Training a deep learning model¶

This example shows how to train a deep learning model using the dataset created in the previous example.

Loading the dataset¶

To load the dataset we need to use the EMGDatasetLoader class.

Two parameters are required:

data_path: Path to the dataset file.
dataloader_parameters: Parameters for the DataLoader.

from pathlib import Path
from doc_octopy.datasets.loader import EMGDatasetLoader

loader = EMGDatasetLoader(Path("data/dataset.zarr").resolve(), dataloader_parameters={"batch_size": 16, "drop_last": True})

Training the model¶

from doc_octopy.models.definitions.raul_net.online.v16 import RaulNetV16
import lightning as L

# Create the model
model = RaulNetV16(
    learning_rate=1e-4,
    nr_of_input_channels=2,
    input_length__samples=192,
    nr_of_outputs=60,
    nr_of_electrode_grids=5,
    nr_of_electrodes_per_grid=64,

    # Multiply following by 4, 8, 16 to have a useful network
    cnn_encoder_channels=(4, 1, 1),
    mlp_encoder_channels=(8, 8),

    event_search_kernel_length=31,
    event_search_kernel_stride=8,
)

trainer = L.Trainer(
    accelerator="auto",
    devices=1,
    precision="16-mixed",
    max_epochs=1,
    log_every_n_steps=50,
    logger=None,
    enable_checkpointing=False,
    deterministic=False,
)

trainer.fit(model, datamodule=loader)

/home/runner/work/DocOctopy/DocOctopy/.venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py:512: You passed `Trainer(accelerator='cpu', precision='16-mixed')` but AMP with fp16 is not supported on CPU. Using `precision='bf16-mixed'` instead.
/home/runner/work/DocOctopy/DocOctopy/.venv/lib/python3.10/site-packages/lightning/pytorch/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=3` in the `DataLoader` to improve performance.

Sanity Checking: |          | 0/? [00:00<?, ?it/s]/home/runner/work/DocOctopy/DocOctopy/.venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.

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Epoch 0:  80%|████████  | 73/91 [01:48<00:26,  0.67it/s, v_num=0, loss_step=39.80]
Epoch 0:  80%|████████  | 73/91 [01:48<00:26,  0.67it/s, v_num=0, loss_step=39.10]
Epoch 0:  81%|████████▏ | 74/91 [01:50<00:25,  0.67it/s, v_num=0, loss_step=39.10]
Epoch 0:  81%|████████▏ | 74/91 [01:50<00:25,  0.67it/s, v_num=0, loss_step=40.60]
Epoch 0:  82%|████████▏ | 75/91 [01:51<00:23,  0.67it/s, v_num=0, loss_step=40.60]
Epoch 0:  82%|████████▏ | 75/91 [01:51<00:23,  0.67it/s, v_num=0, loss_step=39.30]
Epoch 0:  84%|████████▎ | 76/91 [01:53<00:22,  0.67it/s, v_num=0, loss_step=39.30]
Epoch 0:  84%|████████▎ | 76/91 [01:53<00:22,  0.67it/s, v_num=0, loss_step=39.50]
Epoch 0:  85%|████████▍ | 77/91 [01:54<00:20,  0.67it/s, v_num=0, loss_step=39.50]
Epoch 0:  85%|████████▍ | 77/91 [01:54<00:20,  0.67it/s, v_num=0, loss_step=40.30]
Epoch 0:  86%|████████▌ | 78/91 [01:56<00:19,  0.67it/s, v_num=0, loss_step=40.30]
Epoch 0:  86%|████████▌ | 78/91 [01:56<00:19,  0.67it/s, v_num=0, loss_step=40.40]
Epoch 0:  87%|████████▋ | 79/91 [01:57<00:17,  0.67it/s, v_num=0, loss_step=40.40]
Epoch 0:  87%|████████▋ | 79/91 [01:57<00:17,  0.67it/s, v_num=0, loss_step=38.90]
Epoch 0:  88%|████████▊ | 80/91 [01:59<00:16,  0.67it/s, v_num=0, loss_step=38.90]
Epoch 0:  88%|████████▊ | 80/91 [01:59<00:16,  0.67it/s, v_num=0, loss_step=39.40]
Epoch 0:  89%|████████▉ | 81/91 [02:00<00:14,  0.67it/s, v_num=0, loss_step=39.40]
Epoch 0:  89%|████████▉ | 81/91 [02:00<00:14,  0.67it/s, v_num=0, loss_step=39.40]
Epoch 0:  90%|█████████ | 82/91 [02:02<00:13,  0.67it/s, v_num=0, loss_step=39.40]
Epoch 0:  90%|█████████ | 82/91 [02:02<00:13,  0.67it/s, v_num=0, loss_step=39.20]
Epoch 0:  91%|█████████ | 83/91 [02:03<00:11,  0.67it/s, v_num=0, loss_step=39.20]
Epoch 0:  91%|█████████ | 83/91 [02:03<00:11,  0.67it/s, v_num=0, loss_step=39.20]
Epoch 0:  92%|█████████▏| 84/91 [02:05<00:10,  0.67it/s, v_num=0, loss_step=39.20]
Epoch 0:  92%|█████████▏| 84/91 [02:05<00:10,  0.67it/s, v_num=0, loss_step=38.70]
Epoch 0:  93%|█████████▎| 85/91 [02:06<00:08,  0.67it/s, v_num=0, loss_step=38.70]
Epoch 0:  93%|█████████▎| 85/91 [02:06<00:08,  0.67it/s, v_num=0, loss_step=39.30]
Epoch 0:  95%|█████████▍| 86/91 [02:08<00:07,  0.67it/s, v_num=0, loss_step=39.30]
Epoch 0:  95%|█████████▍| 86/91 [02:08<00:07,  0.67it/s, v_num=0, loss_step=38.70]
Epoch 0:  96%|█████████▌| 87/91 [02:09<00:05,  0.67it/s, v_num=0, loss_step=38.70]
Epoch 0:  96%|█████████▌| 87/91 [02:09<00:05,  0.67it/s, v_num=0, loss_step=39.90]
Epoch 0:  97%|█████████▋| 88/91 [02:11<00:04,  0.67it/s, v_num=0, loss_step=39.90]
Epoch 0:  97%|█████████▋| 88/91 [02:11<00:04,  0.67it/s, v_num=0, loss_step=40.30]
Epoch 0:  98%|█████████▊| 89/91 [02:12<00:02,  0.67it/s, v_num=0, loss_step=40.30]
Epoch 0:  98%|█████████▊| 89/91 [02:12<00:02,  0.67it/s, v_num=0, loss_step=38.70]
Epoch 0:  99%|█████████▉| 90/91 [02:14<00:01,  0.67it/s, v_num=0, loss_step=38.70]
Epoch 0:  99%|█████████▉| 90/91 [02:14<00:01,  0.67it/s, v_num=0, loss_step=38.70]
Epoch 0: 100%|██████████| 91/91 [02:15<00:00,  0.67it/s, v_num=0, loss_step=38.70]
Epoch 0: 100%|██████████| 91/91 [02:15<00:00,  0.67it/s, v_num=0, loss_step=40.50]

Validation: |          | 0/? [00:00<?, ?it/s]

Validation:   0%|          | 0/2 [00:00<?, ?it/s]

Validation DataLoader 0:   0%|          | 0/2 [00:00<?, ?it/s]

Validation DataLoader 0:  50%|█████     | 1/2 [00:00<00:00,  2.31it/s]

Validation DataLoader 0: 100%|██████████| 2/2 [00:00<00:00,  3.06it/s]


Epoch 0: 100%|██████████| 91/91 [02:16<00:00,  0.67it/s, v_num=0, loss_step=40.50, val_loss=40.30]
Epoch 0: 100%|██████████| 91/91 [02:16<00:00,  0.67it/s, v_num=0, loss_step=40.50, val_loss=40.30, loss_epoch=47.00]
Epoch 0: 100%|██████████| 91/91 [02:16<00:00,  0.67it/s, v_num=0, loss_step=40.50, val_loss=40.30, loss_epoch=47.00]

Total running time of the script: (2 minutes 17.994 seconds)

Estimated memory usage: 112 MB

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