Documentation examples

examples/frameworks/autokeras/autokeras_imdb_example.py

@@ -5,6 +5,8 @@ from tensorflow import keras
 
 from clearml import Task
 
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name="autokeras", task_name="autokeras imdb example with scalars")
 
 

examples/frameworks/fastai/fastai_with_tensorboard.py

@@ -6,6 +6,8 @@ from fastai.vision import *  # Quick access to computer vision functionality
 
 from clearml import Task
 
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name="example", task_name="fastai with tensorboard callback")
 
 path = untar_data(URLs.MNIST_SAMPLE)

examples/frameworks/ignite/cifar_ignite.py

@@ -17,7 +17,8 @@ from tqdm import tqdm
 
 from clearml import Task, StorageManager
 
-# ClearML Initializations
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 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

examples/frameworks/keras/keras_tensorboard.py

@@ -89,7 +89,8 @@ model.compile(loss='categorical_crossentropy',
               optimizer=RMSprop(),
               metrics=['accuracy'])
 
-# Connecting ClearML
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='Keras with TensorBoard example')
 
 # To set your own configuration:

examples/frameworks/keras/legacy/keras_tensorboard.py

@@ -88,7 +88,8 @@ model.compile(loss='categorical_crossentropy',
               optimizer=RMSprop(),
               metrics=['accuracy'])
 
-# Connecting ClearML
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='Keras with TensorBoard example')
 task.connect_configuration({'test': 1337, 'nested': {'key': 'value', 'number': 1}})
 

examples/frameworks/keras/manual_model_upload.py

@@ -7,7 +7,8 @@ from keras import Input, layers, Model
 
 from clearml import Task
 
-
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='Model configuration and upload')
 
 

examples/frameworks/kerastuner/keras_tuner_cifar.py

@@ -43,6 +43,8 @@ def build_model(hp):
     return model
 
 
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init('examples', 'kerastuner cifar10 tuning')
 
 tuner = kt.Hyperband(

examples/frameworks/lightgbm/train_with_lightbgm.py

@@ -6,6 +6,8 @@ from sklearn.metrics import mean_squared_error
 
 from clearml import Task
 
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name="examples", task_name="LIGHTgbm")
 
 print('Loading data...')

examples/frameworks/matplotlib/matplotlib_example.py

@@ -5,7 +5,8 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 from clearml import Task
 
-
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='Matplotlib example')
 
 # Create a plot

examples/frameworks/pytorch-lightning/pytorch_lightning_example.py

@@ -0,0 +1,95 @@
+from argparse import ArgumentParser
+import torch
+import pytorch_lightning as pl
+from torch.nn import functional as F
+from torch.utils.data import DataLoader, random_split
+from clearml import Task
+
+from torchvision.datasets.mnist import MNIST
+from torchvision import transforms
+
+
+class LitClassifier(pl.LightningModule):
+    def __init__(self, hidden_dim=128, learning_rate=1e-3):
+        super().__init__()
+        self.save_hyperparameters()
+
+        self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
+        self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)
+
+    def forward(self, x):
+        x = x.view(x.size(0), -1)
+        x = torch.relu(self.l1(x))
+        x = torch.relu(self.l2(x))
+        return x
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        y_hat = self(x)
+        loss = F.cross_entropy(y_hat, y)
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        x, y = batch
+        y_hat = self(x)
+        loss = F.cross_entropy(y_hat, y)
+        self.log('valid_loss', loss)
+
+    def test_step(self, batch, batch_idx):
+        x, y = batch
+        y_hat = self(x)
+        loss = F.cross_entropy(y_hat, y)
+        self.log('test_loss', loss)
+
+    def configure_optimizers(self):
+        return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
+
+    @staticmethod
+    def add_model_specific_args(parent_parser):
+        parser = ArgumentParser(parents=[parent_parser], add_help=False)
+        parser.add_argument('--hidden_dim', type=int, default=128)
+        parser.add_argument('--learning_rate', type=float, default=0.0001)
+        return parser
+
+
+if __name__ == '__main__':
+    # Connecting ClearML with the current process,
+    # from here on everything is logged automatically
+    task = Task.init(project_name="examples", task_name="pytorch lightning mnist example")
+
+    pl.seed_everything(0)
+
+    parser = ArgumentParser()
+    parser.add_argument('--batch_size', default=32, type=int)
+    parser.add_argument('--epochs', default=3, type=int)
+    parser = pl.Trainer.add_argparse_args(parser)
+    parser = LitClassifier.add_model_specific_args(parser)
+    args = parser.parse_args()
+
+    # ------------
+    # data
+    # ------------
+    dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
+    mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
+    mnist_train, mnist_val = random_split(dataset, [55000, 5000])
+
+    train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
+    val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
+    test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
+
+    # ------------
+    # model
+    # ------------
+    model = LitClassifier(args.hidden_dim, args.learning_rate)
+
+    # ------------
+    # training
+    # ------------
+    trainer = pl.Trainer.from_argparse_args(args)
+    trainer.max_epochs = args.epochs
+    trainer.fit(model, train_loader, val_loader)
+
+    # ------------
+    # testing
+    # ------------
+    trainer.test(test_dataloaders=test_loader)

examples/frameworks/pytorch-lightning/requirements.txt

@@ -0,0 +1,4 @@
+clearml
+pytorch_lightning ~= 1.1.2
+torch
+torchvision

examples/frameworks/pytorch/manual_model_upload.py

@@ -6,7 +6,8 @@ from tempfile import gettempdir
 import torch
 from clearml import Task
 
-
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='Model configuration and upload')
 
 # create a model

examples/frameworks/pytorch/pytorch_matplotlib.py

@@ -62,7 +62,8 @@ import torchvision.models as models
 import copy
 from clearml import Task
 
-
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='pytorch with matplotlib example', task_type=Task.TaskTypes.testing)
 
 

examples/frameworks/pytorch/pytorch_mnist.py

@@ -74,6 +74,8 @@ def test(args, model, device, test_loader, epoch):
 
 
 def main():
+    # Connecting ClearML with the current process,
+    # from here on everything is logged automatically
     task = Task.init(project_name='examples', task_name='pytorch mnist train')
 
     # Training settings

examples/frameworks/pytorch/pytorch_tensorboard.py

@@ -99,7 +99,11 @@ def main():
     parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                         help='how many batches to wait before logging training status')
     args = parser.parse_args()
-    Task.init(project_name='examples', task_name='pytorch with tensorboard')
+
+    # Connecting ClearML with the current process,
+    # from here on everything is logged automatically
+    task = Task.init(project_name='examples', task_name='pytorch with tensorboard')  # noqa: F841
+
     writer = SummaryWriter('runs')
     writer.add_text('TEXT', 'This is some text', 0)
     args.cuda = not args.no_cuda and torch.cuda.is_available()

examples/frameworks/pytorch/tensorboard_toy_pytorch.py

@@ -6,9 +6,12 @@ from PIL import Image
 from torch.utils.tensorboard import SummaryWriter
 
 from clearml import Task
-task = Task.init(project_name='examples', task_name='pytorch tensorboard toy example')
 
 
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
+task = Task.init(project_name='examples', task_name='pytorch tensorboard toy example')
+
 writer = SummaryWriter(log_dir=os.path.join(gettempdir(), 'tensorboard_logs'))
 
 # convert to 4d [batch, col, row, RGB-channels]

examples/frameworks/scikit-learn/sklearn_joblib_example.py

@@ -9,9 +9,11 @@ from sklearn.model_selection import train_test_split
 import numpy as np
 import matplotlib.pyplot as plt
 
-
 from clearml import Task
 
+
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name="examples", task_name="scikit-learn joblib example")
 
 iris = datasets.load_iris()

examples/frameworks/scikit-learn/sklearn_matplotlib_example.py

@@ -124,6 +124,8 @@ def plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None, n
     return plt
 
 
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 Task.init('examples', 'scikit-learn matplotlib example')
 
 fig, fig_axes = plt.subplots(1, 3, figsize=(30, 10))

examples/frameworks/tensorboardx/pytorch_tensorboardX.py

@@ -100,7 +100,10 @@ def main():
     args = parser.parse_args()
     args.cuda = not args.no_cuda and torch.cuda.is_available()
 
-    task = Task.init(project_name='examples', task_name='pytorch with tensorboardX')  # noqa: F841
+    # Connecting ClearML with the current process,
+    # from here on everything is logged automatically
+    task = Task.init(project_name='examples', task_name='pytorch with tensorboardX')
+
     writer = SummaryWriter('runs')
     writer.add_text('TEXT', 'This is some text', 0)
 

examples/frameworks/tensorflow/legacy/tensorboard_pr_curve.py

@@ -39,6 +39,9 @@ import tensorflow as tf
 from tensorboard.plugins.pr_curve import summary
 from clearml import Task
 
+
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='tensorboard pr_curve')
 
 tf.compat.v1.disable_v2_behavior()

examples/frameworks/tensorflow/legacy/tensorboard_toy.py

@@ -8,9 +8,12 @@ import numpy as np
 from PIL import Image
 
 from clearml import Task
-task = Task.init(project_name='examples', task_name='tensorboard toy example')
 
 
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
+task = Task.init(project_name='examples', task_name='tensorboard toy example')
+
 k = tf.placeholder(tf.float32)
 
 # Make a normal distribution, with a shifting mean

examples/frameworks/tensorflow/legacy/tensorflow_eager.py

@@ -32,11 +32,13 @@ from tensorflow.examples.tutorials.mnist import input_data
 
 from clearml import Task
 
+
 tf.compat.v1.enable_eager_execution()
 
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='Tensorflow eager mode')
 
-
 FLAGS = tf.app.flags.FLAGS
 tf.app.flags.DEFINE_integer('data_num', 100, """Flag of type integer""")
 tf.app.flags.DEFINE_string('img_path', './img', """Flag of type string""")

examples/frameworks/tensorflow/legacy/tensorflow_mnist_with_summaries.py

@@ -34,6 +34,9 @@ from tensorflow.examples.tutorials.mnist import input_data
 from clearml import Task
 
 FLAGS = None
+
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='Tensorflow mnist with summaries example')
 
 

examples/frameworks/tensorflow/manual_model_upload.py

@@ -6,6 +6,9 @@ import tempfile
 import tensorflow as tf
 from clearml import Task
 
+
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='Model configuration and upload')
 
 model = tf.Module()

examples/frameworks/tensorflow/tensorboard_pr_curve.py

@@ -39,6 +39,8 @@ from tensorboard.plugins.pr_curve import summary
 from clearml import Task
 
 
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='tensorboard pr_curve')
 
 tf.compat.v1.disable_v2_behavior()

examples/frameworks/tensorflow/tensorflow_mnist.py

@@ -11,8 +11,9 @@ from tensorflow.keras import Model
 from clearml import Task
 
 
-task = Task.init(project_name='examples',
-                 task_name='Tensorflow v2 mnist with summaries')
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
+task = Task.init(project_name='examples', task_name='Tensorflow v2 mnist with summaries')
 
 
 # Load and prepare the MNIST dataset.

examples/frameworks/xgboost/xgboost_sample.py

@@ -7,7 +7,11 @@ from xgboost import plot_tree
 
 from clearml import Task
 
+
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
 task = Task.init(project_name='examples', task_name='XGBoost simple example')
+
 iris = datasets.load_iris()
 X = iris.data
 y = iris.target
@@ -56,5 +60,6 @@ labels = dtest.get_label()
 
 # plot results
 xgb.plot_importance(model)
+plt.show()
 plot_tree(model)
 plt.show()