Based on our record, Scikit-learn should be more popular than Buildah. It has been mentiond 31 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.
I suspect that the GP was really asking "why not use a different tool", like buildah , buildpacks , nix ,. - Source: Hacker News / about 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 / 5 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
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 12 months ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
Podman - Simple debugging tool for pods and images
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
containerd - An industry-standard container runtime with an emphasis on simplicity, robustness and portability
OpenCV - OpenCV is the world's biggest computer vision library
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.
NumPy - NumPy is the fundamental package for scientific computing with Python