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79 lines
2.7 KiB
Markdown
79 lines
2.7 KiB
Markdown
---
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title: Matplotlib
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---
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:::tip
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If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
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instructions.
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:::
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[Matplotlib](https://matplotlib.org/) is a Python library for data visualization. ClearML automatically captures plots
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and images created with `matplotlib`.
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All you have to do is add two lines of code to your script:
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```python
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from clearml import Task
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task = Task.init(task_name="<task_name>", project_name="<project_name>")
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```
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This will create a ClearML Task that captures:
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* Git details
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* Source code and uncommitted changes
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* Installed packages
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* Matplotlib visualizations
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* And more
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View captured Matplotlib plots and images in the [WebApp](../webapp/webapp_exp_track_visual.md),
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in the experiment's **Plots** and **Debug Samples** tabs respectively.
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## Automatic Logging Control
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By default, when ClearML is integrated into your script, it captures all of your matplotlib visualizations.
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But, you may want to have more control over what your experiment logs.
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To control a task's framework logging, use the `auto_connect_frameworks` parameter of [`Task.init()`](../references/sdk/task.md#taskinit).
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Completely disable all automatic logging by setting the parameter to `False`. For finer grained control of logged
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frameworks, input a dictionary, with framework-boolean pairs.
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For example:
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```python
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auto_connect_frameworks={
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'matplotlib': False, 'tensorflow': False, 'tensorboard': False, 'pytorch': True,
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'xgboost': False, 'scikit': True, 'fastai': True, 'lightgbm': False,
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'hydra': True, 'detect_repository': True, 'tfdefines': True, 'joblib': True,
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'megengine': True, 'catboost': True
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}
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```
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## Manual Logging
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To augment its automatic logging, ClearML also provides an explicit logging interface.
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Use [`Logger.report_matplotlib_figure()`](../references/sdk/logger.md#report_matplotlib_figure) to explicitly log
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a matplotlib figure, and specify its title and series names, and iteration number:
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```python
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logger = task.get_logger()
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area = (40 * np.random.rand(N))**2
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plt.scatter(x, y, s=area, c=colors, alpha=0.5)
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logger.report_matplotlib_figure(title="My Plot Title", series="My Plot Series", iteration=10, figure=plt)
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plt.show()
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```
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The logged figure is displayed in the experiment's **Plots** tab.
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Matplotlib figures can be logged as images by passing `report_image=True` to `Logger.report_matplotlib_figure()`.
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View the images in the experiment's **DEBUG SAMPLES** tab.
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See [Manual Matplotlib Reporting](../guides/reporting/manual_matplotlib_reporting.md) example.
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