Capitalized examples task name, updated example requirements (#573)

examples/advanced/execute_remotely_example.py

@@ -78,7 +78,7 @@ 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='remote_execution pytorch mnist train')
+    task = Task.init(project_name='examples', task_name='Remote_execution PyTorch MNIST train')
     # Training settings
     parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
     parser.add_argument('--batch-size', type=int, default=64, metavar='N',

examples/advanced/multiple_tasks_single_process.py

@@ -2,7 +2,7 @@ from clearml import Task
 
 
 for i in range(3):
-    task = Task.init(project_name="examples", task_name="same process, multiple tasks, Task #{}".format(i))
+    task = Task.init(project_name="examples", task_name="Same process, Multiple tasks, Task #{}".format(i))
     # Doing Task processing here
     print("Task #{} running".format(i))
     #

examples/datasets/data_ingestion.py

@@ -20,7 +20,7 @@ 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")
+task = Task.init(project_name="Image Example", task_name="Image classification CIFAR10")
 params = {
     "number_of_epochs": 20,
     "batch_size": 64,

examples/frameworks/autokeras/autokeras_imdb_example.py

@@ -7,7 +7,7 @@ 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")
+task = Task.init(project_name="autokeras", task_name="AutoKeras IMDB example with scalars")
 
 
 def imdb_raw():

examples/frameworks/click/click_multi_cmd.py

@@ -6,7 +6,7 @@ from clearml import Task
 @click.option('--print-something/--dont-print-something', default=True)
 @click.option('--what-to-print', default='something')
 def cli(print_something, what_to_print):
-    Task.init(project_name='examples', task_name='click multi command')
+    Task.init(project_name='examples', task_name='Click multi command')
     if print_something:
         print(what_to_print)
 

examples/frameworks/click/click_single_cmd.py

@@ -7,7 +7,7 @@ from clearml import Task
 @click.option('--name', prompt='Your name',
               help='The person to greet.')
 def hello(count, name):
-    task = Task.init(project_name='examples', task_name='click single command')
+    task = Task.init(project_name='examples', task_name='Click single command')
 
     """Simple program that greets NAME for a total of COUNT times."""
     for x in range(count):

examples/frameworks/fastai/fastai_with_tensorboard.py

@@ -8,7 +8,7 @@ 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="fastai with tensorboard callback")
+task = Task.init(project_name="examples", task_name="Fastai with TensorBoard callback")
 
 path = untar_data(URLs.MNIST_SAMPLE)
 

examples/frameworks/fire/fire_class_cmd.py

@@ -17,5 +17,5 @@ class BrokenCalculator(object):
 
 
 if __name__ == "__main__":
-    Task.init(project_name="examples", task_name="fire class command")
+    Task.init(project_name="examples", task_name="Fire class command")
     fire.Fire(BrokenCalculator)

examples/frameworks/fire/fire_dict_cmd.py

@@ -14,7 +14,7 @@ def multiply(x, y):
 
 
 if __name__ == "__main__":
-    Task.init(project_name="examples", task_name="fire dict command")
+    Task.init(project_name="examples", task_name="Fire dict command")
     fire.Fire(
         {
             "add": add,

examples/frameworks/fire/fire_grouping_cmd.py

@@ -40,5 +40,5 @@ class Pipeline(object):
 
 
 if __name__ == "__main__":
-    Task.init(project_name="examples", task_name="fire grouping command")
+    Task.init(project_name="examples", task_name="Fire grouping command")
     fire.Fire(Pipeline)

examples/frameworks/fire/fire_multi_cmd.py

@@ -18,5 +18,5 @@ def serve(addr, port, should_serve=False):
 
 
 if __name__ == "__main__":
-    Task.init(project_name="examples", task_name="fire multi command")
+    Task.init(project_name="examples", task_name="Fire multi command")
     fire.Fire()

examples/frameworks/fire/fire_object_cmd.py

@@ -14,6 +14,6 @@ class Calculator(object):
 
 
 if __name__ == "__main__":
-    Task.init(project_name="examples", task_name="fire object command")
+    Task.init(project_name="examples", task_name="Fire object command")
     calculator = Calculator()
     fire.Fire(calculator)

examples/frameworks/fire/fire_single_cmd.py

@@ -11,5 +11,5 @@ def hello(count, name="clearml", prefix="prefix_", suffix="_suffix", **kwargs):
 
 
 if __name__ == "__main__":
-    Task.init(project_name="examples", task_name="fire single command")
+    Task.init(project_name="examples", task_name="Fire single command")
     fire.Fire(hello)

examples/frameworks/hydra/hydra_example.py

@@ -10,7 +10,7 @@ from clearml import Task
 @hydra.main(config_path="config_files", config_name="config")
 def my_app(cfg):
     # type (DictConfig) -> None
-    task = Task.init(project_name="examples", task_name="hydra configuration")
+    task = Task.init(project_name="examples", task_name="Hydra configuration")
     logger = task.get_logger()
     logger.report_text("You can view your full hydra configuration under Configuration tab in the UI")
     print(OmegaConf.to_yaml(cfg))

examples/frameworks/ignite/cifar_ignite.py

@@ -19,7 +19,7 @@ from clearml import Task, StorageManager
 
 # Connecting ClearML with the current process,
 # from here on everything is logged automatically
-task = Task.init(project_name='examples', task_name='image classification CIFAR10')
+task = Task.init(project_name='examples', 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)

examples/frameworks/megengine/megengine_mnist.py

@@ -94,7 +94,7 @@ def train(dataloader, args):
 
 
 def main():
-    task = Task.init(project_name="examples", task_name="megengine mnist train")  # noqa
+    task = Task.init(project_name="examples", task_name="MegEngine MNIST train")  # noqa
 
     parser = argparse.ArgumentParser(description="MegEngine MNIST Example")
     parser.add_argument(

examples/frameworks/pytorch-lightning/pytorch_lightning_example.py

@@ -55,7 +55,7 @@ class LitClassifier(pl.LightningModule):
 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")
+    task = Task.init(project_name="examples", task_name="PyTorch lightning MNIST example")
 
     pl.seed_everything(0)
 

examples/frameworks/pytorch/pytorch_abseil.py

@@ -113,7 +113,7 @@ 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 with abseil")
+    task = Task.init(project_name="examples", task_name="PyTorch MNIST train with abseil")
 
     use_cuda = FLAGS.cuda and torch.cuda.is_available()
 

examples/frameworks/pytorch/pytorch_matplotlib.py

@@ -64,7 +64,7 @@ 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)
+task = Task.init(project_name='examples', task_name='PyTorch with Matplotlib example', task_type=Task.TaskTypes.testing)
 
 
 ######################################################################

examples/frameworks/pytorch/pytorch_mnist.py

@@ -76,7 +76,7 @@ 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')
+    task = Task.init(project_name='examples', task_name='PyTorch MNIST train')
 
     # Training settings
     parser = argparse.ArgumentParser(description='PyTorch MNIST Example')

examples/frameworks/pytorch/pytorch_model_update.py

@@ -23,7 +23,7 @@ def main():
     # from here on everything is logged automatically
     task = Task.init(
         project_name="examples",
-        task_name="Model update pytorch",
+        task_name="Model update PyTorch",
         auto_connect_frameworks={"pytorch": False},
     )
     params = {

examples/frameworks/pytorch/pytorch_tensorboard.py

@@ -102,7 +102,7 @@ def main():
 
     # 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
+    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)

examples/frameworks/pytorch/requirements.txt

@@ -1,7 +1,9 @@
 matplotlib
 tensorboardX
 tensorboard>=1.14.0
-torch>=1.1.0
-torchvision>=0.3.0
+torch >= 1.1.0 ; python_version >= '3.8'
+torch <= 1.5.1 ; python_version < '3.8'
+torchvision
 tqdm
-clearml
+jsonschema==3.2.0 ; python_version <= '3.5'
+clearml

examples/frameworks/pytorch/tensorboard_toy_pytorch.py

@@ -10,7 +10,7 @@ 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 tensorboard toy example')
+task = Task.init(project_name='examples', task_name='PyTorch TensorBoard toy example')
 
 writer = SummaryWriter(log_dir=os.path.join(gettempdir(), 'tensorboard_logs'))
 

examples/frameworks/scikit-learn/sklearn_joblib_example.py

@@ -14,7 +14,7 @@ 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")
+task = Task.init(project_name="examples", task_name="Scikit-learn joblib example")
 
 iris = datasets.load_iris()
 X = iris.data

examples/frameworks/tensorboardx/pytorch_tensorboardX.py

@@ -102,7 +102,7 @@ def main():
 
     # Connecting ClearML with the current process,
     # from here on everything is logged automatically
-    task = Task.init(project_name='examples', task_name='pytorch with tensorboardX')
+    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

@@ -42,7 +42,7 @@ 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')
+task = Task.init(project_name='examples', task_name='TensorBoard pr_curve')
 
 tf.compat.v1.disable_v2_behavior()
 FLAGS = flags.FLAGS

examples/frameworks/tensorflow/legacy/tensorboard_toy.py

@@ -12,7 +12,7 @@ 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 toy example')
+task = Task.init(project_name='examples', task_name='TensorBoard toy example')
 
 k = tf.placeholder(tf.float32)
 

examples/frameworks/tensorflow/legacy/tensorflow_eager.py

@@ -37,7 +37,7 @@ 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')
+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""")

examples/frameworks/tensorflow/legacy/tensorflow_mnist_with_summaries.py

@@ -37,7 +37,7 @@ 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')
+task = Task.init(project_name='examples', task_name='TensorFlow MNIST with summaries example')
 
 
 def train():

examples/frameworks/tensorflow/tensorboard_pr_curve.py

@@ -41,7 +41,7 @@ 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')
+task = Task.init(project_name='examples', task_name='TensorBoard pr_curve')
 
 tf.compat.v1.disable_v2_behavior()
 FLAGS = flags.FLAGS

examples/frameworks/tensorflow/tensorboard_toy.py

@@ -61,7 +61,7 @@ def generate_summary(k, step):
     tf.summary.image("test_rgba", image_rgba, max_outputs=10, step=step)
 
 
-task = Task.init(project_name='examples', task_name='tensorboard toy example')
+task = Task.init(project_name='examples', task_name='TensorbBoard toy example')
 
 # create the tensorboard file writer in a temp folder
 writer = tf.summary.create_file_writer(os.path.join(gettempdir(), "toy_tb_example"))

examples/frameworks/tensorflow/tensorflow_mnist.py

@@ -13,7 +13,7 @@ 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='Tensorflow v2 mnist with summaries')
+task = Task.init(project_name='examples', task_name='TensorFlow v2 MNIST with summaries')
 
 
 # Load and prepare the MNIST dataset.

examples/frameworks/xgboost/xgboost_metrics.py

@@ -4,7 +4,7 @@ from sklearn.model_selection import train_test_split
 
 from clearml import Task
 
-task = Task.init(project_name="examples", task_name="xgboost metric auto reporting")
+task = Task.init(project_name="examples", task_name="XGBoost metric auto reporting")
 
 X, y = load_iris(return_X_y=True)
 X_train, X_test, y_train, y_test = train_test_split(

examples/pipeline/pipeline_from_functions.py

@@ -55,7 +55,7 @@ if __name__ == '__main__':
     # create the pipeline controller
     pipe = PipelineController(
         project='examples',
-        name='pipeline demo',
+        name='Pipeline demo',
         version='1.1',
         add_pipeline_tags=False,
     )

examples/pipeline/pipeline_from_tasks.py

@@ -20,7 +20,7 @@ def post_execute_callback_example(a_pipeline, a_node):
 # Connecting ClearML with the current pipeline,
 # from here on everything is logged automatically
 pipe = PipelineController(
-    name='pipeline demo',
+    name='Pipeline demo',
     project='examples',
     version='0.0.1',
     add_pipeline_tags=False,
@@ -28,16 +28,16 @@ pipe = PipelineController(
 
 pipe.set_default_execution_queue('default')
 
-pipe.add_step(name='stage_data', base_task_project='examples', base_task_name='pipeline step 1 dataset artifact')
+pipe.add_step(name='stage_data', base_task_project='examples', base_task_name='Pipeline step 1 dataset artifact')
 pipe.add_step(name='stage_process', parents=['stage_data', ],
-              base_task_project='examples', base_task_name='pipeline step 2 process dataset',
+              base_task_project='examples', base_task_name='Pipeline step 2 process dataset',
               parameter_override={'General/dataset_url': '${stage_data.artifacts.dataset.url}',
                                   'General/test_size': 0.25},
               pre_execute_callback=pre_execute_callback_example,
               post_execute_callback=post_execute_callback_example
               )
 pipe.add_step(name='stage_train', parents=['stage_process', ],
-              base_task_project='examples', base_task_name='pipeline step 3 train model',
+              base_task_project='examples', base_task_name='Pipeline step 3 train model',
               parameter_override={'General/dataset_task_id': '${stage_process.id}'})
 
 # for debugging purposes use local jobs

examples/pipeline/step1_dataset_artifact.py

@@ -1,7 +1,7 @@
 from clearml import Task, StorageManager
 
 # create an dataset experiment
-task = Task.init(project_name="examples", task_name="pipeline step 1 dataset artifact")
+task = Task.init(project_name="examples", task_name="Pipeline step 1 dataset artifact")
 
 # only create the task, we will actually execute it later
 task.execute_remotely()

examples/pipeline/step2_data_processing.py

@@ -5,7 +5,7 @@ from sklearn.model_selection import train_test_split
 
 # Connecting ClearML with the current process,
 # from here on everything is logged automatically
-task = Task.init(project_name="examples", task_name="pipeline step 2 process dataset")
+task = Task.init(project_name="examples", task_name="Pipeline step 2 process dataset")
 
 # program arguments
 # Use either dataset_task_id to point to a tasks artifact or

examples/pipeline/step3_train_model.py

@@ -8,7 +8,7 @@ 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="pipeline step 3 train model")
+task = Task.init(project_name="examples", task_name="Pipeline step 3 train model")
 
 # Arguments
 args = {

examples/reporting/artifacts.py

@@ -9,7 +9,7 @@ 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='artifacts example')
+task = Task.init(project_name='examples', task_name='Artifacts example')
 
 df = pd.DataFrame(
     {

examples/reporting/artifacts_retrieval.py

@@ -8,7 +8,7 @@ from clearml import Task
 
 def main():
     # Getting the task we want to get the artifacts from
-    artifacts_task = Task.get_task(project_name='examples', task_name='artifacts example')
+    artifacts_task = Task.get_task(project_name='examples', task_name='Artifacts example')
 
     # getting the numpy object back
     numpy_artifact = artifacts_task.artifacts['Numpy Eye'].get()

examples/reporting/html_reporting.py

@@ -219,7 +219,7 @@ def report_html_image(logger, iteration=0):
 def main():
     # Connecting ClearML with the current process,
     # from here on everything is logged automatically
-    task = Task.init(project_name="examples", task_name="html samples reporting")
+    task = Task.init(project_name="examples", task_name="HTML samples reporting")
 
     print('reporting html files into debug samples section')
 

examples/reporting/hyper_parameters.py

@@ -12,7 +12,7 @@ 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='hyper-parameters example')
+task = Task.init(project_name='examples', task_name='Hyper-parameters example')
 
 parameters = {
     'list': [1, 2, 3],

examples/reporting/image_reporting.py

@@ -45,7 +45,7 @@ def report_debug_images(logger, iteration=0):
 def main():
     # Connecting ClearML with the current process,
     # from here on everything is logged automatically
-    task = Task.init(project_name="examples", task_name="image reporting")
+    task = Task.init(project_name="examples", task_name="Image reporting")
 
     print('reporting a few debug images')
 

examples/reporting/media_reporting.py

@@ -6,7 +6,7 @@ from clearml import Task, Logger
 
 # Connecting ClearML with the current process,
 # from here on everything is logged automatically
-task = Task.init(project_name="examples", task_name="audio and video reporting")
+task = Task.init(project_name="examples", task_name="Audio and video reporting")
 
 print('reporting audio and video samples to the debug samples section')
 

examples/reporting/pandas_reporting.py

@@ -45,7 +45,7 @@ def report_table(logger, iteration=0):
 def main():
     # Connecting ClearML with the current process,
     # from here on everything is logged automatically
-    task = Task.init(project_name="examples", task_name="table reporting")
+    task = Task.init(project_name="examples", task_name="Table reporting")
 
     print('reporting pandas tables and python lists as tables into the plots section')
 

examples/reporting/scalar_reporting.py

@@ -23,7 +23,7 @@ def report_scalars(logger):
 def main():
     # Connecting ClearML with the current process,
     # from here on everything is logged automatically
-    task = Task.init(project_name="examples", task_name="scalar reporting")
+    task = Task.init(project_name="examples", task_name="Scalar reporting")
 
     print('reporting scalar graphs')
 

examples/reporting/text_reporting.py

@@ -60,7 +60,7 @@ Vestibulum dictum ipsum at viverra ultrices. Aliquam sed ante massa. Quisque con
 def main():
     # Connecting ClearML with the current process,
     # from here on everything is logged automatically
-    task = Task.init(project_name="examples", task_name="text reporting")
+    task = Task.init(project_name="examples", task_name="Text reporting")
 
     print("reporting text logs")
 

examples/reporting/using_artifacts_example.py

@@ -6,16 +6,16 @@ from clearml import Task
 from time import sleep
 
 
-task1 = Task.init(project_name='examples', task_name='create artifact')
+task1 = Task.init(project_name='examples', task_name='Create artifact')
 # upload data file to the initialized task, inputting a name and file location
 task1.upload_artifact(name='data file', artifact_object='data_samples/sample.json')
 # close the task, to be able to initialize a new task
 task1.close()
 
 # initialize another task to use some other task's artifacts
-task2 = Task.init(project_name='examples', task_name='use artifact from other task')
+task2 = Task.init(project_name='examples', task_name='Use artifact from other task')
 # get instance of Task that created artifact (task1), using Task's project and name. You could also use its ID number.
-preprocess_task = Task.get_task(project_name='examples', task_name='create artifact')
+preprocess_task = Task.get_task(project_name='examples', task_name='Create artifact')
 # access artifact from task1, using the artifact's name
 # get_local_copy() caches the files for later use and returns a path to the cached file
 local_json = preprocess_task.artifacts['data file'].get_local_copy()

examples/scheduler/cron_example.py

@@ -15,7 +15,7 @@ scheduler = TaskScheduler()
 # Run the pipeline Task 'pipeline demo, every day at 10:30am
 scheduler.add_task(
     name='recurring pipeline job',
-    schedule_task_id=Task.get_task(project_name='examples', task_name='pipeline demo'),
+    schedule_task_id=Task.get_task(project_name='examples', task_name='Pipeline demo'),
     queue='default',
     minute=30,
     hour=10,