How open standards guide us in a world of change. As I write this article in my home office in Beaverton, Oregon, a Portland suburb, I'm relying (and reflecting) on years of work that went into standards like TCP/IP, HTTP, NTP, XMPP, SAML, and many others, as well as open source implementations of these standards from organizations such as the Apache Software Foundation.
The combination of these standards and technologies is literally saving lives, as many of us are able to work from home while "flattening the curve. " Nothing has dominated the news more in 2020 than COVID-19. Yet, in the midst of challenging time, I've found opportunities for personal and industrial renewal. By fortunate (some may say unfortunate) timing, I found myself switching roles in the middle of this crisis from helping to build and run Open Source Program Offices (OSPOs) to becoming the executive director at OASIS Open, a standards development organization that is helping bring standards and open source together in practical and productive ways.
Docker vs. Singularity for data processing: UIDs and filesystem access. When you’re processing data, reading in files and writing out the result, containers are a great way to ensure reproducible runs.
You package up all the binaries and libraries necessary to process your data, and each run uses the same files. But while Docker is the most well-known container system, it’s not necessarily the easiest to use for data processing. Filesystem access, including ensuring correct UIDs, can be annoying. The problem is that Docker was not designed for this use case. Docker is not the only way to create and run containers, though. A batch script to process data As a starting point, let’s say we have a simple script that reads in a file, processes it somehow, and writes it out: $ lsinput.txt script.py $ python script.py input.txt output.txt Wrote all data to to output.txt $ ls -l output.txt -rw-r--r-- 1 itamarst itamarst 24 Mar 11 11:41 output.txt. Reproducible Research in Computational Science. CFP: Benchmarking Protocols for Robotics Manipulation - IEEE Robotics and Automation Society. Motivation Benchmarks are crucial for analyzing the effectiveness of an approach against a common basis, providing a quantitative means for interpreting performance.
Carefully designed and widely recognized benchmarks encourage the research community to focus on certain key research challenges, promote competition, foster a climate for novel solutions, and, therefore, contribute dramatically to the advancement of a field. March Tutorials — Project March Melodic documentation. REPRODUCIBLEROBOTICSRESEARCH.ORG. A database of human gait performance on irregular and uneven surfaces collected by wearable sensors. 1.Chen, S., Lach, J., Lo, B.
& Yang, G. Z. Toward pervasive gait analysis with wearable sensors: A systematic review. IEEE J. Biomed. Modenaxe/awesome-biomechanics: A public list collecting resources for biomechanics: datasets, processing tools, software for simulation, educational videos, lectures, etc. Examples, Guide And Process Explained. ReproducibleRobots. Benchmarking Robotics. EUROP EURON 2010 Benchmarking AnneBAJART pdf. Prometheus - Monitoring system & time series database.
Data as code Data management for reproducible research Martin OReilly. [1803.09010] Datasheets for Datasets. JRMeyer/markdown-datasheet-for-datasets: Markdown template for Dataseets for Datasets. For Debian systems - Octave. Simple GitHub repo and ReadTheDocs set up — tutos 0.5 documentation. I’ve just wanted to set up a GitHub repository for this guide and I found that it’s really ubearable to set so simple thing as this.
Here is short guide which should walk you through: Creating repository on GitHubCloning it into your local machineSubmitting changes from your local machine using SSHSubmitting changes from repository on GitHubGenerating documentation with ReadTheDocs and sphinx < Creating repository on GitHub¶ If you’ve found this guide, I guess you are inteligent enough to create account on GitHub, so we’ll skip this step. Same with installing git and SSH on your machine. To create a directory just go to your profile (e.g. When repository is created, copy Subversion checkout URL which can be found in right panel of repository view. Continuous Integration and Delivery with MATLAB. Would you like to run your tests in MATLAB on every change to your codebase?
Or perhaps you want to build and Dockerize your MATLAB software with automatic GitHub integrations, but feel that your hands are tied by proprietary license? Then this one’s for you! I have no objection that Python is one of the most favorable options to develop open-source software for science. It has almost become the norm for many research labs. Yet, many researchers still run MATLAB, some out of habit, some out of affection. Lando, le manager Docker pour les nuls ? - Max Koder. Unittest — Unit testing framework — Python 3.8.3rc1 documentation. Source code: Lib/unittest/__init__.py (If you are already familiar with the basic concepts of testing, you might want to skip to the list of assert methods.)
The unittest unit testing framework was originally inspired by JUnit and has a similar flavor as major unit testing frameworks in other languages. It supports test automation, sharing of setup and shutdown code for tests, aggregation of tests into collections, and independence of the tests from the reporting framework. To achieve this, unittest supports some important concepts in an object-oriented way: test fixture A test fixture represents the preparation needed to perform one or more tests, and any associated cleanup actions. Benchmarking Human Likeness of Bipedal Robot Locomotion: State of the Art and Future Trends. - Chérie, j'ai rétréci Docker - part 2/3. Automating Every Aspect of Your Python Project.
Bobble_controllers/src/analysis_tools at master · super-owesome/bobble_controllers. The best Docker base image for your Python application (January 2020) When you’re building a Docker image for your Python application, you’re building on top of an existing image—and there are many possible choices.
There are OS images like Ubuntu and CentOS, and there are the many different variants of the python base image. Which one should you use? Build a CI/CD Pipeline with Docker and GitLab - Jimmy Adaro - Medium. Before continue, make sure you are logged-in into your GitLab account, you are owner of a project/repository and you have access to that repo in your local computer through Git, you must to be able to pull and push.
I use GitKraken (Git GUI) to make it even easier. GitLab offers a simple way to handle CI/CD pipelines using Docker and shared runners. Every time you run your pipeline GitLab will create an isolated virtual machine and build a Docker image, which you can setup using a YAML configuration file. Those pipelines can have multiple jobs, but more jobs means more build time and we don’t want that, right? You can have up to 2000 build minutes per month using the free plan. Production-ready Docker packaging. Docker packaging guide for Python The broken status quo Broken by default: why you should avoid most Dockerfile examplesMost Dockerfile examples you’ll find on the Web are broken.
And that’s a problem.A review of the official Dockerfile best practices: good, bad, and insecureThe official Docker documentation’s Dockerfile best practices are mostly good—but they omit some important information. Basic concepts. Setting Up GitLab CI for a Python Application – Patrick's Software Blog. Introduction This blog post describes how to configure a Continuous Integration (CI) process on GitLab for a python application. This blog post utilizes one of my python applications (bild) to show how to setup the CI process: In this blog post, I’ll show how I setup a GitLab CI process to run the following jobs on a python application: A perfect way to Dockerize your Python application. In a previous post I described how to set up a perfect Python project with dependency management, formatting, linting and testing tools all set up and ready to go.
After writing your Python application the next stage is deploying and running it. Docker provides a excellent abstraction that guarantees that the environment for running the application is identical on every deployment and run, even when running on different hardware or infrastructure. I will assume you already have Docker installed, if not you can follow the instructions here. Let's jump in at the deep end. Martin Heinz - Personal Website & Blog. Hello Docker CI / CD - Travis. Manually running builds, executing tests and deploying can become a nightmare and an error-prone process: Human errors easily slip in.As soon as the project grows, build processes tend to get more and more complex.Knowing which version is running in which environment is not straightforward.Switching versions is not straightforward.
What if: That's the benefit you get of configuring a CI/CD server (in this post we will use Travis) and mixing it up with Docker container technology. This is the second post of the Hello Docker series, the first post is available in this link. In this post we will use Travis to automatically trigger the following processes on every merge to master or pull request: Documentation as code - Haufe-Lexware.github.io. Technical documentation, if done at all, often tends to get outdated very quickly. One of the reasons for that often lies in having the documentation located somewhere far away from the subject it is documenting: the code. The guys who are supposed to write the documentation face a media break when going to document what they were doing: They need to update a document which is not co-located to the code they were writing and perhaps even is not available in their IDE (e.g., a wiki or a word document somewhere in Sharepoint).
Even if that burden is taken and the documentation gets updated, it may contain duplicated content when it comes to code examples. Having code examples in the documentation quickly violates the single source of truth. If the corresponding code changes, how am I sure that the documentation does not get outdated or even misleading at worst? Moi je code m’ADOC. Antora and Docusaurus duke it out — Eva Parish. For Squarespace's most recent Hack Week, I tried out three different documentation tools, with an eye to replacing our current internal documentation system.
SDK Overview. Please read this carefully. List of customised UIs / showcase sites (#20) · Issues · Antora / antora.org. Continuous Documentation with Antora and Travis. Antora is a documentation pipeline that enables docs, product, and engineering teams to create, manage, remix, and publish documentation sites composed in AsciiDoc and sourced from multiple versioned content repositories. You can see several examples out there from Couchbase documentation to Fedora documentation.
Replace your wiki with a documentation site. Bonjour, la version française de cet article peut être lue sur dev.to. Is "the CLOUD" actually in the EU and is it GDPR COMPLIANT? Top 10 European CSP (Cloud Services Providers) Ranked for IaaS List 2017. Europe's cloud computing plan won't do much to scare the US giants. AWS alternatives: Fuga Cloud, a great alternative to AWS.
How do I create a simple Octave distributable without installing Octave. Converting from AsciiDoc to MS Word. AtsushiSakai/rosbag_to_csv: Converter from ros bag to csv. Datavisualization.ch Selected Tools. Tuto Docker - Démarrer Docker (Partie 2) Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals.
Cross-industry standard test method developments: from manufacturing to wearable robots. Pons Benchmarking. What Is Robot Benchmarking? Repeatable robotics: Standards and benchmarks from frontier science to applied technology. RoCKIn - Benchmarking. YCB Benchmarks – Object and Model Set. Sprunk14iser. Benchmark for Robotic Indoor Navigation. 1 Standard Test Methods for Evaluating, Purchasing, and Training with Response Robots. E2853 - 12 Standard Test Method for Evaluating Emergency Response Robot Capabilities: Human-System Interaction (HSI): Search Tasks: Random Mazes with Complex Terrain. 1 Jacoff. STM NEDO Ray2017. DHS NIST ASTM Robot Test Methods 2. q10r/README.md at master · vlevit/q10r. Django-survey-and-report. C++ - Compile MATLAB function into something that can be run on a linux terminal w.out MATLAB?
GaitAnalysisToolKit/README.rst at master · csu-hmc/GaitAnalysisToolKit. Final Report Summary - ROBOLAW (Regulating Emerging Robotic Technologies in Europe: Robotics facing Law and Ethics) Ultimate guide to handle Big Datasets for Machine Learning using Dask (in Python) Processing Huge Dataset with Python. Serverless Distributed Data Pre-processing using Dask, Amazon ECS and Python (Part 1) DockerComp/README.md at master · arcolife/dockerComp. Docker SDK for Python — Docker SDK for Python 3.7.0 documentation. Luigi vs Airflow vs Pinball. Quick Tip: Speed up your Python data processing scripts with Process Pools. Numba: A High Performance Python Compiler. Thank you for openma · Issue #83 · OpenMA/openma. AMCParser/README.md at master · CalciferZh/AMCParser. Alemi. Omimo/PyMO: A library for machine learning research on motion capture data. OpenMA. Torricelli2015 RAM accepted version. Frontiers benchmark. Working with Spreadsheets in SQL. Gait Recognition from Motion Capture Data.
Base de datos. Open Data Motion Capture: MOCAP-ULL Database. Schema for Motion Capture Data Management. EFFICIENT INDEXING AND RETRIEVAL OF MOTION CAPTURE DATA BASED ON ADAPTIVE SEGMENTATION. Gait Recognition from Motion Capture Data. Serre Lab » HMDB: a large human motion database. How do foundations support open source software? Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions. MVN Analyze - Products - Xsens 3D motion tracking. Free Universal Database Tool. ClickHouse — open source distributed column-oriented DBMS. Pour la recherche de données, l'analyse de logs et autres analytiques. How to Make Your Python Workers Scale Dynamically. Explanation of the c3d file format – Yvan Scher.