Posts
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baldr
Quadcopter flight simulator
An open source flight simulator for aerial drones written entirely in Python. This is currently a Linux-only project. baldr is an extension for the MORSE robotics simulator: https://www.openrobots.org/wiki/morse/.
Here’s a short video demonstration of v1.0: https://www.youtube.com/watch?v=0st4ma6SF8Y&feature=youtu.be.
Tags: #python
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TensorFlow-Xception
TensorFlow implementation of the Xception Model by François Chollet
TensorFlow implementation of the Xception Model by François Chollet, based on the paper: Xception: Deep Learning with Depthwise Separable Convolutions.
As an example, the model will be trained on the Flowers dataset.
Tags: #python • deep-learning • computer-vision
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dropplets
Welcome to an easier way to blog. - A minimalist markdown blogging platform.
Go to
https://(your url)/dashboardand all of the site options will be available.Tags: #php • markdown • blog
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notadd
A microservice development architecture based on nest.js. —— 基于 Nest.js 的微服务开发架构。
Notadd is an open source, Nest.js framework-based microservice development architecture that allows you to build a microservices system using the right modules and addons for different business needs. Notadd officially provides an abstract public service layer. Within the service layer, each module provides the Grpc interface for the Notadd main program to call. For example, a CMS system, you can use the officially provided
nt-module-cmsandnt-module-usermodules as the underlying service layer. Then use the Notadd main program to write your API layer code according to theprotobufmessage protocol defined by the service layer.Tags: #typescript • notadd • cms
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ciri
Ciri is an Ethereum implementation written in Ruby.
clone repo and submodules
git clone --recursive https://github.com/ciri-ethereum/ciri.gitTags: #ruby • ethereum • eth
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scalelite
Scalable load balancer for BigBlueButton.
BigBlueButton is an open source web conferencing system for online learning.
Scalelite is an open source load balancer that manages a pool of BigBlueButton servers. It makes the pool of servers appear as a single (very scalable) BigBlueButton server. A front-end, such as Moodle or Greenlight, sends standard BigBlueButton API requests to the Scalelite server which, in turn, distributes those request to the least loaded BigBlueButton server in the pool.
A single BigBlueButton server that meets the minimum configuration supports around 200 concurrent users.
For many schools and organizations, the ability to 4 simultaneous classes of 50 users, or 8 simultaneous meetings of 25 users, is enough capacity. However, what if a school wants to support 1,500 users across 50 simultaneous classes? A single BigBlueButton server cannot handle such a load.
With Scalelite, a school can create a pool of 4 BigBlueButton servers and handle 16 simultaneous classes of 50 users. Want to scale higher, add more BigBlueButton servers to the pool.
BigBlueButton has been in development for over 10 years now. The latest release is a pure HTML5 client, with extensive documentation. There is even a BigBlueButton install script called bbb-install.sh that lets you setup a BigBlueButton server (with a Let’s Encrypt certificate) in about 15 minutes. Using
bbb-install.shyou can quickly setup a pool of servers for management by Scalelite.To load balance the pool, Scalelite periodically polls each BigBlueButton to check if it is reachable online, ready to receive API requests, and to determine its current load (number of currently running meetings). With this information, when Scalelite receives an incoming API call to create a new meeting, it places the new meeting on the least loaded server in the pool. In this way, Scalelite can balance the load of meeting requests evenly across the pool.
Many BigBlueButton servers will create many recordings. Scalelite can serve a large set of recordings by consolidating them together, indexing them in a database, and, when receiving an incoming getRecordings, use the database index to return quickly the list of available recordings.
Tags: #ruby
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PettingZoo
Gym for multi-agent reinforcement learning
PettingZoo includes the following families of environments:
- Atari: Multi-player Atari 2600 games (cooperative, competitive and mixed sum)
- Butterfly: Cooperative graphical games developed by us, requiring a high degree of coordination
- Classic: Classical games including card games, board games, etc.
- MAgent: Configurable environments with massive numbers of particle agents, originally from https://github.com/geek-ai/MAgent
- MPE: A set of simple nongraphical communication tasks, originally from https://github.com/openai/multiagent-particle-envs
- SISL: 3 cooperative environments, originally from https://github.com/sisl/MADRL
To install the pettingzoo base library, use
pip install pettingzoo.This does not include dependencies for all families of environments (there’s a massive number, and some can be problematic to install on certain systems). You can install these dependencies for one family like
pip install pettingzoo[atari]or usepip install pettingzoo[all]to install all dependencies.We support Python 3.6, 3.7, 3.8 and 3.9 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.
Tags: #python
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seafox
A blazing fast 100% spec compliant, self-hosted javascript parser written in Typescript
- Conforms to the standard ECMAScript® 2021 (ECMA-262 11th Edition) language specification
- Support for additional ECMAScript features for Web Browsers
- Optionally track syntactic node locations
- Emits an ESTree-compatible abstract syntax tree
- Lexical analysis
- No backtracking
- Low memory usage
- Insane performance both on desktop computers and handheld devices
- Twice as fast as other Javascript parsers
- Very well tested (~33 000 unit tests with full code coverage)
- Lightweight - ~84 KB minified
Tags: #typescript • javascript • parser
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riteway
Simple, readable, helpful unit tests.
Simple, readable, helpful unit tests.
- Readable
- Isolated/Integrated
- Thorough
- Explicit
RITEway forces you to write Readable, Isolated, and Explicit tests, because that’s the only way you can use the API. It also makes it easier to be thorough by making test assertions so simple that you’ll want to write more of them.
There are 5 questions every unit test must answer. RITEWay forces you to answer them.
- What is the unit under test (module, function, class, whatever)?
- What should it do? (Prose description)
- What was the actual output?
- What was the expected output?
- How do you reproduce the failure?
Tags: #javascript
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quantum
Hybrid Quantum-Classical Machine Learning in TensorFlow
Quantum computing at Google has hit an exciting milestone with the achievement of Quantum Supremacy. In the wake of this demonstration, Google is now turning its attention to developing and implementing new algorithms to run on its Quantum Computer that have real world applications.
To provide users with the tools they need to program and simulate a quantum computer, Google is working on Cirq. Cirq is designed for quantum computing researchers who are interested in running and designing algorithms that leverage existing (imperfect) quantum computers.
TensorFlow Quantum provides users with the tools they need to interleave quantum algorithms and logic designed in Cirq with the powerful and performant ML tools from TensorFlow. With this connection we hope to unlock new and exciting paths for Quantum Computing research that would not have otherwise been possible.
Tags: #python
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