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4
docs/fundamentals/logger.md
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@ -26,10 +26,10 @@ ClearML supports four types of reports:
ClearML automatically captures metrics reported to leading visualization libraries, such as TensorBoard and Matplotlib,
with no additional code necessary.
In addition, ClearML will capture and log everything written to standard output, from debug messages to errors to
In addition, ClearML captures and logs everything written to standard output, from debug messages to errors to
library warning messages.
GPU, CPU, Memory and Network information is also automatically captured.
GPU, CPU, Memory, and Network information is also automatically captured.
![image](../img/fundamentals_logger_cpu_monitoring.png)

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docs/getting_started/ds/ds_first_steps.md
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@ -95,7 +95,7 @@ Now you can use ClearML in your notebook!
In ClearML, experiments are organized as [Tasks](../../fundamentals/task.md).
ClearML will automatically log your experiment and code, including outputs and parameters from popular ML frameworks,
ClearML automatically logs your experiment and code, including outputs and parameters from popular ML frameworks,
once you integrate the ClearML [SDK](../../clearml_sdk/clearml_sdk.md) with your code. To control what ClearML automatically logs, see this [FAQ](../../faq.md#controlling_logging).
At the beginning of your code, import the `clearml` package:
@ -114,9 +114,9 @@ Then initialize the Task object in your `main()` function, or the beginning of t
task = Task.init(project_name='great project', task_name='best experiment')
```
If the project does not already exist, a new one will be created automatically.
If the project does not already exist, a new one is created automatically.
The console should return the following output:
The console should display the following output:
```
ClearML Task: created new task id=1ca59ef1f86d44bd81cb517d529d9e5a

24
docs/getting_started/ds/ds_second_steps.md
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@ -4,22 +4,22 @@ title: Next Steps
So, you've already [installed ClearML's python package](ds_first_steps.md) and run your first experiment!
Now, you'll learn how to track Hyperparameters, Artifacts and Metrics!
Now, you'll learn how to track Hyperparameters, Artifacts, and Metrics!
## Accessing Experiments
Every previously executed experiment is stored as a Task.
A Task has a project and a name, both can be changed after the experiment has been executed.
A Task's project and name can be changed after the experiment has been executed.
A Task is also automatically assigned an auto-generated unique identifier (UUID string) that cannot be changed and always locates the same Task in the system.
It's possible to retrieve a Task object programmatically by querying the system based on either the Task ID,
or project and name combination. It's also possible to query tasks based on their properties, like Tags.
or project and name combination. It's also possible to query tasks based on their properties, like tags (see [Querying Tasks](../../clearml_sdk/task_sdk.md#querying--searching-tasks)).
```python
prev_task = Task.get_task(task_id='123456deadbeef')
```
Once you have a Task object you can query the state of the Task, get its model, scalars, parameters, etc.
Once you have a Task object you can query the state of the Task, get its model(s), scalars, parameters, etc.
## Log Hyperparameters
@ -36,13 +36,13 @@ params_dictionary = {'epochs': 3, 'lr': 0.4}
task.connect(params_dictionary)
```
Check [this](../../fundamentals/hyperparameters.md) out for all Hyperparameter logging options.
See [Configuration](../../clearml_sdk/task_sdk.md#configuration) for all hyperparameter logging options.
## Log Artifacts
ClearML lets you easily store the output products of an experiment - Model snapshot / weights file, a preprocessing of your data, feature representation of data and more!
Essentially, artifacts are files (or python objects) uploaded from a script and are stored alongside the Task.
Essentially, artifacts are files (or Python objects) uploaded from a script and are stored alongside the Task.
These artifacts can be easily accessed by the web UI or programmatically.
Artifacts can be stored anywhere, either on the ClearML server, or any object storage solution or shared folder.
@ -137,9 +137,9 @@ This feature lets you easily get a full genealogy of every trained and used mode
## Log Metrics
Full metrics logging is the key to finding the best performing model!
By default, everything that's reported to Tensorboard and Matplotlib is automatically captured and logged.
By default, everything that's reported to TensorBoard and Matplotlib is automatically captured and logged.
Since not all metrics are tracked that way, it's also possible to manually report metrics using the `logger` object.
Since not all metrics are tracked that way, it's also possible to manually report metrics using the [`logger`](../../fundamentals/logger.md) object.
It's possible to log everything, from time series data to confusion matrices to HTML, Audio and Video, to custom plotly graphs! Everything goes!
@ -162,16 +162,16 @@ It's possible to filter and sort based on parameters and metrics, so creating cu
Create a dashboard for a project, presenting the latest Models and their accuracy scores, for immediate insights.
It can also be used as a live leaderboard, showing the best performing experiments' status, updated in real time.
This is helpful to monitor your projects' progress, and share it across the organization.
This is helpful to monitor your projects' progress, and to share it across the organization.
Any page is sharable by copying the URL from the address bar, allowing you to bookmark leaderboards or send an exact view of a specific experiment or a comparison view.
Any page is sharable by copying the URL from the address bar, allowing you to bookmark leaderboards or to send an exact view of a specific experiment or a comparison page.
It's also possible to tag Tasks for visibility and filtering allowing you to add more information on the execution of the experiment.
Later you can search based on task name and tag in the search bar, and filter experiments based on their tags, parameters, status and more.
Later you can search based on task name in the search bar, and filter experiments based on their tags, parameters, status, and more.
## What's Next?
This covers the Basics of ClearML! Running through this guide you've learned how to log Parameters, Artifacts and Metrics!
This covers the basics of ClearML! Running through this guide you've learned how to log Parameters, Artifacts and Metrics!
If you want to learn more look at how we see the data science process in our [best practices](best_practices.md) page,
or check these pages out:

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docs/guides/reporting/image_reporting.md
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@ -50,8 +50,8 @@ Logger.current_logger().report_image(
ClearML reports these images as debug samples in the **ClearML Web UI**, under the experiment's
**DEBUG SAMPLES** tab.
![image](../../img/examples_reporting_07.png)
![Debug samples](../../img/examples_reporting_07.png)
Click a thumbnail, and the image viewer opens.
Click a thumbnail to open the image viewer.
![image](../../img/examples_reporting_07a.png)
![Image viewer](../../img/examples_reporting_07a.png)

8
docs/guides/reporting/media_reporting.md
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@ -38,9 +38,9 @@ Logger.current_logger().report_media(
)
```
The reported audio can be viewed in the **DEBUG SAMPLES** tab. Click a thumbnail, and the audio player opens.
The reported audio can be viewed in the **DEBUG SAMPLES** tab. Click a thumbnail to open the audio player.
![image](../../img/examples_reporting_08.png)
![Audio debug samples](../../img/examples_reporting_08.png)
## Reporting (Uploading) Media from a Local File
@ -55,6 +55,6 @@ Logger.current_logger().report_media(
)
```
The reported video can be viewed in the **DEBUG SAMPLES** tab. Click a thumbnail, and the video player opens.
The reported video can be viewed in the **DEBUG SAMPLES** tab. Click a thumbnail to open the video player.
![image](../../img/examples_reporting_09.png)
![Video debug samples](../../img/examples_reporting_09.png)

2
docs/webapp/webapp_exp_track_visual.md
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@ -146,7 +146,7 @@ except experiments whose status is *Published* (read-only).
### Configuration Objects
ClearML tracks experiment (Task) model configuration objects, which appear in **Configuration Objects** **>** **General**.
These objects include those that are automatically tracked, and those connected to a Task in code (see [Task.connect_configuration](../references/sdk/task.md#connect_configuration)).
These objects include those that are automatically tracked, and those connected to a Task in code (see [`Task.connect_configuration`](../references/sdk/task.md#connect_configuration)).
![Configuration objects](../img/webapp_tracking_24.png)