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66 lines
3.1 KiB
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66 lines
3.1 KiB
Markdown
---
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title: TensorboardX
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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).
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:::
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[TensorboardX](https://tensorboardx.readthedocs.io/en/latest/tutorial.html#what-is-tensorboard-x) is a data
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visualization toolkit to log information through PyTorch and visualize it through [TensorBoard](https://www.tensorflow.org/tensorboard).
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ClearML automatically captures all data logged to TensorboardX, including scalars, images, video, plots, and text. All you have
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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](../fundamentals/task.md) that captures your script's information, including Git details,
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uncommitted code, python environment, your TensorboardX metrics, plots, images, and text.
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View the TensorboardX outputs in the [WebApp](../webapp/webapp_overview.md), in the experiment's page.
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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 TensorboardX plots, images, metrics, videos, and text.
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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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'tensorboard': False,'matplotlib': False, 'tensorflow': 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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Note that the `tensorboard` key enables/disables automatic logging for both `TensorboardX` and `TensorBoard`.
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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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See more information about explicitly logging information to a ClearML Task:
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* [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
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* [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
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* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
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* [Scalars](../clearml_sdk/task_sdk.md#scalars)
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* [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
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### Examples
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Take a look at ClearML's TensorboardX examples:
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* [TensorboardX with PyTorch](../guides/frameworks/tensorboardx/tensorboardx.md) - Demonstrates ClearML 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) - Demonstrates ClearML logging TensorboardX scalars in code using MegEngine
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* [TensorboardX Video](../guides/frameworks/tensorboardx/video_tensorboardx.md) - Demonstrates ClearML logging TensorBoardX video data.
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