copy editing lightning-mnist and torch-fashion-mnist examples

Signed-off-by: angelinalg <[email protected]>

doc/source/train/examples/lightning/lightning_mnist_example.ipynb

@@ -9,7 +9,7 @@
  "\n",
  "# Train a Pytorch Lightning Image Classifier\n",
  "\n",
- "This example introduces how to train a Pytorch Lightning Module using Ray Train {class}`TorchTrainer <ray.train.torch.TorchTrainer>`. We will demonstrate how to train a basic neural network on the MNIST dataset with distributed data parallelism.\n"
+ "This example introduces how to train a Pytorch Lightning Module using Ray Train {class}`TorchTrainer <ray.train.torch.TorchTrainer>`. It demonstrates how to train a basic neural network on the MNIST dataset with distributed data parallelism.\n"
  ]
  },
  {
@@ -49,9 +49,9 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "## Prepare Dataset and Module\n",
+ "## Prepare a dataset and module\n",
  "\n",
- "The Pytorch Lightning Trainer takes either `torch.utils.data.DataLoader` or `pl.LightningDataModule` as data inputs. You can keep using them without any changes for the Ray AIR LightningTrainer. "
+ "The Pytorch Lightning Trainer takes either `torch.utils.data.DataLoader` or `pl.LightningDataModule` as data inputs. You can continue using them without any changes for the Ray Train LightningTrainer. "
  ]
  },
  {
@@ -183,15 +183,15 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "## Define the Training Loop\n",
+ "## Define the training loop\n",
  "\n",
- "Here we define a training loop for each worker. Compare with the original PyTorch Lightning code, there are 3 main differences:\n",
+ "This code defines a training loop for each worker. Comparing the training loop with the original PyTorch Lightning code, there are 3 main differences:\n",
  "\n",
  "- Distributed strategy: Use {class}`RayDDPStrategy <ray.train.lightning.RayDDPStrategy>`.\n",
  "- Cluster environment: Use {class}`RayLightningEnvironment <ray.train.lightning.RayLightningEnvironment>`.\n",
  "- Parallel devices: Always sets to `devices=\"auto\"` to use all available devices configured by ``TorchTrainer``.\n",
  "\n",
- "Please refer to {ref}`Getting Started with PyTorch Lightning <train-pytorch-lightning>`.\n",
+ "See {ref}`Getting Started with PyTorch Lightning <train-pytorch-lightning>` for more information.\n",
  "\n",
  "\n",
  "For checkpoint reportining, Ray Train provides a minimal {class}`RayTrainReportCallback <ray.train.lightning.RayTrainReportCallback>` that reports metrics and checkpoint on each train epoch end. For more complex checkpoint logic, please implement custom callbacks as described in {ref}`Saving and Loading Checkpoint <train-checkpointing>` user guide."

python/ray/train/examples/pytorch/torch_fashion_mnist_example.py

@@ -19,23 +19,23 @@ def get_dataloaders(batch_size):
  transform = transforms.Compose([ToTensor(), Normalize((0.5,), (0.5,))])
 
  with FileLock(os.path.expanduser("~/data.lock")):
- # Download training data from open datasets.
+ # Download training data from open datasets
  training_data = datasets.FashionMNIST(
  root="~/data",
  train=True,
  download=True,
  transform=transform,
  )
 
- # Download test data from open datasets.
+ # Download test data from open datasets
  test_data = datasets.FashionMNIST(
  root="~/data",
  train=False,
  download=True,
  transform=transform,
  )
 
- # Create data loaders.
+ # Create data loaders
  train_dataloader = DataLoader(training_data, batch_size=batch_size)
  test_dataloader = DataLoader(test_data, batch_size=batch_size)
 
@@ -69,7 +69,7 @@ def train_func_per_worker(config: Dict):
  epochs = config["epochs"]
  batch_size = config["batch_size_per_worker"]
 
- # Get dataloaders inside worker training function
+ # Get dataloaders inside the worker training function
  train_dataloader, test_dataloader = get_dataloaders(batch_size=batch_size)
 
  # [1] Prepare Dataloader for distributed training
@@ -81,7 +81,7 @@ def train_func_per_worker(config: Dict):
  model = NeuralNetwork()
 
  # [2] Prepare and wrap your model with DistributedDataParallel
- # Move the model the correct GPU/CPU device
+ # Move the model to the correct GPU/CPU device
  # ============================================================
  model = ray.train.torch.prepare_model(model)
 
@@ -137,9 +137,9 @@ def train_fashion_mnist(num_workers=2, use_gpu=False):
  scaling_config=scaling_config,
  )
 
- # [4] Start Distributed Training
+ # [4] Start distributed training
  # Run `train_func_per_worker` on all workers
- # =============================================
+ # ==========================================
  result = trainer.fit()
  print(f"Training result: {result}")