support pytorch lightning 1.7

.github/workflows/test.yaml

@@ -223,4 +223,4 @@ jobs:
         run: |
           pushd ray_lightning/tests
           python -m pytest -v --durations=0 -x test_ddp.py
-          python -m pytest -v --durations=0 -x test_horovod.py
+          python -m pytest -v --durations=0 -x test_horovod.py

README.md

@@ -93,7 +93,7 @@ ray.init("ray://<head_node_host>:10001")
 ```
 Now you can run your training script on the laptop, but have it execute as if your laptop has all the resources of the cluster essentially providing you with an **infinite laptop**.
 
-**Note:** When using with Ray Client, you must disable checkpointing and logging for your Trainer by setting `checkpoint_callback` and `logger` to `False`.
+**Note:** When using with Ray Client, you must disable checkpointing and logging for your Trainer by setting `enable_checkpointing` and `logger` to `False`.
 
 ## Horovod Strategy on Ray
 Or if you prefer to use Horovod as the distributed training protocol, use the `HorovodRayStrategy` instead.

ray_lightning/accelerators/delayed_gpu_accelerator.py

@@ -16,10 +16,10 @@
 import torch
 
 from pytorch_lightning.accelerators import Accelerator,\
-    GPUAccelerator
+    CUDAAccelerator
 
 
-class _GPUAccelerator(GPUAccelerator):
+class _GPUAccelerator(CUDAAccelerator):
     """Accelerator for GPU devices.
 
     adapted from:

ray_lightning/examples/ray_ddp_sharded_example.py

@@ -57,6 +57,8 @@ def download_data():
         num_workers=num_workers, use_gpu=use_gpu, init_hook=download_data)
 
     dm = MNISTDataModule(data_dir, batch_size=batch_size)
+    dm.train_transforms = None
+    dm.val_transforms = None
 
     model = ImageGPT(
         embed_dim=embed_dim, layers=16, heads=4, vocab_size=32, num_pixels=28)
@@ -130,4 +132,4 @@ def download_data():
             batch_size=args.batch_size,
             embed_dim=args.embed_dim,
             max_epochs=args.num_epochs,
-            max_steps=None)
+            max_steps=-1)

ray_lightning/examples/ray_ddp_tune.py

@@ -31,11 +31,13 @@ def download_data():
     trainer = pl.Trainer(
         max_epochs=num_epochs,
         callbacks=callbacks,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         strategy=RayStrategy(
             num_workers=num_workers, use_gpu=use_gpu, init_hook=download_data))
     dm = MNISTDataModule(
         data_dir=data_dir, num_workers=1, batch_size=config["batch_size"])
+    dm.train_transforms = None
+    dm.val_transforms = None
     trainer.fit(model, dm)
 
 

ray_lightning/launchers/ray_horovod_launcher.py

@@ -165,7 +165,6 @@ def _wrapping_function(
         `_wrapping_function` is run on each remote worker.
         `function(*args, **kwargs)` is where the actual training happens.
         """
-
         self._strategy.set_remote(True)
 
         # `function` is a trainer's instance method
@@ -193,6 +192,7 @@ def _wrapping_function(
         rank_zero_only.rank = self.global_rank
         set_cuda_device_if_used(trainer.strategy)
 
+        trainer.strategy.set_remote(True)
         # Move the model to the appropriate device.
         trainer.strategy.model_to_device()
 

ray_lightning/launchers/ray_launcher.py

@@ -293,6 +293,7 @@ def _wrapping_function(
             init_session(rank=global_rank, queue=tune_queue)
 
         self._strategy._worker_setup(process_idx=global_rank)
+        trainer.strategy.set_remote(True)
         trainer.strategy.root_device = self._strategy.root_device
         trainer.strategy.global_rank = self._strategy.global_rank
         trainer.strategy.local_rank = self._strategy.local_rank

ray_lightning/ray_ddp.py

@@ -336,5 +336,6 @@ def teardown(self) -> None:
         This function is overriding ddp_spawn_strategy's method.
         It is run on the driver processes.
         """
-        self.accelerator = None
         super().teardown()
+        if not self._is_remote:
+            self.accelerator = None

ray_lightning/ray_horovod.py

@@ -147,8 +147,9 @@ def teardown(self) -> None:
         It is run on the driver process.
         """
         self.join()
-        self.accelerator = None
         super().teardown()
+        if not self._is_remote:
+            self.accelerator = None
 
     @property
     def is_distributed(self):

ray_lightning/tests/test_ddp.py

@@ -260,6 +260,8 @@ def test_predict(tmpdir, ray_start_2_cpus, seed, num_workers):
     model = LightningMNISTClassifier(config, tmpdir)
     dm = MNISTDataModule(
         data_dir=tmpdir, num_workers=1, batch_size=config["batch_size"])
+    dm.train_transforms = None
+    dm.val_transforms = None
     strategy = RayStrategy(num_workers=num_workers, use_gpu=False)
     trainer = get_trainer(
         tmpdir, limit_train_batches=20, max_epochs=1, strategy=strategy)
@@ -280,6 +282,8 @@ def test_predict_client(tmpdir, start_ray_client_server_2_cpus, seed,
     model = LightningMNISTClassifier(config, tmpdir)
     dm = MNISTDataModule(
         data_dir=tmpdir, num_workers=1, batch_size=config["batch_size"])
+    dm.train_transforms = None
+    dm.val_transforms = None
     strategy = RayStrategy(num_workers=num_workers, use_gpu=False)
     trainer = get_trainer(
         tmpdir, limit_train_batches=20, max_epochs=1, strategy=strategy)
@@ -300,8 +304,7 @@ def test_early_stop(tmpdir, ray_start_2_cpus):
         callbacks=[early_stop],
         num_sanity_val_steps=0,
         limit_train_batches=1.0,
-        limit_val_batches=1.0,
-        progress_bar_refresh_rate=1)
+        limit_val_batches=1.0)
     trainer.fit(model)
     trained_model = BoringModel.load_from_checkpoint(
         trainer.checkpoint_callback.best_model_path)

ray_lightning/tests/test_ddp_gpu.py

@@ -57,6 +57,8 @@ def test_predict(tmpdir, ray_start_2_gpus, seed, num_workers):
     model = LightningMNISTClassifier(config, tmpdir)
     dm = MNISTDataModule(
         data_dir=tmpdir, num_workers=1, batch_size=config["batch_size"])
+    dm.train_transforms = None
+    dm.val_transforms = None
     strategy = RayStrategy(num_workers=num_workers, use_gpu=True)
     trainer = get_trainer(
         tmpdir, limit_train_batches=20, max_epochs=1, strategy=strategy)

ray_lightning/tests/test_horovod.py

@@ -86,6 +86,8 @@ def test_predict(tmpdir, ray_start_2_cpus, seed, num_workers):
     model = LightningMNISTClassifier(config, tmpdir)
     dm = MNISTDataModule(
         data_dir=tmpdir, num_workers=1, batch_size=config["batch_size"])
+    dm.train_transforms = None
+    dm.val_transforms = None
     strategy = HorovodRayStrategy(num_workers=num_workers, use_gpu=False)
     trainer = get_trainer(
         tmpdir, limit_train_batches=20, max_epochs=1, strategy=strategy)
@@ -105,6 +107,8 @@ def test_predict_client(tmpdir, start_ray_client_server_2_cpus, seed,
     model = LightningMNISTClassifier(config, tmpdir)
     dm = MNISTDataModule(
         data_dir=tmpdir, num_workers=1, batch_size=config["batch_size"])
+    dm.train_transforms = None
+    dm.val_transforms = None
     strategy = HorovodRayStrategy(num_workers=num_workers, use_gpu=False)
     trainer = get_trainer(
         tmpdir, limit_train_batches=20, max_epochs=1, strategy=strategy)
@@ -147,6 +151,8 @@ def test_predict_gpu(tmpdir, ray_start_2_gpus, seed, num_workers):
     model = LightningMNISTClassifier(config, tmpdir)
     dm = MNISTDataModule(
         data_dir=tmpdir, num_workers=1, batch_size=config["batch_size"])
+    dm.train_transforms = None
+    dm.val_transforms = None
     strategy = HorovodRayStrategy(num_workers=num_workers, use_gpu=True)
     trainer = get_trainer(
         tmpdir, limit_train_batches=20, max_epochs=1, strategy=strategy)

ray_lightning/tests/test_tune.py

@@ -12,7 +12,7 @@
 
 @pytest.fixture
 def ray_start_4_cpus():
-    address_info = ray.init(num_cpus=4)
+    address_info = ray.init(num_cpus=6)
     yield address_info
     ray.shutdown()
 
@@ -31,7 +31,7 @@ def _inner_train(config):
             dir,
             callbacks=callbacks,
             strategy=strategy,
-            checkpoint_callback=False,
+            enable_checkpointing=False,
             **config)
         trainer.fit(model)
 

ray_lightning/tests/utils.py

@@ -216,7 +216,7 @@ def get_trainer(dir,
                 limit_train_batches: int = 10,
                 limit_val_batches: int = 10,
                 callbacks: Optional[List[Callback]] = None,
-                checkpoint_callback: bool = True,
+                enable_checkpointing: bool = True,
                 **trainer_kwargs) -> Trainer:
     """Returns a Pytorch Lightning Trainer with the provided arguments."""
     callbacks = [] if not callbacks else callbacks
@@ -228,7 +228,7 @@ def get_trainer(dir,
         limit_train_batches=limit_train_batches,
         limit_val_batches=limit_val_batches,
         enable_progress_bar=False,
-        checkpoint_callback=checkpoint_callback,
+        enable_checkpointing=enable_checkpointing,
         **trainer_kwargs)
     return trainer
 
@@ -256,8 +256,11 @@ def load_test(trainer: Trainer, model: LightningModule):
 def predict_test(trainer: Trainer, model: LightningModule,
                  dm: LightningDataModule):
     """Checks if the trained model has high accuracy on the test set."""
+    dm.train_transforms = None
+    dm.val_transforms = None
     trainer.fit(model, datamodule=dm)
     model = trainer.lightning_module
+    dm.test_transforms = None
     dm.setup(stage="test")
     test_loader = dm.test_dataloader()
     acc = torchmetrics.Accuracy()

requirements-test.txt

@@ -4,10 +4,10 @@ flake8-comprehensions
 flake8-quotes
 yapf==0.23.0
 pytest
-pytorch-lightning==1.6.4
-lightning-bolts==0.3.3
+pytorch-lightning==1.7.7
+lightning-bolts==0.4.0
 ray[tune]
-torch==1.12.0
+torch==1.12.1
 torchmetrics
 torchvision
 protobuf<=3.20.1

setup.py

@@ -3,10 +3,10 @@
 setup(
     name="ray_lightning",
     packages=find_packages(where=".", include="ray_lightning*"),
-    version="0.3.0",
+    version="0.4.0",
     author="Ray Team",
     description="Ray distributed strategies for Pytorch Lightning.",
     long_description="Custom Pytorch Lightning distributed strategies "
     "built on top of distributed computing framework Ray.",
     url="https://github.com/ray-project/ray_lightning_accelerators",
-    install_requires=["pytorch-lightning==1.6.*", "ray"])
+    install_requires=["pytorch_lightning>=1.6.4,<=1.7.7", "ray"])