[tune] Update Lightning examples to support PTL 1.5 (ray-project#20562)

To helps resolve the issues users are facing with running Lightning examples with Ray Tune Lightning-AI/pytorch-lightning#10407 Co-authored-by: Amog Kamsetty <[email protected]>
simonsays1980 · Feb 27, 2022 · d4d13bd · d4d13bd
1 parent f0050a0
commit d4d13bd
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Showing 6 changed files with 13 additions and 12 deletions.
diff --git a/python/ray/tune/examples/mnist_ptl_mini.py b/python/ray/tune/examples/mnist_ptl_mini.py
@@ -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
@@ -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()
@@ -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)

diff --git a/python/ray/tune/examples/mnist_pytorch_lightning.py b/python/ray/tune/examples/mnist_pytorch_lightning.py
@@ -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__
@@ -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(
                 {
@@ -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={

diff --git a/python/ray/util/ray_lightning/simple_tune.py b/python/ray/util/ray_lightning/simple_tune.py
@@ -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
@@ -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
         ),
     )

diff --git a/python/ray/util/ray_lightning/tune/__init__.py b/python/ray/util/ray_lightning/tune/__init__.py
@@ -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(
@@ -22,5 +22,5 @@
 __all__ = [
     "TuneReportCallback",
     "TuneReportCheckpointCallback",
-    "get_tune_ddp_resources",
+    "get_tune_resources",
 ]
diff --git a/python/requirements/ml/requirements_tune.txt b/python/requirements/ml/requirements_tune.txt
@@ -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

diff --git a/python/requirements/ml/requirements_upstream.txt b/python/requirements/ml/requirements_upstream.txt
@@ -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