Add model reporting examples (#553)

examples/reporting/model_config.py

@@ -1,48 +0,0 @@
-# ClearML - Example of manual model configuration
-import json
-import yaml
-from clearml import Task, OutputModel
-
-
-# Connecting ClearML with the current process,
-task = Task.init(project_name='examples', task_name='Model configuration example')
-
-# Connect a local configuration file in json format
-config_file_json = 'data_samples/sample.json'
-# In the web UI, this file will appear in the CONFIGURATION OBJECTS tab,
-# under the "json file" subsection because of the `name` parameter entered here
-task.connect_configuration(name="json file", configuration=config_file_json)
-
-# Read configuration as usual, the backend will contain a copy of it.
-# When executing remotely, the returned `config_file_json` will be a temporary file
-# that contains a new copy of the configuration retrieved form the backend
-model_config_dictionary_json = json.load(open(config_file_json, 'rt'))
-
-
-# Connecting a local configuration file in yaml format
-config_file_yaml = 'data_samples/config_yaml.yaml'
-task.connect_configuration(configuration=config_file_yaml, name="yaml file")
-# Read configuration as usual
-model_config_dictionary_yaml = yaml.load(open(config_file_yaml), Loader=yaml.FullLoader)
-
-# Connecting a dictionary of definitions for a specific network design
-model_config_dict = {
-    'CHANGE ME': 13.37,
-    'dict': {'sub_value': 'string', 'sub_integer': 11},
-    'list_of_ints': [1, 2, 3, 4],
-}
-model_config_dict = task.connect_configuration(name='dictionary', configuration=model_config_dict)
-
-# Update the dictionary after connecting it, and the changes will be tracked as well.
-model_config_dict['new value'] = 10
-model_config_dict['CHANGE ME'] *= model_config_dict['new value']
-
-# Connecting label enumeration
-labels = {'background': 0, 'cat': 1, 'dog': 2}
-task.connect_label_enumeration(labels)
-
-# Manually log a local model file, which will have the labels connected above
-OutputModel().update_weights('my_best_model.bin')
-
-# Any saved model (keras / pytorch / tensorflow / etc.) will have the task network configuration and label enumeration
-print('Any model stored from this point onwards, will contain both model_config and label_enumeration')

examples/reporting/model_reporting.py

@@ -0,0 +1,16 @@
+# ClearML - Example of manual model reporting
+from clearml import Task, OutputModel
+
+# Connecting ClearML with the current process,
+task = Task.init(project_name="examples", task_name="Model reporting example")
+
+# Create output model and connect it to the task
+output_model = OutputModel(task=task)
+
+labels = {"background": 0, "cat": 1, "dog": 2}
+output_model.update_labels(labels)
+
+model_url = "https://allegro-examples.s3.amazonaws.com/clearml-public-resources/v1.0/clearml-examples-open/newexamples/examples/pytorch%20lightning%20mnist%20example.fb969db720e241e5859d522aa5226b81/models/training.pt"
+
+# Manually log a model file, which will have the labels connected above
+output_model.update_weights(register_uri=model_url)

examples/reporting/model_update_pytorch.py

@@ -0,0 +1,250 @@
+from pathlib import Path
+
+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 Task, StorageManager, OutputModel
+
+
+def main():
+    # Connecting ClearML with the current process,
+    # from here on everything is logged automatically
+    task = Task.init(
+        project_name="examples",
+        task_name="Model update pytorch",
+        auto_connect_frameworks={"pytorch": False},
+    )
+    params = {
+        "number_of_epochs": 1,
+        "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)
+
+    model = OutputModel(task=task, framework="pytorch")
+    model_config_dict = {
+        "list_of_ints": [1, 2, 3, 4],
+        "dict": {
+            "sub_value": "string",
+            "sub_integer": 11
+            },
+        "value": 13.37
+    }
+    model.update_design(config_dict=model_config_dict)
+
+    manager = StorageManager()
+
+    dataset_path = Path(
+        manager.get_local_copy(
+            remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+        )
+    )
+
+    # 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
+    )
+
+    run(
+        params["number_of_epochs"],
+        params["base_lr"],
+        params["momentum"],
+        10,
+        params,
+        trainloader,
+        testloader,
+        model,
+    )
+
+
+# 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()
+    classes = (
+        "plane",
+        "car",
+        "bird",
+        "cat",
+        "deer",
+        "dog",
+        "frog",
+        "horse",
+        "ship",
+        "truck",
+    )
+    enumeration = {k: v for v, k in enumerate(classes, 1)}
+    Task.current_task().connect_label_enumeration(enumeration)
+
+    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, params):
+        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, params, trainloader, testloader, model):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    net = Net(params).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 = TensorboardLogger(log_dir="cifar-output")
+    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"
+
+    # CONDITION depicts a custom condition for when to save the model. The model is saved and then updated in ClearML
+    CONDITION = True
+
+    if CONDITION:
+        torch.save(net.state_dict(), PATH)
+        model.update_weights(weights_filename=PATH)
+    print("Finished Training")
+    print("Task ID number is: {}".format(Task.current_task().id))
+
+
+if __name__ == "__main__":
+    main()