ClearML/clearml-docs
1
0
Fork 0
mirror of https://github.com/clearml/clearml-docs synced 2025-06-26 18:17:44 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

Change terminology (#1028)

Browse Source
This commit is contained in:
pollfly
2025-02-06 17:31:11 +02:00
committed by GitHub
parent 30805e474d
commit b12b71d835
158 changed files with 857 additions and 855 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
docs/integrations/mmengine.md
View File

@@ -8,12 +8,12 @@ instructions.
:::
[MMEngine](https://github.com/open-mmlab/mmengine) is a library for training deep learning models based on PyTorch.
MMEngine supports ClearML through a builtin logger: It automatically logs experiment environment information, such as
MMEngine supports ClearML through a builtin logger: It automatically logs task environment information, such as
required packages and uncommitted changes, and supports reporting scalars, parameters, and debug samples.
Integrate ClearML with the following steps:
1. Instantiate a [`ClearMLVisBackend`](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.visualization.ClearMLVisBackend.html#mmengine.visualization.ClearMLVisBackend)
object. This creates a ClearML Task that logs the experiment’s environment information.
object. This creates a ClearML Task that logs the task’s environment information.
```python
from mmengine.visualization import ClearMLVisBackend
@@ -33,16 +33,16 @@ Integrate ClearML with the following steps:
* `artifact_suffix` – At the end of training, artifacts with these suffixes will be uploaded to the task's `output_uri`.
Defaults to (`.py`, `pth`).
2. Log experiment parameters using `ClearMLVisBackend.add_config()`. Under the `config` parameter, input a dictionary of parameter key-value pairs.
2. Log task parameters using `ClearMLVisBackend.add_config()`. Under the `config` parameter, input a dictionary of parameter key-value pairs.
```python
cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
vis_backend.add_config(config=cfg)
```
The parameters will be displayed in the ClearML WebApp, under the experiment’s Hyperparameters
The parameters will be displayed in the ClearML WebApp, under the task’s Hyperparameters
3. Log your experiment’s scalars using either `ClearMLVisBackend.add_scalar()` for single values or `ClearMLVisBackend.add_scalars()`
3. Log your task’s scalars using either `ClearMLVisBackend.add_scalar()` for single values or `ClearMLVisBackend.add_scalars()`
for multiple values:
```python
@@ -50,20 +50,20 @@ Integrate ClearML with the following steps:
vis_backend.add_scalars(scalar_dict={'loss': 0.1,'acc':0.8}, step=1)
```
The scalars are displayed in the experiment's Scalars tab.
The scalars are displayed in the task's **Scalars** tab.
5. Report images to your experiment using `ClearMLVisBackend.add_image()`. Under the `image` parameter, input the image
5. Report images to your task using `ClearMLVisBackend.add_image()`. Under the `image` parameter, input the image
to be reported as an `np.ndarray` in RGB format:
```python
img = np.random.randint(0, 256, size=(10, 10, 3))
vis_backend.add_image(name='img.png', image=img, step=1)
```
The images will be displayed in the experiment's Debug Samples
The images will be displayed in the task's **Debug Samples** tab.
5. Once you've finished training, make sure to run `ClearMLVisBackend.close()` so that ClearML can mark the task as
completed. This will also scan the directory for relevant artifacts with the suffixes input when instantiating
`ClearMLVisBackend` and log the artifacts to your experiment.
`ClearMLVisBackend` and log the artifacts to your task.
```python
vis_backend.close()