Command-line Tools

The command line interface for Gradient

Gradient CLI Gradient is an an end-to-end MLOps platform that enables individuals and organizations to quickly develop, train, and deploy Deep Learning models. The Gradient software stack runs on any infrastructure e.g. AWS, GCP, on-premise and low-cost Paperspace GPUs. Leverage automatic versioning, distributed training, built-in graphs & metrics, hyperparameter search, GradientCI, 1-click Jupyter Notebooks, our Python SDK, and more. Key components: Notebooks: 1-click Jupyter Notebooks. Experiments: Run experiments from a web interface, CLI, SDK, or GradientCI bot. Models: Store, analyze, […]

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Database

A PostgreSQL or SQLite orm for Python

Prom An opinionated lightweight orm for PostgreSQL or SQLite. Prom has been used in both single threaded and multi-threaded environments, including environments using Greenthreads. 1 Minute Getting Started with SQLite First, install prom: $ pip install prom Set an environment variable: $ export PROM_DSN=sqlite://:memory: Start python: $ python Create a prom Orm: >>> import prom >>> >>> class Foo(prom.Orm): … table_name = “foo_table_name” … bar = prom.Field(int) … >>> Now go wild and create some Foo objects: >>> for x […]

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HTTP

HTTP client mocking tool for Python

HTTPretty HTTP Client mocking tool for Python created by Gabriel Falcão . It provides a full fake TCP socket module. Inspired by FakeWeb Install pip install httpretty Common Use Cases Test-driven development of API integrations Fake responses of external APIs Record and playback HTTP requests Simple Example import sure import httpretty import requests @httpretty.activate def test_httpbin(): httpretty.register_uri( httpretty.GET, “https://httpbin.org/ip”, body='{“origin”: “127.0.0.1”}’ ) response = requests.get(‘https://httpbin.org/ip’) response.json().should.equal({‘origin’: ‘127.0.0.1’}) httpretty.latest_requests().should.have.length_of(1) httpretty.last_request().should.equal(httpretty.latest_requests()[0]) httpretty.last_request().body.should.equal(‘{“origin”: “127.0.0.1”}’) checking multiple responses @httpretty.activate def test_post_bodies(): url = ‘http://httpbin.org/post’ […]

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Deep Learning

Medical image analysis framework merging ANTsPy and deep learning

ANTsPyNet A collection of deep learning architectures and applications ported to the python language and tools for basic medical image processing. Based on keras and tensorflow with cross-compatibility with our R analog ANTsRNet. Documentation page https://antsx.github.io/ANTsPyNet/. Installation Publications Nicholas J. Tustison, Talissa A. Altes, Kun Qing, Mu He, G. Wilson Miller, Brian B. Avants, Yun M. Shim, James C. Gee, John P. Mugler III, Jaime F. Mata. Image- vs. histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images. (medrxiv) […]

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Animation

A Python description of the Kinematic Bicycle Model with an animated example

Kinematic Bicycle Model A Python description of the Kinematic Bicycle Model with an animated example. Abstract A python library for the Kinematic Bicycle model. :param x: (float) vehicle’s x-coordinate :param y: (float) vehicle’s y-coordinate :param yaw: (float) vehicle’s heading [rad] :param v: (float) vehicle’s velocity in the x-axis [m/s] :param throttle: (float) vehicle’s forward speed [m/s] :param delta: (float) vehicle’s steering angle [rad] :param L: (float) vehicle’s wheelbase [m] :param max_steer: (float) vehicle’s steering limits [rad] :param c_r: (float) vehicle’s […]

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Python

Write Pythonic and Clean Code With namedtuple

Python’s collections module provides a factory function called namedtuple(), which is specially designed to make your code more Pythonic when you’re working with tuples. With namedtuple(), you can create immutable sequence types that allow you to access their values using descriptive field names and the dot notation instead of unclear integer indices. If you have some experience using Python, then you know that writing Pythonic code is a core skill for Python developers. In this tutorial, you’ll level up that […]

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Machine Learning

A rotation-equivariant GNN for learning from biomolecular structure

Geometric Vector Perceptron Implementation of equivariant GVP-GNNs as described in Learning from Protein Structure with Geometric Vector Perceptrons by B Jing, S Eismann, P Suriana, RJL Townshend, and RO Dror. This repository serves two purposes. If you would like to use the GVP architecture for structural biology tasks, we provide building blocks for models and data pipelines. If you are specifically interested in protein design as described in the paper, we provide scripts for training and testing models. Note: This […]

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Face recognition

Deep Face Detection Library in TensorFlow for Python

RetinaFace RetinaFace is the face detection module of insightface project. The original implementation is mainly based on mxnet. Then, its tensorflow based re-implementation is published by Stanislas Bertrand. This repo is heavily inspired from the study of Stanislas Bertrand. Its source code is simplified and it is transformed to pip compatible but the main structure of the reference model and its pre-trained weights are same. Installation The easiest way to install retinaface is to download it from pypi. pip install […]

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GUI

A Pancakeswap v2 trading client with limit orders and much more

Pancakeswap v2 trading client A Pancakeswap trading client (and bot) with limit orders, stop-loss, custom gas strategies, a GUI and much more. If you have any questions or inquiries, you can contact me via telegram: aviddot Check out the demo of my Pancakeswap sniping bot: https://github.com/aviddot/Pancakeswap-sniping-bot-demo Prerequisites An ethereum/bsc address A Windows machine Not sure whether needed anymore: Visual C++ build tools (www.visualstudio.microsoft.com/visual-cpp-build-tools/) Getting started Read prerequisites Download the latest release or download “configfile.py” and “pancakeswap_bot.exe” from the repository. Open […]

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Machine Learning

An unofficial PyTorch implementation of a federated learning algorithm

Federated Averaging (FedAvg) in PyTorch An unofficial implementation of FederatedAveraging (or FedAvg) algorithm proposed in the paper Communication-Efficient Learning of Deep Networks from Decentralized Data in PyTorch. (implemented in Python 3.9.2.) Implementation points Exactly implement the models (‘2NN’ and ‘CNN’ mentioned in the paper) to have the same number of parameters written in the paper. 2NN: TwoNN class in models.py; 199,210 parameters CNN: CNN class in models.py; 1,663,370 parameters Exactly implement the non-IID data split. Each client has at least […]

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