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Colab tutorials (#367)

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* Colab Button & Run added
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Giuseppe Alessio D'Inverno
2024-10-22 15:47:33 +02:00
committed by GitHub
parent 6a4febb33a
commit 78ed2a67a2
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tutorials/tutorial11/tutorial.py vendored
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@@ -3,10 +3,11 @@
# # Tutorial: PINA and PyTorch Lightning, training tips and visualizations
#
# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial11/tutorial.ipynb)
#
# In this tutorial, we will delve deeper into the functionality of the `Trainer` class, which serves as the cornerstone for training **PINA** [Solvers](https://mathlab.github.io/PINA/_rst/_code.html#solvers).
#
# The `Trainer` class offers a plethora of features aimed at improving model accuracy, reducing training time and memory usage, facilitating logging visualization, and more.
# The `Trainer` class offers a plethora of features aimed at improving model accuracy, reducing training time and memory usage, facilitating logging visualization, and more thanks to the amazing job done by the PyTorch Lightning team!
#
# Our leading example will revolve around solving the `SimpleODE` problem, as outlined in the [*Introduction to PINA for Physics Informed Neural Networks training*](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb). If you haven't already explored it, we highly recommend doing so before diving into this tutorial.
#
@@ -15,6 +16,15 @@
# In[18]:
## routine needed to run the notebook on Google Colab
try:
import google.colab
IN_COLAB = True
except:
IN_COLAB = False
if IN_COLAB:
get_ipython().system('pip install "pina-mathlab"')
import torch
from pina import Condition, Trainer
@@ -99,7 +109,7 @@ trainer = Trainer(solver=pinn,
#
# In **PINA** you can log metrics in different ways. The simplest approach is to use the `MetricTraker` class from `pina.callbacks` as seen in the [*Introduction to PINA for Physics Informed Neural Networks training*](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb) tutorial.
#
# However, expecially when we need to train multiple times to get an average of the loss across multiple runs, `pytorch_lightning.loggers` might be useful. Here we will use `TensorBoardLogger` (more on [logging](https://lightning.ai/docs/pytorch/stable/extensions/logging.html) here), but you can choose the one you prefer (or make your own one) thanks to the amazing job done by the PyTorch Lightning team!
# However, expecially when we need to train multiple times to get an average of the loss across multiple runs, `pytorch_lightning.loggers` might be useful. Here we will use `TensorBoardLogger` (more on [logging](https://lightning.ai/docs/pytorch/stable/extensions/logging.html) here), but you can choose the one you prefer (or make your own one).
#
# We will now import `TensorBoardLogger`, do three runs of training and then visualize the results. Notice we set `enable_model_summary=False` to avoid model summary specifications (e.g. number of parameters), set it to true if needed.
#