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Install Ultralytics

Ultralytics provides various installation methods including pip, conda, and Docker. Install YOLOv8 via the ultralytics pip package for the latest stable release or by cloning the Ultralytics GitHub repository for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.

Watch: Ultralytics YOLO Quick Start Guide


PyPI - Python Version

Install the ultralytics package using pip, or update an existing installation by running pip install -U ultralytics. Visit the Python Package Index (PyPI) for more details on the ultralytics package:

PyPI - Version Downloads

# Install the ultralytics package from PyPI
pip install ultralytics

You can also install the ultralytics package directly from the GitHub repository. This might be useful if you want the latest development version. Make sure to have the Git command-line tool installed on your system. The @main command installs the main branch and may be modified to another branch, i.e. @my-branch, or removed entirely to default to main branch.

# Install the ultralytics package from GitHub
pip install git+

Conda is an alternative package manager to pip which may also be used for installation. Visit Anaconda for more details at Ultralytics feedstock repository for updating the conda package is at

Conda Version Conda Downloads Conda Recipe Conda Platforms

# Install the ultralytics package using conda
conda install -c conda-forge ultralytics


If you are installing in a CUDA environment best practice is to install ultralytics, pytorch and pytorch-cuda in the same command to allow the conda package manager to resolve any conflicts, or else to install pytorch-cuda last to allow it override the CPU-specific pytorch package if necessary.

# Install all packages together using conda
conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics

Conda Docker Image

Ultralytics Conda Docker images are also available from DockerHub. These images are based on Miniconda3 and are an simple way to start using ultralytics in a Conda environment.

# Set image name as a variable

# Pull the latest ultralytics image from Docker Hub
sudo docker pull $t

# Run the ultralytics image in a container with GPU support
sudo docker run -it --ipc=host --gpus all $t  # all GPUs
sudo docker run -it --ipc=host --gpus '"device=2,3"' $t  # specify GPUs

Clone the ultralytics repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package in editable mode -e using pip.

GitHub last commit GitHub commit activity

# Clone the ultralytics repository
git clone

# Navigate to the cloned directory
cd ultralytics

# Install the package in editable mode for development
pip install -e .

Utilize Docker to effortlessly execute the ultralytics package in an isolated container, ensuring consistent and smooth performance across various environments. By choosing one of the official ultralytics images from Docker Hub, you not only avoid the complexity of local installation but also benefit from access to a verified working environment. Ultralytics offers 5 main supported Docker images, each designed to provide high compatibility and efficiency for different platforms and use cases:

Docker Image Version Docker Pulls

  • Dockerfile: GPU image recommended for training.
  • Dockerfile-arm64: Optimized for ARM64 architecture, allowing deployment on devices like Raspberry Pi and other ARM64-based platforms.
  • Dockerfile-cpu: Ubuntu-based CPU-only version suitable for inference and environments without GPUs.
  • Dockerfile-jetson: Tailored for NVIDIA Jetson devices, integrating GPU support optimized for these platforms.
  • Dockerfile-python: Minimal image with just Python and necessary dependencies, ideal for lightweight applications and development.
  • Dockerfile-conda: Based on Miniconda3 with conda installation of ultralytics package.

Below are the commands to get the latest image and execute it:

# Set image name as a variable

# Pull the latest ultralytics image from Docker Hub
sudo docker pull $t

# Run the ultralytics image in a container with GPU support
sudo docker run -it --ipc=host --gpus all $t  # all GPUs
sudo docker run -it --ipc=host --gpus '"device=2,3"' $t  # specify GPUs

The above command initializes a Docker container with the latest ultralytics image. The -it flag assigns a pseudo-TTY and maintains stdin open, enabling you to interact with the container. The --ipc=host flag sets the IPC (Inter-Process Communication) namespace to the host, which is essential for sharing memory between processes. The --gpus all flag enables access to all available GPUs inside the container, which is crucial for tasks that require GPU computation.

Note: To work with files on your local machine within the container, use Docker volumes for mounting a local directory into the container:

# Mount local directory to a directory inside the container
sudo docker run -it --ipc=host --gpus all -v /path/on/host:/path/in/container $t

Alter /path/on/host with the directory path on your local machine, and /path/in/container with the desired path inside the Docker container for accessibility.

For advanced Docker usage, feel free to explore the Ultralytics Docker Guide.

See the ultralytics pyproject.toml file for a list of dependencies. Note that all examples above install all required dependencies.


PyTorch requirements vary by operating system and CUDA requirements, so it's recommended to install PyTorch first following instructions at

PyTorch Installation Instructions

Use Ultralytics with CLI

The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment. CLI requires no customization or Python code. You can simply run all tasks from the terminal with the yolo command. Check out the CLI Guide to learn more about using YOLOv8 from the command line.


Ultralytics yolo commands use the following syntax:


See all ARGS in the full Configuration Guide or with the yolo cfg CLI command.

Train a detection model for 10 epochs with an initial learning_rate of 0.01

yolo train data=coco8.yaml epochs=10 lr0=0.01

Predict a YouTube video using a pretrained segmentation model at image size 320:

yolo predict source='' imgsz=320

Val a pretrained detection model at batch-size 1 and image size 640:

yolo val data=coco8.yaml batch=1 imgsz=640

Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)

yolo export format=onnx imgsz=224,128

Run special commands to see version, view settings, run checks and more:

yolo help
yolo checks
yolo version
yolo settings
yolo copy-cfg
yolo cfg


Arguments must be passed as arg=val pairs, split by an equals = sign and delimited by spaces between pairs. Do not use -- argument prefixes or commas , between arguments.

  • yolo predict imgsz=640 conf=0.25
  • yolo predict model imgsz 640 conf 0.25 ❌ (missing =)
  • yolo predict, imgsz=640, conf=0.25 ❌ (do not use ,)
  • yolo predict --model --imgsz 640 --conf 0.25 ❌ (do not use --)

CLI Guide

Use Ultralytics with Python

YOLOv8's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement object detection, segmentation, and classification in their projects. This makes YOLOv8's Python interface an invaluable tool for anyone looking to incorporate these functionalities into their Python projects.

For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX format with just a few lines of code. Check out the Python Guide to learn more about using YOLOv8 within your Python projects.


from ultralytics import YOLO

# Create a new YOLO model from scratch
model = YOLO("yolov8n.yaml")

# Load a pretrained YOLO model (recommended for training)
model = YOLO("")

# Train the model using the 'coco8.yaml' dataset for 3 epochs
results = model.train(data="coco8.yaml", epochs=3)

# Evaluate the model's performance on the validation set
results = model.val()

# Perform object detection on an image using the model
results = model("")

# Export the model to ONNX format
success = model.export(format="onnx")

Python Guide

Ultralytics Settings

The Ultralytics library provides a powerful settings management system to enable fine-grained control over your experiments. By making use of the SettingsManager housed within the ultralytics.utils module, users can readily access and alter their settings. These are stored in a YAML file and can be viewed or modified either directly within the Python environment or via the Command-Line Interface (CLI).

Inspecting Settings

To gain insight into the current configuration of your settings, you can view them directly:

View settings

You can use Python to view your settings. Start by importing the settings object from the ultralytics module. Print and return settings using the following commands:

from ultralytics import settings

# View all settings

# Return a specific setting
value = settings["runs_dir"]

Alternatively, the command-line interface allows you to check your settings with a simple command:

yolo settings

Modifying Settings

Ultralytics allows users to easily modify their settings. Changes can be performed in the following ways:

Update settings

Within the Python environment, call the update method on the settings object to change your settings:

from ultralytics import settings

# Update a setting
settings.update({"runs_dir": "/path/to/runs"})

# Update multiple settings
settings.update({"runs_dir": "/path/to/runs", "tensorboard": False})

# Reset settings to default values

If you prefer using the command-line interface, the following commands will allow you to modify your settings:

# Update a setting
yolo settings runs_dir='/path/to/runs'

# Update multiple settings
yolo settings runs_dir='/path/to/runs' tensorboard=False

# Reset settings to default values
yolo settings reset

Understanding Settings

The table below provides an overview of the settings available for adjustment within Ultralytics. Each setting is outlined along with an example value, the data type, and a brief description.

Name Example Value Data Type Description
settings_version '0.0.4' str Ultralytics settings version (different from Ultralytics pip version)
datasets_dir '/path/to/datasets' str The directory where the datasets are stored
weights_dir '/path/to/weights' str The directory where the model weights are stored
runs_dir '/path/to/runs' str The directory where the experiment runs are stored
uuid 'a1b2c3d4' str The unique identifier for the current settings
sync True bool Whether to sync analytics and crashes to HUB
api_key '' str Ultralytics HUB API Key
clearml True bool Whether to use ClearML logging
comet True bool Whether to use Comet ML for experiment tracking and visualization
dvc True bool Whether to use DVC for experiment tracking and version control
hub True bool Whether to use Ultralytics HUB integration
mlflow True bool Whether to use MLFlow for experiment tracking
neptune True bool Whether to use Neptune for experiment tracking
raytune True bool Whether to use Ray Tune for hyperparameter tuning
tensorboard True bool Whether to use TensorBoard for visualization
wandb True bool Whether to use Weights & Biases logging

As you navigate through your projects or experiments, be sure to revisit these settings to ensure that they are optimally configured for your needs.

Created 2023-11-12, Updated 2024-06-10
Authors: glenn-jocher (11), Burhan-Q (2), RizwanMunawar (2), Laughing-q (1), AyushExel (1)