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Docs partial mdformat improvements (#7378)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2024-01-07 17:13:42 +01:00 • committed by GitHub
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13
docs/en/integrations/mlflow.md
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@ -63,24 +63,31 @@ Make sure that MLflow logging is enabled in Ultralytics settings. Usually, this
### Commands
1. **Set a Project Name**: You can set the project name via an environment variable:
```bash
export MLFLOW_EXPERIMENT_NAME=<your_experiment_name>
```
Or use the `project=<project>` argument when training a YOLO model, i.e. `yolo train project=my_project`.
2. **Set a Run Name**: Similar to setting a project name, you can set the run name via an environment variable:
```bash
export MLFLOW_RUN=<your_run_name>
```
Or use the `name=<name>` argument when training a YOLO model, i.e. `yolo train project=my_project name=my_name`.
3. **Start Local MLflow Server**: To start tracking, use:
```bash
mlflow server --backend-store-uri runs/mlflow'
```
This will start a local server at http://127.0.0.1:5000 by default and save all mlflow logs to the 'runs/mlflow' directory. To specify a different URI, set the `MLFLOW_TRACKING_URI` environment variable.
4. **Kill MLflow Server Instances**: To stop all running MLflow instances, run:
```bash
ps aux | grep 'mlflow' | grep -v 'grep' | awk '{print $2}' | xargs kill -9
```
@ -93,11 +100,9 @@ The logging is taken care of by the `on_pretrain_routine_end`, `on_fit_epoch_end
1. **Logging Custom Metrics**: You can add custom metrics to be logged by modifying the `trainer.metrics` dictionary before `on_fit_epoch_end` is called.
2. **View Experiment**: To view your logs, navigate to your MLflow server (usually http://127.0.0.1:5000) and select your experiment and run.
<img width="1024" src="https://user-images.githubusercontent.com/26833433/274933329-3127aa8c-4491-48ea-81df-ed09a5837f2a.png" alt="YOLO MLflow Experiment">
2. **View Experiment**: To view your logs, navigate to your MLflow server (usually http://127.0.0.1:5000) and select your experiment and run. <img width="1024" src="https://user-images.githubusercontent.com/26833433/274933329-3127aa8c-4491-48ea-81df-ed09a5837f2a.png" alt="YOLO MLflow Experiment">
3. **View Run**: Runs are individual models inside an experiment. Click on a Run and see the Run details, including uploaded artifacts and model weights.
<img width="1024" src="https://user-images.githubusercontent.com/26833433/274933337-ac61371c-2867-4099-a733-147a2583b3de.png" alt="YOLO MLflow Run">
3. **View Run**: Runs are individual models inside an experiment. Click on a Run and see the Run details, including uploaded artifacts and model weights. <img width="1024" src="https://user-images.githubusercontent.com/26833433/274933337-ac61371c-2867-4099-a733-147a2583b3de.png" alt="YOLO MLflow Run">
## Disabling MLflow