Search Results for "memory" - Page 16
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Showing 497 open source projects for "memory"
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gitfs
Version controlled file system
gitfs is a FUSE file system that fully integrates with git. You can mount a remote repository's branch locally, and any subsequent changes made to the files will be automatically committed to the remote. gitfs was developed by the awesome engineering team at Presslabs, a Managed WordPress Hosting provider. gitfs was designed to bring the full powers of git to everyone, no matter how little they know about versioning. A user can mount any repository and all their changes will be automatically... -
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Texar
Toolkit for Machine Learning, Natural Language Processing
Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides a library of easy-to-use ML modules and functionalities for composing whatever models and algorithms. The tool is designed for both researchers and practitioners for fast prototyping and experimentation. Texar was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. A mirror of this... -
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PyTorch-BigGraph
Generate embeddings from large-scale graph-structured data
...Its training loop is built for throughput: asynchronous I/O, memory-mapped tensors, and lock-free updates keep GPUs and CPUs fed even at extreme scale. The toolkit includes evaluation metrics and export tools so learned embeddings can be used in downstream nearest-neighbor search, recommendation, or analytics. In practice, PBG’s design lets practitioners train high-quality graph embeddings. -
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yapydata
Lower-Layer unified data - JSON, XML, YAML + INI, CFG, properties
The yapydata - Yet Another Python Data - provides a unified interface for the access to various data syntaxes. Therefore it encapsulates the libraries by offering a common API with the canonical internal data as JSON compatible Python in-memory structure. The application is foreseen in particular for the lower layer of the software stack including setup-tools. Thus it uses standard libraries only whenever possible. The initial supported DDLs are: * JSON, XML, YAML and the formats * INI, CFG, .properties The yapydata in particular supports the advanced access to data entries by mapping the dotted-OID notation onto mixed in-memory data types, optional including non-conformant tyeps such as tuple and set. -
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pysourceinfo
RTTI for Python Source and Binary Files
...The covered objects include packages, modules, functions, methods, scripts, and classes by two views: - File System View - packages, modules, and linenumbers - based on files and paths - Runtime Object View - callables, classes, and containers - based on in-memory RTTI / introspection The supported platforms are: - Linux, BSD, Unix, OS-X, Cygwin, and Windows - Python2, Python3 - CPython, PyPy Object addresses within modules - Object Identifier OID - and the display of the runtime call flow are supported by 'PyStackInfo'. Online documents: https://pysourceinfo.sourceforge.io/ -
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bristoSOFT Contacts
bristoSOFT Contacts is group oriented contact management software.
bristoSOFT Contacts is a group oriented contact management software application written in Python, PyQt and PostgreSQL based on MVC software architecture. All contacts include email, telephone, address, title, name, company and also include notes, files, integrated google maps, activities such as phone calls, appointments, messaging, calendar. Contacts is a novel project with cutting edge technology. -
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captcha_break
Identification codes
...First, we set our verification code format to numbers and capital letters, and generate a string of verification codes. It is well known that tensorflow occupies all video memory by default, which is not conducive to us conducting multiple experiments at the same time, so we can use the following code when tensorflow uses the video memory it needs instead of directly occupying all video memory. -
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Rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning
Combining improvements in deep reinforcement learning. Results and pretrained models can be found in the releases. Data-efficient Rainbow can be run using several options (note that the "unbounded" memory is implemented here in practice by manually setting the memory capacity to be the same as the maximum number of timesteps). -
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Video Nonlocal Net
Non-local Neural Networks for Video Classification
...The repo provides training recipes and models for standard datasets, as well as ablations that show how many non-local blocks to insert and at which stages. Efficient implementations keep memory and compute manageable so the blocks can be added without rewriting the entire backbone. The result is a practical, drop-in mechanism for upgrading purely local video models into context-aware networks with strong benchmark performance. -
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Wally
Distributed Stream Processing
...Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency data processing. Be portable and deploy easily (i.e., run on-prem or any cloud). Manage in-memory state for the application. Allow applications to scale as needed, even when they are live and up-and-running. The primary API for Wally is written in Pony. Wally applications are written using this Pony API. -
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base64io
A stream implementation for Python that provides transparent base64
...:class:`base64io.Base64IO` provides an io streaming interface with context manager support that transparently Base64-encodes data read from it. You can use it to transform large files without caching the entire context in memory or to transform an existing stream. -
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
...It supports multi-GPU and multi-node data-parallel training, and integrates with Horovod to scale out across large GPU clusters. Mixed-precision support (float16) is optimized for NVIDIA Volta and Turing GPUs, allowing significant speedups and memory savings without sacrificing model quality. The project comes with configuration-driven training scripts, documentation, and examples that demonstrate how to set up pipelines for tasks. -
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Learn Python the Hard Way
Concise study notes derived from “Learn Python the Hard Way”
...The material favors clarity over abstraction, keeping examples runnable and easy to modify in any editor. It works well as a companion to more exhaustive books, giving you a lightweight way to drill fundamentals and build muscle memory. -
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Vaex
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python
...It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted). Cut development cut development time by 80%. Your prototype is your solution. Create automatic pipelines for any model. -
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jsondata
Modular JSON by trees and branches, pointers and patches
The 'jsondata' package provides for the modular in-memory processing of JSON data by trees, branches, pointers, and patches. The main interface classes are: - JSONData - Core for RFC7159 based data structures. Provides modular data components. - JSONDataSerializer - Core for RFC7159 based data persistence. Provides modular data serialization. - JSONPointer - RFC6901 for addressing by pointer paths. -
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Rekall
Rekall Memory Forensic Framework
Rekall is a powerful memory forensics framework that turns raw RAM captures—or live system state—into structured artifacts investigators can query and script. It ships with a large collection of plugins that parse OS internals to recover processes, modules, sockets, registry hives, and file objects, even when rootkits try to hide them. The design emphasizes repeatability: investigators run well-defined analyses that produce timelines, indicators, and reports suitable for case work or automation. ... -
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BlockSparse
Efficient GPU kernels for block-sparse matrix multiplication
...The idea is to exploit block-level sparsity — i.e. treat matrices or weight tensors as composed of blocks, many of which may be zero or unused — to save compute and memory when sparsity patterns are structured. This is particularly useful in models like Sparse Transformers, where attention matrices or intermediate layers may adopt block-sparse patterns to scale better. The repo implements both blocksparse and blockwise convolution/transpose-convolution primitives, with support for preparing, executing, and verifying those ops on NVIDIA GPUs. ... -
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Five video classification methods
Code that accompanies my blog post outlining five video classification
...As I’ve covered in my previous posts, video has the added (and interesting) property of temporal features in addition to the spatial features present in 2D images. While this additional information provides us more to work with, it also requires different network architectures and, often, adds larger memory and computational demands.We won’t use any optical flow images. This reduces model complexity, training time, and a whole whack load of hyperparameters we don’t have to worry about. Every video will be subsampled down to 40 frames. So a 41-frame video and a 500-frame video will both be reduced to 40 frames, with the 500-frame video essentially being fast-forwarded. ... -
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cnn-benchmarks
Benchmarks for popular CNN models
The cnn-benchmarks project is a collection of benchmarking scripts designed to evaluate the performance of convolutional neural networks across different hardware and configurations. It provides standardized implementations of popular CNN architectures, enabling developers to measure training speed, memory usage, and computational efficiency. The project focuses on reproducibility, allowing consistent comparisons between models and environments. It is particularly useful for testing GPUs and optimizing deep learning workloads, as it highlights bottlenecks and performance differences across setups. The repository includes scripts for running benchmarks on various architectures and datasets, making it easy to gather comparative metrics. ... -
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Pootle Django
Online translation tool
...Key localization file formats are supported, including Gettext PO, XLIFF, Java .properties, PHP arrays and many more supported by the Translate Toolkit. Ensures the best quality localizations by automatically detecting common errors made by translators. Integrates user suggestions, terminology, Translation Memory, Machine Translation, and more. A number of translation projects for a number of languages can be hosted on Pootle. Teams can manage their files, permissions, projects, and translate on-line. Files can be downloaded for offline translation. Delegate to your translators and allow them to commit directly to your version control systems. ... -
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persistent-memory-labs
Get started with various persistent memory technologies
Persistent-memory-labs is a repository of step-by-step guides allowing a smooth approach to persistent memory technologies like NVDIMM-N. -
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SNeezy
A lightweight frontend for launching SNES games on the Raspberry Pi.
...SNeezy turns your Raspberry Pi into a portable SNES console, allowing you to quickly load and play SNES games with only a compatible controller and a screen. Fast load times, a low memory profile, easy configuration, consistent retro styling, box art integration, and a sleek interface make SNeezy the perfect lightweight environment for a dedicated Raspberry Pi SNES console. -
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littletable is a lightweight in-memory data manager of collections of Python objects, providing ORM-like access for querying and joining data using object attributes as pseudo-columns.
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P3BSseq
Parallel processing pipeline for analysis of bisulfite sequencing data
Bisulfite sequencing (BSseq) processing is among the most cumbersome next generation sequencing (NGS) applications. Though some BSseq processing tools are available, they are scattered, require puzzling parameters and are running-time and memory-usage demanding. We have developed P3BSseq, a parallel processing pipeline for fast, accurate and automatic analysis of BSseq reads that trims, aligns, annotates, records the intermediate results, performs bisulfite conversion quality assessment, generates BED methylome and report files following the NIH standards. P3BSseq outperforms the known BSseq mappers regarding running time, computer hardware requirements. ... -
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GMES
GMES is a free Python package for FDTD electromagnetic simulations.
GMES is a free finite-difference time-domain (FDTD) simulation Python package developed at GIST to model photonic devices. Its features include simulation in 1D, 2D, and 3D Cartesian coordinates, distributed memory parallelism on any system supporting the MPI standard, portable to any Unix-like system, variuos dispersive ε(ω) models, CPML absorbing boundaries and/or Bloch-periodic boundary conditions, and arbitrary material and source distributions. GMES officially stands for GIST Maxwell’s Equations Solver.