HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Add Docs glossary links (#16448)

Browse source 
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-09-23 23:48:46 +02:00 • committed by GitHub
parent 8b8c25f216
commit 443fbce194
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
193 changed files with 1124 additions and 1124 deletions
Download patch file Download diff file Expand all files Collapse all files

4
docs/en/integrations/mlflow.md
View file

@ -10,7 +10,7 @@ keywords: MLflow, Ultralytics YOLO, machine learning, experiment tracking, metri
## Introduction
Experiment logging is a crucial aspect of machine learning workflows that enables tracking of various metrics, parameters, and artifacts. It helps to enhance model reproducibility, debug issues, and improve model performance. [Ultralytics](https://www.ultralytics.com/) YOLO, known for its real-time object detection capabilities, now offers integration with [MLflow](https://mlflow.org/), an open-source platform for complete machine learning lifecycle management.
Experiment logging is a crucial aspect of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) workflows that enables tracking of various metrics, parameters, and artifacts. It helps to enhance model reproducibility, debug issues, and improve model performance. [Ultralytics](https://www.ultralytics.com/) YOLO, known for its real-time [object detection](https://www.ultralytics.com/glossary/object-detection) capabilities, now offers integration with [MLflow](https://mlflow.org/), an open-source platform for complete machine learning lifecycle management.
This documentation page is a comprehensive guide to setting up and utilizing the MLflow logging capabilities for your Ultralytics YOLO project.
@ -164,7 +164,7 @@ mlflow server --backend-store-uri runs/mlflow
Ultralytics YOLO with MLflow supports logging various metrics, parameters, and artifacts throughout the training process:
- **Metrics Logging**: Tracks metrics at the end of each epoch and upon training completion.
- **Metrics Logging**: Tracks metrics at the end of each [epoch](https://www.ultralytics.com/glossary/epoch) and upon training completion.
- **Parameter Logging**: Logs all parameters used in the training process.
- **Artifacts Logging**: Saves model artifacts like weights and configuration files after training.