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128 lines
6.1 KiB
Markdown
128 lines
6.1 KiB
Markdown
---
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title: Logger
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---
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The ClearML Logger class is used to report experiments' results such as metrics, graphs, and debug samples. It is provided
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through the ClearML [Task](task.md) object.
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A Logger object is used to do the following:
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* [Manual reporting](#manual-reporting), complementing ClearML's [automatic reporting](#automatic-reporting)
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* [Logging configuration](#logger-configuration)
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* Set upload destination for debug sample storage
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* Control ClearML's automatic logging
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* Set default NaN and Inf values
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## Types of Logged Results
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ClearML supports four types of reports:
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- Text - Mostly captured automatically from stdout and stderr but can be logged manually.
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- Scalars - Time series data. X-axis is always a sequential number, usually iterations but can be epochs or others.
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- Plots - General graphs and diagrams, such as histograms, confusion matrices, line plots, and custom plotly charts.
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- Debug Samples - Images, audio, and videos. Can be reported per iteration.
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## Automatic Reporting
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ClearML automatically captures metrics reported to leading visualization libraries, such as TensorBoard and Matplotlib,
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with no additional code necessary.
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In addition, ClearML will capture and log everything written to standard output, from debug messages to errors to
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library warning messages.
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GPU, CPU, Memory and Network information is also automatically captured.
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### Supported Packages
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- [TensorBoard](https://www.tensorflow.org/tensorboard)
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- [TensorBoardX](https://github.com/lanpa/tensorboardX)
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- [Matplotlib](https://matplotlib.org/)
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### Automatic Reporting Examples
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Check out some of ClearML's automatic reporting examples for supported packages:
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* TensorBoard
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* [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md) - logging TensorBoard outputs and
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TensorFlow flags
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* [TensorBoard Toy](../guides/frameworks/tensorflow/tensorboard_toy.md) - logging TensorBoard histograms, scalars, images, text, and
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TensorFlow flags
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* [Tensorboard with PyTorch](../guides/frameworks/pytorch/pytorch_tensorboard.md) - logging TensorBoard scalars, debug samples, and text integrated into
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code that uses PyTorch
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* TensorBoardX
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* [TensorBoardX with Pytorch](../guides/frameworks/tensorboardx/tensorboardx.md) - logging TensorBoardX scalars, debug
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samples, and text in code using PyTorch
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* [MegEngine MNIST](../guides/frameworks/megengine/megengine_mnist.md) - logging scalars using TensorBoardX's `SummaryWriter`
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* Matplotlib
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* [Matplotlib](../guides/frameworks/matplotlib/matplotlib_example.md) - logging scatter diagrams plotted with Matplotlib
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* [Matplotlib with PyTorch](../guides/frameworks/pytorch/pytorch_matplotlib.md) - logging debug images shown
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by Matplotlib
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## Manual Reporting
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ClearML also supports manually reporting multiple types of metrics and plots, such as line plots, histograms, and even plotly
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charts.
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The object used for reporting metrics is called **logger** and is obtained by calling
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```python
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logger = task.get_logger()
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```
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### Media Reporting
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ClearML also supports reporting media (such as audio, video and images) for every iteration.
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This section is mostly used for debugging. It's recommended to use [artifacts](task.md#artifacts) for storing script
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outputs that would be used later on.
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Only the last X results of each title / series are saved to prevent overloading the server.
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See details in [Logger.report_media](../references/sdk/logger.md#report_media).
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### Explicit Reporting Examples
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Check out ClearML's explicit reporting examples for various types of results:
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- [Text](../guides/reporting/text_reporting.md)
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- [Scalars](../guides/reporting/scalar_reporting.md)
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- Plots
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- [2d plots](../guides/reporting/scatter_hist_confusion_mat_reporting.md)
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- Histograms
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- Confusion matrices
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- Scatter plots
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- [3d plots](../guides/reporting/3d_plots_reporting.md)
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- Surface plots
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- Scatter plots
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- [Tables](../guides/reporting/pandas_reporting.md)
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- Pandas DataFrames
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- CSV file
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- [Matplotlib figures](../guides/reporting/manual_matplotlib_reporting.md)
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- [Plotly figures](../guides/reporting/plotly_reporting.md)
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- Debug Samples
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- [Images](../guides/reporting/image_reporting.md)
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- [HTML](../guides/reporting/html_reporting.md)
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- [Media - images, audio, video](../guides/reporting/media_reporting.md)
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- Explicit reporting in Jupyter Notebook [example](../guides/reporting/clearml_logging_example.md)
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## Logger Configuration
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The Logger class provides methods to control aspects of ClearML's logging.
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### Upload Destination
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Set the default storage URI for uploading debug samples using the [`Logger.set_default_upload_destination`](../references/sdk/logger.md#set_default_upload_destination) method.
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The debug samples are uploaded separately. A link to each sample is reported.
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:::note DESTINATION STORAGE CREDENTIALS
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Credentials for the destination storage are specified in the [ClearML configuration file](../configs/clearml_conf.md#sdk-section).
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:::
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### Automatic Logging Settings
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The Logger class provides methods for fine-tuning ClearML's automatic logging behavior with Matplotlib and Tensorboard.
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For example, use the [`Logger.matplotlib_force_report_non_interactive`](../references/sdk/logger.md#loggermatplotlib_force_report_non_interactive)
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class method to control how matplotlib objects are logged. See the [`Logger.tensorboard_auto_group_scalars`](../references/sdk/logger.md#loggertensorboard_auto_group_scalars)
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and [`Logger.tensorboard_single_series_per_graph`](../references/sdk/logger.md#loggertensorboard_single_series_per_graph)
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class methods.
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### Set Default NaN and Inf Values
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When you report metrics that include NaN or Inf values, ClearML logs them as `0` by default. You can specify
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different default values for NaN and Inf using the [`Logger.set_reporting_nan_value`](../references/sdk/logger.md#loggerset_reporting_nan_value)
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and the [`Logger.set_reporting_inf_value`](../references/sdk/logger.md#loggerset_reporting_inf_value) class methods respectively. |