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add tutorial notebooks (PR 7 of N) #10
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Training a model with PyTorch Lightning\n", | ||
"\n", | ||
"This tutorial provides a quick overview of training a toy model with Lightning, using the `tiledbsoma_ml.ExperimentAxisQueryIterableDataset` class, on data from the [CZI CELLxGENE Census](https://chanzuckerberg.github.io/cellxgene-census/) dataset. This is intended only to demonstrate the use of the `ExperimentAxisQueryIterableDataset`, and not as an example of how to train a biologically useful model.\n", | ||
"\n", | ||
"For more information on these API, please refer to the [`tutorial_pytorch` notebook](tutorial_pytorch.ipynb).\n", | ||
"\n", | ||
"**Prerequesites**\n", | ||
"\n", | ||
"Install `tiledbsoma_ml` and `scikit-learn`, for example:\n", | ||
"\n", | ||
"> pip install tiledbsoma_ml scikit-learn\n" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Initialize SOMA Experiment query as training data" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 1, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stderr", | ||
"output_type": "stream", | ||
"text": [ | ||
"/home/bruce/miniforge3/envs/toymodel/lib/python3.11/site-packages/torchdata/datapipes/__init__.py:18: UserWarning: \n", | ||
"################################################################################\n", | ||
"WARNING!\n", | ||
"The 'datapipes', 'dataloader2' modules are deprecated and will be removed in a\n", | ||
"future torchdata release! Please see https://github.com/pytorch/data/issues/1196\n", | ||
"to learn more and leave feedback.\n", | ||
"################################################################################\n", | ||
"\n", | ||
" deprecation_warning()\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"import pytorch_lightning as pl\n", | ||
"import tiledbsoma as soma\n", | ||
"import torch\n", | ||
"from sklearn.preprocessing import LabelEncoder\n", | ||
"\n", | ||
"import tiledbsoma_ml as soma_ml\n", | ||
"\n", | ||
"CZI_Census_Homo_Sapiens_URL = \"s3://cellxgene-census-public-us-west-2/cell-census/2024-07-01/soma/census_data/homo_sapiens/\"\n", | ||
"\n", | ||
"experiment = soma.open(\n", | ||
" CZI_Census_Homo_Sapiens_URL,\n", | ||
" context=soma.SOMATileDBContext(tiledb_config={\"vfs.s3.region\": \"us-west-2\"}),\n", | ||
")\n", | ||
"obs_value_filter = \"tissue_general == 'tongue' and is_primary_data == True\"\n", | ||
"\n", | ||
"with experiment.axis_query(\n", | ||
" measurement_name=\"RNA\", obs_query=soma.AxisQuery(value_filter=obs_value_filter)\n", | ||
") as query:\n", | ||
" obs_df = query.obs(column_names=[\"cell_type\"]).concat().to_pandas()\n", | ||
" cell_type_encoder = LabelEncoder().fit(obs_df[\"cell_type\"].unique())\n", | ||
"\n", | ||
" experiment_dataset = soma_ml.ExperimentAxisQueryIterableDataset(\n", | ||
" query,\n", | ||
" X_name=\"raw\",\n", | ||
" obs_column_names=[\"cell_type\"],\n", | ||
" batch_size=128,\n", | ||
" shuffle=True,\n", | ||
" )" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Define the Lightning module" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 2, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"class LogisticRegressionLightning(pl.LightningModule):\n", | ||
" def __init__(self, input_dim, output_dim, cell_type_encoder, learning_rate=1e-5):\n", | ||
" super(LogisticRegressionLightning, self).__init__()\n", | ||
" self.linear = torch.nn.Linear(input_dim, output_dim)\n", | ||
" self.cell_type_encoder = cell_type_encoder\n", | ||
" self.learning_rate = learning_rate\n", | ||
" self.loss_fn = torch.nn.CrossEntropyLoss()\n", | ||
"\n", | ||
" def forward(self, x):\n", | ||
" outputs = torch.sigmoid(self.linear(x))\n", | ||
" return outputs\n", | ||
"\n", | ||
" def training_step(self, batch, batch_idx):\n", | ||
" X_batch, y_batch = batch\n", | ||
" # X_batch = X_batch.float()\n", | ||
" X_batch = torch.from_numpy(X_batch).float().to(self.device)\n", | ||
"\n", | ||
" # Perform prediction\n", | ||
" outputs = self(X_batch)\n", | ||
"\n", | ||
" # Determine the predicted label\n", | ||
" probabilities = torch.nn.functional.softmax(outputs, 1)\n", | ||
" predictions = torch.argmax(probabilities, axis=1)\n", | ||
"\n", | ||
" # Compute loss\n", | ||
" y_batch = torch.from_numpy(\n", | ||
" self.cell_type_encoder.transform(y_batch[\"cell_type\"])\n", | ||
" ).to(self.device)\n", | ||
" loss = self.loss_fn(outputs, y_batch.long())\n", | ||
"\n", | ||
" # Compute accuracy\n", | ||
" train_correct = (predictions == y_batch).sum().item()\n", | ||
" train_accuracy = train_correct / len(predictions)\n", | ||
"\n", | ||
" # Log loss and accuracy\n", | ||
" self.log(\"train_loss\", loss, prog_bar=True)\n", | ||
" self.log(\"train_accuracy\", train_accuracy, prog_bar=True)\n", | ||
"\n", | ||
" return loss\n", | ||
"\n", | ||
" def configure_optimizers(self):\n", | ||
" optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n", | ||
" return optimizer" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Train the model" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 3, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stderr", | ||
"output_type": "stream", | ||
"text": [ | ||
"GPU available: True (cuda), used: True\n", | ||
"TPU available: False, using: 0 TPU cores\n", | ||
"HPU available: False, using: 0 HPUs\n", | ||
"/home/bruce/miniforge3/envs/toymodel/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", | ||
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | ||
"\n", | ||
" | Name | Type | Params | Mode \n", | ||
"-----------------------------------------------------\n", | ||
"0 | linear | Linear | 726 K | train\n", | ||
"1 | loss_fn | CrossEntropyLoss | 0 | train\n", | ||
"-----------------------------------------------------\n", | ||
"726 K Trainable params\n", | ||
"0 Non-trainable params\n", | ||
"726 K Total params\n", | ||
"2.905 Total estimated model params size (MB)\n", | ||
"2 Modules in train mode\n", | ||
"0 Modules in eval mode\n", | ||
"/home/bruce/miniforge3/envs/toymodel/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=19` in the `DataLoader` to improve performance.\n", | ||
"/home/bruce/miniforge3/envs/toymodel/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:122: Your `IterableDataset` has `__len__` defined. In combination with multi-process data loading (when num_workers > 1), `__len__` could be inaccurate if each worker is not configured independently to avoid having duplicate data.\n" | ||
] | ||
}, | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"Epoch 19: 100%|██████████| 118/118 [00:08<00:00, 14.31it/s, v_num=5, train_loss=1.670, train_accuracy=0.977]" | ||
] | ||
}, | ||
{ | ||
"name": "stderr", | ||
"output_type": "stream", | ||
"text": [ | ||
"`Trainer.fit` stopped: `max_epochs=20` reached.\n" | ||
] | ||
}, | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"Epoch 19: 100%|██████████| 118/118 [00:08<00:00, 14.28it/s, v_num=5, train_loss=1.670, train_accuracy=0.977]\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"dataloader = soma_ml.experiment_dataloader(experiment_dataset)\n", | ||
"\n", | ||
"# The size of the input dimension is the number of genes\n", | ||
"input_dim = experiment_dataset.shape[1]\n", | ||
"\n", | ||
"# The size of the output dimension is the number of distinct cell_type values\n", | ||
"output_dim = len(cell_type_encoder.classes_)\n", | ||
"\n", | ||
"# Initialize the PyTorch Lightning model\n", | ||
"model = LogisticRegressionLightning(\n", | ||
" input_dim, output_dim, cell_type_encoder=cell_type_encoder\n", | ||
")\n", | ||
"\n", | ||
"# Define the PyTorch Lightning Trainer\n", | ||
"trainer = pl.Trainer(max_epochs=20)\n", | ||
"\n", | ||
"# set precision\n", | ||
"torch.set_float32_matmul_precision(\"high\")\n", | ||
"\n", | ||
"# Train the model\n", | ||
"trainer.fit(model, train_dataloaders=dataloader)" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "toymodel", | ||
"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": "3.11.9" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 2 | ||
} |
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Does
pytorch_lightning
need to be listed here too?