126 lines
3.5 KiB
ReStructuredText
126 lines
3.5 KiB
ReStructuredText
:orphan:
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.. _quick_start:
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############
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Quick Start
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############
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In this guide, we'll run an application that trains
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an image classification model with the `MNIST Dataset <https://en.wikipedia.org/wiki/MNIST_database>`_,
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and uses `Gradio <https://gradio.app>`_ to serve it.
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----
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**********************
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Step 1 - Installation
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**********************
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First, you'll need to install Lightning. You can find the complete guide here.
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Then, you'll need to install the `Lightning Quick Start package <https://github.com/Lightning-AI/lightning-quick-start>`_.
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.. code-block:: bash
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lightning install app lightning/quick-start
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And download the training script used by the App:
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----
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**********************
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Step 2 - Run the app
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**********************
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To run your app, copy the following command to your local terminal:
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.. code-block:: bash
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lightning run app app.py
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And that's it!
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.. admonition:: You should see the app logs in your terminal.
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:class: dropdown
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.. code-block:: console
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Your Lightning App is starting. This won't take long.
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INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
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Global seed set to 42
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GPU available: True (mps), used: False
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TPU available: False, using: 0 TPU cores
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| Name | Type | Params | In sizes | Out sizes
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------------------------------------------------------------------
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0 | model | Net | 1.2 M | [1, 1, 28, 28] | [1, 10]
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1 | val_acc | Accuracy | 0 | ? | ?
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------------------------------------------------------------------
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1.2 M Trainable params
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0 Non-trainable params
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1.2 M Total params
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Epoch 4: 100%|█████████████████████████| 16/16 [00:00<00:00, 32.31it/s, loss=0.0826, v_num=0]
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`Trainer.fit` stopped: `max_epochs=5` reached.
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Running on local URL: http://127.0.0.1:62782/
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...
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The app will open your browser and show an interactive demo:
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.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/qiuck-start-tensorboard-tab.png
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:alt: Quick Start UI - Model Training Tab
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:width: 100 %
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.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/quick-start-gradio-tab.png
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:alt: Quick Start UI - Interactive Demo Tab
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:width: 100 %
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----
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This app behind the scenes
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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This application has one flow component which coordinates two works executing their own python script.
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Once the training is finished, the trained model weights are passed to the serve component.
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Here is how the components of a Lightning app are structured:
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.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/quick_start_components.gif
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:alt: Quick Start Application
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:width: 100 %
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Here is the application timeline:
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.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/timeline.gif
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:alt: Quick Start Timeline Application
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:width: 100 %
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----
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**************************************
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Steps 3 - Build your app in the cloud
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**************************************
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Simply add ``--cloud`` to run this application in the cloud 🤯
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.. code-block:: bash
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lightning run app app.py --cloud
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Congratulations! You've now run your first application with Lightning.
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----
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***********
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Next Steps
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***********
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To learn how to build and modify apps, go to the :ref:`basics`.
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To learn how to create UIs for your apps, read :ref:`ui_and_frontends`.
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