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[tune] Update Lightning examples to support PTL 1.5 #20562

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merged 6 commits into from
Feb 12, 2022
Merged

[tune] Update Lightning examples to support PTL 1.5 #20562

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b28bed0
Update Lightning examples with the latest updates
kaushikb11 Nov 19, 2021
9cdcff9
Update lightning version
kaushikb11 Nov 28, 2021
7a3ea1d
Fix sanity check calls
kaushikb11 Nov 28, 2021
821f33f
Format with black
amogkam Feb 11, 2022
35957c3
Merge branch 'master' of https://github.com/ray-project/ray into upda…
amogkam Feb 11, 2022
153d3a8
update api
amogkam Feb 11, 2022
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5 changes: 3 additions & 2 deletions python/ray/tune/examples/mnist_ptl_mini.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import torch
from filelock import FileLock
from torch.nn import functional as F
from torchmetrics import Accuracy
import pytorch_lightning as pl
from pl_bolts.datamodules.mnist_datamodule import MNISTDataModule
import os
Expand All @@ -24,7 +25,7 @@ def __init__(self, config, data_dir=None):
self.layer_1 = torch.nn.Linear(28 * 28, layer_1)
self.layer_2 = torch.nn.Linear(layer_1, layer_2)
self.layer_3 = torch.nn.Linear(layer_2, 10)
self.accuracy = pl.metrics.Accuracy()
self.accuracy = Accuracy()

def forward(self, x):
batch_size, channels, width, height = x.size()
Expand Down Expand Up @@ -75,7 +76,7 @@ def train_mnist_tune(config, num_epochs=10, num_gpus=0):
max_epochs=num_epochs,
# If fractional GPUs passed in, convert to int.
gpus=math.ceil(num_gpus),
progress_bar_refresh_rate=0,
enable_progress_bar=False,
callbacks=[TuneReportCallback(metrics, on="validation_end")],
)
trainer.fit(model, dm)
Expand Down
6 changes: 3 additions & 3 deletions python/ray/tune/examples/mnist_pytorch_lightning.py
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Expand Up @@ -121,7 +121,7 @@ def configure_optimizers(self):

def train_mnist(config):
model = LightningMNISTClassifier(config)
trainer = pl.Trainer(max_epochs=10, show_progress_bar=False)
trainer = pl.Trainer(max_epochs=10, enable_progress_bar=False)

trainer.fit(model)
# __lightning_end__
Expand All @@ -148,7 +148,7 @@ def train_mnist_tune(config, num_epochs=10, num_gpus=0, data_dir="~/data"):
gpus=math.ceil(num_gpus),
logger=TensorBoardLogger(
save_dir=tune.get_trial_dir(), name="", version="."),
progress_bar_refresh_rate=0,
enable_progress_bar=False,
callbacks=[
TuneReportCallback(
{
Expand All @@ -174,7 +174,7 @@ def train_mnist_tune_checkpoint(config,
"gpus": math.ceil(num_gpus),
"logger": TensorBoardLogger(
save_dir=tune.get_trial_dir(), name="", version="."),
"progress_bar_refresh_rate": 0,
"enable_progress_bar": False,
"callbacks": [
TuneReportCheckpointCallback(
metrics={
Expand Down
4 changes: 2 additions & 2 deletions python/ray/util/ray_lightning/simple_tune.py
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Expand Up @@ -8,7 +8,7 @@
import pytorch_lightning as pl

from ray.util.ray_lightning import RayPlugin
from ray.util.ray_lightning.tune import TuneReportCallback, get_tune_ddp_resources
from ray.util.ray_lightning.tune import TuneReportCallback, get_tune_resources

num_cpus_per_actor = 1
num_workers = 1
Expand Down Expand Up @@ -70,7 +70,7 @@ def main():
num_samples=1,
metric="loss",
mode="min",
resources_per_trial=get_tune_ddp_resources(
resources_per_trial=get_tune_resources(
num_workers=num_workers, cpus_per_worker=num_cpus_per_actor
),
)
Expand Down
6 changes: 3 additions & 3 deletions python/ray/util/ray_lightning/tune/__init__.py
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Expand Up @@ -4,13 +4,13 @@

TuneReportCallback = None
TuneReportCheckpointCallback = None
get_tune_ddp_resources = None
get_tune_resources = None

try:
from ray_lightning.tune import (
TuneReportCallback,
TuneReportCheckpointCallback,
get_tune_ddp_resources,
get_tune_resources,
)
except ImportError:
logger.info(
Expand All @@ -22,5 +22,5 @@
__all__ = [
"TuneReportCallback",
"TuneReportCheckpointCallback",
"get_tune_ddp_resources",
"get_tune_resources",
]
2 changes: 1 addition & 1 deletion python/requirements/ml/requirements_tune.txt
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Expand Up @@ -29,7 +29,7 @@ nevergrad==0.4.3.post7
optuna==2.9.1
pytest-remotedata==0.3.2
lightning-bolts==0.4.0
pytorch-lightning==1.4.9
pytorch-lightning==1.5.10
shortuuid==1.0.1
scikit-learn==0.24.2
scikit-optimize==0.8.1
Expand Down
2 changes: 1 addition & 1 deletion python/requirements/ml/requirements_upstream.txt
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Expand Up @@ -2,7 +2,7 @@
# Because they depend on Ray, we can't pin the subdependencies.
# So we separate its own requirements file.

ray_lightning==0.1.1
ray_lightning==0.2.0
tune-sklearn==0.4.1
xgboost_ray==0.1.4
lightgbm_ray==0.0.2
Expand Down