export tutorials changed in db9df8b

tutorials/tutorial11/tutorial.py

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 # # Tutorial: Introduction to `Trainer` class
 # [![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). 
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+#
+# 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 thanks to the amazing job done by the PyTorch Lightning team!
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 # Our leading example will revolve around solving a simple regression problem where we want to approximate the following function with a Neural Net model $\mathcal{M}_{\theta}$:
 # $$y = x^3$$
 # by having only a set of $20$ observations $\{x_i, y_i\}_{i=1}^{20}$, with  $x_i \sim\mathcal{U}[-3, 3]\;\;\forall i\in(1,\dots,20)$.
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+#
 # Let's start by importing useful modules!
 
 # In[ ]:
@@ -70,16 +70,16 @@ trainer = Trainer(solver=solver)
 
 
 # ## Trainer Accelerator
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 # When creating the `Trainer`, **by default** the most performing `accelerator` for training which is available in your system will be chosen, ranked as follows:
 # 1. [TPU](https://cloud.google.com/tpu/docs/intro-to-tpu)
 # 2. [IPU](https://www.graphcore.ai/products/ipu)
 # 3. [HPU](https://habana.ai/)
 # 4. [GPU](https://www.intel.com/content/www/us/en/products/docs/processors/what-is-a-gpu.html#:~:text=What%20does%20GPU%20stand%20for,video%20editing%2C%20and%20gaming%20applications) or [MPS](https://developer.apple.com/metal/pytorch/)
 # 5. CPU
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 # For setting manually the `accelerator` run:
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 # * `accelerator = {'gpu', 'cpu', 'hpu', 'mps', 'cpu', 'ipu'}` sets the accelerator to a specific one
 
 # In[15]:
@@ -91,11 +91,11 @@ trainer = Trainer(solver=solver, accelerator="cpu")
 # As you can see, even if a `GPU` is available on the system, it is not used since we set `accelerator='cpu'`.
 
 # ## Trainer Logging
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 # In **PINA** you can log metrics in different ways. The simplest approach is to use the `MetricTracker` class from `pina.callbacks`, as seen in the [*Introduction to Physics Informed Neural Networks training*](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb) tutorial.
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 # However, especially when we need to train multiple times to get an average of the loss across multiple runs, `lightning.pytorch.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).
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 # 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.
 
 # In[17]:
@@ -133,21 +133,21 @@ for _ in range(3):
 # </p>
 
 # As you can see, by default, **PINA** logs the losses which are shown in the progress bar, as well as the number of epochs. You can always insert more loggings by either defining a **callback** ([more on callbacks](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html)), or inheriting the solver and modifying the programs with different **hooks** ([more on hooks](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#hooks)).
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 # ## Trainer Callbacks
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 # Whenever we need to access certain steps of the training for logging, perform static modifications (i.e. not changing the `Solver`), or update `Problem` hyperparameters (static variables), we can use **Callbacks**. Notice that **Callbacks** allow you to add arbitrary self-contained programs to your training. At specific points during the flow of execution (hooks), the Callback interface allows you to design programs that encapsulate a full set of functionality. It de-couples functionality that does not need to be in **PINA** `Solver`s.
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 # Lightning has a callback system to execute them when needed. **Callbacks** should capture NON-ESSENTIAL logic that is NOT required for your lightning module to run.
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 # The following are best practices when using/designing callbacks:
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 # * Callbacks should be isolated in their functionality.
 # * Your callback should not rely on the behavior of other callbacks in order to work properly.
 # * Do not manually call methods from the callback.
 # * Directly calling methods (e.g., on_validation_end) is strongly discouraged.
 # * Whenever possible, your callbacks should not depend on the order in which they are executed.
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 # We will try now to implement a naive version of `MetricTraker` to show how callbacks work. Notice that this is a very easy application of callbacks, fortunately in **PINA** we already provide more advanced callbacks in `pina.callbacks`.
 
 # In[18]:
@@ -172,7 +172,7 @@ class NaiveMetricTracker(Callback):
 
 
 # Let's see the results when applied to the problem. You can define **callbacks** when initializing the `Trainer` by using the `callbacks` argument, which expects a list of callbacks.
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 # In[19]:
 
@@ -206,8 +206,8 @@ trainer.train()
 trainer.callbacks[0].saved_metrics[:3]  # only the first three epochs
 
 
-# PyTorch Lightning also has some built-in `Callbacks` which can be used in **PINA**, [here is an extensive list](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html#built-in-callbacks). 
-# 
+# PyTorch Lightning also has some built-in `Callbacks` which can be used in **PINA**, [here is an extensive list](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html#built-in-callbacks).
+#
 # We can, for example, try the `EarlyStopping` routine, which automatically stops the training when a specific metric converges (here the `train_loss`). In order to let the training keep going forever, set `max_epochs=-1`.
 
 # In[22]:
@@ -237,17 +237,17 @@ trainer.train()
 # As we can see the model automatically stop when the logging metric stopped improving!
 
 # ## Trainer Tips to Boost Accuracy, Save Memory and Speed Up Training
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 # Until now we have seen how to choose the right `accelerator`, how to log and visualize the results, and how to interface with the program in order to add specific parts of code at specific points via `callbacks`.
 # Now, we will focus on how to boost your training by saving memory and speeding it up, while maintaining the same or even better degree of accuracy!
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 # There are several built-in methods developed in PyTorch Lightning which can be applied straightforward in **PINA**. Here we report some:
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 # * [Stochastic Weight Averaging](https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/) to boost accuracy
 # * [Gradient Clipping](https://deepgram.com/ai-glossary/gradient-clipping) to reduce computational time (and improve accuracy)
 # * [Gradient Accumulation](https://lightning.ai/docs/pytorch/stable/common/optimization.html#id3) to save memory consumption
 # * [Mixed Precision Training](https://lightning.ai/docs/pytorch/stable/common/optimization.html#id3) to save memory consumption
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 # We will just demonstrate how to use the first two and see the results compared to standard training.
 # We use the [`Timer`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.Timer.html#lightning.pytorch.callbacks.Timer) callback from `pytorch_lightning.callbacks` to track the times. Let's start by training a simple model without any optimization (train for 500 epochs).
 
@@ -312,7 +312,7 @@ print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s')
 # As you can see, the training time does not change at all! Notice that around epoch 350
 # the scheduler is switched from the defalut one `ConstantLR` to the Stochastic Weight Average Learning Rate (`SWALR`).
 # This is because by default `StochasticWeightAveraging` will be activated after `int(swa_epoch_start * max_epochs)` with `swa_epoch_start=0.7` by default. Finally, the final `train_loss` is lower when `StochasticWeightAveraging` is used.
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 # We will now do the same but clippling the gradient to be relatively small.
 
 # In[25]:
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 # As we can see, by applying gradient clipping, we were able to achieve even lower error!
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 # ## What's Next?
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 # Now you know how to use the `Trainer` class efficiently in **PINA**! There are several directions you can explore next:
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 # 1. **Explore Training on Different Devices**: Test training times on various devices (e.g., `TPU`) to compare performance.
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 # 2. **Reduce Memory Costs**: Experiment with mixed precision training and gradient accumulation to optimize memory usage, especially when training Neural Operators.
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 # 3. **Benchmark `Trainer` Speed**: Benchmark the training speed of the `Trainer` class for different precisions to identify potential optimizations.
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 # 4. **...and many more!**: Consider expanding to **multi-GPU** setups or other advanced configurations for large-scale training.
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 # For more resources and tutorials, check out the [PINA Documentation](https://mathlab.github.io/PINA/).
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