guides/conda-quickstart/

[giscus[bot]]

guides/conda-quickstart/

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.

https://docs.ultralytics.com/guides/conda-quickstart/

[nadinator]

Does the python version have to be 3.8? Can it be a newer version? What would/wouldn't break if we used 3.11, for example (I'm thinking of the [ray]tuning feature)?

[glenn-jocher]

Hey there! 😊

You can definitely use Python versions newer than 3.8, including 3.11. However, it's important to note that while most features should work fine, there might be some compatibility issues with newer Python versions, especially with external libraries or specific features like [ray] tuning.

If you're keen on using Python 3.11, I'd recommend testing the key functionalities you plan to use in your project to ensure everything runs smoothly. For the [ray] tuning feature, you might want to check Ray's documentation or GitHub issues for any known compatibility concerns with Python 3.11.

Here's a quick example of how you might set up your environment with Python 3.11:

conda create --name ultralytics-env python=3.11 -y
conda activate ultralytics-env
conda install -c conda-forge ultralytics

And remember, if you encounter any issues, the Ultralytics community is here to help! 🚀

[Malvinlam]

Hi is there a way to install the yolov8 dependencies using Anaconda prompt to the virtual environment other than pip install ultralytics? For example I already downloaded the ultralytics package from GitHub and opened it as project folder using PyCharm. Is there a way to only install the required libraries?

[glenn-jocher]

@Malvinlam hi there! 👋

Absolutely, you can install the required dependencies for YOLOv8 using Conda without relying on pip install ultralytics. Here's a step-by-step guide to help you set up your Conda environment and install the necessary libraries:

1. Create a New Conda Environment:
   First, create a new Conda environment with Python 3.8 (or your preferred version):

   conda create --name ultralytics-env python=3.8 -y

2. Activate the Environment:
   Activate your newly created environment:

   conda activate ultralytics-env

3. Install Required Libraries:
   You can install the required libraries from the requirements.txt file in the Ultralytics repository. First, navigate to the directory where your requirements.txt file is located, then run:

   conda install --file requirements.txt

   Alternatively, if you want to install specific packages manually, you can use:

   conda install -c conda-forge numpy pandas matplotlib opencv scikit-learn
   conda install -c pytorch pytorch torchvision torchaudio

4. Note on CUDA Environment:
   If you're working in a CUDA-enabled environment, it's a good practice to install pytorch and pytorch-cuda together to resolve any conflicts:

   conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision torchaudio pytorch-cuda=11.8

5. Using Ultralytics:
   With the dependencies installed, you can now start using Ultralytics. For example, to perform inference with YOLOv8:

   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

For more detailed instructions, you can refer to our Conda Quickstart Guide.

If you encounter any issues or have further questions, feel free to ask. Happy coding! 😊

[phpmegha]

The step-by-step instructions on creating a Conda environment and installing Ultralytics made everything so much simpler.

The section about CUDA compatibility was a huge help too—I had no idea how to avoid those conflicts before. And just when I thought it couldn’t get better, the mention of Docker images with Conda included is like icing on the cake!

For anyone setting this up on Ubuntu, the install miniconda ubuntu guide is a perfect starting point. Highly recommend this to anyone looking to streamline their data science or machine learning setup. Kudos for such a thorough and easy-to-follow guide!

[glenn-jocher]

@phpmegha thank you for your kind words! We're glad the guide was helpful in simplifying your setup and addressing CUDA compatibility concerns. The inclusion of Docker images with Conda indeed makes deployment smoother. Your recommendation of the Miniconda Ubuntu guide is also a great resource for new users. Credit goes to the YOLO community and the Ultralytics team for creating such comprehensive resources. Let us know if you have any further questions!

[phpmegha]

The step-by-step instructions on creating a Conda environment and installing Ultralytics made everything so much simpler.

The section about CUDA compatibility was a huge help too—I had no idea how to avoid those conflicts before. And just when I thought it couldn’t get better, the mention of Docker images with Conda included is like icing on the cake!

For anyone setting this up on Ubuntu, the install miniconda ubuntu guide is a perfect starting point. Highly recommend this to anyone looking to streamline their data science or machine learning setup. Kudos for such a thorough and easy-to-follow guide!

[glenn-jocher]

@phpmegha thank you for the kind words and detailed feedback! We're glad you found the Conda setup guide and CUDA compatibility tips helpful. The inclusion of Docker images with Conda is indeed a powerful addition for streamlined workflows. If you have further suggestions or questions, feel free to share—your input helps improve the Ultralytics tools and community.