: Detailed description
-   :col_css: col-md-6
-   :button_link: lightning_work/index.html
-   :height: 180

@@ -1,53 +0,0 @@
-:orphan:
-
-###################################################
-Scheduled DAG with pandas and sklearn from scratch.
-###################################################
-
-**Audience:** Users coming from MLOps to Lightning Apps, looking for more flexibility.
-
-**Level:** Intermediate.
-
-In this example, you will learn how to create a simple DAG which:
-
-* Download and process some data
-* Train several models and report their associated metrics
-
-and learn how to schedule this entire process.
-
-Find the complete example `here <https://github.com/Lightning-AI/lightning/blob/master/examples/app/dag/app.py>`_.
-
-----
-
-**************************
-Step 1: Implement your DAG
-**************************
-
-Here is an illustration of the DAG to implement:
-
-.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/simple_dag.png
-    :alt: Simple DAG
-    :width: 100 %
-
-First, let's define the component we need:
-
-* DataCollector is responsible to download the data
-* Processing is responsible to execute a ``processing.py`` script.
-* A collection of model work to train all models in parallel.
-
-.. literalinclude:: ../../../../examples/app/dag/app.py
-    :lines: 52-74
-
-And its run method executes the steps described above.
-
-.. literalinclude:: ../../../../examples/app/dag/app.py
-    :lines: 76-99
-
-----
-
-*****************************
-Step 2: Define the scheduling
-*****************************
-
-.. literalinclude:: ../../../../examples/app/dag/app.py
-    :lines: 102-130

@@ -1,81 +0,0 @@
-:orphan:
-
-######################################
-Develop a Directed Acyclic Graph (DAG)
-######################################
-
-.. _dag_example:
-
-**Audience:** Users coming from MLOps to Lightning Apps, looking for more flexibility.
-
-A typical ML training workflow can be implemented with a simple DAG.
-
-Below is a pseudo-code using the lightning framework that uses a LightningFlow to orchestrate the serial workflow: process data, train a model, and serve the model.
-
-.. code-block:: python
-
-    import lightning as L
-
-    class DAGFlow(L.LightningFlow):
-
-        def __init__(self):
-            super().__init__()
-            self.processor = DataProcessorWork(...)
-            self.train_work = TrainingWork(...)
-            self.serve_work = ServeWork(...)
-
-        def run(self):
-            self.processor.run(...)
-            self.train_work.run(...)
-            self.serve_work.run(...)
-
-Below is a pseudo-code to run several works in parallel using a built-in :class:`~lightning.app.structures.Dict`.
-
-.. code-block:: python
-
-    import lightning as L
-
-    class DAGFlow(L.LightningFlow):
-
-        def __init__(self):
-            super().__init__()
-            ...
-            self.train_works = L.structures.Dict(**{
-                "1": TrainingWork(..., parallel=True),
-                "2": TrainingWork(..., parallel=True),
-                "3": TrainingWork(..., parallel=True),
-                ...
-                })
-            ...
-
-        def run(self):
-            self.processor.run(...)
-
-            # The flow runs through them all, so we need to guard self.serve_work.run
-            for work in self.train_works.values():
-                work.run(...)
-
-            # Wait for all to have finished without errors.
-            if not all(w.has_succeeded for w in self.train_works):
-                continue
-
-            self.serve_work.run(...)
-
-----
-
-**********
-Next Steps
-**********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Scheduled DAG with pandas and sklearn from scratch.
-   :description: DAG example in pure Lightning.
-   :col_css: col-md-4
-   :button_link: dag_from_scratch.html
-   :height: 180
-   :tag: intermediate

@@ -1,5 +0,0 @@
-:orphan:
-
-##########################
-Build a Data Exploring App
-##########################

@@ -1,5 +0,0 @@
-:orphan:
-
-###############
-Build a ETL App
-###############

@@ -1,243 +0,0 @@
-import json
-import os
-import tarfile
-import uuid
-import zipfile
-from pathlib import Path
-
-from lightning.app import LightningWork, LightningApp
-from lightning.app.storage import Drive
-
-
-class FileServer(LightningWork):
-    def __init__(
-        self,
-        drive: Drive,
-        base_dir: str = "file_server",
-        chunk_size=10240,
-        **kwargs
-    ):
-        """This component uploads, downloads files to your application.
-
-        Arguments:
-            drive: The drive can share data inside your application.
-            base_dir: The local directory where the data will be stored.
-            chunk_size: The quantity of bytes to download/upload at once.
-
-        """
-        super().__init__(
-            cloud_build_config=BuildConfig(["flask, flask-cors"]),
-            parallel=True,
-            **kwargs,
-        )
-        # 1: Attach the arguments to the state.
-        self.drive = drive
-        self.base_dir = base_dir
-        self.chunk_size = chunk_size
-
-        # 2: Create a folder to store the data.
-        os.makedirs(self.base_dir, exist_ok=True)
-
-        # 3: Keep a reference to the uploaded filenames.
-        self.uploaded_files = dict()
-
-    def get_filepath(self, path: str) -> str:
-        """Returns file path stored on the file server."""
-        return os.path.join(self.base_dir, path)
-
-    def get_random_filename(self) -> str:
-        """Returns a random hash for the file name."""
-        return uuid.uuid4().hex
-
-    def upload_file(self, file):
-        """Upload a file while tracking its progress."""
-        # 1: Track metadata about the file
-        filename = file.filename
-        uploaded_file = self.get_random_filename()
-        meta_file = uploaded_file + ".meta"
-        self.uploaded_files[filename] = {
-            "progress": (0, None), "done": False
-        }
-
-        # 2: Create a stream and write bytes of
-        # the file to the disk under `uploaded_file` path.
-        with open(self.get_filepath(uploaded_file), "wb") as out_file:
-            content = file.read(self.chunk_size)
-            while content:
-                # 2.1 Write the file bytes
-                size = out_file.write(content)
-
-                # 2.2 Update the progress metadata
-                self.uploaded_files[filename]["progress"] = (
-                    self.uploaded_files[filename]["progress"][0] + size,
-                    None,
-                )
-                # 4: Read next chunk of data
-                content = file.read(self.chunk_size)
-
-        # 3: Update metadata that the file has been uploaded.
-        full_size = self.uploaded_files[filename]["progress"][0]
-        self.drive.put(self.get_filepath(uploaded_file))
-        self.uploaded_files[filename] = {
-            "progress": (full_size, full_size),
-            "done": True,
-            "uploaded_file": uploaded_file,
-        }
-
-        # 4: Write down the metadata about the file to the disk
-        meta = {
-            "original_path": filename,
-            "display_name": os.path.splitext(filename)[0],
-            "size": full_size,
-            "drive_path": uploaded_file,
-        }
-        with open(self.get_filepath(meta_file), "w") as f:
-            json.dump(meta, f)
-
-        # 5: Put the file to the drive.
-        # It means other components can access get or list them.
-        self.drive.put(self.get_filepath(meta_file))
-        return meta
-
-    def list_files(self, file_path: str):
-        # 1: Get the local file path of the file server.
-        file_path = self.get_filepath(file_path)
-
-        # 2: If the file exists in the drive, transfer it locally.
-        if not os.path.exists(file_path):
-            self.drive.get(file_path)
-
-        if os.path.isdir(file_path):
-            result = set()
-            for _, _, f in os.walk(file_path):
-                for file in f:
-                    if not file.endswith(".meta"):
-                        for filename, meta in self.uploaded_files.items():
-                            if meta["uploaded_file"] == file:
-                                result.add(filename)
-            return {"asset_names": [v for v in result]}
-
-        # 3: If the filepath is a tar or zip file, list their contents
-        if zipfile.is_zipfile(file_path):
-            with zipfile.ZipFile(file_path, "r") as zf:
-                result = zf.namelist()
-        elif tarfile.is_tarfile(file_path):
-            with tarfile.TarFile(file_path, "r") as tf:
-                result = tf.getnames()
-        else:
-            raise ValueError("Cannot open archive file!")
-
-        # 4: Returns the matching files.
-        return {"asset_names": result}
-
-    def run(self):
-        # 1: Imports flask requirements.
-        from flask import Flask, request
-        from flask_cors import CORS
-
-        # 2: Create a flask app
-        flask_app = Flask(__name__)
-        CORS(flask_app)
-
-        # 3: Define the upload file endpoint
-        @flask_app.post("/upload_file/")
-        def upload_file():
-            """Upload a file directly as form data."""
-            f = request.files["file"]
-            return self.upload_file(f)
-
-        @flask_app.get("/")
-        def list_files():
-            return self.list_files(str(Path(self.base_dir).resolve()))
-
-        # 5: Start the flask app while providing the `host` and `port`.
-        flask_app.run(host=self.host, port=self.port, load_dotenv=False)
-
-    def alive(self):
-        """Hack: Returns whether the server is alive."""
-        return self.url != ""
-
-
-import requests
-
-from lightning import LightningWork
-
-
-class TestFileServer(LightningWork):
-    def __init__(self, drive: Drive):
-        super().__init__(cache_calls=True)
-        self.drive = drive
-
-    def run(self, file_server_url: str, first=True):
-        if first:
-            with open("test.txt", "w") as f:
-                f.write("Some text.")
-
-            response = requests.post(
-                file_server_url + "/upload_file/",
-                files={'file': open("test.txt", 'rb')}
-            )
-            assert response.status_code == 200
-        else:
-            response = requests.get(file_server_url)
-            assert response.status_code == 200
-            assert response.json() == {"asset_names": ["test.txt"]}
-
-
-from lightning import LightningApp, LightningFlow
-
-
-class Flow(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        # 1: Create a drive to share data between works
-        self.drive = Drive("lit://file_server")
-        # 2: Create  the filer server
-        self.file_server = FileServer(self.drive)
-        # 3: Create the file ser
-        self.test_file_server = TestFileServer(self.drive)
-
-    def run(self):
-        # 1: Start the file server.
-        self.file_server.run()
-
-        # 2: Trigger the test file server work when ready.
-        if self.file_server.alive():
-            # 3 Execute the test file server work.
-            self.test_file_server.run(self.file_server.url)
-            self.test_file_server.run(self.file_server.url, first=False)
-
-        # 4 When both execution are successful, exit the app.
-        if self.test_file_server.num_successes == 2:
-            self.stop()
-
-    def configure_layout(self):
-        # Expose the file_server component
-        # in the UI using its `/` endpoint.
-        return {"name": "File Server", "content": self.file_server}
-
-
-from lightning.app.runners import MultiProcessRuntime
-
-
-def test_file_server():
-    app = LightningApp(Flow())
-    MultiProcessRuntime(app).dispatch()
-
-
-from lightning.app.testing import run_app_in_cloud
-
-
-def test_file_server_in_cloud():
-    # You need to provide the directory containing the app file.
-    app_dir = "docs/source-app/examples/file_server"
-    with run_app_in_cloud(app_dir) as (admin_page, view_page, get_logs_fn, name):
-        """# 1. `admin_page` and `view_page` are playwright Page Objects.
-
-        # Check out https://playwright.dev/python/ doc to learn more.
-        # You can click the UI and trigger actions.
-
-        # 2. By calling logs = get_logs_fn(),
-        # you get all the logs currently on the admin page.
-
-        """

@@ -1,85 +0,0 @@
-
-
-*********
-Our Goal
-*********
-
-Create a simple Lightning App (App) that allows users to upload files and list the uploaded files.
-
-----
-
-*************
-Completed App
-*************
-
-Here is a recording of the final App built in this example, tested with pytest.
-
-.. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/file_server.mp4
-    :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/file_server.png
-    :width: 600
-    :class: background-video
-    :autoplay:
-    :loop:
-    :muted:
-
-----
-
-**********
-App Design
-**********
-
-In order to create this App, we need to develop two components and an App:
-
-* A **File Server Component** that gives you the ability to download or list files shared with your App. This is particularly useful when you want to trigger an ML job but your users need to provide their own data or if the user wants to download the trained checkpoints.
-
-* A **Test File Server** Component to interact with the file server.
-
-* An App putting everything together and the App's associated pytest tests.
-
-----
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Step 1: Implement the File Server general structure
-   :description: Put together the shape of the Component
-   :col_css: col-md-4
-   :button_link: file_server_step_1.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Step 2: Implement the File Server upload and list files methods
-   :description: Add the core functionalities to the Component
-   :col_css: col-md-4
-   :button_link: file_server_step_2.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Step 3: Implement a File Server Testing Component
-   :description: Create a Component to test the file server
-   :col_css: col-md-4
-   :button_link: file_server_step_3.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Step 4: Implement tests for the File Server component with pytest
-   :description: Create an App to validate the upload and list files endpoints
-   :col_css: col-md-4
-   :button_link: file_server_step_4.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,13 +0,0 @@
-:orphan:
-
-.. _fileserver_example:
-
-#####################
-Develop a File Server
-#####################
-
-**Prerequisite**: Reach :ref:`level 16+ <intermediate_level>` and read the :ref:`Drive article <drive_storage>`.
-
-----
-
-.. include:: file_server_content.rst

@@ -1,49 +0,0 @@
-:orphan:
-
-##################################################
-Step 1: Implement the FileServer general structure
-##################################################
-
-Let’s dive in on how to develop the component with the following code:
-
-.. literalinclude:: ./app.py
-    :lines: 1-41, 132-158
-    :emphasize-lines: 16, 51-
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Step 2: Implement the File Server upload and list files methods
-   :description: Add the core functionalities to the Component
-   :col_css: col-md-4
-   :button_link: file_server_step_2.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Step 3: Implement a File Server Testing Component
-   :description: Create a Component to test the file server
-   :col_css: col-md-4
-   :button_link: file_server_step_3.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Step 4: Implement tests for the File Server component with pytest
-   :description: Create an App to validate the upload and list files endpoints
-   :col_css: col-md-4
-   :button_link: file_server_step_4.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,75 +0,0 @@
-:orphan:
-
-################################################################
-Step 2: Implement the File Server upload and list_files methods
-################################################################
-
-Let's dive in on how to implement these methods.
-
-***************************
-Implement the upload method
-***************************
-
-In this method, we are creating a stream between the uploaded file and the uploaded file stored on the file server disk.
-
-Once the file is uploaded, we are putting the file into the :class:`~lightning.app.storage.drive.Drive`, so it becomes persistent and accessible to all Components.
-
-.. literalinclude:: ./app.py
-    :lines: 12, 51-99
-    :emphasize-lines: 49
-
-*******************************
-Implement the fist_files method
-*******************************
-
-First, in this method, we get the file in the file server filesystem, if available in the Drive. Once done, we list the the files under the provided paths and return the results.
-
-.. literalinclude:: ./app.py
-    :lines: 12, 100-130
-    :emphasize-lines: 9
-
-
-*******************
-Implement utilities
-*******************
-
-.. literalinclude:: ./app.py
-    :lines: 12, 43-49
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Step 1: Implement the File Server general structure
-   :description: Put together the shape of the Component
-   :col_css: col-md-4
-   :button_link: file_server_step_1.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Step 3: Implement a File Server Testing Component
-   :description: Create a Component to test the file server
-   :col_css: col-md-4
-   :button_link: file_server_step_3.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Step 4: Implement tests for the File Server component with pytest
-   :description: Create an App to validate the upload and list files endpoints
-   :col_css: col-md-4
-   :button_link: file_server_step_4.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,54 +0,0 @@
-:orphan:
-
-#################################################
-Step 3: Implement a File Server Testing Component
-#################################################
-
-Let's dive in on how to implement a testing component for a server.
-
-This component needs to test two things:
-
-* The **/upload_file/** endpoint by creating a file and sending its content to it.
-
-* The **/** endpoint listing files, by validating the that previously uploaded file is present in the response.
-
-.. literalinclude:: ./app.py
-   :lines: 165-182
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Step 1: Implement the File Server general structure
-   :description: Put together the shape of the Component
-   :col_css: col-md-4
-   :button_link: file_server_step_1.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Step 2: Implement the File Server upload and list files methods
-   :description: Add the core functionalities to the Component
-   :col_css: col-md-4
-   :button_link: file_server_step_2.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Step 4: Implement tests for the File Server component with pytest
-   :description: Create an App to validate the upload and list files endpoints
-   :col_css: col-md-4
-   :button_link: file_server_step_4.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,127 +0,0 @@
-:orphan:
-
-#################################################################
-Step 4: Implement tests for the File Server component with pytest
-#################################################################
-
-Let's create a simple App with our **File Server** and **File Server Test** components.
-
-Once the File Server is up and running, we'll execute the **test_file_server** LightningWork and when both calls are successful, we exit the App using ``self._exit``.
-
-.. literalinclude:: ./app.py
-   :lines: 187-218
-
-
-Simply create a ``test.py`` file with the following code and run ``pytest tests.py``:
-
-.. literalinclude:: ./app.py
-   :lines: 221-226
-
-To test the App in the cloud, create a ``cloud_test.py`` file with the following code and run ``pytest cloud_test.py``.
-Under the hood, we are using the end-to-end testing `playwright <https://playwright.dev/python/>`_ library, so you can interact with the UI.
-
-.. literalinclude:: ./app.py
-   :lines: 229-
-
-----
-
-********************
-Test the application
-********************
-
-Clone the Lightning repo and run the following command:
-
-.. code-block:: bash
-
-   pytest docs/source/examples/file_server/app.py --capture=no -v
-
-----
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Step 1: Implement the File Server general structure
-   :description: Put together the shape of the Component
-   :col_css: col-md-4
-   :button_link: file_server_step_1.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Step 2: Implement the File Server upload and list files methods
-   :description: Add the core functionalities to the Component
-   :col_css: col-md-4
-   :button_link: file_server_step_2.html
-   :height: 180
-   :tag: Basic
-
-.. displayitem::
-   :header: Step 3: Implement a File Server Testing Component
-   :description: Create a Component to test the file server
-   :col_css: col-md-4
-   :button_link: file_server_step_3.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>
-
-----
-
-******************
-Find more examples
-******************
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. Add callout items below this line
-
-.. displayitem::
-   :header: Develop a DAG
-   :description: Create a dag pipeline
-   :col_css: col-md-4
-   :button_link: ../dag/dag.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Develop a Github Repo Script Runner
-   :description: Run any script on github in the cloud
-   :col_css: col-md-4
-   :button_link: ../github_repo_runner/github_repo_runner.html
-   :height: 150
-   :tag: Intermediate
-
-
-.. displayitem::
-   :header: Develop a HPO Sweeper
-   :description: Train multiple models with different parameters
-   :col_css: col-md-4
-   :button_link: ../hpo/hpo.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Develop a Model Server
-   :description: Serve multiple models with different parameters
-   :col_css: col-md-4
-   :button_link: ../model_server_app/model_server_app.html
-   :height: 150
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,309 +0,0 @@
-import io
-import os
-import subprocess
-import sys
-from copy import deepcopy
-from functools import partial
-from subprocess import Popen
-from typing import Dict, List, Optional
-
-from lightning import BuildConfig, CloudCompute, LightningApp, LightningFlow
-from lightning.app import structures
-from lightning.app.components import TracerPythonScript
-from lightning.app.frontend import StreamlitFrontend
-from lightning.app.storage.path import Path
-from lightning.app.utilities.state import AppState
-
-
-class GithubRepoRunner(TracerPythonScript):
-    def __init__(
-        self,
-        id: str,
-        github_repo: str,
-        script_path: str,
-        script_args: List[str],
-        requirements: List[str],
-        cloud_compute: Optional[CloudCompute] = None,
-        **kwargs,
-    ):
-        """The GithubRepoRunner Component clones a repo, runs a specific script with provided arguments and collect
-        logs.
-
-        Arguments:
-            id: Identified of the component.
-            github_repo: The Github Repo URL to clone.
-            script_path: The path to the script to execute.
-            script_args: The arguments to be provided to the script.
-            requirements: The python requirements tp run the script.
-            cloud_compute: The object to select the cloud instance.
-
-        """
-        super().__init__(
-            script_path=script_path,
-            script_args=script_args,
-            cloud_compute=cloud_compute,
-            cloud_build_config=BuildConfig(requirements=requirements),
-            **kwargs,
-        )
-        self.id = id
-        self.github_repo = github_repo
-        self.logs = []
-
-    def run(self, *args, **kwargs):
-        # 1. Hack: Patch stdout so we can capture the logs.
-        string_io = io.StringIO()
-        sys.stdout = string_io
-
-        # 2: Use git command line to clone the repo.
-        repo_name = self.github_repo.split("/")[-1].replace(".git", "")
-        cwd = os.path.dirname(__file__)
-        subprocess.Popen(
-            f"git clone {self.github_repo}", cwd=cwd, shell=True).wait()
-
-        # 3: Execute the parent run method of the TracerPythonScript class.
-        os.chdir(os.path.join(cwd, repo_name))
-        super().run(*args, **kwargs)
-
-        # 4: Get all the collected logs and add them to the state.
-        # This isn't optimal as heavy, but works for this demo purpose.
-        self.logs = string_io.getvalue()
-        string_io.close()
-
-    def configure_layout(self):
-        return {"name": self.id, "content": self}
-
-
-class PyTorchLightningGithubRepoRunner(GithubRepoRunner):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.best_model_path = None
-        self.best_model_score = None
-
-    def configure_tracer(self):
-        from lightning.pytorch import Trainer
-        from lightning.pytorch.callbacks import Callback
-
-        tracer = super().configure_tracer()
-
-        class TensorboardServerLauncher(Callback):
-            def __init__(self, work):
-                # The provided `work` is the
-                # current ``PyTorchLightningScript`` work.
-                self.w = work
-
-            def on_train_start(self, trainer, *_):
-                # Add `host` and `port` for tensorboard to work in the cloud.
-                cmd = f"tensorboard --logdir='{trainer.logger.log_dir}'"
-                server_args = f"--host {self.w.host} --port {self.w.port}"
-                Popen(cmd + " " + server_args, shell=True)
-
-        def trainer_pre_fn(self, *args, work=None, **kwargs):
-            # Intercept Trainer __init__ call
-            # and inject a ``TensorboardServerLauncher`` component.
-            kwargs["callbacks"].append(TensorboardServerLauncher(work))
-            return {}, args, kwargs
-
-        # 5. Patch the `__init__` method of the Trainer
-        # to inject our callback with a reference to the work.
-        tracer.add_traced(
-            Trainer, "__init__", pre_fn=partial(trainer_pre_fn, work=self))
-        return tracer
-
-    def on_after_run(self, end_script_globals):
-        import torch
-
-        # 1. Once the script has finished to execute,
-        # we can collect its globals and access any objects.
-        trainer = end_script_globals["cli"].trainer
-        checkpoint_callback = trainer.checkpoint_callback
-        lightning_module = trainer.lightning_module
-
-        # 2. From the checkpoint_callback,
-        # we are accessing the best model weights
-        checkpoint = torch.load(checkpoint_callback.best_model_path)
-
-        # 3. Load the best weights and torchscript the model.
-        lightning_module.load_state_dict(checkpoint["state_dict"])
-        lightning_module.to_torchscript(f"{self.name}.pt")
-
-        # 4. Use lightning.app.storage.Pathto create a reference to the
-        # torch scripted model. In the cloud with multiple machines,
-        # by simply passing this reference to another work,
-        # it triggers automatically a file transfer.
-        self.best_model_path = Path(f"{self.name}.pt")
-
-        # 5. Keep track of the metrics.
-        self.best_model_score = float(checkpoint_callback.best_model_score)
-
-
-class KerasGithubRepoRunner(GithubRepoRunner):
-    """Left to the users to implement."""
-
-
-class TensorflowGithubRepoRunner(GithubRepoRunner):
-    """Left to the users to implement."""
-
-
-GITHUB_REPO_RUNNERS = {
-    "PyTorch Lightning": PyTorchLightningGithubRepoRunner,
-    "Keras": KerasGithubRepoRunner,
-    "Tensorflow": TensorflowGithubRepoRunner,
-}
-
-
-class Flow(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        # 1: Keep track of the requests within the state
-        self.requests = []
-        # 2: Create a dictionary of components.
-        self.ws = structures.Dict()
-
-    def run(self):
-        # Iterate continuously over all requests
-        for request_id, request in enumerate(self.requests):
-            self._handle_request(request_id, deepcopy(request))
-
-    def _handle_request(self, request_id: int, request: Dict):
-        # 1: Create a name and find selected framework
-        name = f"w_{request_id}"
-        ml_framework = request["train"].pop("ml_framework")
-
-        # 2: If the component hasn't been created yet, create it.
-        if name not in self.ws:
-            work_cls = GITHUB_REPO_RUNNERS[ml_framework]
-            work = work_cls(id=request["id"], **request["train"])
-            self.ws[name] = work
-
-        # 3: Run the component
-        self.ws[name].run()
-
-        # 4: Once the component has finished,
-        # add metadata to the original request for the UI.
-        if self.ws[name].best_model_path:
-            request = self.requests[request_id]
-            request["best_model_score"] = self.ws[name].best_model_score
-            request["best_model_path"] = self.ws[name].best_model_path
-
-    def configure_layout(self):
-        # Create a StreamLit UI for the user to run his Github Repo.
-        return StreamlitFrontend(render_fn=render_fn)
-
-
-def page_1__create_new_run(state):
-    import streamlit as st
-
-    st.markdown("# Create a new Run 🎈")
-
-    # 1: Collect arguments from the users
-    id = st.text_input("Name your run", value="my_first_run")
-    github_repo = st.text_input(
-        "Enter a Github Repo URL", value="https://github.com/Lightning-AI/lightning-quick-start.git"
-    )
-
-    default_script_args = (
-        "--trainer.max_epochs=5"
-        " --trainer.limit_train_batches=4"
-        " --trainer.limit_val_batches=4"
-        " --trainer.callbacks=ModelCheckpoint"
-        " --trainer.callbacks.monitor=val_acc"
-    )
-    default_requirements = "torchvision, pytorch_lightning, jsonargparse[signatures]"
-
-    script_path = st.text_input("Enter your script to run", value="train_script.py")
-    script_args = st.text_input("Enter your base script arguments", value=default_script_args)
-    requirements = st.text_input("Enter your requirements", value=default_requirements)
-    ml_framework = st.radio(
-        "Select your ML Training Frameworks", options=["PyTorch Lightning", "Keras", "Tensorflow"]
-    )
-
-    if ml_framework not in ("PyTorch Lightning"):
-        st.write(f"{ml_framework} isn't supported yet.")
-        return
-
-    clicked = st.button("Submit")
-
-    # 2: If clicked, create a new request.
-    if clicked:
-        new_request = {
-            "id": id,
-            "train": {
-                "github_repo": github_repo,
-                "script_path": script_path,
-                "script_args": script_args.split(" "),
-                "requirements": requirements.split(" "),
-                "ml_framework": ml_framework,
-            },
-        }
-        # 3: IMPORTANT: Add a new request to the state in-place.
-        # The flow receives the UI request and dynamically create
-        # and run the associated work from the request information.
-        state.requests = state.requests + [new_request]
-
-
-def page_2__view_run_lists(state):
-    import streamlit as st
-
-    st.markdown("# Run Lists 🎈")
-    # 1: Iterate through all the requests in the state.
-    for i, r in enumerate(state.requests):
-        i = str(i)
-        # 2: Display information such as request, logs, work state, model score.
-        work = state._state["structures"]["ws"]["works"][f"w_{i}"]
-        with st.expander(f"Expand to view Run {i}", expanded=False):
-            if st.checkbox("Expand to view your configuration", key=i):
-                st.json(r)
-            if st.checkbox("Expand to view logs", key=i):
-                st.code(body=work["vars"]["logs"])
-            if st.checkbox("Expand to view your work state", key=i):
-                work["vars"].pop("logs")
-                st.json(work)
-            best_model_score = r.get("best_model_score", None)
-            if best_model_score:
-                if st.checkbox("Expand to view your run performance", key=i):
-                    st.json({"best_model_score": best_model_score, "best_model_path": r.get("best_model_path")})
-
-
-def page_3__view_app_state(state):
-    import streamlit as st
-
-    st.markdown("# App State 🎈")
-    st.write(state._state)
-
-
-def render_fn(state: AppState):
-    import streamlit as st
-
-    page_names_to_funcs = {
-        "Create a new Run": partial(page_1__create_new_run, state=state),
-        "View your Runs": partial(page_2__view_run_lists, state=state),
-        "View the App state": partial(page_3__view_app_state, state=state),
-    }
-    selected_page = st.sidebar.selectbox(
-        "Select a page", page_names_to_funcs.keys())
-    page_names_to_funcs[selected_page]()
-
-
-class RootFlow(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        # Create the flow
-        self.flow = Flow()
-
-    def run(self):
-        # Run the flow
-        self.flow.run()
-
-    def configure_layout(self):
-        # 1: Add the main StreamLit UI
-        selection_tab = [{
-            "name": "Run your Github Repo",
-            "content": self.flow,
-        }]
-        # 2: Add a new tab whenever a new work is dynamically created
-        run_tabs = [e.configure_layout() for e in self.flow.ws.values()]
-        # 3: Returns the list of tabs.
-        return selection_tab + run_tabs
-
-
-app = LightningApp(RootFlow())

@@ -1,97 +0,0 @@
-
-********
-Our Goal
-********
-
-Create a simple Lightning App (App) where users can enter information in a UI to run a given PyTorch Lightning Script from a given Github Repo with some optional extra Python requirements and arguments.
-
-Users should be able to monitor their training progress in real-time, view the logs, and get the best monitored metric and associated checkpoint for their models.
-
-----
-
-Completed App
-^^^^^^^^^^^^^
-
-Here is a recording of the final application built in this example. The example is around 200 lines in total and should give you a great foundation to build your own Lightning App.
-
-.. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/github_app.mp4
-    :poster: "https://pl-public-data.s3.amazonaws.com/assets_lightning/github_app.png
-    :width: 600
-    :class: background-video
-    :autoplay:
-    :loop:
-    :muted:
-
-----
-
-**********
-App Design
-**********
-
-In order to develop the App, we need to build several components:
-
-* A GithubRepoRunner Component that clones a repo, runs a specific script with provided arguments and collect logs.
-
-* A PyTorch Lightning GithubRepoRunner Component that augments the GithubRepoRunner component to track PyTorch Lightning Trainer.
-
-* A UI for the users to provide to trigger dynamically a new execution.
-
-* A Flow to dynamically create GithubRepoRunner once a user submits information from the UI.
-
-----
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Step 1: Implement the GithubRepoRunner Component
-   :description: Clone and execute script from a GitHub Repo.
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_1.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Step 2: Implement the PyTorch Lightning GithubRepoRunner Component
-   :description: Automate PyTorch Lightning execution
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_2.html
-   :height: 180
-   :tag: Advanced
-
-.. displayitem::
-   :header: Step 3: Implement the Flow to manage user requests
-   :description: Dynamically create GithubRepoRunner
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_3.html
-   :height: 180
-   :tag: Intermediate
-
-
-.. displayitem::
-   :header: Step 4: Implement the UI with StreamLit
-   :description: Several pages application
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_4.html
-   :height: 180
-   :tag: Intermediate
-
-
-.. displayitem::
-   :header: Step 5: Put it all together
-   :description:
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_5.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,15 +0,0 @@
-:orphan:
-
-.. _github_repo_script_runner_example:
-
-###################################
-Develop a Github Repo Script Runner
-###################################
-
-**Audience:** Users that want to develop interactive applications which runs Github Repo in the cloud at any scale for multiple users.
-
-**Prerequisite**: Reach :ref:`level 16+ <intermediate_level>` and read the docstring of of :class:`~lightning.app.components.python.tracer.TracerPythonScript` component.
-
-----
-
-.. include:: github_repo_runner_content.rst

@@ -1,62 +0,0 @@
-:orphan:
-
-************************************************
-Step 1: Implement the GithubRepoRunner Component
-************************************************
-
-The GithubRepoRunner Component clones a repo, runs a specific script with provided arguments, and collect logs.
-
-Let's dive in on how to develop the component with the following code:
-
-.. literalinclude:: ./app.py
-    :lines: -72
-
-----
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Step 2: Implement the PyTorch Lightning GithubRepoRunner Component
-   :description: Automate PyTorch Lightning execution
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_2.html
-   :height: 180
-   :tag: Advanced
-
-.. displayitem::
-   :header: Step 3: Implement the Flow to manage user requests
-   :description: Dynamically create GithubRepoRunner
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_3.html
-   :height: 180
-   :tag: Intermediate
-
-
-.. displayitem::
-   :header: Step 4: Implement the UI with StreamLit
-   :description: Several pages application
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_4.html
-   :height: 180
-   :tag: Intermediate
-
-
-.. displayitem::
-   :header: Step 5: Put it all together
-   :description:
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_5.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,68 +0,0 @@
-:orphan:
-
-******************************************************************
-Step 2: Implement the PyTorch Lightning GithubRepoRunner Component
-******************************************************************
-
-The PyTorch Lightning GithubRepoRunner Component subclasses the GithubRepoRunner but tailors the execution experience to PyTorch Lightning.
-
-As a matter of fact, this component adds two primary tailored features for PyTorch Lightning users:
-
-* It injects dynamically a custom callback ``TensorboardServerLauncher`` in the PyTorch Lightning Trainer to start a tensorboard server so it can be exposed in Lightning App UI.
-
-* Once the script has run, the ``on_after_run`` hook of the :class:`~lightning.app.components.python.tracer.TracerPythonScript` is invoked with the script globals, meaning we can collect anything we need. In particular, we are reloading the best model, torch scripting it, and storing its path in the state along side the best metric score.
-
-Let's dive in on how to develop the component with the following code:
-
-.. literalinclude:: ./app.py
-    :lines: 75-136
-
-----
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Step 1: Implement the GithubRepoRunner Component
-   :description: Clone and execute script from a GitHub Repo.
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_1.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Step 3: Implement the Flow to manage user requests
-   :description: Dynamically create GithubRepoRunner
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_3.html
-   :height: 180
-   :tag: Intermediate
-
-
-.. displayitem::
-   :header: Step 4: Implement the UI with StreamLit
-   :description: Several pages application
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_4.html
-   :height: 180
-   :tag: Intermediate
-
-
-.. displayitem::
-   :header: Step 5: Put it all together
-   :description:
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_5.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,62 +0,0 @@
-:orphan:
-
-**************************************************
-Step 3: Implement the Flow to manage user requests
-**************************************************
-
-In step 1 and 2, we have implemented the ``GithubRepoRunner`` and ``PyTorchLightningGithubRepoRunner`` components.
-
-Now, we are going to develop a component to dynamically handle user requests.
-Let's dive in on how to develop the component with the following code:
-
-.. literalinclude:: ./app.py
-    :lines: 142-190
-
-----
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Step 1: Implement the GithubRepoRunner Component
-   :description: Clone and execute script from a GitHub Repo.
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_1.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Step 2: Implement the PyTorch Lightning GithubRepoRunner Component
-   :description: Automate PyTorch Lightning execution
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_2.html
-   :height: 180
-   :tag: Advanced
-
-.. displayitem::
-   :header: Step 4: Implement the UI with StreamLit
-   :description: Several pages application
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_4.html
-   :height: 180
-   :tag: Intermediate
-
-
-.. displayitem::
-   :header: Step 5: Put it all together
-   :description:
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_5.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,86 +0,0 @@
-:orphan:
-
-***************************************
-Step 4: Implement the UI with StreamLit
-***************************************
-
-In step 3, we have implemented a Flow which dynamically creates a Work when a new request is added to the requests list.
-
-From the UI, we create 3 pages with `StreamLit <https://streamlit.io/>`_:
-
-* **Page 1**: Create a form with add a new request to the Flow state **requests**.
-
-* **Page 2**: Iterate through all the requests and display the associated information.
-
-* **Page 3**: Display the entire App State.
-
-
-Render All Pages
-^^^^^^^^^^^^^^^^
-
-.. literalinclude:: ./app.py
-    :lines: 274-284
-
-**Page 1**
-
-.. literalinclude:: ./app.py
-    :lines: 193-241
-    :emphasize-lines: 43
-
-**Page 2**
-
-.. literalinclude:: ./app.py
-    :lines: 244-264
-
-**Page 3**
-
-.. literalinclude:: ./app.py
-    :lines: 267-271
-
-----
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: 1. Implement the GithubRepoRunner Component
-   :description: Clone and execute script from a GitHub Repo.
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_1.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: 2. Implement the PyTorch Lightning GithubRepoRunner Component
-   :description: Automate PyTorch Lightning execution
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_2.html
-   :height: 180
-   :tag: Advanced
-
-.. displayitem::
-   :header: 3. Implement the Flow to manage user requests
-   :description: Dynamically create GithubRepoRunner
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_3.html
-   :height: 180
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Step 5: Put it all together
-   :description:
-   :col_css: col-md-4
-   :button_link: github_repo_runner_step_5.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,75 +0,0 @@
-:orphan:
-
-***************************
-Step 5: Put it all together
-***************************
-
-Let's dive in on how to develop the component with the following code:
-
-.. literalinclude:: ./app.py
-    :lines: 287-
-
-Run the application
-^^^^^^^^^^^^^^^^^^^
-
-Clone the Lightning repo and run the following command:
-
-.. code-block:: bash
-
-   lightning run app docs/source/examples/github_repo_runner/app.py
-
-Add ``--cloud`` to run this application in the cloud.
-
-.. code-block:: bash
-
-   lightning run app docs/source/examples/github_repo_runner/app.py --cloud
-
-----
-
-**********************
-More hands-on examples
-**********************
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. Add callout items below this line
-
-.. displayitem::
-   :header: Develop a DAG
-   :description: Create a dag pipeline
-   :col_css: col-md-4
-   :button_link: ../dag/dag.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Develop a File Server
-   :description: Train multiple models with different parameters
-   :col_css: col-md-4
-   :button_link: ../file_server/file_server.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Develop a HPO Sweeper
-   :description: Train multiple models with different parameters
-   :col_css: col-md-4
-   :button_link: ../hpo/hpo.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Develop a Model Server
-   :description: Serve multiple models with different parameters
-   :col_css: col-md-4
-   :button_link: ../model_server/model_server.html
-   :height: 150
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,50 +0,0 @@
-:orphan:
-
-#################
-Hands-on Examples
-#################
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Build a DAG
-   :description: Learn how to orchestrate workflows
-   :col_css: col-md-6
-   :button_link: dag/dag.html
-   :height: 180
-
-.. displayitem::
-   :header: Build a File Server
-   :description: Learn how to upload and download files
-   :col_css: col-md-6
-   :button_link: file_server/file_server.html
-   :height: 180
-
-.. displayitem::
-   :header: Build a Github Repo Script Runner
-   :description: Learn how to configure dynamic execution from the UI
-   :col_css: col-md-6
-   :button_link: github_repo_runner/github_repo_runner.html
-   :height: 180
-
-.. displayitem::
-   :header: Build a HPO Sweeper
-   :description: Learn how to scale your training
-   :col_css: col-md-6
-   :button_link: hpo/hpo.html
-   :height: 180
-
-.. displayitem::
-   :header: Build a Model Server
-   :description: Learn how to server your models
-   :col_css: col-md-6
-   :button_link: model_server_app_content.html
-   :height: 180
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,36 +0,0 @@
-########
-Examples
-########
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Develop a DAG workflow
-   :description: Develop sequential, non-reactive workflows
-   :col_css: col-md-4
-   :button_link: dag/dag.html
-   :height: 150
-
-.. displayitem::
-   :header:  Develop a File Server
-   :description: Develop a file server
-   :col_css: col-md-4
-   :button_link: file_server/file_server.html
-   :height: 150
-
-.. displayitem::
-   :header: Develop a Github Repo Script Runner
-   :description: Build an app to run a Github repo
-   :col_css: col-md-4
-   :button_link: github_repo_runner/github_repo_runner.html
-   :height: 150
-
-.. displayitem::
-   :header: Deploy a model
-   :description: Learn how to deploy a model
-   :col_css: col-md-4
-   :button_link: model_server_app/model_server_app.html
-   :height: 150

@@ -1,34 +0,0 @@
-from locust_component import Locust
-from model_server import MLServer
-from train import TrainModel
-
-from lightning import LightningApp, LightningFlow
-
-
-class TrainAndServe(LightningFlow):
-    def __init__(self):
-        super().__init__()
-        self.train_model = TrainModel()
-        self.model_server = MLServer(
-            name="mnist-svm",
-            implementation="mlserver_sklearn.SKLearnModel",
-            workers=8,
-        )
-        self.performance_tester = Locust(num_users=100)
-
-    def run(self):
-        self.train_model.run()
-        self.model_server.run(self.train_model.best_model_path)
-        if self.model_server.alive():
-            # The performance tester needs the model server to be up
-            # and running to be started, so the URL is added in the UI.
-            self.performance_tester.run(self.model_server.url)
-
-    def configure_layout(self):
-        return [
-            {"name": "Server", "content": self.model_server.url + "/docs"},
-            {"name": "Server Testing", "content": self.performance_tester},
-        ]
-
-
-app = LightningApp(TrainAndServe())

@@ -1,57 +0,0 @@
-:orphan:
-
-***********************************
-3. Build the Load Testing Component
-***********************************
-
-Now, we are going to create a component to test the performance of your model server.
-
-We are going to use a python performance testing tool called `Locust <https://github.com/locustio/locust>`_.
-
-.. literalinclude:: ./locust_component.py
-
-
-Finally, once the component is done, we need to create a ``locustfile.py`` file which defines the format of the request to send to your model server.
-
-The endpoint to hit has the following format: ``/v2/models/{MODEL_NAME}/versions/{VERSION}/infer``.
-
-.. literalinclude:: ./locustfile.py
-
-
-----
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. Add callout items below this line
-
-.. displayitem::
-   :header: 1.  Build a Train Component
-   :description: Train a model and store its checkpoints with SKlearn
-   :col_css: col-md-4
-   :button_link: train.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: 2. Build a Model Server Component
-   :description: Use MLServer to server your models
-   :col_css: col-md-4
-   :button_link: model_server.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: 4. Putting everything together.
-   :description: Ensemble the components together and run the app
-   :col_css: col-md-4
-   :button_link: putting_everything_together.html
-   :height: 150
-   :tag: basic
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,43 +0,0 @@
-import os
-import subprocess
-
-from lightning import BuildConfig, LightningWork
-
-
-class Locust(LightningWork):
-    def __init__(self, num_users: int = 100):
-        """This component checks the performance of a server. The server url is passed to its run method.
-
-        Arguments:
-            num_users: Number of users emulated by Locust
-
-        """
-        # Note: Using the default port 8089 of Locust.
-        super().__init__(
-            port=8089,
-            parallel=True,
-            cloud_build_config=BuildConfig(requirements=["locust"]),
-        )
-        self.num_users = num_users
-
-    def run(self, load_tested_url: str):
-        # 1: Create the locust command line.
-        cmd = " ".join(
-            [
-                "locust",
-                "--master-host",
-                str(self.host),
-                "--master-port",
-                str(self.port),
-                "--host",
-                str(load_tested_url),
-                "-u",
-                str(self.num_users),
-            ]
-        )
-        # 2: Create another process with locust
-        process = subprocess.Popen(cmd, cwd=os.path.dirname(__file__), shell=True)
-
-        # 3: Wait for the process to finish. As locust is a server,
-        # this waits infinitely or if killed.
-        process.wait()

@@ -1,41 +0,0 @@
-from locust import FastHttpUser, task
-from sklearn import datasets
-from sklearn.model_selection import train_test_split
-
-
-class HelloWorldUser(FastHttpUser):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._prepare_inference_request()
-
-    @task
-    def predict(self):
-        self.client.post(
-            "/v2/models/mnist-svm/versions/v0.0.1/infer",
-            json=self.inference_request,
-        )
-
-    def _prepare_inference_request(self):
-        # The digits dataset
-        digits = datasets.load_digits()
-
-        # To apply a classifier on this data,
-        # we need to flatten the image, to
-        # turn the data in a (samples, feature) matrix:
-        n_samples = len(digits.images)
-        data = digits.images.reshape((n_samples, -1))
-
-        # Split data into train and test subsets
-        _, X_test, _, _ = train_test_split(data, digits.target, test_size=0.5, shuffle=False)
-
-        x_0 = X_test[0:1]
-        self.inference_request = {
-            "inputs": [
-                {
-                    "name": "predict",
-                    "shape": x_0.shape,
-                    "datatype": "FP32",
-                    "data": x_0.tolist(),
-                }
-            ]
-        }

@@ -1,84 +0,0 @@
-
-*********
-Objective
-*********
-
-Create a simple application that trains and serves a `Sklearn <https://scikit-learn.org/stable/>`_ machine learning model with `MLServer from SeldonIO <https://github.com/SeldonIO/MLServer>`_
-
-----
-
-*****************
-Final Application
-*****************
-
-Here is a gif of the final application built in this example.
-
-.. figure::  https://pl-public-data.s3.amazonaws.com/assets_lightning/ml_server_2.gif
-
-----
-
-*************
-System Design
-*************
-
-In order to create such application, we need to build several components:
-
-* A Model Train Component that trains a model and provides its trained weights
-
-* A Model Server Component that serves as an API endpoint for the model generated by the **Model Train Component**.
-
-* A Load Testing Component that tests the model server works as expected. This could be used to CI/CD the performance of newly generated models (left to the users).
-
-.. figure::  https://pl-public-data.s3.amazonaws.com/assets_lightning/model_server_app_2.png
-
-Let's dive into the tutorial.
-
-----
-
-********
-Tutorial
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. Add callout items below this line
-
-.. displayitem::
-   :header: 1.  Build a Train Component
-   :description: Train a model and store its checkpoints with SKlearn
-   :col_css: col-md-4
-   :button_link: train.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: 2. Build a Model Server Component
-   :description: Use MLServer to server your models
-   :col_css: col-md-4
-   :button_link: model_server.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: 3. Build a Load Testing Component
-   :description: Use Locust to test your model servers
-   :col_css: col-md-4
-   :button_link: load_testing.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: 4. Putting everything together.
-   :description: Ensemble the components together and run the app
-   :col_css: col-md-4
-   :button_link: putting_everything_together.html
-   :height: 150
-   :tag: basic
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,15 +0,0 @@
-:orphan:
-
-.. _model_server_example:
-
-######################
-Develop a Model Server
-######################
-
-**Audience:** Users who want to serve their trained models.
-
-**Prerequisite**: Reach :ref:`level 16+ <intermediate_level>`.
-
-----
-
-.. include:: model_server_app_content.rst

@@ -1,90 +0,0 @@
-import json
-import subprocess
-
-from lightning import BuildConfig, LightningWork
-from lightning.app.storage.path import Path
-
-# ML_SERVER_URL = https://github.com/SeldonIO/MLServer
-
-
-class MLServer(LightningWork):
-    """This components uses SeldonIO MLServer library.
-
-    The model endpoint: /v2/models/{MODEL_NAME}/versions/{VERSION}/infer.
-
-    Arguments:
-        name: The name of the model for the endpoint.
-        implementation: The model loader class.
-            Example: "mlserver_sklearn.SKLearnModel".
-            Learn more here: $ML_SERVER_URL/tree/master/runtimes
-        workers: Number of server worker.
-
-    """
-
-    def __init__(
-        self,
-        name: str,
-        implementation: str,
-        workers: int = 1,
-        **kwargs,
-    ):
-        super().__init__(
-            parallel=True,
-            cloud_build_config=BuildConfig(
-                requirements=["mlserver", "mlserver-sklearn"],
-            ),
-            **kwargs,
-        )
-        # 1: Collect the config's.
-        self.settings = {
-            "debug": True,
-            "parallel_workers": workers,
-        }
-        self.model_settings = {
-            "name": name,
-            "implementation": implementation,
-        }
-        # 2: Keep track of latest version
-        self.version = 1
-
-    def run(self, model_path: Path):
-        """The model is downloaded when the run method is invoked.
-
-        Arguments:
-            model_path: The path to the trained model.
-
-        """
-        # 1: Use the host and port at runtime so it works in the cloud.
-        # $ML_SERVER_URL/blob/master/mlserver/settings.py#L50
-        if self.version == 1:
-            # TODO: Reload the next version model of the model.
-
-            self.settings.update({"host": self.host, "http_port": self.port})
-
-            with open("settings.json", "w") as f:
-                json.dump(self.settings, f)
-
-            # 2. Store the model-settings
-            # $ML_SERVER_URL/blob/master/mlserver/settings.py#L120
-            self.model_settings["parameters"] = {
-                "version": f"v0.0.{self.version}",
-                "uri": str(model_path.absolute()),
-            }
-            with open("model-settings.json", "w") as f:
-                json.dump(self.model_settings, f)
-
-            # 3. Launch the Model Server
-            subprocess.Popen("mlserver start .", shell=True)
-
-            # 4. Increment the version for the next time run is called.
-            self.version += 1
-
-        else:
-            # TODO: Load the next model and unload the previous one.
-            pass
-
-    def alive(self):
-        # Current hack, when the url is available,
-        # the server is up and running.
-        # This would be cleaned out and automated.
-        return self.url != ""

@@ -1,48 +0,0 @@
-:orphan:
-
-*************************************
-2. Develop the Model Server Component
-*************************************
-
-In the code below, we use `MLServer <https://github.com/SeldonIO/MLServer>`_ which aims to provide an easy way to start serving your machine learning models through a REST and gRPC interface,
-fully compliant with KFServing's V2 Dataplane spec.
-
-.. literalinclude:: ./model_server.py
-
-----
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. Add callout items below this line
-
-.. displayitem::
-   :header: 1.  Develop a Train Component
-   :description: Train a model and store its checkpoints with SKlearn
-   :col_css: col-md-4
-   :button_link: train.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: 3. Develop a Load Testing Component
-   :description: Use Locust to test your model servers
-   :col_css: col-md-4
-   :button_link: load_testing.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: 4. Putting everything together.
-   :description: Ensemble the Components together and run the App
-   :col_css: col-md-4
-   :button_link: putting_everything_together.html
-   :height: 150
-   :tag: basic
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,80 +0,0 @@
-:orphan:
-
-******************************
-4. Putting everything together
-******************************
-
-In the code below, we put together the **TrainWork**, the **MLServer** and the **Locust** components in an ``app.py`` file.
-
-.. literalinclude:: ./app.py
-
-
-***********
-Run the App
-***********
-
-To run the app, simply open a terminal and execute this command:
-
-.. code-block:: bash
-
-    lightning run app docs/source/examples/model_deploy_app/app.py
-
-Here is a gif of the UI.
-
-.. figure::  https://pl-public-data.s3.amazonaws.com/assets_lightning/ml_server_2.gif
-
-.. raw:: html
-
-    <br />
-
-Congrats, you have finished the **Build a Model Server** example !
-
-----
-
-******************
-Find more examples
-******************
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. Add callout items below this line
-
-.. displayitem::
-   :header: Develop a DAG
-   :description: Develop a DAG pipeline
-   :col_css: col-md-4
-   :button_link: ../dag/dag.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Develop a File Server
-   :description: Train multiple models with different parameters
-   :col_css: col-md-4
-   :button_link: ../file_server/file_server.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Develop a Github Repo Script Runner
-   :description: Run code from the internet in the cloud
-   :col_css: col-md-4
-   :button_link: ../github_repo_runner/github_repo_runner.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: Develop a HPO Sweeper
-   :description: Train multiple models with different parameters
-   :col_css: col-md-4
-   :button_link: ../hpo/hpo.html
-   :height: 150
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,41 +0,0 @@
-import joblib
-from sklearn import datasets, svm
-from sklearn.model_selection import train_test_split
-
-from lightning import LightningWork
-from lightning.app.storage.path import Path
-
-
-class TrainModel(LightningWork):
-    """This component trains a Sklearn SVC model on digits dataset."""
-
-    def __init__(self):
-        super().__init__()
-        # 1: Add element to the state.
-        self.best_model_path = None
-
-    def run(self):
-        # 2: Load the Digits
-        digits = datasets.load_digits()
-
-        # 3: To apply a classifier on this data,
-        # we need to flatten the image, to
-        # turn the data in a (samples, feature) matrix:
-        n_samples = len(digits.images)
-        data = digits.images.reshape((n_samples, -1))
-
-        # 4: Create a classifier: a support vector classifier
-        classifier = svm.SVC(gamma=0.001)
-
-        # 5: Split data into train and test subsets
-        X_train, _, y_train, _ = train_test_split(data, digits.target, test_size=0.5, shuffle=False)
-
-        # 6: We learn the digits on the first half of the digits
-        classifier.fit(X_train, y_train)
-
-        # 7: Save the Sklearn model with `joblib`.
-        model_file_name = "mnist-svm.joblib"
-        joblib.dump(classifier, model_file_name)
-
-        # 8: Keep a reference the the generated model.
-        self.best_model_path = Path("mnist-svm.joblib")

@@ -1,49 +0,0 @@
-:orphan:
-
-****************************
-1. Build the Train Component
-****************************
-
-In the code below, we create a work which trains a simple `SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_ model on the digits dataset (classification).
-
-Once the model is trained, it is saved and a reference :class:`~lightning.app.storage.path.Path` with ``best_model_path`` state attribute.
-
-.. literalinclude:: ./train.py
-
-----
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. Add callout items below this line
-
-.. displayitem::
-   :header: 2. Build a Model Server Component
-   :description: Use MLServer to server your models
-   :col_css: col-md-4
-   :button_link: model_server.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: 3. Build a Load Testing Component
-   :description: Use Locust to test your model servers
-   :col_css: col-md-4
-   :button_link: load_testing.html
-   :height: 150
-   :tag: Intermediate
-
-.. displayitem::
-   :header: 4. Putting everything together.
-   :description: Ensemble the components together and run the app
-   :col_css: col-md-4
-   :button_link: putting_everything_together.html
-   :height: 150
-   :tag: basic
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,5 +0,0 @@
-:orphan:
-
-#########################
-Build a Research Demo App
-#########################

@@ -1,15 +0,0 @@
-:orphan:
-
-#######################
-Add an Interactive Demo
-#######################
-
-.. _add_an_interactive_Demo:
-
-**Required background:** Basic Python familiarity and complete the install guide.
-
-**Goal:** We'll walk you through the 4 key steps to run a Lightning App that trains and demos a model.
-
-----
-
-.. include:: go_beyond_training_content.rst

@@ -1,73 +0,0 @@
-:orphan:
-
-.. _build_model:
-
-#######################
-Build and Train a Model
-#######################
-
-**Required background:** Basic Python familiarity and complete the  guide.
-
-**Goal:** We'll walk you through the creation of a model using PyTorch Lightning.
-
-----
-
-*********************************
-A simple PyTorch Lightning script
-*********************************
-
-Let's assume you already have a folder with those two files.
-
-.. code-block:: bash
-
-    pl_project/
-        train.py            # your own script to train your models
-        requirements.txt    # your python requirements.
-
-If you don't, simply create a ``pl_project`` folder with those two files and add the following `PyTorch Lightning <https://lightning.ai/docs/pytorch/latest/>`_ code  in the ``train.py`` file. This code trains a simple ``AutoEncoder`` on `MNIST Dataset <https://en.wikipedia.org/wiki/MNIST_database>`_.
-
-.. literalinclude:: ../code_samples/convert_pl_to_app/train.py
-
-Add the following to the ``requirements.txt`` file.
-
-.. literalinclude:: ../code_samples/convert_pl_to_app/requirements.txt
-
-Simply run the following commands in your terminal to install the requirements and train the model.
-
-.. code-block:: bash
-
-    pip install -r requirements.txt
-    python train.py
-
-Get through `PyTorch Lightning Introduction <https://lightning.ai/docs/pytorch/stable/starter/introduction.html#step-1-define-lightningmodule>`_ to learn more.
-
-----
-
-**********
-Next Steps
-**********
-
-.. raw:: html
-
-   <br />
-   <div class="display-card-container">
-      <div class="row">
-
-.. displayitem::
-   :header: Evolve a Model into an ML System
-   :description: Develop an App to train a model in the cloud
-   :col_css: col-md-6
-   :button_link: training_with_apps.html
-   :height: 180
-
-.. displayitem::
-   :header: Start from a Template ML System
-   :description: Learn about Apps, from a template.
-   :col_css: col-md-6
-   :button_link: go_beyond_training.html
-   :height: 180
-
-.. raw:: html
-
-      </div>
-   </div>

@@ -1,405 +0,0 @@
-************************************************
-The *Train & Demo PyTorch Lightning* Application
-************************************************
-
-Find the *Train & Demo PyTorch Lightning* application in the `Lightning.ai App Gallery <https://lightning.ai/app/AU3WoWwdAP-Train%20%26%20Demo%20PyTorch%20Lightning>`_.
-
-Here is a recording of this App running locally and in the cloud with the same behavior.
-
-.. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning_app_experience_cut.mp4
-    :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning_app_experience_cut.png
-    :width: 600
-    :class: background-video
-    :autoplay:
-    :loop:
-    :muted:
-
-In the steps below, we are going to show you how to build this application.
-
-Here are `the entire App's code <https://github.com/Lightning-AI/lightning-quick-start>`_ and `its commented components. <https://github.com/Lightning-AI/lightning-quick-start/blob/main/quick_start/components.py>`_
-
-----
-
-*************************
-Step 1: Install Lightning
-*************************
-
-If you are using a virtual env, don't forget to activate it before running commands.
-You must do so in every new shell.
-
-.. tip:: We highly recommend using virtual environments.
-
-.. code:: bash
-
-    pip install lightning
-
-----
-
-****************************************
-Step 2: Install the *Train and Demo* App
-****************************************
-The first Lightning App we'll explore is an App to train and demo a machine learning model.
-
-..
-    [|qs_code|], [|qs_live_app|].
-
-    .. |qs_live_app| raw:: html
-
-       <a href="https://01g3w6gqdvjqjnqw05ccy69nwy.litng-ai-03.litng.ai/view/Interactive%20demo" target="_blank">live app</a>
-
-    .. |qs_code| raw:: html
-
-       <a href="https://github.com/Lightning-AI/lightning-quick-start" target="_blank">code</a>
-
-
-Install this App by typing:
-
-.. code-block:: bash
-
-    lightning_app install app lightning/quick-start
-
-Verify the App was successfully installed:
-
-.. code-block:: bash
-
-    cd lightning-quick-start
-
-----
-
-***************************
-Step 3: Run the App locally
-***************************
-
-Run the app locally with the ``run`` command 🤯
-
-.. code:: bash
-
-    lightning_app run app app.py
-
-----
-
-********************************
-Step 4: Run the App in the cloud
-********************************
-
-Add the ``--cloud`` argument to run on the `Lightning.AI cloud <http://lightning.ai/>`_. 🤯🤯🤯
-
-.. code:: bash
-
-    lightning_app run app app.py --cloud
-
-..
-    Your app should look like this one (|qs_live_app|)
-
-----
-
-*******************
-Understand the code
-*******************
-The App that we just launched trained a PyTorch Lightning model (although any framework works), then added an interactive demo.
-
-This is the App's code:
-
-.. code:: python
-
-    # lightning-quick-start/app.py
-    import os.path as ops
-    import lightning as L
-    from quick_start.components import PyTorchLightningScript, ImageServeGradio
-
-    class TrainDeploy(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.train_work = PyTorchLightningScript(
-                script_path=ops.join(ops.dirname(__file__), "./train_script.py"),
-                script_args=["--trainer.max_epochs=5"],
-            )
-
-            self.serve_work = ImageServeGradio(L.CloudCompute())
-
-        def run(self):
-            # 1. Run the python script that trains the model
-            self.train_work.run()
-
-            # 2. when a checkpoint is available, deploy
-            if self.train_work.best_model_path:
-                self.serve_work.run(self.train_work.best_model_path)
-
-        def configure_layout(self):
-            tab_1 = {"name": "Model training", "content": self.train_work}
-            tab_2 = {"name": "Interactive demo", "content": self.serve_work}
-            return [tab_1, tab_2]
-
-    app = L.LightningApp(TrainDeploy())
-
-Let's break down the code section by section to understand what it is doing.
-
-----
-
-1: Define root component
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-A Lightning App provides a cohesive product experience for a set of unrelated components.
-
-The top-level component (Root) must subclass ``L.LightningFlow``
-
-
-.. code:: python
-    :emphasize-lines: 6
-
-    # lightning-quick-start/app.py
-    import os.path as ops
-    import lightning as L
-    from quick_start.components import PyTorchLightningScript, ImageServeGradio
-
-    class TrainDeploy(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.train_work = PyTorchLightningScript(
-                script_path=ops.join(ops.dirname(__file__), "./train_script.py"),
-                script_args=["--trainer.max_epochs=5"],
-            )
-
-            self.serve_work = ImageServeGradio(L.CloudCompute("cpu-small"))
-
-        def run(self):
-            # 1. Run the python script that trains the model
-            self.train_work.run()
-
-            # 2. when a checkpoint is available, deploy
-            if self.train_work.best_model_path:
-                self.serve_work.run(self.train_work.best_model_path)
-
-        def configure_layout(self):
-            tab_1 = {"name": "Model training", "content": self.train_work}
-            tab_2 = {"name": "Interactive demo", "content": self.serve_work}
-            return [tab_1, tab_2]
-
-    app = L.LightningApp(TrainDeploy())
-
-----
-
-2: Define components
-^^^^^^^^^^^^^^^^^^^^
-In the __init__ method, we define the components that make up the App. In this case, we have 2 components,
-a component to execute any PyTorch Lightning script (model training) and a second component to
-start a Gradio server for demo purposes.
-
-.. code:: python
-    :emphasize-lines: 9, 14
-
-    # lightning-quick-start/app.py
-    import os.path as ops
-    import lightning as L
-    from quick_start.components import PyTorchLightningScript, ImageServeGradio
-
-    class TrainDeploy(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.train_work = PyTorchLightningScript(
-                script_path=ops.join(ops.dirname(__file__), "./train_script.py"),
-                script_args=["--trainer.max_epochs=5"],
-            )
-
-            self.serve_work = ImageServeGradio(L.CloudCompute("cpu-small"))
-
-        def run(self):
-            # 1. Run the python script that trains the model
-            self.train_work.run()
-
-            # 2. when a checkpoint is available, deploy
-            if self.train_work.best_model_path:
-                self.serve_work.run(self.train_work.best_model_path)
-
-        def configure_layout(self):
-            tab_1 = {"name": "Model training", "content": self.train_work}
-            tab_2 = {"name": "Interactive demo", "content": self.serve_work}
-            return [tab_1, tab_2]
-
-    app = L.LightningApp(TrainDeploy())
-
-----
-
-3: Define how components Flow
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Every component has a ``run`` method. The run method defines the 🌊 Flow 🌊 of how components interact together.
-
-In this case, we train a model (until completion). When it's done AND there exists a checkpoint, we launch a
-demo server:
-
-.. code:: python
-    :emphasize-lines: 18, 21, 22
-
-    # lightning-quick-start/app.py
-    import os.path as ops
-    import lightning as L
-    from quick_start.components import PyTorchLightningScript, ImageServeGradio
-
-    class TrainDeploy(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.train_work = PyTorchLightningScript(
-                script_path=ops.join(ops.dirname(__file__), "./train_script.py"),
-                script_args=["--trainer.max_epochs=5"],
-            )
-
-            self.serve_work = ImageServeGradio(L.CloudCompute("cpu-small"))
-
-        def run(self):
-            # 1. Run the python script that trains the model
-            self.train_work.run()
-
-            # 2. when a checkpoint is available, deploy
-            if self.train_work.best_model_path:
-                self.serve_work.run(self.train_work.best_model_path)
-
-        def configure_layout(self):
-            tab_1 = {"name": "Model training", "content": self.train_work}
-            tab_2 = {"name": "Interactive demo", "content": self.serve_work}
-            return [tab_1, tab_2]
-
-    app = L.LightningApp(TrainDeploy())
-
-.. note:: If you've used other ML systems you'll be pleasantly surprised to not find decorators or YAML files.
-
-----
-
-4: Connect web user interfaces
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-All our favorite tools normally have their own web user interfaces (UI).
-
-Implement the ``configure_layout`` method to connect them together:
-
-.. code:: python
-    :emphasize-lines: 24-27
-
-    # lightning-quick-start/app.py
-    import os.path as ops
-    import lightning as L
-    from quick_start.components import PyTorchLightningScript, ImageServeGradio
-
-    class TrainDeploy(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.train_work = PyTorchLightningScript(
-                script_path=ops.join(ops.dirname(__file__), "./train_script.py"),
-                script_args=["--trainer.max_epochs=5"],
-            )
-
-            self.serve_work = ImageServeGradio(L.CloudCompute("cpu-small"))
-
-        def run(self):
-            # 1. Run the python script that trains the model
-            self.train_work.run()
-
-            # 2. when a checkpoint is available, deploy
-            if self.train_work.best_model_path:
-                self.serve_work.run(self.train_work.best_model_path)
-
-        def configure_layout(self):
-            tab_1 = {"name": "Model training", "content": self.train_work}
-            tab_2 = {"name": "Interactive demo", "content": self.serve_work}
-            return [tab_1, tab_2]
-
-    app = L.LightningApp(TrainDeploy())
-
-----
-
-5: Init the ``app`` object
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-Initialize an ``app`` object with the ``TrainDeploy`` component (this won't run the App yet):
-
-.. code:: python
-    :emphasize-lines: 29
-
-    # lightning-quick-start/app.py
-    import os.path as ops
-    import lightning as L
-    from quick_start.components import PyTorchLightningScript, ImageServeGradio
-
-    class TrainDeploy(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.train_work = PyTorchLightningScript(
-                script_path=ops.join(ops.dirname(__file__), "./train_script.py"),
-                script_args=["--trainer.max_epochs=5"],
-            )
-
-            self.serve_work = ImageServeGradio(L.CloudCompute("cpu-small"))
-
-        def run(self):
-            # 1. Run the python script that trains the model
-            self.train_work.run()
-
-            # 2. when a checkpoint is available, deploy
-            if self.train_work.best_model_path:
-                self.serve_work.run(self.train_work.best_model_path)
-
-        def configure_layout(self):
-            tab_1 = {"name": "Model training", "content": self.train_work}
-            tab_2 = {"name": "Interactive demo", "content": self.serve_work}
-            return [tab_1, tab_2]
-
-    app = L.LightningApp(TrainDeploy())
-
-----
-
-******************************
-What components are supported?
-******************************
-Any component can work with Lightning AI!
-
-.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/Lightning.gif
-    :alt: What is Lightning gif.
-    :width: 100 %
-
-----
-
-**********
-Next Steps
-**********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Add components to your App
-   :description: Expand your App by adding components.
-   :col_css: col-md-4
-   :button_link: ../workflows/extend_app.html
-   :height: 180
-
-.. displayitem::
-   :header: Build a component
-   :description: Learn to build your own component.
-   :col_css: col-md-4
-   :button_link: ../workflows/build_lightning_component/index.html
-   :height: 180
-
-.. displayitem::
-   :header: Explore more Apps
-   :description: Explore more apps for inspiration.
-   :col_css: col-md-4
-   :button_link: https://lightning.ai/apps
-   :height: 180
-
-.. displayitem::
-   :header: Under the hood
-   :description: Explore how it works under the hood.
-   :col_css: col-md-4
-   :button_link: ../core_api/lightning_app/index.html
-   :height: 180
-
-.. displayitem::
-   :header: Run on your private cloud
-   :description: Run Lightning Apps on your private VPC or on-prem.
-   :button_link: ../workflows/run_on_private_cloud.html
-   :col_css: col-md-4
-   :height: 180
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,14 +0,0 @@
-:orphan:
-
-################################
-Start from an ML system template
-################################
-
-.. _go_beyond_training:
-
-**Required background:** Basic Python familiarity and complete the install guide.
-
-**Goal:** We'll walk you through the 4 key steps to run a Lightning App that trains and demos a model.
-
-
-.. include:: go_beyond_training_content.rst

@@ -1,123 +0,0 @@
-:orphan:
-
-#####################################
-Start from Ready-to-Run Template Apps
-#####################################
-
-.. _jumpstart_from_app_gallery:
-
-Anyone can build Apps for their own use cases and promote them on the `App Gallery <https://lightning.ai/apps>`_.
-
-In return, you can benefit from the work of others and get started faster by re-using a ready-to-run App close to your own use case.
-
-
-*************
-User Workflow
-*************
-
-#. Visit the `App Gallery <https://lightning.ai/apps>`_ and look for an App close to your own use case.
-
-    .. raw:: html
-
-       <br />
-
-#. If **Launch** is available, it means the App is live and ready to be used! Take it for a spin.
-
-    .. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/launch_button.png
-        :alt: Launch Button on lightning.ai
-        :width: 100 %
-
-#. By clicking **Clone & Run**, a copy of the App is added to your account and an instance starts running.
-
-
-    .. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/clone_and_run.mp4
-        :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/clone_and_run.png
-        :width: 600
-        :class: background-video
-        :autoplay:
-        :loop:
-        :muted:
-
-#. If you found an App that matches what you need, move to **step 5**! Otherwise, go back to **step 1**.
-
-    .. raw:: html
-
-       <br />
-
-#. Copy the installation command (optionally from the clipboard on the right).
-
-    .. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/install_command.png
-        :alt: Install command on lightning.ai
-        :width: 100 %
-
-#. Copy the command to your local terminal.
-
-    .. code-block:: bash
-
-        lightning_app install app lightning/hackernews-app
-
-#. Go through the installation steps.
-
-    .. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/install_an_app.mp4
-        :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/install_an_app.png
-        :width: 600
-        :class: background-video
-        :autoplay:
-        :loop:
-        :muted:
-
-#. Run the App locally.
-
-    .. code-block:: bash
-
-        cd LAI-Hackernews-App
-        lightning_app run app app.py
-
-    .. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/hackernews.mp4
-        :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/hackernews.png
-        :width: 600
-        :class: background-video
-        :autoplay:
-        :loop:
-        :muted:
-
-#. Open the code with your favorite IDE, modify it, and run it back in the cloud.
-
-    .. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/hackernews_modified.mp4
-        :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/hackernews_modified.png
-        :width: 600
-        :class: background-video
-        :autoplay:
-        :loop:
-        :muted:
-
-----
-
-**********
-Next Steps
-**********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Add Component made by others to your App
-   :description: Add more functionality to your projects
-   :col_css: col-md-6
-   :button_link: jumpstart_from_component_gallery.html
-   :height: 180
-
-.. displayitem::
-   :header: Level-up your skills with Lightning Apps
-   :description: From Basic to Advanced Skills
-   :col_css: col-md-6
-   :button_link: ../levels/basic/index.html
-   :height: 180
-
-.. raw:: html
-
-      </div>
-   </div>
-   <br />

@@ -1,151 +0,0 @@
-:orphan:
-
-########################################
-Add Component made by others to your App
-########################################
-
-.. _jumpstart_from_component_gallery:
-
-Anyone can build components for their own use case and promote them on the `Component Gallery <https://lightning.ai/components>`_.
-
-In return, you can benefit from the work of others and add new functionalities to your Apps with minimal effort.
-
-
-*************
-User Workflow
-*************
-
-#. Visit the `Component Gallery <https://lightning.ai/components>`_ and look for a Component close to something you want to do.
-
-    .. raw:: html
-
-       <br />
-
-#. Check out the code for inspiration or simply install the component from PyPi and use it.
-
-----
-
-*************
-Success Story
-*************
-
-The default `Train and Demo Application <https://github.com/Lightning-AI/lightning-quick-start>`_ trains a PyTorch Lightning
-model and then starts a demo with `Gradio <https://gradio.app/>`_.
-
-.. code-block:: python
-
-    import os.path as ops
-    import lightning as L
-    from quick_start.components import PyTorchLightningScript, ImageServeGradio
-
-
-    class TrainDeploy(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.train_work = PyTorchLightningScript(
-                script_path=ops.join(ops.dirname(__file__), "./train_script.py"),
-                script_args=["--trainer.max_epochs=5"],
-            )
-
-            self.serve_work = ImageServeGradio(L.CloudCompute("cpu"))
-
-        def run(self):
-            # 1. Run the python script that trains the model
-            self.train_work.run()
-
-            # 2. when a checkpoint is available, deploy
-            if self.train_work.best_model_path:
-                self.serve_work.run(self.train_work.best_model_path)
-
-        def configure_layout(self):
-            tab_1 = {"name": "Model training", "content": self.train_work}
-            tab_2 = {"name": "Interactive demo", "content": self.serve_work}
-            return [tab_1, tab_2]
-
-
-    app = L.LightningApp(TrainDeploy())
-
-However, someone who wants to use this Aop (maybe you) found `Lightning HPO <https://lightning.ai/component/BA2slXI093-Lightning%20HPO>`_
-from browsing the `Component Gallery <https://lightning.ai/components>`_ and decided to give it a spin after checking the associated
-`Github Repository <https://github.com/Lightning-AI/LAI-lightning-hpo-App>`_.
-
-Once ``lightning_hpo`` installed, they improved the default App by easily adding HPO support to their project.
-
-Here is the resulting App. It is almost the same code, but it's way more powerful now!
-
-This is the power of `lightning.ai <https://lightning.ai/>`_ ecosystem 🔥⚡🔥
-
-.. code-block:: python
-
-    import os.path as ops
-    import lightning as L
-    from quick_start.components import PyTorchLightningScript, ImageServeGradio
-    import optuna
-    from optuna.distributions import LogUniformDistribution
-    from lightning_hpo import Optimizer, BaseObjective
-
-
-    class HPOPyTorchLightningScript(PyTorchLightningScript, BaseObjective):
-        @staticmethod
-        def distributions():
-            return {"model.lr": LogUniformDistribution(0.0001, 0.1)}
-
-
-    class TrainDeploy(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.train_work = Optimizer(
-                script_path=ops.join(ops.dirname(__file__), "./train_script.py"),
-                script_args=["--trainer.max_epochs=5"],
-                objective_cls=HPOPyTorchLightningScript,
-                n_trials=4,
-            )
-
-            self.serve_work = ImageServeGradio(L.CloudCompute("cpu"))
-
-        def run(self):
-            # 1. Run the python script that trains the model
-            self.train_work.run()
-
-            # 2. when a checkpoint is available, deploy
-            if self.train_work.best_model_path:
-                self.serve_work.run(self.train_work.best_model_path)
-
-        def configure_layout(self):
-            tab_1 = {"name": "Model training", "content": self.train_work.hi_plot}
-            tab_2 = {"name": "Interactive demo", "content": self.serve_work}
-            return [tab_1, tab_2]
-
-
-    app = L.LightningApp(TrainDeploy())
-
-----
-
-**********
-Next Steps
-**********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Start from Ready-to-Run Template Apps
-   :description: Jump-start your projects development
-   :col_css: col-md-6
-   :button_link: jumpstart_from_app_gallery.html
-   :height: 180
-
-.. displayitem::
-   :header: Level-up your skills with Lightning Apps
-   :description: From Basic to Advanced Skills
-   :col_css: col-md-6
-   :button_link: ../levels/basic/index.html
-   :height: 180
-
-.. raw:: html
-
-      </div>
-   </div>
-   <br />

@@ -1,125 +0,0 @@
-:orphan:
-
-################################
-Evolve a model into an ML system
-################################
-
-.. _convert_pl_to_app:
-
-**Required background:** Basic Python familiarity and complete the :ref:`build_model` guide.
-
-**Goal:** We'll walk you through the two key steps to build your first Lightning App from your existing PyTorch Lightning scripts.
-
-
-*******************
-Training and beyond
-*******************
-
-With `PyTorch Lightning <https://github.com/Lightning-AI/lightning/tree/master/src/lightning/pytorch>`__, we abstracted distributed training and hardware, by organizing PyTorch code.
-With `Lightning Apps <https://github.com/Lightning-AI/lightning/tree/master/src/lightning/app>`__, we unified the local and cloud experience while abstracting infrastructure.
-
-By using `PyTorch Lightning <https://github.com/Lightning-AI/lightning/tree/master/src/lightning/pytorch>`__ and `Lightning Apps <https://github.com/Lightning-AI/lightning/tree/master/src/lightning/app>`__
-together, a completely new world of possibilities emerges.
-
-.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/pl_to_app_4.png
-    :alt: From PyTorch Lightning to Lightning App
-    :width: 100 %
-
-----
-
-******************************************
-1. Write an App to run the train.py script
-******************************************
-
-This article continues where the :ref:`build_model` guide finished.
-
-Create an additional file ``app.py`` in the ``pl_project`` folder as follows:
-
-.. code-block:: bash
-
-    pl_project/
-        app.py
-        train.py
-        requirements.txt
-
-Inside the ``app.py`` file, add the following code.
-
-.. literalinclude:: ../code_samples/convert_pl_to_app/app.py
-
-This App runs the PyTorch Lightning script contained in the ``train.py`` file using the powerful :class:`~lightning.app.components.python.tracer.TracerPythonScript` component. This is really worth checking out!
-
-----
-
-************************************************
-2. Run the train.py file locally or in the cloud
-************************************************
-
-First, go to the ``pl_folder`` folder from the local terminal and install the requirements.
-
-.. code-block:: bash
-
-    cd pl_folder
-    pip install -r requirements.txt
-
-To run your app, copy the following command to your local terminal:
-
-.. code-block:: bash
-
-    lightning_app run app app.py
-
-Simply add ``--cloud`` to run this application in the cloud with a GPU machine 🤯
-
-.. code-block:: bash
-
-    lightning_app run app app.py --cloud
-
-
-Congratulations! Now, you know how to run a `PyTorch Lightning <https://github.com/Lightning-AI/lightning/tree/master/src/lightning/pytorch>`_ script with Lightning Apps.
-
-Lightning Apps can make your ML system way more powerful, keep reading to learn how.
-
-----
-
-**********
-Next Steps
-**********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Level-up with Lightning Apps
-   :description: From Basics to Advanced Skills
-   :col_css: col-md-4
-   :button_link: ../levels/basic/index.html
-   :height: 180
-
-.. displayitem::
-   :header: Add an Interactive Demo
-   :description: Add a Gradio Demo once the training is finished
-   :col_css: col-md-4
-   :button_link: add_an_interactive_demo.html
-   :height: 180
-
-.. displayitem::
-   :header: Add Model Serving
-   :description: Serve and load testing with MLServer and Locust
-   :col_css: col-md-4
-   :button_link: ../examples/model_server_app/model_server_app.html
-   :height: 180
-
-.. displayitem::
-   :header: Add DAG Orchestration
-   :description: Organize your processing, training and metrics collection
-   :col_css: col-md-4
-   :button_link: ../examples/dag/dag.html
-   :height: 180
-
-.. displayitem::
-   :header: Add Team Collaboration
-   :description: Create an app to run any PyTorch Lightning Script from Github
-   :col_css: col-md-4
-   :button_link: ../examples/github_repo_runner/github_repo_runner.html
-   :height: 180

@@ -1,187 +0,0 @@
-:orphan:
-
-############################################
-Discover what Lightning Apps can do in 5 min
-############################################
-
-.. _what_app_can_do:
-
-Lightning Apps can be plenty things, and while a picture is worth a thousand words, videos showing you examples should be worth even more.
-
-
-*****************************
-Flashy - Auto ML App (Public)
-*****************************
-
-Train a model on any image or text dataset without writing any code. Flashy uses `React.js <https://reactjs.org/>`_ for its frontend.
-
-Find `Flashy <https://lightning.ai/app/PgM82rHUWu-Flashy>`_ on the App Gallery and the `Flashy codebase. <https://github.com/Lightning-AI/LAI-Flashy-App>`_ on GitHub.
-
-.. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/flashy.mp4
-    :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/flashy.png
-    :width: 600
-    :class: background-video
-    :autoplay:
-    :loop:
-    :muted:
-
-.. ----
-
-.. ***************************************
-.. NVIDIA Omniverse Sampling App (Private)
-.. ***************************************
-
-.. Use `Nvidia Sampling Omniverse <https://www.nvidia.com/en-gb/omniverse/>`_ to generate synthetic samples from 3D meshes and train an object detector on that data.
-
-.. .. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/Omniverse-Sampling.mp4
-        :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/Omniverse-Sampling.png
-        :width: 600
-        :class: background-video
-        :autoplay:
-        :loop:
-        :muted:
-
-----
-
-*********************
-Research App (Public)
-*********************
-
-Share your paper ``bundled`` with the arxiv link, poster, live jupyter notebook, interactive demo to try the model, and more!
-
-Find the `Research App <https://lightning.ai/app/KDKgKk3HVW-Research%20Poster>`_  on the App Gallery and the `Research App codebase. <https://github.com/Lightning-AI/LAI-research-template-App>`_ on GitHub.
-
-.. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/research_app.mp4
-    :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/research_app.png
-    :width: 600
-    :class: background-video
-    :autoplay:
-    :loop:
-    :muted:
-
-----
-
-************************************************
-ScratchPad - Notebook Manager for Team  (Public)
-************************************************
-
-Run multiple Jupyter Notebooks on cloud CPUs or machines with multiple GPUs.
-
-Find the `ScratchPad App <https://lightning.ai/app/hvUwbEG70B-ScratchPad%2C%20Notebook%20Manager%20for%20Teams>`_  on the App Gallery and the `ScratchPad App codebase <https://github.com/Lightning-AI/LAI-lightning-template-jupyterlab-App>`_ on GitHub.
-
-.. note:: ScratchPad is `tested end-to-end <https://github.com/Lightning-AI/LAI-lightning-template-jupyterlab-App/blob/master/tests/test_template_jupyterlab.py#L6>`_ on every Lightning App commit with `pytest <https://docs.pytest.org/en/7.1.x/>`_.
-
-.. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/notebook_apps.mp4
-    :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/notebook_apps.png
-    :width: 600
-    :class: background-video
-    :autoplay:
-    :loop:
-    :muted:
-
-----
-
-***********************
-InVideo Search (Public)
-***********************
-
-This App lets you find anything you're looking for inside a video. The engine is powered by `Open AI CLIP <https://www.openai.com/index/clip/>`_.
-
-Find the `InVideo Search App <https://lightning.ai/app/7pmQNIDxAE-InVideo%20Search>`_  on the App Gallery and the `InVideo Search App codebase. <https://github.com/Lightning-AI/LAI-InVideo-search-App>`_ in GitHub.
-
-.. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/video_search_2.mp4
-    :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/video_search_2.png
-    :width: 600
-    :class: background-video
-    :autoplay:
-    :loop:
-    :muted:
-
-----
-
-******************************
-AI-powered HackerNews (Public)
-******************************
-
-Save yourself time, and get Hacker News story recommendations, chosen for you specifically. This Lightning App was designed to illustrate a full end-to-end MLOPs workflow aimed at enterprise recommendation systems.
-
-Find the `AI-powered HackerNews App <https://lightning.ai/app/g1VJ8GZ7XF-AI-powered%20HackerNews>`_  on the App Gallery and the `AI-powered HackerNews App codebase. <https://github.com/Lightning-AI/LAI-Hackernews-App>`_ on GitHub.
-
-.. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/hackernews_app.mp4
-    :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/hackernews_app.png
-    :width: 600
-    :class: background-video
-    :autoplay:
-    :loop:
-    :muted:
-
-----
-
-*********************************************************************
-Lightning Apps can turn ML into scalable systems in days — not months
-*********************************************************************
-
-Use the Lightning framework to develop any ML system: train and deploy a model, create an ETL pipeline,
-or spin up a research demo — using the intuitive principles we pioneered with PyTorch Lightning.
-
-.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/apps_logos_2.png
-   :alt: Apps with Logos
-   :width: 100 %
-
-Anyone who knows Python can build a Lightning App, even without machine learning experience.
-
-Lightning Apps are:
-
-- cloud agnostic
-- fault-tolerant, distributed, cost optimized
-- production ready
-- local and cloud debuggable
-- highly reactive & interactive
-- connect multiple UIs together
-- built for team collaboration
-- framework agnostic, use your own stack
-- and much more
-
-.. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning_app_experience_cut.mp4
-    :poster: https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning_app_experience_cut.png
-    :width: 600
-    :class: background-video
-    :autoplay:
-    :loop:
-    :muted:
-
-**********
-Next Steps
-**********
-
-.. raw:: html
-
-   <br />
-   <div class="display-card-container">
-      <div class="row">
-
-.. displayitem::
-   :header: Build & Train a Model
-   :description: Discover PyTorch Lightning and train your first Model.
-   :col_css: col-md-4
-   :button_link: build_model.html
-   :height: 180
-
-.. displayitem::
-   :header: Evolve a Model into an ML System
-   :description: Develop an App to train a model in the cloud
-   :col_css: col-md-4
-   :button_link: training_with_apps.html
-   :height: 180
-
-.. displayitem::
-   :header: Start from an ML system template
-   :description: Learn about Apps, from a template.
-   :col_css: col-md-4
-   :button_link: go_beyond_training.html
-   :height: 180
-
-.. raw:: html
-
-      </div>
-   </div>

@@ -1,113 +0,0 @@
-:orphan:
-
-.. _app_component_tree:
-
-###################
-App Component Tree
-###################
-
-**Audience:** Users who want to know how components can be composed with each other.
-
-**Level:** Basic
-
-----
-
-**************************************
-What is an Application Component Tree?
-**************************************
-
-Components can be nested to form component trees where the LightningFlows are its branches and LightningWorks are its leaves.
-
-This design enables users to organize and maintain their code with more ease, but more importantly, this helps creating an ecosystem with reusable components.
-
-Here's a basic application with four flows and two works (associated tree structure):
-
-.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/tree.gif
-    :alt: Basic App Components
-    :width: 100 %
-
-
-.. literalinclude:: ../code_samples/quickstart/app_comp.py
-
-A Lightning app runs all flows into a single process. Its flows coordinate the execution of the works each running in their own independent processes.
-
-----
-
-***********************************************
-How do I define my application component tree?
-***********************************************
-
-In order to define your application component tree, you need create a tree of components and attach them to your root flow.
-
-You can attach your components in the **__init__** method of a flow.
-
-.. code-block:: python
-
-    import lightning as L
-
-
-    class RootFlow(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            # The `Work` component is attached here.
-            self.work = Work()
-
-            # The `NestedFlow` component is attached here.
-            self.nested_flow = NestedFlow()
-
-Once done, simply add the root flow to a Lightning app as follows:
-
-.. code-block:: python
-
-    app = L.LightningApp(RootFlow())
-
-----
-
-******************************************
-Is my application component tree static?
-******************************************
-
-No, Lightning supports dynamic flows and works.
-
-You can simply attach your components in the **run** method of a flow using the Python functions **hasattr**, **setattr**, and **getattr**.
-
-.. code-block:: python
-
-    class RootFlow(L.LightningFlow):
-        def run(self):
-
-            if not hasattr(self, "work"):
-                # The `Work` component is attached here.
-                setattr(self, "work", Work())
-            # Run the `Work` component.
-            getattr(self, "work").run()
-
-            if not hasattr(self, "nested_flow"):
-                # The `NestedFlow` component is attached here.
-                setattr(self, "nested_flow", NestedFlow())
-            # Run the `NestedFlow` component.
-            getattr(self, "wonested_flowrk").run()
-
-
-But it is usually more readable to use Lightning built-in :class:`~lightning.app.structures.Dict` or :class:`~lightning.app.structures.List` as follows:
-
-.. code-block:: python
-
-    from lightning.app.structures import Dict
-
-
-    class RootFlow(L.LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.dict = Dict()
-
-        def run(self):
-            if "work" not in self.dict:
-                # The `Work` component is attached here.
-                self.dict["work"] = Work()
-            self.dict["work"].run()
-
-            if "nested_flow" not in self.dict:
-                # The `NestedFlow` component is attached here.
-                self.dict["nested_flow"] = NestedFlow()
-            self.dict["nested_flow"].run()

@@ -1,63 +0,0 @@
-:orphan:
-
-##############################
-Build Configuration (Advanced)
-##############################
-
-**Audience:** Users who want full control over the docker image that is being installed in the cloud.
-
-**Level:** Advanced
-
-Advanced users who need full control over the environment a LightningWork runs in can specify a custom docker image that will be deployed in the cloud.
-
-
-----
-
-******************
-Use a docker image
-******************
-
-Create a :class:`~lightning.app.utilities.packaging.build_config.BuildConfig` and provide a **publicly accessible** link to where the image is hosted:
-
-.. code-block:: python
-
-    from lightning.app import LightningWork, BuildConfig
-
-
-    class MyWork(LightningWork):
-        def __init__(self):
-            super().__init__()
-
-            # Using a publicly hosted docker image:
-            self.cloud_build_config = BuildConfig(
-                # This is one of the base images Lightning uses by default
-                image="ghcr.io/gridai/base-images:v1.8-gpu"
-            )
-
-            # Can also be combined with extra requirements
-            self.cloud_build_config = BuildConfig(image="...", requirements=["torchmetrics"])
-
-
-.. warning::
-    Many public hosters like DockerHub apply rate limits for public images. We recommend to pull images from your own registry.
-    For example, you can set up a
-    `docker registry on GitHub <https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry>`_.
-
-
-.. note::
-    - The build config only applies when running in the cloud and gets ignored otherwise. A local build config is currently not supported.
-    - Images from private registries are currently not supported.
-
-.. note::
-    Custom docker images must have python installed. We'll use `virtualenv` from this system python to create a virtual environment.
-    We'll also configure the `virtualenv` to use the packages installed under system's python so your packages are not lost
-
-----
-
-
-*********************
-Provide a docker file
-*********************
-
-.. note::
-    Not yet supported. Coming soon.

@@ -1,68 +0,0 @@
-:orphan:
-
-###########################
-Build Configuration (Basic)
-###########################
-
-**Audience:** Users who need to install Python packages for an individual LightningWork.
-
-**Level:** Basic
-
-----
-
-***********************************
-List dependencies in separate files
-***********************************
-
-If you are building an app with multiple LightningWorks that have different or even conflicting requirements, split your dependencies into individual files
-for more granular control.
-
-.. code-block:: bash
-
-    ├── app.py
-    ├── requirements.txt          # Global requirements for the entire app
-    └── works
-        ├── serve
-        │   ├── requirements.txt  # Requirements specific to the 'serve' work
-        │   └── serve.py          # Source file for the LightningWork
-        └── train
-            ├── requirements.txt  # Requirements specific to the 'train' work
-            └── train.py          # Source file for the LightningWork
-
-The requirements.txt file must be located in the same directory as the source file of the LightningWork.
-When the LightningWork starts up, it will pick up the requirements file if present and install all listed packages.
-
-.. note::
-    This only applies when running in the cloud. The requirements.txt files get ignored when running locally.
-
-----
-
-***********************************
-Define the requirements in the code
-***********************************
-
-Instead of listing the requirements in a file, you can also pass them to the LightningWork at runtime using the
-:class:`~lightning.app.utilities.packaging.build_config.BuildConfig`:
-
-.. code-block:: python
-    :emphasize-lines: 7
-
-    from lightning.app import LightningWork, BuildConfig
-
-
-    class MyWork(LightningWork):
-        def __init__(self):
-            super().__init__()
-            self.cloud_build_config = BuildConfig(requirements=["torch>=1.8", "torchmetrics"])
-
-.. note::
-    The build config only applies when running in the cloud and gets ignored otherwise. A local build config is currently not supported.
-
-.. warning::
-     Custom base images are not supported with the default CPU cloud compute. For example:
-
-     .. code-block:: py
-
-         class MyWork(LightningWork):
-             def __init__(self):
-              super().__init__(cloud_build_config=BuildConfig(image="my-custom-image")) # no cloud compute, for example default work

@@ -1,56 +0,0 @@
-:orphan:
-
-##################################
-Build Configuration (Intermediate)
-##################################
-
-**Audience:** Users who need to execute commands to configure the machine before a LightningWork runs on it.
-
-**Level:** Intermediate
-
-When a LightningWork machine starts up in the cloud, it uses a lightweight operating system with essential packages pre-installed.
-If you need to install additional system packages or run other configuration steps before your code executes on that machine, it is possible to do so by creating a custom
-:class:`~lightning.app.utilities.packaging.build_config.BuildConfig`:
-
-1.  Subclass :class:`~lightning.app.utilities.packaging.build_config.BuildConfig`:
-
-    .. code-block:: python
-
-        from lightning.app import BuildConfig
-
-
-        @dataclass
-        class CustomBuildConfig(BuildConfig):
-            def build_commands(self):
-                return ["sudo apt-get install libsparsehash-dev"]
-
-
-2.  Set the build config on the LightningWork:
-
-    .. code-block:: python
-
-        from lightning.app import LightningWork
-
-
-        class MyWork(LightningWork):
-            def __init__(self):
-                super().__init__()
-
-                # Use the custom build config
-                self.cloud_build_config = CustomBuildConfig()
-
-                # Can also be combined with extra requirements
-                self.cloud_build_config = CustomBuildConfig(requirements=["torchmetrics"])
-
-.. note::
-    - When you need to execute commands or install tools that require more privileges than the current user has, you can use ``sudo`` without needing to provide a password, e.g., when installing system packages.
-    - The build config only applies when running in the cloud and gets ignored otherwise. A local build config is currently not supported.
-
-.. warning::
-     Custom base images are not supported with the default CPU cloud compute. For example:
-
-     .. code-block:: py
-
-         class MyWork(LightningWork):
-             def __init__(self):
-              super().__init__(cloud_build_config=BuildConfig(image="my-custom-image")) # no cloud compute, for example default work

@@ -1,43 +0,0 @@
-:orphan:
-
-.. _build_config:
-
-###################
-Build Configuration
-###################
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. Add callout items below this line
-
-.. displayitem::
-   :header: Basic
-   :description: Learn how to manage Python dependencies for an individual LightningWork
-   :col_css: col-md-6
-   :button_link: build_config_basic.html
-   :height: 150
-   :tag: basic
-
-.. displayitem::
-   :header: Intermediate
-   :description: Learn how to run custom build commands for a LightningWork
-   :col_css: col-md-6
-   :button_link: build_config_intermediate.html
-   :height: 150
-   :tag: intermediate
-
-.. displayitem::
-   :header: Advanced
-   :description: Learn how to use a custom Docker image for a LightningWork
-   :col_css: col-md-6
-   :button_link: build_config_advanced.html
-   :height: 150
-   :tag: advanced
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,69 +0,0 @@
-:orphan:
-
-############################
-Command-line Interface (CLI)
-############################
-
-**Audience:** Users looking to create a command line interface (CLI) for their application.
-
-----
-
-**************
-What is a CLI?
-**************
-
-A Command-line Interface (CLI) is an user interface (UI) in a terminal to interact with a specific program.
-
-.. note::
-
-    The Lightning guideline to build CLI is `lightning_app <VERB> <NOUN> ...` or `<ACTION> <OBJECT> ...`.
-
-As an example, Lightning provides a CLI to interact with your Lightning Apps and the `lightning.ai <https://lightning.ai/>`_ platform as follows:
-
-.. code-block:: bash
-
-    main
-    ├── fork - Forks an App.
-    ├── init - Initializes a Lightning App and/or Component.
-    │   ├── app
-    │   ├── component
-    │   ├── pl-app - Creates an App from your PyTorch Lightning source files.
-    │   └── react-ui - Creates a React UI to give a Lightning Component a React.js web UI
-    ├── install - Installs a Lightning App and/or Component.
-    │   ├── app
-    │   └── component
-    ├── list - Lists Lightning AI self-managed resources (apps)
-    │   └── apps - Lists your Lightning AI Apps.
-    ├── login - Logs in to your lightning.ai account.
-    ├── logout - Logs out of your lightning.ai account.
-    ├── run - Runs a Lightning App locally or on the cloud.
-    │   └── app - Runs an App from a file.
-    ├── show - Shows given resource.
-    │   └── logs - Shows cloud application logs. By default prints logs for all currently available Components.
-    ├── stop - Stops your App.
-    └── tree - Shows the command tree of your CLI.
-
-Learn more about `Command-line interfaces here <https://en.wikipedia.org/wiki/Command-line_interface>`_.
-
-----
-
-**********
-Learn more
-**********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Develop a Command Line Interface
-   :description: Learn how to develop a CLI for your App.
-   :col_css: col-md-6
-   :button_link: ../../workflows/build_command_line_interface/index_content.html
-   :height: 150
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,46 +0,0 @@
-######################
-Directed Acyclic Graph
-######################
-**Audience:** Users coming from MLOps to Lightning Apps, looking for more flexibility.
-
-----
-
-*****************************
-Is Lightning a DAG framework?
-*****************************
-No.
-
-A Lightning App enables developers to express complex, interactive applications that are impossible to create with DAGs.
-
-----
-
-*********************************
-Can I Build a DAG with Lightning?
-*********************************
-Yes!
-
-DAGs are one of the easiest Lightning Apps to build. For example, here's a :doc:`full app that defines a DAG <../examples/dag/dag>`.
-
-----
-
-********
-Examples
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Build a DAG
-   :description: Learn how to create a DAG with Lightning
-   :col_css: col-md-4
-   :button_link: ../examples/dag/dag.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,3 +0,0 @@
-:orphan:
-
-.. include:: ../workflows/debug_locally.rst

@@ -1,5 +0,0 @@
-:orphan:
-
-#####################
-Distributed Front-End
-#####################

@@ -1,5 +0,0 @@
-:orphan:
-
-####################
-Distributed Hardware
-####################

@@ -1,27 +0,0 @@
-.. _environment_variables:
-
-*********************
-Environment Variables
-*********************
-
-If your App is using configuration values you don't want to commit with your App source code, you can use environment variables.
-
-Lightning allows you to set environment variables when running the App from the CLI with the `lightning_app run app` command. You can use environment variables to pass any values to the App, and avoiding sticking those values in the source code.
-
-Set one or multiple variables using the **--env** option:
-
-.. code:: bash
-
-    lightning_app run app app.py --cloud --env FOO=BAR --env BAZ=FAZ
-
-Environment variables are available in all Flows and Works, and can be accessed as follows:
-
-.. code:: python
-
-    import os
-
-    print(os.environ["FOO"])  # BAR
-    print(os.environ["BAZ"])  # FAZ
-
-.. note::
-    Environment variables are not encrypted. For sensitive values, we recommend using :ref:`Encrypted Secrets <secrets>`.

@@ -1,11 +0,0 @@
-##########
-Event loop
-##########
-
-Drawing inspiration from modern web frameworks like `React.js <https://reactjs.org/>`_, the Lightning App runs all flows in an **event loop** (forever), which is triggered several times a second after collecting any works' state change.
-
-.. figure::  https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning_loop.gif
-
-When running a Lightning App in the cloud, the ``LightningWork`` run on different machines. LightningWork communicates any state changes to the **event loop** which re-executes the flow with the newly-collected works' state.
-
-.. _app_event_loop:

@@ -1,7 +0,0 @@
-:orphan:
-
-###############
-Fault tolerance
-###############
-
-.. note:: documentation under construction

@@ -1,155 +0,0 @@
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   ios_and_android
-   app_tree
-   build_config/build_config
-   command_lines/command_lines
-   dag
-   event_loop
-   environment_variables
-   secrets
-   front ends <../workflows/add_web_ui/glossary_front_end>
-   Lightning app <../core_api/lightning_app/index>
-   sharing_components
-   scheduling
-   storage/storage
-   restful_api/restful_api
-   add web ui <../workflows/add_web_ui/glossary_ui>
-   use_local_lightning
-
-########
-Glossary
-########
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Android Lightning App
-   :description: Use Lightning with android apps.
-   :col_css: col-md-12
-   :button_link: ios_and_android.html
-   :height: 100
-
-.. displayitem::
-   :header: App Components Tree
-   :description: Learn how components can be nested to form component trees where the LightningFlows are its branches and LightningWorks are its leaves.
-   :col_css: col-md-12
-   :button_link: app_tree.html
-   :height: 100
-
-.. displayitem::
-   :header: Build Configuration
-   :description: Prepare your requirements, add custom build commands or use docker image
-   :col_css: col-md-12
-   :button_link: build_config/build_config.html
-   :height: 100
-
-.. displayitem::
-   :header: Command Line Interface (CLI)
-   :description: Learn about the Lightning CLI
-   :col_css: col-md-12
-   :button_link: command_lines/command_lines.html
-   :height: 100
-
-.. displayitem::
-   :header: DAG
-   :description: Learn about directed acyclic graph, their properties and usage
-   :col_css: col-md-12
-   :button_link: dag.html
-   :height: 100
-
-.. displayitem::
-   :header: Event Loop
-   :description: Learn how the Infinite Event Loop enables high distributed reactivity by triggering after collecting state changes.
-   :col_css: col-md-12
-   :button_link: event_loop.html
-   :height: 100
-
-.. displayitem::
-   :header: Environment Variables
-   :description: Add secrets such as API keys or access tokens
-   :col_css: col-md-12
-   :button_link: environment_variables.html
-   :height: 100
-
-.. displayitem::
-   :header: Encrypted Secrets
-   :description: Learn how to add passwords to your Lightning apps
-   :col_css: col-md-12
-   :button_link: secrets.html
-   :height: 100
-
-.. displayitem::
-   :header: Frontend
-   :description: Customize your App View with any framework you want
-   :col_css: col-md-12
-   :button_link: ../workflows/add_web_ui/glossary_front_end.html
-   :height: 100
-
-.. displayitem::
-   :header: iOS Lightning App
-   :description: Use Lightning with iOS apps.
-   :col_css: col-md-12
-   :button_link: ios_and_android.html
-   :height: 100
-
-.. displayitem::
-   :header: Lightning App
-   :description: A Lightning app is a collection of connected components that form a workflow
-   :col_css: col-md-12
-   :button_link: ../core_api/lightning_app/index.html
-   :height: 100
-
-.. displayitem::
-   :header: Mounts
-   :description: Mount Cloud Data
-   :col_css: col-md-12
-   :button_link: mount.html
-   :height: 100
-
-.. displayitem::
-   :header: Sharing Components
-   :description: Let's create an ecosystem altogether
-   :col_css: col-md-12
-   :button_link: sharing_components.html
-   :height: 100
-
-.. displayitem::
-   :header: Scheduling
-   :description: Orchestrate execution at specific times
-   :col_css: col-md-12
-   :button_link: scheduling.html
-   :height: 100
-
-.. displayitem::
-   :header: Storage
-   :description: Easily share files even across multiple machines
-   :col_css: col-md-12
-   :button_link: storage/storage.html
-   :height: 100
-
-.. displayitem::
-   :header: REST API
-   :description: Learn how to set up a RESTful API endpoint
-   :col_css: col-md-12
-   :button_link: restful_api/restful_api.html
-   :height: 100
-
-.. displayitem::
-   :header: UI
-   :description: Combine multiple frameworks to create your own UI
-   :col_css: col-md-12
-   :button_link: ../workflows/add_web_ui/glossary_ui.html
-   :height: 100
-
-.. displayitem::
-   :header: Using a development branch of Lightning on the Cloud
-   :description: Learn how to contribute to the Lightning framework in the cloud
-   :col_css: col-md-12
-   :button_link: use_local_lightning.html
-   :height: 100

@@ -1,26 +0,0 @@
-
-###############################################
-Apple and Android mobile devices with Lightning
-###############################################
-
-Audience: Users who want to develop Lightning Apps for Apple or Android mobile devices.
-
-----
-
-***********************************************************
-Develop a Lightning App for Apple or Android mobile devices
-***********************************************************
-
-There are a couple of ways you can go about building Lightning Apps that work on Apple or Android mobile devices.
-
-Option 1
-^^^^^^^^
-
-You can develop a Lightning App that interacts with an iOS or Android app.
-The ML and backend services live on the Lightning App, but the iOS or Android code (obj-c/swift or android) lives on the mobile devices.
-
-Option 2
-^^^^^^^^
-
-You can build a mobile-first React Lightning App that works on both Apple and Android mobile devices.
-The `InVideo app <https://lightning.ai/app/7pmQNIDxAE-InVideo%20Search>`_ is a good example of a Lightning App that does just that.

@@ -1,11 +0,0 @@
-:orphan:
-
-###########################
-Lightning Apps Key concepts
-###########################
-
-**Audience:** Users who want to know how the 🤯 magic works under the hood.
-
-----
-
-.. note:: This page is under construction

@@ -1 +0,0 @@
-.. include:: ../workflows/mount_cloud_object_store.rst

@@ -1,53 +0,0 @@
-:orphan:
-
-###########
-RESTful API
-###########
-
-**Audience:** Users looking to create an API in their App to allow users to activate functionalities from external sources.
-
-----
-
-**********************
-What is a RESTful API?
-**********************
-
-A RESTful API is a set of external URL routes exposed by a server that enables clients to trigger some functionalities, such as getting or putting some data, uploading files, etc..
-
-This provides great flexibility for users as they can easily discover functionalities made available by the App Builders.
-
-The Lightning App framework supports the four primary HTTP methods: `GET`, `POST`, `PUT`, `DELETE`.
-
-These methods are guidelines to organize your RESTful Services and help users understand your functionalities.
-
-* **`GET`:** Reads data from the server.
-* **`POST`:** Creates new resources.
-* **`PUT`:** Updates/replaces existing resources.
-* **`DELETE`:** Deletes resources.
-
-Learn more about `HTTP Methods for RESTful Services here <https://www.restapitutorial.com/introduction/whatisrest>`_.
-
-The Lightning App framework uses the popular `FastAPI <https://fastapi.tiangolo.com/>`_ and `Pydantic <https://pydantic-docs.helpmanual.io/>`_ frameworks under the hood. This means you can use all their features while building your App.
-
-----
-
-**********
-Learn more
-**********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Develop a RESTful API
-   :description: Learn how to develop an API for your App.
-   :col_css: col-md-6
-   :button_link: ../../workflows/build_rest_api/index_content.html
-   :height: 150
-
-.. raw:: html
-
-        </div>
-    </div>

@@ -1,185 +0,0 @@
-:orphan:
-
-##########
-Scheduling
-##########
-
-The Lightning Scheduling system makes it easy to schedule your components execution with any arbitrary conditions.
-
-
-----
-
-************************
-Schedule your components
-************************
-
-The LightningFlow has a ``schedule`` method which can be used to schedule your components.
-
-.. code-block:: python
-
-    from lightning.app import LightningWork, LightningFlow
-    from lightning.app.storage import Path
-
-
-    class MyFlow(LightningFlow):
-
-        def run(self):
-            if self.schedule("hourly"):
-                # run some code once every hour.
-
-            if self.schedule("daily"):
-                # run some code once day.
-
-            if self.schedule("daily") and anything_else:
-                # run some code once day if the anything else is also True.
-
-            if self.schedule("2 4 * * mon,fri"):
-                # defined with cron syntax, run some code at 04:02 on every Monday and Friday.
-
-Learn more about the cron syntax `here <https://github.com/kiorky/croniter>`_
-
-----
-
-**************
-Best Practices
-**************
-
-In the example above, the line ``self.schedule("hourly")`` will return ``True`` for a **single** flow execution every hour. Mathematically, this is known as a dirac.
-
-1. Instantiate your component under the schedule method and run outside as follows:
-
-.. code-block:: python
-
-    from lightning.app import LightningFlow
-    from lightning.app.structures import List
-
-    class ScheduledDAG(LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.list = List()
-
-        def run(self):
-            if self.schedule("hourly"):
-                # dynamically instantiate
-                # don't forget to always attach
-                # your components to the flow !!!
-                self.list.append(MyDAGFlow(...))
-
-            # run all dags, but the completed ones
-            # are cached and don't re-execute.
-            for dag in self.list:
-                dag.run()
-
-
-2. Run a single work under the schedule with different arguments to have it re-run.
-
-.. code-block:: python
-
-    from lightning.app import LightningFlow
-    from time import time
-
-    class ScheduledDAG(LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.data_processor = DataProcessorWork(...)
-
-        def run(self):
-            ...
-            if self.schedule("hourly"):
-                self.data_processor.run(trigger_time=time())
-
-
-3. Capture the event in the state and execute your sequential works outside.
-
-.. code-block:: python
-
-    from lightning.app import LightningFlow
-    from time import time
-
-    class ScheduledDAG(LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.should_execute = False
-            self.data_processor = DataProcessorWork(...)
-            self.training_work = KerasTrainingWork(...)
-
-        def run(self):
-            ...
-            if self.schedule("hourly"):
-                self.should_execute = True
-
-            # Runs in 10 min
-            if self.should_execute:
-                # Runs in 5 min
-                self.data_processor.run(trigger_time=time())
-                if self.data_processor.has_succeeded:
-                    # Runs in 5 min
-                    self.training_work.run(self.data_processor.data)
-                if self.training_work.has_succeeded:
-                    self.should_execute = False
-
-----
-
-***********
-Limitations
-***********
-
-As stated above, the schedule acts as a dirac and is **True** for a single flow execution.
-Therefore, sequential works execution under the schedule won't work as they don't complete within a single flow execution.
-
-Here is an example of something which **WON'T** work:
-
-.. code-block:: python
-
-    from lightning.app import LightningFlow
-    from time import time
-
-    class ScheduledDAG(LightningFlow):
-        def __init__(self):
-            super().__init__()
-            self.data_processor = DataProcessorWork(...)
-            self.training_work = KerasTrainingWork(...)
-
-        def run(self):
-            ...
-            if self.schedule("hourly"):
-                # This finishes 5 min later
-                self.data_processor.run(trigger_time=time())
-                if self.data_processor.has_succeeded:
-                    # This will never be reached as the
-                    # data processor will keep processing forever...
-                    self.training_work.run(self.data_processor.data)
-
-----
-
-**************************
-Frequently Asked Questions
-**************************
-
-- **Q: Can I use multiple nested scheduler?** No, as they might cancel themselves out, but you can capture the event of one to trigger the next one.
-
-- **Q: Can I use any arbitrary logic to schedule?** Yes, this design enables absolute flexibility, but you need to be careful to avoid bad practices.
-
-----
-
-********
-Examples
-********
-
-.. raw:: html
-
-    <div class="display-card-container">
-        <div class="row">
-
-.. displayitem::
-   :header: Build a DAG
-   :description: Learn how to schedule a DAG execution
-   :col_css: col-md-4
-   :button_link: ../examples/dag/dag.html
-   :height: 180
-   :tag: Intermediate
-
-.. raw:: html
-
-        </div>
-    </div>