This open source project provides hands-on materials on different aspects of working in the clouds like AWS and GCP.
Automation of static website testing and deployment using CircleCI, Terraform and Cypress
In this article you'll find a set of instructions and builerplate code, which will help you to organize end-to-end CI/CD pipeline for your static website deployment to AWS S3 bucket. Technology stack To demonstrate our solution we'll be using the following technology stack:
Running Jaeger v1.11 backed by Elasticsearch OSS v6.7.0 using Docker Compose
A couple of days already I'm playing with Jager - distributed tracing system inspired by Dapper and OpenZipkin, which been opensourced by Uber Technologies. This system solves the following problems: Distributed context propagation Distributed transaction monitoring Root cause analysis Service dependency analysis Performance / latency optimization After acomplishing a couple of tutorials I decided to try launch it using Elasticsearch as a backend storage.
How to build Python Data Science Docker container based on Anaconda
Last time we’ve created Docker container with Jupiter, Keras, Tensorflow, Pandas, Sklearn and Matplotlib. Suddenly, I understood, that I’ve missed OpenCV for Docker image and video manipulations. Well, I spent whole day preparing new image build. And in this article I’ll show you how to do it much faster using Anaconda official Docker Image.
How to run Jupiter, Keras, Tensorflow, Pandas, Sklearn and Matplotlib in Docker container
Environment setup is a very common question when you’re trying to start learning Machine Learning (ML). In this article I’ll show you how to create your own Docker container including the following frameworks for comfortable start: Python 3 Jupyter Keras Tensorflow TensorBoard Pandas Sklearn Matplotlib Seaborn pyyaml h5py That are TOP 10 widely used Python frameworks for Data Science and you’ll find most of them in any HOWTO article on the Internet.