Based on our record, NumPy seems to be a lot more popular than Buildbot. While we know about 107 links to NumPy, we've tracked only 9 mentions of Buildbot. 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.
Buildbot is a versatile CI framework designed to automate all aspects of the software development cycle, enhancing efficiency and reliability. As an open-source platform, it is highly customizable, allowing teams to tailor the automation process to their specific needs. Buildbot excels in integrating various stages of development, from code integration, testing, to deployment, ensuring a seamless and coherent... - Source: dev.to / 4 months ago
If you want more than the builtin CIs in Github and Gitlab, https://buildbot.net is the way. Source: about 1 year ago
If you don't have one already integrated with your source control, buildbot is pretty nice and doesn't force you to use docker like most others. Source: over 1 year ago
Https://buildbot.net/ existed before Jenkins Hudson and was quite well known. Source: over 1 year ago
I have used python based CI tool buildbot which is a great tool but we want to move away from buildbot only because in some scenarios we want to compile low-level microseconds which are in c++ to a different architecture. Buildbot doesn't have such a feature. - Source: dev.to / almost 2 years ago
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 2 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 2 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 7 months ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Travis CI - Focus on writing code. Let Travis CI take care of running your tests and deploying your apps.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
CircleCI - CircleCI gives web developers powerful Continuous Integration and Deployment with easy setup and maintenance.
OpenCV - OpenCV is the world's biggest computer vision library