851 lines
29 KiB
Plaintext
Vendored
851 lines
29 KiB
Plaintext
Vendored
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Tutorial: PINA and PyTorch Lightning, training tips and visualizations \n",
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"\n",
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"[](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial11/tutorial.ipynb)\n",
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"\n",
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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). \n",
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"\n",
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"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!\n",
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"\n",
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"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.\n",
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"\n",
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"Let's start by importing useful modules, define the `SimpleODE` problem and the `PINN` solver."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" import google.colab\n",
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"\n",
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" IN_COLAB = True\n",
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"except:\n",
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" IN_COLAB = False\n",
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"if IN_COLAB:\n",
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" !pip install \"pina-mathlab\"\n",
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"\n",
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"import torch\n",
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"import warnings\n",
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"\n",
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"from pina import Condition, Trainer\n",
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"from pina.solver import PINN\n",
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"from pina.model import FeedForward\n",
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"from pina.problem import SpatialProblem\n",
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"from pina.operator import grad\n",
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"from pina.domain import CartesianDomain\n",
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"from pina.equation import Equation, FixedValue\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Define problem and solver."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# defining the ode equation\n",
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"def ode_equation(input_, output_):\n",
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"\n",
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" # computing the derivative\n",
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" u_x = grad(output_, input_, components=[\"u\"], d=[\"x\"])\n",
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"\n",
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" # extracting the u input variable\n",
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" u = output_.extract([\"u\"])\n",
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"\n",
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" # calculate the residual and return it\n",
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" return u_x - u\n",
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"\n",
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"\n",
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"class SimpleODE(SpatialProblem):\n",
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"\n",
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" output_variables = [\"u\"]\n",
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" spatial_domain = CartesianDomain({\"x\": [0, 1]})\n",
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"\n",
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" domains = {\n",
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" \"x0\": CartesianDomain({\"x\": 0.0}),\n",
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" \"D\": CartesianDomain({\"x\": [0, 1]}),\n",
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" }\n",
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"\n",
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" # conditions to hold\n",
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" conditions = {\n",
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" \"bound_cond\": Condition(domain=\"x0\", equation=FixedValue(1.0)),\n",
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" \"phys_cond\": Condition(domain=\"D\", equation=Equation(ode_equation)),\n",
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" }\n",
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"\n",
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" # defining the true solution\n",
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" def solution(self, pts):\n",
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" return torch.exp(pts.extract([\"x\"]))\n",
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"\n",
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"\n",
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"# sampling for training\n",
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"problem = SimpleODE()\n",
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"problem.discretise_domain(1, \"random\", domains=[\"x0\"])\n",
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"problem.discretise_domain(20, \"lh\", domains=[\"D\"])\n",
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"\n",
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"# build the model\n",
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"model = FeedForward(\n",
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" layers=[10, 10],\n",
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" func=torch.nn.Tanh,\n",
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" output_dimensions=len(problem.output_variables),\n",
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" input_dimensions=len(problem.input_variables),\n",
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")\n",
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"\n",
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"# create the PINN object\n",
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"pinn = PINN(problem, model)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Till now we just followed the extact step of the previous tutorials. The `Trainer` object\n",
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"can be initialized by simiply passing the `PINN` solver"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"GPU available: True (mps), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n"
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]
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}
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],
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"source": [
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"trainer = Trainer(solver=pinn)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Trainer Accelerator\n",
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"\n",
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"When creating the trainer, **by defualt** the `Trainer` will choose the most performing `accelerator` for training which is available in your system, ranked as follow:\n",
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"1. [TPU](https://cloud.google.com/tpu/docs/intro-to-tpu)\n",
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"2. [IPU](https://www.graphcore.ai/products/ipu)\n",
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"3. [HPU](https://habana.ai/)\n",
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"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/)\n",
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"5. CPU"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For setting manually the `accelerator` run:\n",
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"\n",
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"* `accelerator = {'gpu', 'cpu', 'hpu', 'mps', 'cpu', 'ipu'}` sets the accelerator to a specific one"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"GPU available: True (mps), used: False\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n"
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]
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}
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],
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"source": [
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"trainer = Trainer(solver=pinn, accelerator=\"cpu\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"as you can see, even if in the used system `GPU` is available, it is not used since we set `accelerator='cpu'`."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Trainer Logging\n",
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"\n",
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"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.\n",
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"\n",
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"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).\n",
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"\n",
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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.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"GPU available: True (mps), used: False\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 233.15it/s, v_num=0, bound_cond_loss=1.22e-5, phys_cond_loss=0.000517, train_loss=0.000529]"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 137.95it/s, v_num=0, bound_cond_loss=1.22e-5, phys_cond_loss=0.000517, train_loss=0.000529]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"GPU available: True (mps), used: False\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 248.63it/s, v_num=1, bound_cond_loss=2.29e-5, phys_cond_loss=0.00106, train_loss=0.00108] "
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 149.06it/s, v_num=1, bound_cond_loss=2.29e-5, phys_cond_loss=0.00106, train_loss=0.00108]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"GPU available: True (mps), used: False\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 254.65it/s, v_num=2, bound_cond_loss=0.00029, phys_cond_loss=0.00253, train_loss=0.00282] "
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 150.72it/s, v_num=2, bound_cond_loss=0.00029, phys_cond_loss=0.00253, train_loss=0.00282]\n"
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]
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}
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],
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"source": [
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"from lightning.pytorch.loggers import TensorBoardLogger\n",
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"\n",
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"# three run of training, by default it trains for 1000 epochs\n",
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"# we reinitialize the model each time otherwise the same parameters will be optimized\n",
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"for _ in range(3):\n",
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" model = FeedForward(\n",
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" layers=[10, 10],\n",
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" func=torch.nn.Tanh,\n",
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" output_dimensions=len(problem.output_variables),\n",
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" input_dimensions=len(problem.input_variables),\n",
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" )\n",
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" pinn = PINN(problem, model)\n",
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" trainer = Trainer(\n",
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" solver=pinn,\n",
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" accelerator=\"cpu\",\n",
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" logger=TensorBoardLogger(save_dir=\"training_log\"),\n",
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" enable_model_summary=False,\n",
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" train_size=1.0,\n",
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" val_size=0.0,\n",
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" test_size=0.0,\n",
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" )\n",
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" trainer.train()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can now visualize the logs by simply running `tensorboard --logdir=training_log/` on terminal, you should obtain a webpage as the one shown below:"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<p align=\\\"center\\\">\n",
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"<img src=\"logging.png\" alt=\\\"Logging API\\\" width=\\\"400\\\"/>\n",
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"</p>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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 modify the programs with different **hooks** ([more on hooks](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#hooks))."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Trainer Callbacks"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Whenever we need to access certain steps of the training for logging, do static modifications (i.e. not changing the `Solver`) or updating `Problem` hyperparameters (static variables), we can use `Callabacks`. 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.\n",
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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.\n",
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"\n",
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"The following are best practices when using/designing callbacks.\n",
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"\n",
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"* Callbacks should be isolated in their functionality.\n",
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"* Your callback should not rely on the behavior of other callbacks in order to work properly.\n",
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"* Do not manually call methods from the callback.\n",
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"* Directly calling methods (eg. on_validation_end) is strongly discouraged.\n",
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"* Whenever possible, your callbacks should not depend on the order in which they are executed.\n",
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"\n",
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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`.\n",
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"\n",
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"<!-- Suppose we want to log the accuracy on some validation poit -->"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"from lightning.pytorch.callbacks import Callback\n",
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"from lightning.pytorch.callbacks import EarlyStopping\n",
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"import torch\n",
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"\n",
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"\n",
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"# define a simple callback\n",
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"class NaiveMetricTracker(Callback):\n",
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" def __init__(self):\n",
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" self.saved_metrics = []\n",
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"\n",
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" def on_train_epoch_end(\n",
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" self, trainer, __\n",
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" ): # function called at the end of each epoch\n",
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" self.saved_metrics.append(\n",
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" {key: value for key, value in trainer.logged_metrics.items()}\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's see the results when applyed to the `SimpleODE` problem. You can define callbacks when initializing the `Trainer` by the `callbacks` argument, which expects a list of callbacks. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"GPU available: True (mps), used: False\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 278.93it/s, v_num=0, bound_cond_loss=6.94e-5, phys_cond_loss=0.00116, train_loss=0.00123] "
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 140.62it/s, v_num=0, bound_cond_loss=6.94e-5, phys_cond_loss=0.00116, train_loss=0.00123]\n"
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]
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}
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],
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"source": [
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"model = FeedForward(\n",
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" layers=[10, 10],\n",
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" func=torch.nn.Tanh,\n",
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" output_dimensions=len(problem.output_variables),\n",
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" input_dimensions=len(problem.input_variables),\n",
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")\n",
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"pinn = PINN(problem, model)\n",
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"trainer = Trainer(\n",
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" solver=pinn,\n",
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" accelerator=\"cpu\",\n",
|
|
" logger=True,\n",
|
|
" callbacks=[NaiveMetricTracker()], # adding a callbacks\n",
|
|
" enable_model_summary=False,\n",
|
|
" train_size=1.0,\n",
|
|
" val_size=0.0,\n",
|
|
" test_size=0.0,\n",
|
|
")\n",
|
|
"trainer.train()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can easily access the data by calling `trainer.callbacks[0].saved_metrics` (notice the zero representing the first callback in the list given at initialization)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[{'bound_cond_loss': tensor(0.9935),\n",
|
|
" 'phys_cond_loss': tensor(0.0303),\n",
|
|
" 'train_loss': tensor(1.0239)},\n",
|
|
" {'bound_cond_loss': tensor(0.9875),\n",
|
|
" 'phys_cond_loss': tensor(0.0293),\n",
|
|
" 'train_loss': tensor(1.0169)},\n",
|
|
" {'bound_cond_loss': tensor(0.9815),\n",
|
|
" 'phys_cond_loss': tensor(0.0284),\n",
|
|
" 'train_loss': tensor(1.0099)}]"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"trainer.callbacks[0].saved_metrics[:3] # only the first three epochs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"PyTorch Lightning also has some built in `Callbacks` which can be used in **PINA**, [here an extensive list](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html#built-in-callbacks). \n",
|
|
"\n",
|
|
"We can for example try the `EarlyStopping` routine, which automatically stops the training when a specific metric converged (here the `train_loss`). In order to let the training keep going forever set `max_epochs=-1`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"GPU available: True (mps), used: False\n",
|
|
"TPU available: False, using: 0 TPU cores\n",
|
|
"HPU available: False, using: 0 HPUs\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 2343: 100%|██████████| 1/1 [00:00<00:00, 64.24it/s, v_num=1, val_loss=4.79e-6, bound_cond_loss=1.15e-7, phys_cond_loss=2.33e-5, train_loss=2.34e-5] \n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model = FeedForward(\n",
|
|
" layers=[10, 10],\n",
|
|
" func=torch.nn.Tanh,\n",
|
|
" output_dimensions=len(problem.output_variables),\n",
|
|
" input_dimensions=len(problem.input_variables),\n",
|
|
")\n",
|
|
"pinn = PINN(problem, model)\n",
|
|
"trainer = Trainer(\n",
|
|
" solver=pinn,\n",
|
|
" accelerator=\"cpu\",\n",
|
|
" max_epochs=-1,\n",
|
|
" enable_model_summary=False,\n",
|
|
" val_size=0.2,\n",
|
|
" train_size=0.8,\n",
|
|
" test_size=0.0,\n",
|
|
" callbacks=[EarlyStopping(\"val_loss\")],\n",
|
|
") # adding a callbacks\n",
|
|
"trainer.train()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"As we can see the model automatically stop when the logging metric stopped improving!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Trainer Tips to Boost Accuracy, Save Memory and Speed Up Training\n",
|
|
"\n",
|
|
"Untill 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 by `callbacks`.\n",
|
|
"Now, we well focus on how boost your training by saving memory and speeding it up, while mantaining the same or even better degree of accuracy!\n",
|
|
"\n",
|
|
"\n",
|
|
"There are several built in methods developed in PyTorch Lightning which can be applied straight forward in **PINA**, here we report some:\n",
|
|
"\n",
|
|
"* [Stochastic Weight Averaging](https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/) to boost accuracy\n",
|
|
"* [Gradient Clippling](https://deepgram.com/ai-glossary/gradient-clipping) to reduce computational time (and improve accuracy)\n",
|
|
"* [Gradient Accumulation](https://lightning.ai/docs/pytorch/stable/common/optimization.html#id3) to save memory consumption \n",
|
|
"* [Mixed Precision Training](https://lightning.ai/docs/pytorch/stable/common/optimization.html#id3) to save memory consumption \n",
|
|
"\n",
|
|
"We will just demonstrate how to use the first two, and see the results compared to a standard training.\n",
|
|
"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 take the times. Let's start by training a simple model without any optimization (train for 2000 epochs)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Seed set to 42\n",
|
|
"GPU available: True (mps), used: False\n",
|
|
"TPU available: False, using: 0 TPU cores\n",
|
|
"HPU available: False, using: 0 HPUs\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 156.69it/s, v_num=2, bound_cond_loss=1.53e-6, phys_cond_loss=0.000169, train_loss=0.000171]"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"`Trainer.fit` stopped: `max_epochs=2000` reached.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 108.75it/s, v_num=2, bound_cond_loss=1.53e-6, phys_cond_loss=0.000169, train_loss=0.000171]\n",
|
|
"Total training time 15.36648 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from lightning.pytorch.callbacks import Timer\n",
|
|
"from lightning.pytorch import seed_everything\n",
|
|
"\n",
|
|
"# setting the seed for reproducibility\n",
|
|
"seed_everything(42, workers=True)\n",
|
|
"\n",
|
|
"model = FeedForward(\n",
|
|
" layers=[10, 10],\n",
|
|
" func=torch.nn.Tanh,\n",
|
|
" output_dimensions=len(problem.output_variables),\n",
|
|
" input_dimensions=len(problem.input_variables),\n",
|
|
")\n",
|
|
"\n",
|
|
"pinn = PINN(problem, model)\n",
|
|
"trainer = Trainer(\n",
|
|
" solver=pinn,\n",
|
|
" accelerator=\"cpu\",\n",
|
|
" deterministic=True, # setting deterministic=True ensure reproducibility when a seed is imposed\n",
|
|
" max_epochs=2000,\n",
|
|
" enable_model_summary=False,\n",
|
|
" callbacks=[Timer()],\n",
|
|
") # adding a callbacks\n",
|
|
"trainer.train()\n",
|
|
"print(f'Total training time {trainer.callbacks[0].time_elapsed(\"train\"):.5f} s')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now we do the same but with StochasticWeightAveraging"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Seed set to 42\n",
|
|
"GPU available: True (mps), used: False\n",
|
|
"TPU available: False, using: 0 TPU cores\n",
|
|
"HPU available: False, using: 0 HPUs\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 224.16it/s, v_num=3, bound_cond_loss=5.7e-6, phys_cond_loss=0.000257, train_loss=0.000263] "
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Swapping scheduler `ConstantLR` for `SWALR`\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 261.43it/s, v_num=3, bound_cond_loss=2.58e-7, phys_cond_loss=9.4e-5, train_loss=9.43e-5] "
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"`Trainer.fit` stopped: `max_epochs=2000` reached.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 145.96it/s, v_num=3, bound_cond_loss=2.58e-7, phys_cond_loss=9.4e-5, train_loss=9.43e-5]\n",
|
|
"Total training time 17.78182 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from lightning.pytorch.callbacks import StochasticWeightAveraging\n",
|
|
"\n",
|
|
"# setting the seed for reproducibility\n",
|
|
"seed_everything(42, workers=True)\n",
|
|
"\n",
|
|
"model = FeedForward(\n",
|
|
" layers=[10, 10],\n",
|
|
" func=torch.nn.Tanh,\n",
|
|
" output_dimensions=len(problem.output_variables),\n",
|
|
" input_dimensions=len(problem.input_variables),\n",
|
|
")\n",
|
|
"pinn = PINN(problem, model)\n",
|
|
"trainer = Trainer(\n",
|
|
" solver=pinn,\n",
|
|
" accelerator=\"cpu\",\n",
|
|
" deterministic=True,\n",
|
|
" max_epochs=2000,\n",
|
|
" enable_model_summary=False,\n",
|
|
" callbacks=[Timer(), StochasticWeightAveraging(swa_lrs=0.005)],\n",
|
|
") # adding StochasticWeightAveraging callbacks\n",
|
|
"trainer.train()\n",
|
|
"print(f'Total training time {trainer.callbacks[0].time_elapsed(\"train\"):.5f} s')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"As you can see, the training time does not change at all! Notice that around epoch `1600`\n",
|
|
"the scheduler is switched from the defalut one `ConstantLR` to the Stochastic Weight Average Learning Rate (`SWALR`).\n",
|
|
"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 `mean_loss` is lower when `StochasticWeightAveraging` is used.\n",
|
|
"\n",
|
|
"We will now now do the same but clippling the gradient to be relatively small."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Seed set to 42\n",
|
|
"GPU available: True (mps), used: False\n",
|
|
"TPU available: False, using: 0 TPU cores\n",
|
|
"HPU available: False, using: 0 HPUs\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 251.76it/s, v_num=4, bound_cond_loss=5.98e-8, phys_cond_loss=3.88e-5, train_loss=3.88e-5] "
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Swapping scheduler `ConstantLR` for `SWALR`\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 239.11it/s, v_num=4, bound_cond_loss=0.000333, phys_cond_loss=0.000676, train_loss=0.00101] "
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"`Trainer.fit` stopped: `max_epochs=2000` reached.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 127.88it/s, v_num=4, bound_cond_loss=0.000333, phys_cond_loss=0.000676, train_loss=0.00101]\n",
|
|
"Total training time 15.12576 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# setting the seed for reproducibility\n",
|
|
"seed_everything(42, workers=True)\n",
|
|
"\n",
|
|
"model = FeedForward(\n",
|
|
" layers=[10, 10],\n",
|
|
" func=torch.nn.Tanh,\n",
|
|
" output_dimensions=len(problem.output_variables),\n",
|
|
" input_dimensions=len(problem.input_variables),\n",
|
|
")\n",
|
|
"pinn = PINN(problem, model)\n",
|
|
"trainer = Trainer(\n",
|
|
" solver=pinn,\n",
|
|
" accelerator=\"cpu\",\n",
|
|
" max_epochs=2000,\n",
|
|
" enable_model_summary=False,\n",
|
|
" gradient_clip_val=0.1, # clipping the gradient\n",
|
|
" callbacks=[Timer(), StochasticWeightAveraging(swa_lrs=0.005)],\n",
|
|
")\n",
|
|
"trainer.train()\n",
|
|
"print(f'Total training time {trainer.callbacks[0].time_elapsed(\"train\"):.5f} s')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"As we can see we by applying gradient clipping we were able to even obtain lower error!\n",
|
|
"\n",
|
|
"## What's next?\n",
|
|
"\n",
|
|
"Now you know how to use efficiently the `Trainer` class **PINA**! There are multiple directions you can go now:\n",
|
|
"\n",
|
|
"1. Explore training times on different devices (e.g.) `TPU` \n",
|
|
"\n",
|
|
"2. Try to reduce memory cost by mixed precision training and gradient accumulation (especially useful when training Neural Operators)\n",
|
|
"\n",
|
|
"3. Benchmark `Trainer` speed for different precisions."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "pina",
|
|
"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": "3.9.21"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|