---
title: Matplotlib
---

:::tip
If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
instructions.
:::

[Matplotlib](https://matplotlib.org/) is a Python library for data visualization. ClearML automatically captures plots 
and images created with `matplotlib`. 

All you have to do is add two lines of code to your script:

```python
from clearml import Task

task = Task.init(task_name="<task_name>", project_name="<project_name>")
```

This will create a ClearML Task that captures:
* Git details
* Source code and uncommitted changes 
* Installed packages
* Matplotlib visualizations
* And more

View captured Matplotlib plots and images in the [WebApp](../webapp/webapp_exp_track_visual.md), 
in the experiment's **Plots** and **Debug Samples** tabs respectively.

![Experiment plots](../img/examples_matplotlib_example_01.png)

## Automatic Logging Control 
By default, when ClearML is integrated into your script, it captures all of your matplotlib visualizations. 
But, you may want to have more control over what your experiment logs.

To control a task's framework logging, use the `auto_connect_frameworks` parameter of [`Task.init()`](../references/sdk/task.md#taskinit). 
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:

```python
auto_connect_frameworks={
   'matplotlib': False, 'tensorflow': False, 'tensorboard': False, 'pytorch': True,
   'xgboost': False, 'scikit': True, 'fastai': True, 'lightgbm': False,
   'hydra': True, 'detect_repository': True, 'tfdefines': True, 'joblib': True,
   'megengine': True, 'catboost': True
}
```

## Manual Logging
To augment its automatic logging, ClearML also provides an explicit logging interface.

Use [`Logger.report_matplotlib_figure()`](../references/sdk/logger.md#report_matplotlib_figure) to explicitly log 
a matplotlib figure, and specify its title and series names, and iteration number:


```python
logger = task.get_logger()

area = (40 * np.random.rand(N))**2
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
logger.report_matplotlib_figure(title="My Plot Title", series="My Plot Series", iteration=10, figure=plt)
plt.show()
```

The logged figure is displayed in the experiment's **Plots** tab. 

![Experiment Matplotlib plots](../img/manual_matplotlib_reporting_01.png)

Matplotlib figures can be logged as images by passing `report_image=True` to `Logger.report_matplotlib_figure()`. 
View the images in the experiment's **DEBUG SAMPLES** tab.

![Experiment debug sample](../img/manual_matplotlib_reporting_03.png)

See [Manual Matplotlib Reporting](../guides/reporting/manual_matplotlib_reporting.md) example.