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2
docs/integrations/autokeras.md
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@@ -16,7 +16,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* AutoKeras model files

4
docs/integrations/catboost.md
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@@ -16,7 +16,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* CatBoost model files
@@ -115,6 +115,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.

2
docs/integrations/fastai.md
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@@ -16,7 +16,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* `fastai` model files

6
docs/integrations/hydra.md
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@@ -41,10 +41,10 @@ The agent executes the code with the modifications you made in the UI, even over
Clone your experiment, then modify your Hydra parameters via the UI in one of the following ways:
* Modify the OmegaConf directly:
1. In the experiment’s **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, set `_allow_omegaconf_edit_` to `True`
1. In the experiment’s **CONFIGURATION > CONFIGURATION OBJECTS > OmegaConf** section, modify the OmegaConf values
1. In the experiment's **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, set `_allow_omegaconf_edit_` to `True`
1. In the experiment's **CONFIGURATION > CONFIGURATION OBJECTS > OmegaConf** section, modify the OmegaConf values
* Add an experiment hyperparameter:
1. In the experiment’s **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, make sure `_allow_omegaconf_edit_` is set
1. In the experiment's **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, make sure `_allow_omegaconf_edit_` is set
to `False`
1. In the same section, click `Edit`, which gives you the option to add parameters. Input parameters from the OmegaConf
that you want to modify using dot notation. For example, if your OmegaConf looks like this:

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docs/integrations/ignite.md
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@@ -8,7 +8,7 @@ instructions.
:::
[PyTorch Ignite](https://pytorch.org/ignite/index.html) is a library for training and evaluating neural networks in
PyTorch. You can integrate ClearML into your code using Ignite’s built-in loggers: [TensorboardLogger](#tensorboardlogger)
PyTorch. You can integrate ClearML into your code using Ignite's built-in loggers: [TensorboardLogger](#tensorboardlogger)
and [ClearMLLogger](#clearmllogger).
## TensorboardLogger
@@ -92,7 +92,7 @@ Integrate ClearML with the following steps:
# Attach the logger to the trainer to log model's weights as a histogram
clearml_logger.attach(trainer, log_handler=WeightsHistHandler(model), event_name=Events.EPOCH_COMPLETED(every=100))
# Attach the logger to the trainer to log model’s gradients as scalars
# Attach the logger to the trainer to log model's gradients as scalars
clearml_logger.attach(
trainer, log_handler=GradsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100)
)

6
docs/integrations/keras.md
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@@ -17,7 +17,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* Keras models
@@ -77,7 +77,7 @@ See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).
## Examples
Take a look at ClearML’s Keras examples. The examples use Keras and ClearML in different configurations with
Take a look at ClearML's Keras examples. The examples use Keras and ClearML in different configurations with
additional tools like TensorBoard and Matplotlib:
* [Keras with Tensorboard](../guides/frameworks/keras/keras_tensorboard.md) - Demonstrates ClearML logging a Keras model,
and plots and scalars logged to TensorBoard
@@ -127,6 +127,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.

2
docs/integrations/keras_tuner.md
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@@ -36,7 +36,7 @@ Integrate ClearML into your Keras Tuner optimization script by doing the followi
)
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Output Keras model
* Optimization trial scalars - scalar plot showing metrics for all runs
* Hyperparameter optimization summary plot - Tabular summary of hyperparameters tested and their metrics by trial ID

4
docs/integrations/lightgbm.md
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@@ -17,7 +17,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* LightGBM model files
@@ -116,6 +116,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.

4
docs/integrations/megengine.md
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@@ -16,7 +16,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* MegEngine model files
@@ -112,6 +112,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.

2
docs/integrations/monai.md
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@@ -66,7 +66,7 @@ change the task's name or project, use the `task_name` and `project_name` parame
The task captures the images logged by the image handler, metrics logged with the stats handler, as well as source code,
uncommitted changes, installed packages, console output, and more.
You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
You can see all the captured data in the task's page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
View the logged images in the WebApp, in the experiment's **Debug Samples** tab.

2
docs/integrations/optuna.md
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@@ -6,7 +6,7 @@ title: Optuna
which makes use of different samplers such as grid search, random, bayesian, and evolutionary algorithms. You can integrate
Optuna into ClearML's automated hyperparameter optimization.
The [HyperParameterOptimizer](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class contains ClearML’s
The [HyperParameterOptimizer](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class contains ClearML's
hyperparameter optimization modules. Its modular design enables using different optimizers, including existing software
frameworks, like Optuna, enabling simple,
accurate, and fast hyperparameter optimization. The Optuna ([`automation.optuna.OptimizerOptuna`](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md)),

4
docs/integrations/pytorch.md
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@@ -16,7 +16,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* PyTorch models
@@ -86,7 +86,7 @@ Take a look at ClearML's PyTorch examples. The examples use PyTorch and ClearML
additional tools, like argparse, TensorBoard, and matplotlib:
* [PyTorch MNIST](../guides/frameworks/pytorch/pytorch_mnist.md) - Demonstrates ClearML automatically logging models created with PyTorch, and `argparse` command line parameters
* [PyTorch with Matplotlib](../guides/frameworks/pytorch/pytorch_matplotlib.md) - Demonstrates ClearML’s automatic logging PyTorch models and matplotlib images. The images are stored in the resulting ClearML experiment's **Debug Samples**
* [PyTorch with Matplotlib](../guides/frameworks/pytorch/pytorch_matplotlib.md) - Demonstrates ClearML's automatic logging PyTorch models and matplotlib images. The images are stored in the resulting ClearML experiment's **Debug Samples**
* [PyTorch with TensorBoard](../guides/frameworks/pytorch/pytorch_tensorboard.md) - Demonstrates ClearML automatically logging PyTorch models, and scalars, debug samples, and text logged using TensorBoard's `SummaryWriter`
* [PyTorch TensorBoard Toy](../guides/frameworks/pytorch/tensorboard_toy_pytorch.md) - Demonstrates ClearML automatically logging debug samples logged using TensorBoard's `SummaryWriter`
* [PyTorch TensorBoardX](../guides/frameworks/pytorch/pytorch_tensorboardx.md) - Demonstrates ClearML automatically logging PyTorch models, and scalars, debug samples, and text logged using TensorBoardX's `SummaryWriter`

7
docs/integrations/pytorch_lightning.md
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@@ -18,7 +18,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* PyTorch Models
@@ -43,8 +43,7 @@ To control a task's framework logging, use the `auto_connect_frameworks` paramet
Completely disable all automatic logging by setting the parameter to `False`. For finer grained control of logged
frameworks, input a dictionary, with framework-boolean pairs.
For example, the following code will log PyTorch models, but will not log any information reported to TensorBoard.
:
For example, the following code will log PyTorch models, but will not log any information reported to TensorBoard:
```python
auto_connect_frameworks={
@@ -143,7 +142,7 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.

2
docs/integrations/scikit_learn.md
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@@ -17,7 +17,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* Joblib model files

2
docs/integrations/tensorboard.md
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@@ -56,7 +56,7 @@ See more information about explicitly logging information to a ClearML Task:
* [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
### Examples
Take a look at ClearML’s TensorBoard examples:
Take a look at ClearML's TensorBoard examples:
* [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md) - Demonstrates logging TensorBoard outputs and TensorFlow flags
* [TensorBoard Toy](../guides/frameworks/tensorflow/tensorboard_toy.md) - Demonstrates logging TensorBoard histograms, scalars, images, text, and TensorFlow flags
* [Tensorboard with PyTorch](../guides/frameworks/pytorch/pytorch_tensorboard.md) - Demonstrates logging TensorBoard scalars, debug samples, and text integrated in code that uses PyTorch

2
docs/integrations/tensorboardx.md
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@@ -56,7 +56,7 @@ See more information about explicitly logging information to a ClearML Task:
### Examples
Take a look at ClearML’s TensorboardX examples:
Take a look at ClearML's TensorboardX examples:
* [TensorboardX with PyTorch](../guides/frameworks/tensorboardx/tensorboardx.md) - Demonstrates ClearML logging TensorboardX scalars, debug
samples, and text in code using PyTorch

12
docs/integrations/tensorflow.md
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@@ -17,7 +17,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* TensorFlow definitions
@@ -75,17 +75,17 @@ See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).
## Examples
Take a look at ClearML’s TensorFlow examples. The examples use TensorFlow and ClearML in different configurations with
Take a look at ClearML's TensorFlow examples. The examples use TensorFlow and ClearML in different configurations with
additional tools, like Abseil and TensorBoard:
* [TensorFlow MNIST](../guides/frameworks/tensorflow/tensorflow_mnist.md) - Demonstrates ClearML's automatic logging of
model checkpoints, TensorFlow definitions, and scalars logged using TensorFlow methods
* [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md) - Demonstrates ClearML’s automatic
* [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md) - Demonstrates ClearML's automatic
logging of TensorBoard output and TensorFlow definitions.
* [TensorBoard Toy](../guides/frameworks/tensorflow/tensorboard_toy.md) - Demonstrates ClearML’s automatic logging of
* [TensorBoard Toy](../guides/frameworks/tensorflow/tensorboard_toy.md) - Demonstrates ClearML's automatic logging of
TensorBoard scalars, histograms, images, and text, as well as all console output and TensorFlow Definitions.
* [Absl flags](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/absl_flags.py) - Demonstrates
ClearML’s automatic logging of parameters defined using `absl.flags`
ClearML's automatic logging of parameters defined using `absl.flags`
## Remote Execution
ClearML logs all the information required to reproduce an experiment on a different machine (installed packages,
@@ -129,6 +129,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.

10
docs/integrations/transformers.md
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@@ -25,7 +25,7 @@ All you have to do is install and set up ClearML:
clearml-init
```
That’s it! In every training run from now on, the ClearML experiment
That's it! In every training run from now on, the ClearML experiment
manager will capture:
* Source code and uncommitted changes
* Hyperparameters - PyTorch trainer [parameters](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/trainer#transformers.TrainingArguments)
@@ -38,7 +38,7 @@ and TensorFlow definitions
* And more
All of this is captured into a [ClearML Task](../fundamentals/task.md). By default, a task called `Trainer` is created
in the `HuggingFace Transformers` project. To change the task’s name or project, use the `CLEARML_PROJECT` and `CLEARML_TASK`
in the `HuggingFace Transformers` project. To change the task's name or project, use the `CLEARML_PROJECT` and `CLEARML_TASK`
environment variables
:::tip project names
@@ -48,7 +48,7 @@ task within the `example` project.
In order to log the models created during training, set the `CLEARML_LOG_MODEL` environment variable to `True`.
You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
You can see all the captured data in the task's page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
![transformers scalars](../img/integrations_transformers_scalars.png)
@@ -79,7 +79,7 @@ and shuts down instances as needed, according to a resource budget that you set.
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
Use ClearML’s web interface to edit task details, like configuration parameters or input models, then execute the task
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:
* Clone the experiment
* Edit the hyperparameters and/or other details
@@ -88,6 +88,6 @@ with the new configuration on a remote machine:
The ClearML Agent executing the task will use the new values to [override any hard coded values](../clearml_agent.md).
## Hyperparameter Optimization
Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.

4
docs/integrations/xgboost.md
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@@ -17,7 +17,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* XGBoost model files
@@ -143,6 +143,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.

12
docs/integrations/yolov5.md
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@@ -27,7 +27,7 @@ built in logger:
clearml-init
```
That’s it! Now, whenever you train a model using YOLOv5, the run will be captured and tracked by ClearML – no additional
That's it! Now, whenever you train a model using YOLOv5, the run will be captured and tracked by ClearML – no additional
code necessary.
## Training YOLOv5 with ClearML
@@ -54,7 +54,7 @@ manager will capture:
* And more
All of this is captured into a [ClearML Task](../fundamentals/task.md). By default, a task called `Training` is created
in the `YOLOv5` project. To change the task’s name or project, use the `--project` and `--name` arguments when running
in the `YOLOv5` project. To change the task's name or project, use the `--project` and `--name` arguments when running
the `train.py` script.
```commandline
@@ -66,7 +66,7 @@ ClearML uses `/` as a delimiter for subprojects: using `example/sample` as a nam
task within the `example` project.
:::
You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
You can see all the captured data in the task's page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
Additionally, you can view all of your YOLOv5 runs tracked by ClearML in the [Experiments Table](../webapp/webapp_model_table.md).
Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model.
You can also select multiple experiments and directly [compare](../webapp/webapp_exp_comparing.md) them.
@@ -94,7 +94,7 @@ dataset using the link in the yaml file or the scripts provided by YOLOv5, you g
```
You can use any dataset, as long as you maintain this folder structure.
Copy the dataset’s corresponding yaml file to the root of the dataset folder.
Copy the dataset's corresponding yaml file to the root of the dataset folder.
```
..
@@ -171,7 +171,7 @@ and shuts down instances as needed, according to a resource budget that you set.
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
Use ClearML’s web interface to edit task details, like configuration parameters or input models, then execute the task
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:
* Clone the experiment
* Edit the hyperparameters and/or other details
@@ -200,7 +200,7 @@ if RANK in {-1, 0}:
```
## Hyperparameter Optimization
Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models.
To run hyperparameter optimization locally, you can use the [template script](https://github.com/ultralytics/yolov5/blob/master/utils/loggers/clearml/hpo.py)

8
docs/integrations/yolov8.md
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@@ -38,7 +38,7 @@ segmentation, and classification. Get the most out of YOLOv8 with ClearML:
clearml-init
```
That’s it! Now, whenever you train a model using YOLOv8, the run will be captured and tracked by ClearML – no additional
That's it! Now, whenever you train a model using YOLOv8, the run will be captured and tracked by ClearML – no additional
code necessary.
## Training YOLOv8 with ClearML
@@ -64,7 +64,7 @@ manager will capture:
* And more
All of this is captured into a [ClearML Task](../fundamentals/task.md): a task with your training script's name
created in a `YOLOv8` ClearML project. To change the task’s name or project, pass the `name` and `project` arguments in one of
created in a `YOLOv8` ClearML project. To change the task's name or project, pass the `name` and `project` arguments in one of
the following ways:
* Via the SDK:
@@ -89,7 +89,7 @@ ClearML uses `/` as a delimiter for subprojects: using `example/sample` as a nam
task within the `example` project.
:::
You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
You can see all the captured data in the task's page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
Additionally, you can view all of your YOLOv8 runs tracked by ClearML in the [Experiments Table](../webapp/webapp_model_table.md).
Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model.
You can also select multiple experiments and directly [compare](../webapp/webapp_exp_comparing.md) them.
@@ -115,7 +115,7 @@ shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
ClearML logs all the information required to reproduce an experiment, but you may also want to change a few parameters
and task details when you re-run an experiment, which you can do through ClearML’s UI.
and task details when you re-run an experiment, which you can do through ClearML's UI.
In order to be able to override parameters via the UI,
you have to run your code to [create a ClearML Task](../clearml_sdk/task_sdk.md#task-creation), which will log all the