Update tutorials 1 through 12 to current version 0.2
This commit is contained in:
Matteo Bertocchi
committed by
Nicola Demo
Nicola Demo
parent
8b797d589a
commit
d83ca3af6e
209
tutorials/tutorial11/tutorial.ipynb
vendored
209
tutorials/tutorial11/tutorial.ipynb
vendored
@@ -19,7 +19,7 @@
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},
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -35,10 +35,10 @@
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"import torch\n",
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"\n",
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"from pina import Condition, Trainer\n",
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"from pina.solvers import PINN\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.operators import grad\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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@@ -91,16 +91,15 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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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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"GPU available: False, used: False\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n"
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@@ -134,16 +133,15 @@
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"text": [
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"GPU available: True (mps), used: False\n",
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"GPU available: False, used: False\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n"
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@@ -175,107 +173,100 @@
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},
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"cell_type": "code",
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"execution_count": 7,
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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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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n"
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"INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
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"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
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"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
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"/home/matte_b/.local/lib/python3.12/site-packages/lightning/pytorch/loops/utilities.py:73: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\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 8: 100%|██████████| 1/1 [00:00<00:00, 232.78it/s, v_num=6, x0_loss=0.436, D_loss=0.129, mean_loss=0.283] "
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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, 222.52it/s, v_num=6, x0_loss=1.48e-5, D_loss=0.000655, mean_loss=0.000335]"
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 27.90it/s, v_num=51, val_loss=0.000392, bound_cond_loss=5.29e-6, phys_cond_loss=0.000459, train_loss=0.000465] "
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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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"INFO:pytorch_lightning.utilities.rank_zero:`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, 133.46it/s, v_num=6, x0_loss=1.48e-5, D_loss=0.000655, mean_loss=0.000335]\n"
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 23.95it/s, v_num=51, val_loss=0.000392, bound_cond_loss=5.29e-6, phys_cond_loss=0.000459, train_loss=0.000465]\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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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n"
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"INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
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"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
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"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
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"/home/matte_b/.local/lib/python3.12/site-packages/lightning/pytorch/loops/utilities.py:73: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\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, 274.80it/s, v_num=7, x0_loss=6.21e-6, D_loss=0.000221, mean_loss=0.000114]"
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 28.54it/s, v_num=52, val_loss=0.00267, bound_cond_loss=2.42e-5, phys_cond_loss=0.00144, train_loss=0.00146] "
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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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"INFO:pytorch_lightning.utilities.rank_zero:`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, 154.49it/s, v_num=7, x0_loss=6.21e-6, D_loss=0.000221, mean_loss=0.000114]\n"
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 24.59it/s, v_num=52, val_loss=0.00267, bound_cond_loss=2.42e-5, phys_cond_loss=0.00144, train_loss=0.00146]\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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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n"
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"INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
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"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
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"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
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"/home/matte_b/.local/lib/python3.12/site-packages/lightning/pytorch/loops/utilities.py:73: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\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, 78.56it/s, v_num=8, x0_loss=1.44e-5, D_loss=0.000572, mean_loss=0.000293] "
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 29.41it/s, v_num=53, val_loss=0.00363, bound_cond_loss=1.02e-5, phys_cond_loss=0.000846, train_loss=0.000856] "
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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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"INFO:pytorch_lightning.utilities.rank_zero:`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, 62.60it/s, v_num=8, x0_loss=1.44e-5, D_loss=0.000572, mean_loss=0.000293]\n"
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 24.69it/s, v_num=53, val_loss=0.00363, bound_cond_loss=1.02e-5, phys_cond_loss=0.000846, train_loss=0.000856]\n"
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]
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}
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],
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@@ -351,11 +342,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pytorch_lightning.callbacks import Callback\n",
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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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"# define a simple callback\n",
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@@ -378,44 +370,38 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 6,
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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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]
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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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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n"
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"INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
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"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
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"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
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"/home/matte_b/.local/lib/python3.12/site-packages/lightning/pytorch/loops/utilities.py:73: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\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, 241.30it/s, v_num=1, x0_loss=7.27e-5, D_loss=0.0016, mean_loss=0.000838] "
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 28.21it/s, v_num=18, val_loss=0.000348, bound_cond_loss=7.54e-5, phys_cond_loss=0.000956, train_loss=0.00103] "
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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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"INFO:pytorch_lightning.utilities.rank_zero:`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, 149.27it/s, v_num=1, x0_loss=7.27e-5, D_loss=0.0016, mean_loss=0.000838]\n"
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"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 23.99it/s, v_num=18, val_loss=0.000348, bound_cond_loss=7.54e-5, phys_cond_loss=0.000956, train_loss=0.00103]\n"
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]
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}
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],
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@@ -443,24 +429,27 @@
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},
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"cell_type": "code",
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"execution_count": 9,
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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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"data": {
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"text/plain": [
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"[{'x0_loss': tensor(0.9141),\n",
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" 'D_loss': tensor(0.0304),\n",
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" 'mean_loss': tensor(0.4722)},\n",
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" {'x0_loss': tensor(0.8906),\n",
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" 'D_loss': tensor(0.0287),\n",
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" 'mean_loss': tensor(0.4596)},\n",
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" {'x0_loss': tensor(0.8674),\n",
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" 'D_loss': tensor(0.0274),\n",
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" 'mean_loss': tensor(0.4474)}]"
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"[{'val_loss': tensor(1.0595),\n",
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" 'bound_cond_loss': tensor(1.0607),\n",
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" 'phys_cond_loss': tensor(0.0043),\n",
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" 'train_loss': tensor(1.0650)},\n",
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" {'val_loss': tensor(1.0503),\n",
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" 'bound_cond_loss': tensor(1.0522),\n",
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" 'phys_cond_loss': tensor(0.0038),\n",
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" 'train_loss': tensor(1.0560)},\n",
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" {'val_loss': tensor(1.0412),\n",
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" 'bound_cond_loss': tensor(1.0439),\n",
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" 'phys_cond_loss': tensor(0.0033),\n",
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" 'train_loss': tensor(1.0471)}]"
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]
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},
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"execution_count": 9,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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"source": [
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"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",
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"\n",
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"We can for example try the `EarlyStopping` routine, which automatically stops the training when a specific metric converged (here the `mean_loss`). In order to let the training keep going forever set `max_epochs=-1`."
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"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`."
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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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"execution_count": null,
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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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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n"
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"INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
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"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
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"INFO:pytorch_lightning.utilities.rank_zero: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 4: 100%|██████████| 1/1 [00:00<00:00, 255.67it/s, v_num=9, x0_loss=0.876, D_loss=0.00542, mean_loss=0.441]"
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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 6157: 100%|██████████| 1/1 [00:00<00:00, 139.84it/s, v_num=9, x0_loss=4.21e-9, D_loss=9.93e-6, mean_loss=4.97e-6] \n"
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"Epoch 6468: 100%|██████████| 1/1 [00:00<00:00, 19.10it/s, v_num=19, val_loss=0.000129, bound_cond_loss=1.57e-8, phys_cond_loss=3.01e-6, train_loss=3.02e-6] \n"
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]
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}
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],
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"source": [
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"# ~2 mins\n",
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"from pytorch_lightning.callbacks import EarlyStopping\n",
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"# ~5 mins\n",
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"\n",
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"model = FeedForward(\n",
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" layers=[10, 10],\n",
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" accelerator='cpu',\n",
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" max_epochs = -1,\n",
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" enable_model_summary=False,\n",
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" callbacks=[EarlyStopping('mean_loss')]) # adding a callbacks\n",
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" callbacks=[EarlyStopping('train_loss')]) # adding a callbacks\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": "code",
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"execution_count": 19,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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@@ -565,38 +545,37 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Seed set to 42\n",
|
||||
"GPU available: True (mps), used: False\n",
|
||||
"TPU available: False, using: 0 TPU cores\n",
|
||||
"IPU available: False, using: 0 IPUs\n",
|
||||
"HPU available: False, using: 0 HPUs\n"
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 275.87it/s, v_num=31, x0_loss=1.12e-6, D_loss=0.000127, mean_loss=6.4e-5] "
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 22.76it/s, v_num=20, val_loss=4.61e-5, bound_cond_loss=1.22e-6, phys_cond_loss=0.000171, train_loss=0.000172] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"`Trainer.fit` stopped: `max_epochs=2000` reached.\n"
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=2000` reached.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 163.58it/s, v_num=31, x0_loss=1.12e-6, D_loss=0.000127, mean_loss=6.4e-5]\n",
|
||||
"Total training time 17.36381 s\n"
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 19.28it/s, v_num=20, val_loss=4.61e-5, bound_cond_loss=1.22e-6, phys_cond_loss=0.000171, train_loss=0.000172]\n",
|
||||
"Total training time 92.35361 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from pytorch_lightning.callbacks import Timer\n",
|
||||
"from pytorch_lightning import seed_everything\n",
|
||||
"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",
|
||||
@@ -628,7 +607,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -636,51 +615,50 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Seed set to 42\n",
|
||||
"GPU available: True (mps), used: False\n",
|
||||
"TPU available: False, using: 0 TPU cores\n",
|
||||
"IPU available: False, using: 0 IPUs\n",
|
||||
"HPU available: False, using: 0 HPUs\n"
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 210.04it/s, v_num=47, x0_loss=4.17e-6, D_loss=0.000204, mean_loss=0.000104]"
|
||||
"Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 30.61it/s, v_num=21, val_loss=4.54e-5, bound_cond_loss=5.03e-6, phys_cond_loss=0.000247, train_loss=0.000252] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Swapping scheduler `ConstantLR` for `SWALR`\n"
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:Swapping scheduler `ConstantLR` for `SWALR`\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 259.39it/s, v_num=47, x0_loss=1.56e-7, D_loss=7.49e-5, mean_loss=3.75e-5] "
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 28.21it/s, v_num=21, val_loss=3.45e-5, bound_cond_loss=2.41e-7, phys_cond_loss=9.02e-5, train_loss=9.04e-5] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"`Trainer.fit` stopped: `max_epochs=2000` reached.\n"
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=2000` reached.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 120.85it/s, v_num=47, x0_loss=1.56e-7, D_loss=7.49e-5, mean_loss=3.75e-5]\n",
|
||||
"Total training time 17.10627 s\n"
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 22.97it/s, v_num=21, val_loss=3.45e-5, bound_cond_loss=2.41e-7, phys_cond_loss=9.02e-5, train_loss=9.04e-5]\n",
|
||||
"Total training time 86.25178 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from pytorch_lightning.callbacks import StochasticWeightAveraging\n",
|
||||
"from lightning.pytorch.callbacks import StochasticWeightAveraging\n",
|
||||
"\n",
|
||||
"# setting the seed for reproducibility\n",
|
||||
"seed_everything(42, workers=True)\n",
|
||||
@@ -716,7 +694,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -724,46 +702,45 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Seed set to 42\n",
|
||||
"GPU available: True (mps), used: False\n",
|
||||
"TPU available: False, using: 0 TPU cores\n",
|
||||
"IPU available: False, using: 0 IPUs\n",
|
||||
"HPU available: False, using: 0 HPUs\n"
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 261.80it/s, v_num=46, x0_loss=9e-8, D_loss=2.39e-5, mean_loss=1.2e-5] "
|
||||
"Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 27.78it/s, v_num=22, val_loss=1.52e-5, bound_cond_loss=5.29e-8, phys_cond_loss=4.07e-5, train_loss=4.08e-5] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Swapping scheduler `ConstantLR` for `SWALR`\n"
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:Swapping scheduler `ConstantLR` for `SWALR`\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 261.78it/s, v_num=46, x0_loss=7.08e-7, D_loss=1.77e-5, mean_loss=9.19e-6] "
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 28.11it/s, v_num=22, val_loss=0.000427, bound_cond_loss=0.000311, phys_cond_loss=0.000849, train_loss=0.00116] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"`Trainer.fit` stopped: `max_epochs=2000` reached.\n"
|
||||
"INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=2000` reached.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 148.99it/s, v_num=46, x0_loss=7.08e-7, D_loss=1.77e-5, mean_loss=9.19e-6]\n",
|
||||
"Total training time 17.01149 s\n"
|
||||
"Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 22.88it/s, v_num=22, val_loss=0.000427, bound_cond_loss=0.000311, phys_cond_loss=0.000849, train_loss=0.00116]\n",
|
||||
"Total training time 87.27789 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
42
tutorials/tutorial11/tutorial.py
vendored
42
tutorials/tutorial11/tutorial.py
vendored
@@ -13,7 +13,7 @@
|
||||
#
|
||||
# Let's start by importing useful modules, define the `SimpleODE` problem and the `PINN` solver.
|
||||
|
||||
# In[18]:
|
||||
# In[1]:
|
||||
|
||||
|
||||
## routine needed to run the notebook on Google Colab
|
||||
@@ -28,10 +28,10 @@ if IN_COLAB:
|
||||
import torch
|
||||
|
||||
from pina import Condition, Trainer
|
||||
from pina.solvers import PINN
|
||||
from pina.solver import PINN
|
||||
from pina.model import FeedForward
|
||||
from pina.problem import SpatialProblem
|
||||
from pina.operators import grad
|
||||
from pina.operator import grad
|
||||
from pina.domain import CartesianDomain
|
||||
from pina.equation import Equation, FixedValue
|
||||
|
||||
@@ -77,7 +77,7 @@ pinn = PINN(problem, model)
|
||||
# Till now we just followed the extact step of the previous tutorials. The `Trainer` object
|
||||
# can be initialized by simiply passing the `PINN` solver
|
||||
|
||||
# In[3]:
|
||||
# In[2]:
|
||||
|
||||
|
||||
trainer = Trainer(solver=pinn)
|
||||
@@ -96,7 +96,7 @@ trainer = Trainer(solver=pinn)
|
||||
#
|
||||
# * `accelerator = {'gpu', 'cpu', 'hpu', 'mps', 'cpu', 'ipu'}` sets the accelerator to a specific one
|
||||
|
||||
# In[5]:
|
||||
# In[3]:
|
||||
|
||||
|
||||
trainer = Trainer(solver=pinn,
|
||||
@@ -114,7 +114,7 @@ trainer = Trainer(solver=pinn,
|
||||
# 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[7]:
|
||||
# In[4]:
|
||||
|
||||
|
||||
from pytorch_lightning.loggers import TensorBoardLogger
|
||||
@@ -161,10 +161,11 @@ for _ in range(3):
|
||||
#
|
||||
# <!-- Suppose we want to log the accuracy on some validation poit -->
|
||||
|
||||
# In[8]:
|
||||
# In[5]:
|
||||
|
||||
|
||||
from pytorch_lightning.callbacks import Callback
|
||||
from lightning.pytorch.callbacks import Callback
|
||||
from lightning.pytorch.callbacks import EarlyStopping
|
||||
import torch
|
||||
|
||||
# define a simple callback
|
||||
@@ -180,7 +181,7 @@ class NaiveMetricTracker(Callback):
|
||||
|
||||
# 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.
|
||||
|
||||
# In[10]:
|
||||
# In[6]:
|
||||
|
||||
|
||||
model = FeedForward(
|
||||
@@ -199,7 +200,7 @@ trainer.train()
|
||||
|
||||
# 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).
|
||||
|
||||
# In[9]:
|
||||
# In[7]:
|
||||
|
||||
|
||||
trainer.callbacks[0].saved_metrics[:3] # only the first three epochs
|
||||
@@ -207,13 +208,12 @@ 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 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 converged (here the `mean_loss`). In order to let the training keep going forever set `max_epochs=-1`.
|
||||
# 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`.
|
||||
|
||||
# In[7]:
|
||||
# In[ ]:
|
||||
|
||||
|
||||
# ~2 mins
|
||||
from pytorch_lightning.callbacks import EarlyStopping
|
||||
# ~5 mins
|
||||
|
||||
model = FeedForward(
|
||||
layers=[10, 10],
|
||||
@@ -226,7 +226,7 @@ trainer = Trainer(solver=pinn,
|
||||
accelerator='cpu',
|
||||
max_epochs = -1,
|
||||
enable_model_summary=False,
|
||||
callbacks=[EarlyStopping('mean_loss')]) # adding a callbacks
|
||||
callbacks=[EarlyStopping('train_loss')]) # adding a callbacks
|
||||
trainer.train()
|
||||
|
||||
|
||||
@@ -248,11 +248,11 @@ trainer.train()
|
||||
# We will just demonstrate how to use the first two, and see the results compared to a 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 take the times. Let's start by training a simple model without any optimization (train for 2000 epochs).
|
||||
|
||||
# In[19]:
|
||||
# In[9]:
|
||||
|
||||
|
||||
from pytorch_lightning.callbacks import Timer
|
||||
from pytorch_lightning import seed_everything
|
||||
from lightning.pytorch.callbacks import Timer
|
||||
from lightning.pytorch import seed_everything
|
||||
|
||||
# setting the seed for reproducibility
|
||||
seed_everything(42, workers=True)
|
||||
@@ -277,10 +277,10 @@ print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s')
|
||||
|
||||
# Now we do the same but with StochasticWeightAveraging
|
||||
|
||||
# In[36]:
|
||||
# In[10]:
|
||||
|
||||
|
||||
from pytorch_lightning.callbacks import StochasticWeightAveraging
|
||||
from lightning.pytorch.callbacks import StochasticWeightAveraging
|
||||
|
||||
# setting the seed for reproducibility
|
||||
seed_everything(42, workers=True)
|
||||
@@ -309,7 +309,7 @@ print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s')
|
||||
#
|
||||
# We will now now do the same but clippling the gradient to be relatively small.
|
||||
|
||||
# In[35]:
|
||||
# In[11]:
|
||||
|
||||
|
||||
# setting the seed for reproducibility
|
||||
|
||||
Reference in New Issue
Block a user