--- comments: true description: Comprehensive guide to setting up and using Ultralytics YOLO models in a Conda environment. Learn how to install the package, manage dependencies, and get started with object detection projects. keywords: Ultralytics, YOLO, Conda, environment setup, object detection, package installation, deep learning, machine learning, guide --- # Conda Quickstart Guide for Ultralytics

Ultralytics Conda Package Visual

This guide provides a comprehensive introduction to setting up a Conda environment for your Ultralytics projects. Conda is an open-source package and environment management system that offers an excellent alternative to pip for installing packages and dependencies. Its isolated environments make it particularly well-suited for data science and machine learning endeavors. For more details, visit the Ultralytics Conda package on [Anaconda](https://anaconda.org/conda-forge/ultralytics) and check out the Ultralytics feedstock repository for package updates on [GitHub](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) ## What You Will Learn - Setting up a Conda environment - Installing Ultralytics via Conda - Initializing Ultralytics in your environment - Using Ultralytics Docker images with Conda --- ## Prerequisites - You should have Anaconda or Miniconda installed on your system. If not, download and install it from [Anaconda](https://www.anaconda.com/) or [Miniconda](https://docs.conda.io/projects/miniconda/en/latest/). --- ## Setting up a Conda Environment First, let's create a new Conda environment. Open your terminal and run the following command: ```bash conda create --name ultralytics-env python=3.8 -y ``` Activate the new environment: ```bash conda activate ultralytics-env ``` --- ## Installing Ultralytics You can install the Ultralytics package from the conda-forge channel. Execute the following command: ```bash conda install -c conda-forge ultralytics ``` ### Note on CUDA Environment If you're working in a CUDA-enabled environment, it's a good practice to install `ultralytics`, `pytorch`, and `pytorch-cuda` together to resolve any conflicts: ```bash conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics ``` --- ## Using Ultralytics With Ultralytics installed, you can now start using its robust features for object detection, instance segmentation, and more. For example, to predict an image, you can run: ```python from ultralytics import YOLO model = YOLO('yolov8n.pt') # initialize model results = model('path/to/image.jpg') # perform inference results[0].show() # display results for the first image ``` --- ## Ultralytics Conda Docker Image If you prefer using Docker, Ultralytics offers Docker images with a Conda environment included. You can pull these images from [DockerHub](https://hub.docker.com/r/ultralytics/ultralytics). Pull the latest Ultralytics image: ```bash # Set image name as a variable t=ultralytics/ultralytics:latest-conda # Pull the latest Ultralytics image from Docker Hub sudo docker pull $t ``` Run the image: ```bash # 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 ``` --- Certainly, you can include the following section in your Conda guide to inform users about speeding up installation using `libmamba`: --- ## Speeding Up Installation with Libmamba If you're looking to [speed up the package installation](https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community) process in Conda, you can opt to use `libmamba`, a fast, cross-platform, and dependency-aware package manager that serves as an alternative solver to Conda's default. ### How to Enable Libmamba To enable `libmamba` as the solver for Conda, you can perform the following steps: 1. First, install the `conda-libmamba-solver` package. This can be skipped if your Conda version is 4.11 or above, as `libmamba` is included by default. ```bash conda install conda-libmamba-solver ``` 2. Next, configure Conda to use `libmamba` as the solver: ```bash conda config --set solver libmamba ``` And that's it! Your Conda installation will now use `libmamba` as the solver, which should result in a faster package installation process. --- Congratulations! You have successfully set up a Conda environment, installed the Ultralytics package, and are now ready to explore its rich functionalities. Feel free to dive deeper into the [Ultralytics documentation](../index.md) for more advanced tutorials and examples.