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Docs improvements and redirect fixes (#16287)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-09-15 00:27:46 +02:00 • committed by GitHub
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docs/en/modes/train.md
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@ -123,14 +123,14 @@ To enable training on Apple M1 and M2 chips, you should specify 'mps' as your de
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Train the model with 2 GPUs
# Train the model with MPS
results = model.train(data="coco8.yaml", epochs=100, imgsz=640, device="mps")
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model using GPUs 0 and 1
# Start training from a pretrained *.pt model using MPS
yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640 device=mps
```
@ -169,7 +169,7 @@ Below is an example of how to resume an interrupted training using Python and vi
By setting `resume=True`, the `train` function will continue training from where it left off, using the state stored in the 'path/to/last.pt' file. If the `resume` argument is omitted or set to `False`, the `train` function will start a new training session.
Remember that checkpoints are saved at the end of every epoch by default, or at fixed interval using the `save_period` argument, so you must complete at least 1 epoch to resume a training run.
Remember that checkpoints are saved at the end of every epoch by default, or at fixed intervals using the `save_period` argument, so you must complete at least 1 epoch to resume a training run.
## Train Settings