
CircleCI
Jenkins
Codeship
Travis CI
Bamboo
Bitrise
TeamCity
Buddy
Deeplearning4j
Keras
TFlearn
TensorFlow
PyTorch
Darknet
Clarifai
DeepPy
CircleCIBased on our record, CircleCI seems to be a lot more popular than Deeplearning4j. While we know about 83 links to CircleCI, we've tracked only 6 mentions of Deeplearning4j. 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.
CircleCI is another popular and mature platform, with extensive support for plugins / reusable workflows in the form of "orbs". - Source: dev.to / 9 months ago
Everyone is free to use alternative CI/CD workflow pipelines. These are often better than Github Actions. There include - https://circleci.com/ - https://www.travis-ci.com/ - Gitlab Anyone can complain as much as they want, but unless they put the money where their mouth is, it's just noise. - Source: Hacker News / 9 months ago
CircleCI Account: You need an active CircleCI account connected to your GitHub repository where the application code resides. If you don’t have one, sign up at circleci.com. - Source: dev.to / about 1 year ago
In this guide, you will explore how to build a fully automated pipeline for processing and updating a vector database using AWS Lambda and CircleCI. The solution involves extracting text from PDFs, generating embeddings with OpenAI, and storing them in Zilliz Cloud, a managed vector database. You will also set up AWS infrastructure (S3, ECR, and Lambda) and implement a CI/CD pipeline with CircleCI to automate... - Source: dev.to / about 1 year ago
CircleCI: Still solid, but watch pricing and concurrency limits. - Source: dev.to / about 1 year ago
This integration is not only a technical marvel but also a case study in how open source funding and a transparent business model powered by blockchain are fostering collaboration among developers, academics, and institutional investors. With links to key resources such as the DL4J GitHub repository and the DL4J official website, the project serves as an inspiration for merging complex domains in a unified framework. - Source: dev.to / over 1 year ago
DeepLearning4j Blockchain Integration is more than just a convergence of technologies; it’s a paradigm shift in how AI projects are developed, funded, and maintained. By utilizing the robust framework of DL4J, enhanced with secure blockchain features and an inclusive open source model, the project is not only pushing the boundaries for artificial intelligence but also establishing a resilient model for future... - Source: dev.to / over 1 year ago
While KotlinDL seems to be a good solution by Jetbrains, I would personally stick to Java frameworks like DL4J for a better community support and likely more features. Source: about 5 years ago
Would recommend taking a look at dl4j: https://deeplearning4j.org. Source: over 5 years ago
We use DeepLearning4j in this chapter because it is written in Java and easy to use with Clojure. In a later chapter we will use the Clojure library libpython-clj to access other deep learning-based tools like the Hugging Face Transformer models for question answering systems as well as the spaCy Python library for NLP. Source: over 5 years ago
Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Codeship - Codeship is a fast and secure hosted Continuous Delivery platform that scales with your needs.
TFlearn - TFlearn is a modular and transparent deep learning library built on top of Tensorflow.
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.
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.