172 Repositories
Python memory-consumption Libraries
LSTMs (Long Short Term Memory) RNN for prediction of price trends
Price Prediction with Recurrent Neural Networks LSTMs BTC-USD price prediction with deep learning algorithm. Artificial Neural Networks specifically L
Simple package to enhance Python's concurrent.futures for memory efficiency
future-map is a Python library to use together with the official concurrent.futures module.
PyTorch implementation of "Dataset Knowledge Transfer for Class-Incremental Learning Without Memory" (WACV2022)
Dataset Knowledge Transfer for Class-Incremental Learning Without Memory [Paper] [Slides] Summary Introduction Installation Reproducing results Citati
Speeding-Up Back-Propagation in DNN: Approximate Outer Product with Memory
Approximate Outer Product Gradient Descent with Memory Code for the numerical experiment of the paper Speeding-Up Back-Propagation in DNN: Approximate
This is the repo for the paper "Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement".
Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement This is the repository for the paper "Improving the Accuracy-Memory Trad
Offcial implementation of "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction, ICCV-2021".
HF2-VAD Offcial implementation of "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictio
This repository contains a set of benchmarks of different implementations of Parquet (storage format) - Arrow (in-memory format).
Parquet benchmarks This repository contains a set of benchmarks of different implementations of Parquet (storage format) - Arrow (in-memory format).
Source Code for our paper: Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network
KaGRMN-DSG_ABSA This repository contains the PyTorch source Code for our paper: Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated
A torch.Tensor-like DataFrame library supporting multiple execution runtimes and Arrow as a common memory format
TorchArrow (Warning: Unstable Prototype) This is a prototype library currently under heavy development. It does not currently have stable releases, an
A testcase generation tool for Persistent Memory Programs.
PMFuzz PMFuzz is a testcase generation tool to generate high-value tests cases for PM testing tools (XFDetector, PMDebugger, PMTest and Pmemcheck) If
MEDS: Enhancing Memory Error Detection for Large-Scale Applications
MEDS: Enhancing Memory Error Detection for Large-Scale Applications Prerequisites cmake and clang Build MEDS supporting compiler $ make Build Using Do
[ICSE2020] MemLock: Memory Usage Guided Fuzzing
MemLock: Memory Usage Guided Fuzzing This repository provides the tool and the evaluation subjects for the paper "MemLock: Memory Usage Guided Fuzzing
Emulation and Feedback Fuzzing of Firmware with Memory Sanitization
BaseSAFE This repository contains the BaseSAFE Rust APIs, introduced by "BaseSAFE: Baseband SAnitized Fuzzing through Emulation". The example/ directo
A python script developed to process Windows memory images based on triage type.
Overview A python script developed to process Windows memory images based on triage type. Requirements Python3 Bulk Extractor Volatility2 with Communi
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)
Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)
Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim
300+ Python Interview Questions
300+ Python Interview Questions
Produces a summary CSV report of an Amber Electric customer's energy consumption and cost data.
Amber Electric Usage Summary This is a command line tool that produces a summary CSV report of an Amber Electric customer's energy consumption and cos
Implementation of Hierarchical Transformer Memory (HTM) for Pytorch
Hierarchical Transformer Memory (HTM) - Pytorch Implementation of Hierarchical Transformer Memory (HTM) for Pytorch. This Deepmind paper proposes a si
Home Assistant integration for energy consumption data from UK SMETS (Smart) meters using the Hildebrand Glow API.
Hildebrand Glow (DCC) Integration Home Assistant integration for energy consumption data from UK SMETS (Smart) meters using the Hildebrand Glow API. T
Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom
Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom Sample on-line plotting while training(avg loss)/testing(writ
A memory-efficient implementation of DenseNets
efficient_densenet_pytorch A PyTorch =1.0 implementation of DenseNets, optimized to save GPU memory. Recent updates Now works on PyTorch 1.0! It uses
Using a raspberry pi, we listen to the coffee machine and count the number of coffee consumption
A typical datarootsian consumes high-quality fresh coffee in their office environment. The board of dataroots had a very critical decision by the end of 2021-Q2 regarding coffee consumption.
Exploring Classification Equilibrium in Long-Tailed Object Detection, ICCV2021
Exploring Classification Equilibrium in Long-Tailed Object Detection (LOCE, ICCV 2021) Paper Introduction The conventional detectors tend to make imba
Tool for generating Memory.scan() compatible instruction search patterns
scanpat Tool for generating Frida Memory.scan() compatible instruction search patterns. Powered by r2. Examples $ ./scanpat.py arm.ks:64 'sub sp, sp,
Run Effective Large Batch Contrastive Learning on Limited Memory GPU
Gradient Cache Gradient Cache is a simple technique for unlimitedly scaling contrastive learning batch far beyond GPU memory constraint. This means tr
Differentiable Neural Computers, Sparse Access Memory and Sparse Differentiable Neural Computers, for Pytorch
Differentiable Neural Computers and family, for Pytorch Includes: Differentiable Neural Computers (DNC) Sparse Access Memory (SAM) Sparse Differentiab
Continuum Learning with GEM: Gradient Episodic Memory
Gradient Episodic Memory for Continual Learning Source code for the paper: @inproceedings{GradientEpisodicMemory, title={Gradient Episodic Memory
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
In-Place Activated BatchNorm In-Place Activated BatchNorm for Memory-Optimized Training of DNNs In-Place Activated BatchNorm (InPlace-ABN) is a novel
STMTrack: Template-free Visual Tracking with Space-time Memory Networks
STMTrack This is the official implementation of the paper: STMTrack: Template-free Visual Tracking with Space-time Memory Networks. Setup Prepare Anac
IJCAI2020 & IJCV 2020 :city_sunrise: Unsupervised Scene Adaptation with Memory Regularization in vivo
Seg_Uncertainty In this repo, we provide the code for the two papers, i.e., MRNet:Unsupervised Scene Adaptation with Memory Regularization in vivo, IJ
MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift
MemStream Implementation of MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift . Siddharth Bhatia, Arjit Jain, Shivi
Custom component to calculate estimated power consumption of lights and other appliances
Custom component to calculate estimated power consumption of lights and other appliances. Provides easy configuration to get virtual power consumption sensors in Home Assistant for all your devices which don't have a build in power meter.
ClusterMonitor - a very simple python script which monitors and records the CPU and RAM consumption of submitted cluster jobs
ClusterMonitor A very simple python script which monitors and records the CPU and RAM consumption of submitted cluster jobs. Usage To start recording
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation
STCN Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [a
PyTorch Code of "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics"
Memory In Memory Networks It is based on the paper Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spati
Official code repository of the paper Learning Associative Inference Using Fast Weight Memory by Schlag et al.
Learning Associative Inference Using Fast Weight Memory This repository contains the offical code for the paper Learning Associative Inference Using F
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
ActNN : Activation Compressed Training This is the official project repository for ActNN: Reducing Training Memory Footprint via 2-Bit Activation Comp
Python utility function to communicate with a subprocess using iterables: for when data is too big to fit in memory and has to be streamed
iterable-subprocess Python utility function to communicate with a subprocess using iterables: for when data is too big to fit in memory and has to be
Elara DB is an easy to use, lightweight NoSQL database that can also be used as a fast in-memory cache.
Elara DB is an easy to use, lightweight NoSQL database written for python that can also be used as a fast in-memory cache for JSON-serializable data. Includes various methods and features to manipulate data structures in-memory, protect database files and export data.
Python function to stream unzip all the files in a ZIP archive: without loading the entire ZIP file or any of its files into memory at once
Python function to stream unzip all the files in a ZIP archive: without loading the entire ZIP file or any of its files into memory at once
This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting.
GAN Memory for Lifelong learning This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting. Please consider citing our paper
Implementation of "Efficient Regional Memory Network for Video Object Segmentation" (Xie et al., CVPR 2021).
RMNet This repository contains the source code for the paper Efficient Regional Memory Network for Video Object Segmentation. Cite this work @inprocee
Official pytorch implementation of Rainbow Memory (CVPR 2021)
Rainbow Memory: Continual Learning with a Memory of Diverse Samples
Training code of Spatial Time Memory Network. Semi-supervised video object segmentation.
Training-code-of-STM This repository fully reproduces Space-Time Memory Networks Performance on Davis17 val set&Weights backbone training stage traini
Visualization Toolbox for Long Short Term Memory networks (LSTMs)
Visualization Toolbox for Long Short Term Memory networks (LSTMs)
Segcache: a memory-efficient and scalable in-memory key-value cache for small objects
Segcache: a memory-efficient and scalable in-memory key-value cache for small objects This repo contains the code of Segcache described in the followi
Asynchronous Client for the worlds fastest in-memory geo-database Tile38
This is an asynchonous Python client for Tile38 that allows for fast and easy interaction with the worlds fastest in-memory geodatabase Tile38.
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.
InfoPro-Pytorch The Information Propagation algorithm for training deep networks with local supervision. (ICLR 2021) Revisiting Locally Supervised Lea
PINCE is a front-end/reverse engineering tool for the GNU Project Debugger (GDB), focused on games.
PINCE is a front-end/reverse engineering tool for the GNU Project Debugger (GDB), focused on games. However, it can be used for any reverse-engi
Guide: Finetune GPT2-XL (1.5 Billion Parameters) and GPT-NEO (2.7 B) on a single 16 GB VRAM V100 Google Cloud instance with Huggingface Transformers using DeepSpeed
Guide: Finetune GPT2-XL (1.5 Billion Parameters) and GPT-NEO (2.7 Billion Parameters) on a single 16 GB VRAM V100 Google Cloud instance with Huggingfa
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
PyTorch Implementation of Differentiable ODE Solvers This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpr
jupyter/ipython experiment containers for GPU and general RAM re-use
ipyexperiments jupyter/ipython experiment containers and utils for profiling and reclaiming GPU and general RAM, and detecting memory leaks. About Thi
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
BatchFlow BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflo
Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀
What is Vaex? Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular data
Lazy Profiler is a simple utility to collect CPU, GPU, RAM and GPU Memory stats while the program is running.
lazyprofiler Lazy Profiler is a simple utility to collect CPU, GPU, RAM and GPU Memory stats while the program is running. Installation Use the packag
BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.
BitPack is a practical tool that can efficiently save quantized neural network models with mixed bitwidth.
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
NuPIC Numenta Platform for Intelligent Computing The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implem
Monitor Memory usage of Python code
Memory Profiler This is a python module for monitoring memory consumption of a process as well as line-by-line analysis of memory consumption for pyth
Diamond is a python daemon that collects system metrics and publishes them to Graphite (and others). It is capable of collecting cpu, memory, network, i/o, load and disk metrics. Additionally, it features an API for implementing custom collectors for gathering metrics from almost any source.
Diamond Diamond is a python daemon that collects system metrics and publishes them to Graphite (and others). It is capable of collecting cpu, memory,
Scalene: a high-performance, high-precision CPU and memory profiler for Python
scalene: a high-performance CPU and memory profiler for Python by Emery Berger 中文版本 (Chinese version) About Scalene % pip install -U scalene Scalen
Development tool to measure, monitor and analyze the memory behavior of Python objects in a running Python application.
README for pympler Before installing Pympler, try it with your Python version: python setup.py try If any errors are reported, check whether your Pyt
Monitor Memory usage of Python code
Memory Profiler This is a python module for monitoring memory consumption of a process as well as line-by-line analysis of memory consumption for pyth
Cross-platform lib for process and system monitoring in Python
Home Install Documentation Download Forum Blog Funding What's new Summary psutil (process and system utilities) is a cross-platform library for retrie
FlatBuffers: Memory Efficient Serialization Library
FlatBuffers FlatBuffers is a cross platform serialization library architected for maximum memory efficiency. It allows you to directly access serializ
Implementation of Feedback Transformer in Pytorch
Feedback Transformer - Pytorch Simple implementation of Feedback Transformer in Pytorch. They improve on Transformer-XL by having each token have acce
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Annoy Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given quer
Colibri core is an NLP tool as well as a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams (i.e patterns with one or more gaps, either of fixed or dynamic size) in a quick and memory-efficient way. At the core is the tool ``colibri-patternmodeller`` whi ch allows you to build, view, manipulate and query pattern models.
Colibri Core by Maarten van Gompel, [email protected], Radboud University Nijmegen Licensed under GPLv3 (See http://www.gnu.org/licenses/gpl-3.0.html
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Annoy Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given quer
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
NuPIC Numenta Platform for Intelligent Computing The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implem
Cross-platform lib for process and system monitoring in Python
Home Install Documentation Download Forum Blog Funding What's new Summary psutil (process and system utilities) is a cross-platform library for retrie
Monitor Memory usage of Python code
Memory Profiler This is a python module for monitoring memory consumption of a process as well as line-by-line analysis of memory consumption for pyth