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71 lines
2.9 KiB
Markdown
71 lines
2.9 KiB
Markdown
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---
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title: MMEngine
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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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[MMEngine](https://github.com/open-mmlab/mmengine) is a library for training deep learning models based on PyTorch.
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MMEngine supports ClearML through a builtin logger: It automatically logs experiment environment information, such as
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required packages and uncommitted changes, and supports reporting scalars, parameters, and debug samples.
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Integrate ClearML with the following steps:
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1. Instantiate a [`ClearMLVisBackend`](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.visualization.ClearMLVisBackend.html#mmengine.visualization.ClearMLVisBackend)
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object. This creates a ClearML Task that logs the experiment’s environment information.
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```python
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from mmengine.visualization import ClearMLVisBackend
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vis_backend = ClearMLVisBackend(
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artifact_suffix=('.py', 'pth'),
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init_kwargs=dict(
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project_name='examples',
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task_name='OpenMMLab cifar10',
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output_uri=True
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)
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)
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```
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You can specify the following parameters:
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* `init_kwargs` – A dictionary that contains the arguments to pass to ClearML's [`Task.init()`](../references/sdk/task.md#taskinit).
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* `artifact_suffix` – At the end of training, artifacts with these suffixes will be uploaded to the task's `output_uri`.
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Defaults to (`.py`, `pth`).
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2. Log experiment parameters using `ClearMLVisBackend.add_config()`. Under the `config` parameter, input a dictionary of parameter key-value pairs.
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```python
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cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
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vis_backend.add_config(config=cfg)
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```
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The parameters will be displayed in the ClearML WebApp, under the experiment’s Hyperparameters
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3. Log your experiment’s scalars using either `ClearMLVisBackend.add_scalar()` for single values or `ClearMLVisBackend.add_scalars()`
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for multiple values:
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```python
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vis_backend.add_scalar(name='mAP', value=0.6, step=1)
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vis_backend.add_scalars(scalar_dict={'loss': 0.1,'acc':0.8}, step=1)
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```
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The scalars are displayed in the experiment's Scalars tab.
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5. Report images to your experiment using `ClearMLVisBackend.add_image()`. Under the `image` parameter, input the image
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to be reported as an `np.ndarray` in RGB format:
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```python
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img = np.random.randint(0, 256, size=(10, 10, 3))
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vis_backend.add_image(name='img.png', image=img, step=1)
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```
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The images will be displayed in the experiment's Debug Samples
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5. Once you've finished training, make sure to run `ClearMLVisBackend.close()` so that ClearML can mark the task as
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completed. This will also scan the directory for relevant artifacts with the suffixes input when instantiating
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`ClearMLVisBackend` and log the artifacts to your experiment.
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```python
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vis_backend.close()
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```
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