---
comments: true
description: Explore various methods to install Ultralytics using pip, conda, git and Docker. Learn how to use Ultralytics with command line interface or within your Python projects.
keywords: Ultralytics installation, pip install Ultralytics, Docker install Ultralytics, Ultralytics command line interface, Ultralytics Python interface
---

## 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](https://github.com/ultralytics/ultralytics) for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.

!!! example "Install"

    === "Pip install (recommended)"
        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: [https://pypi.org/project/ultralytics/](https://pypi.org/project/ultralytics/).

        [![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)

        ```bash
        # 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 alltogether to default to `main` branch.
    
        ```bash
        # Install the ultralytics package from GitHub
        pip install git+https://github.com/ultralytics/ultralytics.git@main
        ```


    === "Conda install"
        Conda is an alternative package manager to pip which may also be used for installation. Visit Anaconda for more details at [https://anaconda.org/conda-forge/ultralytics](https://anaconda.org/conda-forge/ultralytics). Ultralytics feedstock repository for updating the conda package is at [https://github.com/conda-forge/ultralytics-feedstock/](https://github.com/conda-forge/ultralytics-feedstock/).


        [![Conda Recipe](https://img.shields.io/badge/recipe-ultralytics-green.svg)](https://anaconda.org/conda-forge/ultralytics) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics) [![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics)

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

        !!! note
        
            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.
            ```bash
            # Install all packages together using conda
            conda install -c conda-forge -c pytorch -c nvidia ultralytics pytorch torchvision pytorch-cuda=11.8 
            ```

    === "Git clone"
        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.
        ```bash
        # Clone the ultralytics repository
        git clone https://github.com/ultralytics/ultralytics

        # Navigate to the cloned directory
        cd ultralytics

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

    === "Docker"

        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](https://hub.docker.com/r/ultralytics/ultralytics), 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:
        
        <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>

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

        ```bash
        # Set image name as a variable
        t=ultralytics/ultralytics:latest

        # 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:

        ```bash
        # 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.

See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) file for a list of dependencies. Note that all examples above install all required dependencies.

!!! tip "Tip"

    PyTorch requirements vary by operating system and CUDA requirements, so it's recommended to install PyTorch first following instructions at [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally).

    <a href="https://pytorch.org/get-started/locally/">
        <img width="800" alt="PyTorch Installation Instructions" src="https://user-images.githubusercontent.com/26833433/228650108-ab0ec98a-b328-4f40-a40d-95355e8a84e3.png">
    </a>

## 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](usage/cli.md) to learn more about using YOLOv8 from the command line.

!!! example

    === "Syntax"

        Ultralytics `yolo` commands use the following syntax:
        ```bash
        yolo TASK MODE ARGS

        Where   TASK (optional) is one of [detect, segment, classify]
                MODE (required) is one of [train, val, predict, export, track]
                ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
        ```
        See all ARGS in the full [Configuration Guide](usage/cfg.md) or with `yolo cfg`

    === "Train"

        Train a detection model for 10 epochs with an initial learning_rate of 0.01
        ```bash
        yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
        ```

    === "Predict"

        Predict a YouTube video using a pretrained segmentation model at image size 320:
        ```bash
        yolo predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
        ```

    === "Val"

        Val a pretrained detection model at batch-size 1 and image size 640:
        ```bash
        yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
        ```

    === "Export"

        Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
        ```bash
        yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
        ```

    === "Special"

        Run special commands to see version, view settings, run checks and more:
        ```bash
        yolo help
        yolo checks
        yolo version
        yolo settings
        yolo copy-cfg
        yolo cfg
        ```

!!! warning "Warning"

    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 model=yolov8n.pt imgsz=640 conf=0.25` &nbsp; ✅
    - `yolo predict model yolov8n.pt imgsz 640 conf 0.25` &nbsp; ❌
    - `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25` &nbsp; ❌

[CLI Guide](usage/cli.md){ .md-button .md-button--primary}

## 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](usage/python.md) to learn more about using YOLOv8 within your Python projects.

!!! example

    ```python
    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('yolov8n.pt')

    # Train the model using the 'coco128.yaml' dataset for 3 epochs
    results = model.train(data='coco128.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('https://ultralytics.com/images/bus.jpg')

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

[Python Guide](usage/python.md){.md-button .md-button--primary}

## 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:

!!! example "View settings"

    === "Python"
        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:
        ```python
        from ultralytics import settings

        # View all settings
        print(settings)

        # Return a specific setting
        value = settings['runs_dir']
        ```

    === "CLI"
        Alternatively, the command-line interface allows you to check your settings with a simple command:
        ```bash
        yolo settings
        ```

### Modifying Settings

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

!!! example "Update settings"

    === "Python"
        Within the Python environment, call the `update` method on the `settings` object to change your settings:
        ```python
        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
        settings.reset()
        ```

    === "CLI"
        If you prefer using the command-line interface, the following commands will allow you to modify your settings:
        ```bash
        # 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](https://pypi.org/project/ultralytics/) 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](https://hub.ultralytics.com/settings?tab=api+keys)                                     |
| `clearml`          | `True`                | `bool`    | Whether to use ClearML logging                                                                                   |
| `comet`            | `True`                | `bool`    | Whether to use [Comet ML](https://bit.ly/yolov8-readme-comet) for experiment tracking and visualization          |
| `dvc`              | `True`                | `bool`    | Whether to use [DVC for experiment tracking](https://dvc.org/doc/dvclive/ml-frameworks/yolo) and version control |
| `hub`              | `True`                | `bool`    | Whether to use [Ultralytics HUB](https://hub.ultralytics.com) 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.
