Add TorchGeo CLI tutorial (#2479)

* Add TorchGeo CLI tutorial

* Pass checkpoint path

docs/tutorials/basic_usage.rst

@@ -7,6 +7,7 @@ The following tutorials introduce the basic concepts and components of TorchGeo:
 * `Spectral Indices <indices.ipynb>`_: Visualizing and appending spectral indices
 * `Pretrained Weights <pretrained_weights.ipynb>`_: Models and pretrained weights
 * `Lightning Trainers <trainers.ipynb>`_: PyTorch Lightning data modules and trainers
+* `Command-Line Interface <cli.ipynb>`_: TorchGeo's command-line interface
 
 .. toctree::
    :hidden:
@@ -16,3 +17,4 @@ The following tutorials introduce the basic concepts and components of TorchGeo:
    indices
    pretrained_weights
    trainers
+   cli

docs/tutorials/cli.ipynb

@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16421d50-8d7a-4972-b06f-160fd890cc86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "# Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e563313d",
+   "metadata": {},
+   "source": [
+    "# Command-Line Interface\n",
+    "\n",
+    "_Written by: Adam J. Stewart_\n",
+    "\n",
+    "TorchGeo provides a command-line interface based on [LightningCLI](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.cli.LightningCLI.html) that allows users to combine our data modules and trainers from the comfort of the command line. This no-code solution can be attractive for both beginners and experts, as it offers flexibility and reproducibility. In this tutorial, we demonstrate some of the features of this interface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c1f4156",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, we install TorchGeo. In addition to the Python library, this also installs a `torchgeo` executable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f0d31a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install torchgeo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7801ab8b-0ee3-40ac-88c2-4bdc29bb4e1b",
+   "metadata": {},
+   "source": [
+    "## Subcommands\n",
+    "\n",
+    "The `torchgeo` command has a number of *subcommands* that can be run. The `--help` flag can be used to list them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6ccac4e-7f20-4aa8-b851-27234ffd259f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!torchgeo --help"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19ee017d-0d8f-41c6-8e7c-68495c7e62b6",
+   "metadata": {},
+   "source": [
+    "## Trainer\n",
+    "\n",
+    "Below, we run `--help` on the `fit` subcommand to see what options are available to us. `fit` is used to train and validate a model, and we can customize many aspects of the training process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afe1dc9d-4cee-43b0-ae30-200c64d3401a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!torchgeo fit --help"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b437860c-b406-4150-b30b-8aa895eebfcd",
+   "metadata": {},
+   "source": [
+    "## Model\n",
+    "\n",
+    "We must first select an `nn.Module` model architecture to train and a `lightning.pytorch.LightningModule` trainer to train it. We will experiment with the `ClassificationTask` trainer and see what options we can customize. Any of TorchGeo's builtin trainers, or trainers written by the user, can be used in this way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cd9bbd0-17c9-4e87-b10d-ea846c39bc24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!torchgeo fit --model.help ClassificationTask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3daacd8d-64f4-4357-bdf3-759295a14224",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "We must also select a `Dataset` we would like to train on and a `lightning.pytorch.LightningDataModule` we can use to access the train/val/test split and any augmentations to apply to the data. Similarly, we use the `--help` flag to see what options are available for the `EuroSAT100` dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "136eb59f-6662-44af-82e9-c55bdb3f17ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!torchgeo fit --data.help EuroSAT100DataModule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8039cb67-ee18-4b41-8bf5-0e939493f5bb",
+   "metadata": {},
+   "source": [
+    "## Config\n",
+    "\n",
+    "Now that we have seen all important configuration options, we can put them together in a YAML file. LightingCLI supports YAML, JSON, and command-line configuration. While we will write this file using Python in this tutorial, normally this file would be written in your favorite text editor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e25c8efb-ed8c-4795-862c-bfb84cc84e1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "root = os.path.join(tempfile.gettempdir(), 'eurosat100')\n",
+    "config = f\"\"\"\n",
+    "trainer:\n",
+    "  max_epochs: 1\n",
+    "  default_root_dir: '{root}'\n",
+    "model:\n",
+    "  class_path: ClassificationTask\n",
+    "  init_args:\n",
+    "    model: 'resnet18'\n",
+    "    in_channels: 13\n",
+    "    num_classes: 10\n",
+    "data:\n",
+    "  class_path: EuroSAT100DataModule\n",
+    "  init_args:\n",
+    "    batch_size: 8\n",
+    "  dict_kwargs:\n",
+    "    root: '{root}'\n",
+    "    download: true\n",
+    "\"\"\"\n",
+    "os.makedirs(root, exist_ok=True)\n",
+    "with open(os.path.join(root, 'config.yaml'), 'w') as f:\n",
+    "    f.write(config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a661b8d7-2dc9-4a30-8842-bd52d130e080",
+   "metadata": {},
+   "source": [
+    "This YAML file has three sections:\n",
+    "\n",
+    "* trainer: Arguments to pass to the [Trainer](https://lightning.ai/docs/pytorch/stable/common/trainer.html)\n",
+    "* model: Arguments to pass to the task\n",
+    "* data: Arguments to pass to the data module\n",
+    "\n",
+    "The `class_path` gives the class to instantiate, `init_args` lists standard arguments, and `dict_kwargs` lists keyword arguments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e132f933-4edf-42bb-b585-e0d8ceb65eab",
+   "metadata": {},
+   "source": [
+    "## Training\n",
+    "\n",
+    "We can now train our model like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f84b0739-c9e7-4057-8864-98ab69a11f64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!torchgeo fit --config {root}/config.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb1557f1-6cc0-46da-909c-836911acb248",
+   "metadata": {},
+   "source": [
+    "## Validation\n",
+    "\n",
+    "Now that we have a trained model, we can evaluate performance on the validation set. Note that we need to explicitly pass in the location of the checkpoint from the previous run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9cbb4f4-1879-4ae7-bae4-2c24d49a4a61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "\n",
+    "checkpoint = glob.glob(\n",
+    "    os.path.join(root, 'lightning_logs', 'version_0', 'checkpoints', '*.ckpt')\n",
+    ")[0]\n",
+    "\n",
+    "!torchgeo validate --config {root}/config.yaml --ckpt_path {checkpoint}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba816fc3-5cac-4cbc-a6ef-effc6c9faa61",
+   "metadata": {},
+   "source": [
+    "## Testing\n",
+    "\n",
+    "After finishing our hyperparameter tuning, we can calculate and report the final test performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1faa997-9f81-4847-94fc-5a8bb7687369",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!torchgeo test --config {root}/config.yaml --ckpt_path {checkpoint}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5383d30-8f76-44a2-8366-e6fcbd1e6042",
+   "metadata": {},
+   "source": [
+    "## Additional Reading\n",
+    "\n",
+    "Lightning CLI has many more features that are worth learning. You can learn more by reading the following set of tutorials:\n",
+    "\n",
+    "* [Configure hyperparameters from the CLI](https://lightning.ai/docs/pytorch/stable/cli/lightning_cli.html)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "provenance": []
+  },
+  "execution": {
+   "timeout": 1200
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}