Add Dataset examples (#485)

* Add data_management example

* split dataset_management into two - dataset creation and data ingerstion examples

* add underscore to project name

* delete space

* black format

examples/datasets/data_ingestion.py

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+# Using ClearML's Dataset class to register data
+# Make sure to execute dataset_creation.py first
+import matplotlib.pyplot as plt
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+from ignite.contrib.handlers import TensorboardLogger
+from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
+from ignite.handlers import global_step_from_engine
+from ignite.metrics import Accuracy, Loss, Recall
+from ignite.utils import setup_logger
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+from clearml import Dataset, Task
+
+dataset_name = "cifar_dataset"
+dataset_project = "dataset_examples"
+
+task = Task.init(project_name="Image Example", task_name="image classification CIFAR10")
+params = {
+    "number_of_epochs": 20,
+    "batch_size": 64,
+    "dropout": 0.25,
+    "base_lr": 0.001,
+    "momentum": 0.9,
+    "loss_report": 100,
+}
+params = task.connect(params)  # enabling configuration override by clearml/
+print(params)  # printing actual configuration (after override in remote mode)
+
+
+# The below gets the dataset and stores in the cache. If you want to download the dataset regardless if it's in the
+# cache, use the Dataset.get(dataset_name, dataset_project).get_mutable_local_copy(path to download)
+dataset_path = Dataset.get(
+    dataset_name=dataset_name, dataset_project=dataset_project
+).get_local_copy()
+
+# Dataset and Dataloader initializations
+transform = transforms.Compose([transforms.ToTensor()])
+
+trainset = datasets.CIFAR10(
+    root=dataset_path, train=True, download=False, transform=transform
+)
+trainloader = torch.utils.data.DataLoader(
+    trainset, batch_size=params.get("batch_size", 4), shuffle=True, num_workers=10
+)
+
+testset = datasets.CIFAR10(
+    root=dataset_path, train=False, download=False, transform=transform
+)
+testloader = torch.utils.data.DataLoader(
+    testset, batch_size=params.get("batch_size", 4), shuffle=False, num_workers=10
+)
+
+classes = (
+    "plane",
+    "car",
+    "bird",
+    "cat",
+    "deer",
+    "dog",
+    "frog",
+    "horse",
+    "ship",
+    "truck",
+)
+
+tb_logger = TensorboardLogger(log_dir="cifar-output")
+
+
+# Helper function to store predictions and scores using matplotlib
+def predictions_gt_images_handler(engine, logger, *args, **kwargs):
+    x, _ = engine.state.batch
+    y_pred, y = engine.state.output
+
+    num_x = num_y = 4
+    le = num_x * num_y
+    fig = plt.figure(figsize=(20, 20))
+    trans = transforms.ToPILImage()
+    for idx in range(le):
+        preds = torch.argmax(F.softmax(y_pred[idx], dim=0))
+        probs = torch.max(F.softmax(y_pred[idx], dim=0))
+        ax = fig.add_subplot(num_x, num_y, idx + 1, xticks=[], yticks=[])
+        ax.imshow(trans(x[idx]))
+        ax.set_title(
+            "{0} {1:.1f}% (label: {2})".format(
+                classes[preds], probs * 100, classes[y[idx]]
+            ),
+            color=("green" if preds == y[idx] else "red"),
+        )
+    logger.writer.add_figure(
+        "predictions vs actuals", figure=fig, global_step=engine.state.epoch
+    )
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 3)
+        self.conv2 = nn.Conv2d(6, 16, 3)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(16 * 6 * 6, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.dorpout = nn.Dropout(p=params.get("dropout", 0.25))
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 6 * 6)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(self.dorpout(x))
+        return x
+
+
+# Training
+def run(epochs, lr, momentum, log_interval):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    net = Net().to(device)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.SGD(net.parameters(), lr=lr, momentum=momentum)
+
+    trainer = create_supervised_trainer(net, optimizer, criterion, device=device)
+    trainer.logger = setup_logger("trainer")
+
+    val_metrics = {"accuracy": Accuracy(), "loss": Loss(criterion), "recall": Recall()}
+    evaluator = create_supervised_evaluator(net, metrics=val_metrics, device=device)
+    evaluator.logger = setup_logger("evaluator")
+
+    # Attach handler to plot trainer's loss every 100 iterations
+    tb_logger.attach_output_handler(
+        trainer,
+        event_name=Events.ITERATION_COMPLETED(every=params.get("loss_report")),
+        tag="training",
+        output_transform=lambda loss: {"loss": loss},
+    )
+
+    # Attach handler to dump evaluator's metrics every epoch completed
+    for tag, evaluator in [("training", trainer), ("validation", evaluator)]:
+        tb_logger.attach_output_handler(
+            evaluator,
+            event_name=Events.EPOCH_COMPLETED,
+            tag=tag,
+            metric_names="all",
+            global_step_transform=global_step_from_engine(trainer),
+        )
+
+    # Attach function to build debug images and report every epoch end
+    tb_logger.attach(
+        evaluator,
+        log_handler=predictions_gt_images_handler,
+        event_name=Events.EPOCH_COMPLETED(once=1),
+    )
+
+    desc = "ITERATION - loss: {:.2f}"
+    pbar = tqdm(initial=0, leave=False, total=len(trainloader), desc=desc.format(0))
+
+    @trainer.on(Events.ITERATION_COMPLETED(every=log_interval))
+    def log_training_loss(engine):
+        pbar.desc = desc.format(engine.state.output)
+        pbar.update(log_interval)
+
+    @trainer.on(Events.EPOCH_COMPLETED)
+    def log_training_results(engine):
+        pbar.refresh()
+        evaluator.run(trainloader)
+        metrics = evaluator.state.metrics
+        avg_accuracy = metrics["accuracy"]
+        avg_nll = metrics["loss"]
+        tqdm.write(
+            "Training Results - Epoch: {}  Avg accuracy: {:.2f} Avg loss: {:.2f}".format(
+                engine.state.epoch, avg_accuracy, avg_nll
+            )
+        )
+
+    @trainer.on(Events.EPOCH_COMPLETED)
+    def log_validation_results(engine):
+        evaluator.run(testloader)
+        metrics = evaluator.state.metrics
+        avg_accuracy = metrics["accuracy"]
+        avg_nll = metrics["loss"]
+        tqdm.write(
+            "Validation Results - Epoch: {}  Avg accuracy: {:.2f} Avg loss: {:.2f}".format(
+                engine.state.epoch, avg_accuracy, avg_nll
+            )
+        )
+
+        pbar.n = pbar.last_print_n = 0
+
+    @trainer.on(Events.EPOCH_COMPLETED | Events.COMPLETED)
+    def log_time():
+        tqdm.write(
+            "{} took {} seconds".format(
+                trainer.last_event_name.name,
+                trainer.state.times[trainer.last_event_name.name],
+            )
+        )
+
+    trainer.run(trainloader, max_epochs=epochs)
+    pbar.close()
+
+    PATH = "./cifar_net.pth"
+    torch.save(net.state_dict(), PATH)
+
+    print("Finished Training")
+    print("Task ID number is: {}".format(task.id))
+
+
+run(params.get("number_of_epochs"), params.get("base_lr"), params.get("momentum"), 10)

examples/datasets/dataset_creation.py

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+# Download CIFAR dataset and create a dataset with ClearML's Dataset class
+from clearml import StorageManager, Dataset
+
+manager = StorageManager()
+
+dataset_path = manager.get_local_copy(
+    remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+)
+
+dataset = Dataset.create(
+    dataset_name="cifar_dataset", dataset_project="dataset_examples"
+)
+
+# Prepare and clean data here before it is added to the dataset
+
+dataset.add_files(path=dataset_path)
+
+# Dataset is uploaded to the ClearML Server by default
+dataset.upload()
+
+dataset.finalize()