Note
Go to the end to download the full example code
Train Neural Network Potential From NeuroChem Input File¶
This example shows how to use TorchANI’s NeuroChem trainer to read and run NeuroChem’s training config file to train a neural network potential.
To begin with, let’s first import the modules we will use:
import torchani
import torch
import os
import sys
import tqdm
Now let’s setup path for the dataset and NeuroChem input file. Note that
these paths assumes the user run this script under the examples directory
of TorchANI’s repository. If you download this script, you should manually
set the path of these files in your system before this script can run
successfully. Also note that here for our demo purpose, we set both training
set and validation set the ani_gdb_s01.h5 in TorchANI’s repository. This
allows this program to finish very quick, because that dataset is very small.
But this is wrong and should be avoided for any serious training.
try:
path = os.path.dirname(os.path.realpath(__file__))
except NameError:
path = os.getcwd()
cfg_path = os.path.join(path, '../tests/test_data/inputtrain.ipt')
training_path = os.path.join(path, '../dataset/ani1-up_to_gdb4/ani_gdb_s01.h5') # noqa: E501
validation_path = os.path.join(path, '../dataset/ani1-up_to_gdb4/ani_gdb_s01.h5') # noqa: E501
We also need to set the device to run the training:
device_str = 'cuda' if torch.cuda.is_available() else 'cpu'
device = torch.device(device_str)
trainer = torchani.neurochem.Trainer(cfg_path, device, True, 'runs')
trainer.load_data(training_path, validation_path)
=> loading /home/runner/work/torchani/torchani/examples/../dataset/ani1-up_to_gdb4/ani_gdb_s01.h5, total molecules: 1
1/1 [==============================] - 0.0s
2/1 [============================================================] - 0.0s
3/1 [==========================================================================================] - 0.1s=> loading /home/runner/work/torchani/torchani/examples/../dataset/ani1-up_to_gdb4/ani_gdb_s01.h5, total molecules: 1
1/1 [==============================] - 0.0s
2/1 [============================================================] - 0.0s
3/1 [==========================================================================================] - 0.1s
Once everything is set up, running NeuroChem is very easy. We simplify need a
trainer.run(). But here, in order for sphinx-gallery to be able to
capture the output of tqdm, let’s do some hacking first to make tqdm to print
its progressbar to stdout.
def my_tqdm(*args, **kwargs):
return tqdm.tqdm(*args, **kwargs, file=sys.stdout)
trainer.tqdm = my_tqdm
Now, let’s go!
trainer.run()
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epoch 1: 40%|████ | 2/5 [00:00<00:00, 5.45it/s]
epoch 1: 60%|██████ | 3/5 [00:00<00:00, 5.41it/s]
epoch 1: 80%|████████ | 4/5 [00:00<00:00, 5.09it/s]
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epoch 2: 60%|██████ | 3/5 [00:00<00:00, 5.53it/s]
epoch 2: 80%|████████ | 4/5 [00:00<00:00, 5.53it/s]
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epoch 3: 60%|██████ | 3/5 [00:00<00:00, 5.42it/s]
epoch 3: 80%|████████ | 4/5 [00:00<00:00, 5.36it/s]
epoch 3: 100%|██████████| 5/5 [00:00<00:00, 6.30it/s]
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epoch 4: 60%|██████ | 3/5 [00:00<00:00, 5.41it/s]
epoch 4: 80%|████████ | 4/5 [00:00<00:00, 5.47it/s]
epoch 4: 100%|██████████| 5/5 [00:00<00:00, 6.33it/s]
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epoch 5: 60%|██████ | 3/5 [00:00<00:00, 5.38it/s]
epoch 5: 80%|████████ | 4/5 [00:00<00:00, 5.50it/s]
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epoch 6: 80%|████████ | 4/5 [00:00<00:00, 5.18it/s]
epoch 6: 100%|██████████| 5/5 [00:00<00:00, 5.82it/s]
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epoch 7: 60%|██████ | 3/5 [00:00<00:00, 5.59it/s]
epoch 7: 80%|████████ | 4/5 [00:00<00:00, 5.51it/s]
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epoch 8: 60%|██████ | 3/5 [00:00<00:00, 5.45it/s]
epoch 8: 80%|████████ | 4/5 [00:00<00:00, 5.44it/s]
epoch 8: 100%|██████████| 5/5 [00:00<00:00, 6.35it/s]
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epoch 9: 80%|████████ | 4/5 [00:00<00:00, 5.57it/s]
epoch 9: 100%|██████████| 5/5 [00:00<00:00, 6.50it/s]
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epoch 10: 20%|██ | 1/5 [00:00<00:00, 5.55it/s]
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epoch 10: 80%|████████ | 4/5 [00:00<00:00, 5.57it/s]
epoch 10: 100%|██████████| 5/5 [00:00<00:00, 6.47it/s]
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epoch 11: 60%|██████ | 3/5 [00:00<00:00, 5.36it/s]
epoch 11: 80%|████████ | 4/5 [00:00<00:00, 5.31it/s]
epoch 11: 100%|██████████| 5/5 [00:00<00:00, 6.20it/s]
Alternatively, you can run NeuroChem trainer directly using command line.
There is no need for programming. Just run the following command for help
python -m torchani.neurochem.trainer -h for usage. For this demo, the
equivalent command is:
cmd = ['python', '-m', 'torchani.neurochem.trainer', '-d', device_str,
'--tqdm', '--tensorboard', 'runs', cfg_path, training_path,
validation_path]
print(' '.join(cmd))
python -m torchani.neurochem.trainer -d cpu --tqdm --tensorboard runs /home/runner/work/torchani/torchani/examples/../tests/test_data/inputtrain.ipt /home/runner/work/torchani/torchani/examples/../dataset/ani1-up_to_gdb4/ani_gdb_s01.h5 /home/runner/work/torchani/torchani/examples/../dataset/ani1-up_to_gdb4/ani_gdb_s01.h5
Now let’s invoke this command to see what we get. Again, we redirect stderr to stdout simplify for sphinx-gallery to be able to capture it when generating this document:
/opt/hostedtoolcache/Python/3.8.17/x64/lib/python3.8/site-packages/torchani-2.2.3-py3.8.egg/torchani/aev.py:16: UserWarning: cuaev not installed
warnings.warn("cuaev not installed")
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epoch 10: 100%|██████████| 5/5 [00:00<00:00, 6.42it/s]
Total running time of the script: (0 minutes 28.653 seconds)