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## Install
Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning
the [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) repository for the most
up-to-date version.
!!! example "Pip install method (recommended)"
```bash
pip install ultralytics
```
!!! example "Git clone method (for development)"
```bash
git clone https://github.com/ultralytics/ultralytics
cd ultralytics
pip install -e '.[dev]'
```
See contributing section to know more about contributing to the project
## Use with CLI
The YOLO command line interface (CLI) lets you simply train, validate or infer models on various tasks and versions.
CLI requires no customization or code. You can simply run all tasks from the terminal with the `yolo` command.
!!! example
=== "Syntax"
```bash
yolo task=detect mode=train model=yolov8n.yaml args...
classify predict yolov8n-cls.yaml args...
segment val yolov8n-seg.yaml args...
export yolov8n.pt format=onnx args...
```
=== "Example training"
```bash
yolo detect train model=yolov8n.pt data=coco128.yaml device=0
```
=== "Example Multi-GPU training"
```bash
yolo detect train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
```
[CLI Guide](usage/cli.md){ .md-button .md-button--primary}
## Use with Python
Python usage allows users to easily use YOLOv8 inside their Python projects. It provides functions for loading and
running the model, as well as for processing the model's output. The interface is designed to be easy to use, so that
users can quickly implement object detection in their projects.
Overall, the Python interface is a useful tool for anyone looking to incorporate object detection, segmentation or
classification into their Python projects using YOLOv8.
!!! example
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
results = model.train(data="coco128.yaml", epochs=3) # train the model
results = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
success = model.export(format="onnx") # export the model to ONNX format
```
[Python Guide](usage/python.md){.md-button .md-button--primary}