Spaces:
Sleeping
Sleeping
File size: 5,314 Bytes
339f0eb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
---
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
<p align="center">
<img width="800" src="https://user-images.githubusercontent.com/26833433/266324397-32119e21-8c86-43e5-a00e-79827d303d10.png" alt="Ultralytics Conda Package Visual">
</p>
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/).
[](https://anaconda.org/conda-forge/ultralytics) [](https://anaconda.org/conda-forge/ultralytics) [](https://anaconda.org/conda-forge/ultralytics) [](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.show() # display results
```
---
## 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](https://docs.ultralytics.com/) for more advanced tutorials and examples.
|