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* Use the LightningCLI in the image classification example * FlashCLI * Finetune support * Port LightningCLI * Update requirements * Initial commit * Updates * Updates * Updates * Temp fill reqs * Bump PL req * Test * Update * Update * Remove debug code * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Updates * Add speech recognition * Add text classification * Add tabular and seq2seq * Remove extra reqs * Fix test * Add pointcloud * Add graph * Fix test * A fix * Try fix * Try fix * Try fix * Add tests * Add click CLI * Add click CLI * Add some docs * Update docs * Punctuation * Add some tests * Update CHANGELOG.md * Fix test * Add some tests * Test * Updates * Try fix Co-authored-by: Carlos Mocholi <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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.. _flash_zero: | ||
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********** | ||
Flash Zero | ||
********** | ||
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Flash Zero is a zero-code machine learning platform built directly into lightning-flash. | ||
To get started and view the available tasks, run: | ||
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.. code-block:: bash | ||
flash --help | ||
Customize Trainer and Model arguments | ||
_____________________________________ | ||
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Flash Zero is built on top of the | ||
`lightning CLI <https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_cli.html>`_, so the trainer and | ||
model arguments can be configured either from the command line or from a config file. | ||
For example, to run the image classifier for 10 epochs with a `resnet50` backbone you can use: | ||
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.. code-block:: bash | ||
flash image-classification --trainer.max_epochs 10 --model.backbone resnet50 | ||
To view all of the available options for a task, run: | ||
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.. code-block:: bash | ||
flash image-classification --help | ||
Using Custom Data | ||
_________________ | ||
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Flash Zero works with your own data through subcommands. The available subcommands for each task are given at the bottom | ||
of their help pages (e.g. when running :code:`flash image-classification --help`). You can then use the required | ||
subcommand to train on your own data. Let's look at an example using the Hymenoptera data from the | ||
:ref:`image_classification` guide. First, download and unzip your data: | ||
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.. code-block:: bash | ||
curl https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip -o hymenoptera_data | ||
unzip hymenoptera_data.zip | ||
Now train with Flash Zero: | ||
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.. code-block:: bash | ||
flash image-classification from_folders --train_folder ./hymenoptera_data/train | ||
You can view the help page for each subcommand. For example, to view the options for training an image classifier from | ||
folders, you can run: | ||
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.. code-block:: bash | ||
flash image-classification from_folders --help |
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