Software Alternatives, Accelerators & Startups

Travis CI VS NumPy

Compare Travis CI VS NumPy and see what are their differences

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Travis CI logo Travis CI

Simple, flexible, trustworthy CI/CD tools. Join hundreds of thousands who define tests and deployments in minutes, then scale up simply with parallel or multi-environment builds using Travis CI’s precision syntax—all with the developer in mind.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Travis CI Travis CI for Simple, Flexible, Trustworthy CI/CD Tools
    Travis CI for Simple, Flexible, Trustworthy CI/CD Tools //
    2024-10-22

Founded in Berlin, Germany, in 2011, Travis CI grew quickly and became a trusted name in CI/CD, gaining popularity among software developers and engineers starting their careers. In 2019, Travis CI became part of Idera, Inc., the parent company of global B2B software productivity brands whose solutions enable technical users to work faster and do more with less.

Today, developers at 300,000 organizations use Travis CI. We often hear about the pangs of nostalgia these folks feel when they use Travis CI, as it was one of the first tools they used at the beginning of their career journey. We are still much here, supporting those who have stuck with us along the way and remaining the best next destination on your CI/CD journey, whether you’re building your first pipelines or trying to bring some thrill back into work that’s become overloaded with AI and DevSecOps complexity.

Our Mission:

We deliver the simplest and most flexible CI/CD tool to developers eager for ownership of their code quality, transparency in how they problem-solve with peers, and pride in the results they create—one LOC at a time.

Our Promise:

We aim for nothing less than to guide every developer to the next phase of their CI/CD adventure—even if that means growing beyond our platform.

  • NumPy Landing page
    Landing page //
    2023-05-13

Travis CI

$ Details
paid Free Trial $13.75 / Monthly (Per Month, Per User)
Release Date
2011 January

Travis CI features and specs

  • Ease of Use
    Travis CI offers a very user-friendly interface and straightforward setup process, making it accessible even for those new to CI/CD.
  • Integration with GitHub
    Seamlessly integrates with GitHub, allowing for automatic builds and tests triggered on pull requests and commits.
  • Wide Range of Language Support
    Supports numerous programming languages out of the box, providing built-in configurations for many common languages such as Python, Ruby, JavaScript, and Java.
  • Extensive Documentation
    Offers comprehensive and well-organized documentation, which can help users troubleshoot and understand complex setups.
  • Build Matrix
    Run your unit and integration tests across any combination of environments for comprehensive automation and absolute quality guarantees on your way to production.

Possible disadvantages of Travis CI

  • Pricing for Private Repositories
    Can become expensive for private repositories and larger teams, especially compared to some competitors that offer more generous free tiers.
  • Performance Issues
    Users have reported occasional performance issues, including slower build times and longer wait periods for queued jobs.
  • Limited Advanced Features
    Might lack some advanced features and customizations that are available in other CI/CD platforms, making it less suitable for very complex workflows.
  • Concurrency Limits
    Has limitations on the number of concurrent builds that can run, which can slow down development cycles for larger projects with many contributors.
  • Complex Configuration for Large Projects
    Configuration can become cumbersome and complex for large projects with intricate dependencies and multiple build steps.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Travis CI videos

Setting Up Your First Build

More videos:

  • Tutorial - CI/CD Core Concepts
  • Tutorial - How to Get Started with Travis CI in 0 to 5 Minutes

NumPy videos

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to Travis CI and NumPy)
Continuous Integration
100 100%
0% 0
Data Science And Machine Learning
DevOps Tools
100 100%
0% 0
Data Science Tools
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Travis CI and NumPy

Travis CI Reviews

The Best Alternatives to Jenkins for Developers
Travis CI is another popular cloud-based CI/CD solution that integrates well with GitHub. Known for its simplicity and ease of setup, Travis CI is a great choice for open-source projects or teams that primarily work with GitHub repositories. Its configuration is based on a YAML file, making it easy to define and manage build workflows.
Source: morninglif.com
Top 10 Most Popular Jenkins Alternatives for DevOps in 2024
Travis CI is known for its simple setup, quick parallel builds, and support for multiple architectures, including popular enterprise options like IBM PowerPC and IBM Z. It’s claimed that pipelines require approximately 33% less configurable code than other CI/CD solutions, which helps make the platform more approachable. Use it instead of Jenkins when you want a fast...
Source: spacelift.io
10 Jenkins Alternatives in 2021 for Developers
You might find that Travis CI proudly promotes the fact that they have more than 900,000 open-source projects and 600,000 users on their platform with Travis CI. Automated deployment can be quickly established by following the tutorials and documentation that are currently available on their website.
The Best Alternatives to Jenkins for Developers
Travis CI is a continuous integration and testing CI/CD tool. It is free of cost for open source projects and provides seamless integration with GitHub. It supports more than 20 languages, like Node.js, PHP, Python, etc. along with Docker.
Continuous Integration. CircleCI vs Travis CI vs Jenkins
Travis CI is recommended for cases when you are working on the open-source projects, that should be tested in different environments.
Source: djangostars.com

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Travis CI. While we know about 119 links to NumPy, we've tracked only 6 mentions of Travis CI. 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.

Travis CI mentions (6)

  • Front-end Guide
    We used Travis CI for our continuous integration (CI) pipeline. Travis is a highly popular CI on Github and its build matrix feature is useful for repositories which contain multiple projects like Grab's. We configured Travis to do the following:. - Source: dev.to / over 2 years ago
  • Flutter
    CI/CD for autobuild + autotests (Codemagic or Travis CI). Source: over 2 years ago
  • How To Build Your First CI/CD Pipeline With Travis CI?
    Step 2: Log on to Travis CI and sign up with your GitHub account used above. - Source: dev.to / over 2 years ago
  • What does a DevOps engineer actually do?
    Some other hosted CI products, such as CircleCI and Travis Cl, are completely hosted in the cloud. It is becoming more popular for small organizations to use hosted CI products, as they allow engineering teams to begin continuous integration as soon as possible. Source: almost 4 years ago
  • Hosting an Angular application on GitHub Pages using Travis CI
    1. Let's create the account. Access the site https://travis-ci.com/ and click on the button Sign up. - Source: dev.to / almost 4 years ago
View more

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 3 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 7 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 8 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 9 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 9 months ago
View more

What are some alternatives?

When comparing Travis CI and NumPy, you can also consider the following products

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.

CircleCI - CircleCI gives web developers powerful Continuous Integration and Deployment with easy setup and maintenance.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Codeship - Codeship is a fast and secure hosted Continuous Delivery platform that scales with your needs.

OpenCV - OpenCV is the world's biggest computer vision library