--- comments: true description: Learn to set up a Conda environment for Ultralytics projects. Follow our comprehensive guide for easy installation and initialization. keywords: Ultralytics, Conda, setup, installation, environment, guide, machine learning, data science --- # 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](https://www.ultralytics.com/glossary/machine-learning-ml) 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 Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](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 Recipe](https://img.shields.io/badge/recipe-ultralytics-green.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=3.11 -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](https://www.ultralytics.com/glossary/object-detection), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and more. For example, to predict an image, you can run: ```python from ultralytics import YOLO model = YOLO("yolo11n.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 ``` ## 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. ## FAQ ### What is the process for setting up a Conda environment for Ultralytics projects? Setting up a Conda environment for Ultralytics projects is straightforward and ensures smooth package management. First, create a new Conda environment using the following command: ```bash conda create --name ultralytics-env python=3.11 -y ``` Then, activate the new environment with: ```bash conda activate ultralytics-env ``` Finally, install Ultralytics from the conda-forge channel: ```bash conda install -c conda-forge ultralytics ``` ### Why should I use Conda over pip for managing dependencies in Ultralytics projects? Conda is a robust package and environment management system that offers several advantages over pip. It manages dependencies efficiently and ensures that all necessary libraries are compatible. Conda's isolated environments prevent conflicts between packages, which is crucial in data science and machine learning projects. Additionally, Conda supports binary package distribution, speeding up the installation process. ### Can I use Ultralytics YOLO in a CUDA-enabled environment for faster performance? Yes, you can enhance performance by utilizing a CUDA-enabled environment. Ensure that you install `ultralytics`, `pytorch`, and `pytorch-cuda` together to avoid conflicts: ```bash conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics ``` This setup enables GPU acceleration, crucial for intensive tasks like [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) model training and inference. For more information, visit the [Ultralytics installation guide](../quickstart.md). ### What are the benefits of using Ultralytics Docker images with a Conda environment? Using Ultralytics Docker images ensures a consistent and reproducible environment, eliminating "it works on my machine" issues. These images include a pre-configured Conda environment, simplifying the setup process. You can pull and run the latest Ultralytics Docker image with the following commands: ```bash sudo docker pull ultralytics/ultralytics:latest-conda sudo docker run -it --ipc=host --gpus all ultralytics/ultralytics:latest-conda ``` This approach is ideal for deploying applications in production or running complex workflows without manual configuration. Learn more about [Ultralytics Conda Docker Image](../quickstart.md). ### How can I speed up Conda package installation in my Ultralytics environment? You can speed up the package installation process by using `libmamba`, a fast dependency solver for Conda. First, install the `conda-libmamba-solver` package: ```bash conda install conda-libmamba-solver ``` Then configure Conda to use `libmamba` as the solver: ```bash conda config --set solver libmamba ``` This setup provides faster and more efficient package management. For more tips on optimizing your environment, read about [libmamba installation](../quickstart.md).