The 2019 edition of PyCon USA takes place over the next few days in Cleveland, Ohio. Scout is delighted to be there, sharing our APM tool with the Python community. Plus, we'll have great t-shirts and stickers for you, and we love to get geeky - one of our lead product engineers, plus two of our smart support engineers, are working the booth, ready to help you figure out your Python performance problems.
This post demonstrates a *basic* example of how to build a deep learning model with Keras, serve it as REST API with Flask, and deploy it using Docker and Kubernetes. This is NOT a robust, production example. This is a quick guide for anyone out there who has heard about Kubernetes but hasn’t tried it out yet. To that end, I use Google Cloud for every step of this process.
Python’s built-in logging module is designed to give you critical visibility into your applications with minimal setup. Whether you’re just getting started or already using Python’s logging module, this guide will show you how to configure this module to log all the data you need, route it to your desired destinations, and centralize your logs to get deeper insights into your Python applications.
Learning to build webapps is an exciting process, but it comes with its own set of challenges. As a newer developer, deciding what programming language will bring your big idea to life is a common challenge. There are lots of terrific choices for building webapps on the market. Today, we’ll focus on two of 2019’s most popular options: Node.js vs Python.
Serious software development calls for performance optimization. When you start optimizing application performance, you can’t escape looking at profilers. Whether monitoring production servers or tracking frequency and duration of method calls, profilers run the gamut. In this article, I’ll cover the basics of using a Python profiler, breaking down the key concepts, and introducing the various libraries and tools for each key concept in Python profiling.
Around 1996, when I attended my first programming classes, C++ was the language of choice if you wanted to have a job in this industry. The Internet was young and not as widely available as it is now. Ruby and Python were still in their infancy. Now, in 2018, both have evolved and matured well enough to be in the top 10 most in-demand programming languages. In this article, I’m going to highlight features and contrasts between Python and Ruby.
We launched our Python agent a few months ago, supporting Python 3.4+. With out of the box instrumentation for Django, Flask, Bottle, Pyramid, Elasticsearch, MongoDB, SQLAlchemy and more, we covered a lot of ground!
PM2 is a production-grade process manager that makes management of background process easy. In the Python world we could compare PM2 to Supervisord, but PM2 has some nifty features you might like. With PM2, rolling restarts, monitoring, checking logs and even deploying application has never been that simple. We really value CLI UX, so PM2 is really simple to use and master.
Flask is a Python framework known for its ease of use. It inherits Python’s advantages of extensibility, broad support, and relative simplicity. It’s known as a microframework because it relies on extensions for much of its functionality. Flask avoids constraining the developer to a predetermined database or authentication mechanism, for example, and instead leaves room for choice.
We’ve been on a roll this year with Beelines, our integrations for quick, easy, and automagic instrumentation of your apps. You may have already seen our Node.js, Ruby, and Go beelines – today, we’re excited to announce the release of the Honeycomb Beeline for Python!
