Python
DataNinja
Introduction DataNinja is a full-stack machine learning workbench designed for object detection tasks. It provides a browser-based interface to manage datasets, configure and train detection models, evaluate results, and, most distinctively, push trained models to embedded hardware in a single workflow. The motivation behind the project was to bridge the gap between the server side (where GPUs live and models are trained) and the edge side (where inference actually runs, often on low-power embedded SoCs or FPGAs). Most open-source ML tools at the time handled one or the other; DataNinja treats them as a single, connected pipeline.
Compile Caffe without Root Privileges
On shared servers or HPC clusters, you often don’t have sudo access, which makes installing system-level dependencies impossible. In this tutorial, we are going to introduce how to install Caffe entirely in user space, without root privileges. We assume that you have installed Anaconda and CUDA on your PC. Note: This tutorial is based on CUDA 9.0, Caffe 1.0, protobuf 3.2.0, and OpenCV 3.4.3. You may need to adjust the version numbers for your own setup.
Similar Patient Finder
GitLab Open Source Introduction Similar Patient Finder (SPF) is a web-based clinical decision support tool that helps physicians explore patient similarities at the molecular level. Given a dataset of high-throughput gene expression profiles, optionally paired with clinical metadata, SPF runs a configurable analysis pipeline and renders the results as an interactive 2D patient map, making it easier to identify disease subtypes, staging, and appropriate therapy options. The motivation was that raw genomic data is too high-dimensional for direct inspection. SPF bridges the gap between bioinformatics methods and clinical workflows by wrapping a full preprocessing-to-visualization pipeline in a browser-based interface that requires no command-line work from the physician.
Making Requests Non-blocking in Tornado
Tornado is one of the most popular web frameworks for Python, which is based on a single thread IO loop (aka event loop). You can handle high concurrency with optimal performance. However, Tornado is single-threaded (in its common usage, although it supports multiple threads in advanced configurations), therefore any “blocking” task will block the whole server. This means that a blocking task will not allow the framework to pick the next task waiting to be processed.
Medical Image Tagger
Gitee Recommended Open Source 科蓝杯 Silver Award Introduction Medical Image Tagger (MITagger) is a collaborative web-based annotation platform developed with Harvard Medical School to accelerate semi-automatic tagging of biomedical figures. By combining NLP-extracted figure legends with the BioPortal Annotator REST API, the system recommends structured tags from major medical ontologies, reducing the manual effort of medical annotators by 20%. The platform later evolved into a broader medical big data initiative, ShuYi Technology (数翼科技), which won the Silver Award at the “科蓝杯” 9th HFUT Student Entrepreneurship Competition in 2014.