Based on our record, OpenCV should be more popular than Buildah. It has been mentiond 60 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but... - Source: dev.to / 26 days ago
Ideal For: Computer vision, NLP, deep learning, and machine learning. - Source: dev.to / about 1 month ago
Almost everyone has heard of libraries like OpenCV, Pytorch, and Torchvision. But there have been incredible leaps and bounds in other libraries to help support new tasks that have helped push research even further. It would be impossible to thank each and every project and the thousands of contributors who have helped make the entire community better. MedSAM2 has been helping bring the awesomeness of SAM2 to the... - Source: dev.to / 5 months ago
OpenCV is an open-source computer vision and machine learning software library that allows users to perform various ML tasks, from processing images and videos to identifying objects, faces, or handwriting. Besides object detection, this platform can also be used for complex computer vision tasks like Geometry-based monocular or stereo computer vision. - Source: dev.to / 7 months ago
This library is used for image and video processing, offering functions for tasks like object detection, filtering, and transformations in computer vision. - Source: dev.to / 8 months ago
I suspect that the GP was really asking "why not use a different tool", like buildah , buildpacks , nix ,. - Source: Hacker News / 2 months ago
Buildah specializes in building OCI-compliant container images, offering a more granular and secure approach to image creation compared to traditional Dockerfile builds. - Source: dev.to / 6 months ago
Lockdown your Dockerized build environments --- Because privileged mode is insecure, you should restrict your CI/CD environments to known users and projects. If this isn't feasible, then instead of using Docker, you could try using a standalone image builder like Buildah to eliminate the risk. Alternatively, configuring rootless Docker-in-Docker can mitigate some --- but not all --- of the security concerns... - Source: dev.to / about 1 year ago
In my experience, not using docker to build docker images is a good idea. E.g. buildah[0] with chroot isolation can build images in a GitLab pipeline, where docker would fail. It can still use the same Dockerfile though. If you want to get rid of your Dockerfiles anyway, nix can also build docker images[1] with all the added benefits of nix (reproducibility, efficient building and caching, automatic layering,... - Source: Hacker News / over 1 year ago
Buildah: This lightweight, open-source command-line tool for building and managing container images. It is an efficient alternative to Docker. With Buildah, you can build images in various ways, including using a Dockerfile, a podmanfile or by running commands in a container. Buildah is a flexible, secure and powerful tool for building container images. - Source: dev.to / almost 2 years ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Podman - Simple debugging tool for pods and images
NumPy - NumPy is the fundamental package for scientific computing with Python
containerd - An industry-standard container runtime with an emphasis on simplicity, robustness and portability
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Crane - Crane is a docker image builder to approach light-weight ML users who want to expand a container image with custom apt/conda/pip packages without writing any Dockerfile.