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Edit tensorboardX logging control (#638)
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@ -43,6 +43,8 @@ auto_connect_frameworks={
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```
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Note that the `tensorboard` key enables/disables automatic logging for both `TensorBoard` and `TensorboardX`.
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## Manual Logging
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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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To augment its automatic logging, ClearML also provides an explicit logging interface.
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@ -23,6 +23,27 @@ View the TensorboardX outputs in the [WebApp](../webapp/webapp_overview.md), in
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![TensorboardX WebApp scalars](../img/examples_pytorch_tensorboardx_03.png)
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![TensorboardX WebApp scalars](../img/examples_pytorch_tensorboardx_03.png)
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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, 'jsonargparse': 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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## Manual Logging
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To augment its automatic logging, ClearML also provides an explicit logging interface.
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To augment its automatic logging, ClearML also provides an explicit logging interface.
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