Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement object detection, segmentation, and classification in their projects. YOLOv8's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. yolo predict -model yolov8n.pt -imgsz 640 -conf 0.25 ❌ (do not use -).yolo predict model=yolov8n.pt, imgsz=640, conf=0.25 ❌ (do not use ,).yolo predict model yolov8n.pt imgsz 640 conf 0.25 ❌ (missing =).yolo predict model=yolov8n.pt imgsz=640 conf=0.25 ✅.Do not use - argument prefixes or commas, between arguments. Dockerfile-conda: Based on Miniconda3 with conda installation of ultralytics package.īelow are the commands to get the latest image and execute it:Īrguments must be passed as arg=val pairs, split by an equals = sign and delimited by spaces between pairs.Dockerfile-python: Minimal image with just Python and necessary dependencies, ideal for lightweight applications and development.Dockerfile-jetson: Tailored for NVIDIA Jetson devices, integrating GPU support optimized for these platforms.Dockerfile-cpu: Ubuntu-based CPU-only version suitable for inference and environments without GPUs.Dockerfile-arm64: Optimized for ARM64 architecture, allowing deployment on devices like Raspberry Pi and other ARM64-based platforms.Dockerfile: GPU image recommended for training.Ultralytics offers 5 main supported Docker images, each designed to provide high compatibility and efficiency for different platforms and use cases: By choosing one of the official ultralytics images from Docker Hub, you not only avoid the complexity of local installation but also benefit from access to a verified working environment. Utilize Docker to effortlessly execute the ultralytics package in an isolated container, ensuring consistent and smooth performance across various environments. Nothing added to commit but untracked files present (use 'git add' to track)Īdd all the files in ~/test-repo-789 to the Git staging area: git add. Push to the Forked RepoĬreate files on the development Linode and push them to the forked repository on GitHub.įrom the ~/test-repo-789 directory, create and checkout a new branch: git checkout -b newbranchĬreate a project directory: mkdir projectĬreate sample files: touch repoTest1.js repoTest2.htm project/prjtTest1.js project/prjtTest1.htmĬheck the status of the Git project, with the git status command: git status Next, return to the terminal of the development Linode. You now have a copy of the repo on your GitHub account. The “Fork” button is under the username icon pictured in step 2 above. Search for test-repo-789.Īfter you select NwayNway/test-repo-789, fork the repo using the “Fork” button on the top right of the page. To fork test-repo-789, use the search bar at the top left of the page. Select your username at the top right of the page, pictured below, which links to your profile. At the “Welcome to GitHub” page, select the green, “Finish sign up” button at the bottom. A fork is a copy of a repo held on your GitHub account.Ĭreate a username on GitHub. To share new files or file revisions, you’ll need a GitHub account and a project fork. To ensure that your master branch is up-to-date, use the pull command: git pull masterĬreate a GitHub Account and Fork the Test Repo In the Linode terminal from the home directory, use the command git clone, then paste the link from your clipboard, or copy the command and link from below: git clone Ĭhange directories to the new ~/test-repo-789 directory: cd ~/test-repo-789/ Select test-repo-789, it should be the first result, listed as NwayNway/test-repo-789.Ĭopy the “HTTPS clone URL” link using the clipboard icon at the bottom right of the page’s side-bar, pictured below. If you would like to contribute to Linode’s guides, search for linode docs. For tutorial purposes, there is a test repository setup on GitHub, which is listed below.

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