Link to this sectionHow to Install Ultralytics YOLO with Conda#

This guide walks through 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 work. For more details, visit the Ultralytics Conda package on Anaconda and check out the Ultralytics feedstock repository for package updates on GitHub.

This guide covers how to create an environment, install Ultralytics, run inference, use the Conda Docker image, and speed up installs with libmamba.

Link to this sectionPrerequisites#

You should have Anaconda or Miniconda installed on your system. If not, download and install it from Anaconda or Miniconda.

Link to this sectionSetting up a Conda Environment#

First, create a new Conda environment. Open your terminal and run the following command:

conda create --name ultralytics-env python=3.11 -y

Activate the new environment:

conda activate ultralytics-env

Link to this sectionInstalling Ultralytics#

You can install the Ultralytics package from the conda-forge channel. Execute the following command:

conda install -c conda-forge ultralytics

conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=12.1 ultralytics

Link to this sectionUsing 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:

from ultralytics import YOLO

model = YOLO("yolo26n.pt")  # initialize model
results = model("path/to/image.jpg")  # perform inference
results[0].show()  # display results for the first image

Link to this sectionUltralytics Conda Docker Image#

If you prefer using Docker, Ultralytics offers Docker images with a Conda environment included. You can pull these images from DockerHub.

Pull the latest Ultralytics image:

# 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:

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

Link to this sectionSpeeding Up Installation with Libmamba#

libmamba is a fast, cross-platform, dependency-aware solver that replaces Conda's classic solver. Conda 23.10 and later already use libmamba as the default solver, so most installations are faster out of the box.

If you're on an older Conda version, you can enable libmamba manually:

1. First, install the conda-libmamba-solver package:

   conda install conda-libmamba-solver

2. Next, configure Conda to use libmamba as the solver:

   conda config --set solver libmamba

You have successfully set up a Conda environment, installed the Ultralytics package, and are now ready to explore its features. For more advanced tutorials and examples, see the Ultralytics documentation.

Link to this sectionFAQ#

Link to this sectionWhat 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:

conda create --name ultralytics-env python=3.11 -y

Then, activate the new environment with:

conda activate ultralytics-env

Finally, install Ultralytics from the conda-forge channel:

conda install -c conda-forge ultralytics

Link to this sectionWhy 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.

Link to this sectionCan 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:

conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=12.1 ultralytics

This setup enables GPU acceleration, crucial for intensive tasks like deep learning model training and inference. For more information, visit the Ultralytics installation guide.

Link to this sectionWhat 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:

sudo docker pull ultralytics/ultralytics:latest-conda
sudo docker run -it --ipc=host --runtime=nvidia --gpus all ultralytics/ultralytics:latest-conda            # all GPUs
sudo docker run -it --ipc=host --runtime=nvidia --gpus '"device=2,3"' ultralytics/ultralytics:latest-conda # specify GPUs

This approach is ideal for deploying applications in production or running complex workflows without manual configuration. Learn more about Ultralytics Conda Docker Image.

Link to this sectionHow can I speed up Conda package installation in my Ultralytics environment?#

Conda 23.10 and later already use the fast libmamba solver by default. On older Conda versions, you can enable it manually by first installing the conda-libmamba-solver package:

conda install conda-libmamba-solver

Then configure Conda to use libmamba as the solver:

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.