Search Results for "sparse" - Page 3
Sort By:
Showing 180 open source projects for "sparse"
View related business solutions-
Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
UMAP
Uniform Manifold Approximation and Projection
...It can handle large datasets and high dimensional data without too much difficulty, scaling beyond what most t-SNE packages can manage. This includes very high dimensional sparse datasets. UMAP has successfully been used directly on data with over a million dimensions. Second, UMAP scales well in the embedding dimension—it isn't just for visualization. You can use UMAP as a general-purpose dimension reduction technique as a preliminary step to other machine learning tasks. -
2
3FS
A high-performance distributed file system
...Its primary aim is to support efficient and scalable feature transformation pipelines—especially for inference environments—by batching, caching, and integrating feature-based modules like segmenters, sparse retrievers, and scorers seamlessly. The repo includes APIs to define components (e.g. seg, ret, scor) that wrap or interface with external or internal modules, as well as logic to schedule and compose these feature transforms. By handling caching and batching at a system level, 3FS helps reduce overhead when many features or modules must be evaluated per input (e.g. in an LLM agent pipeline). ... -
3
Ring
Ring is a reasoning MoE LLM provided and open-sourced by InclusionAI
Ring is a reasoning Mixture-of-Experts (MoE) large language model (LLM) developed by inclusionAI. It is built from or derived from Ling. Its design emphasizes reasoning, efficiency, and modular expert activation. In its “flash” variant (Ring-flash-2.0), it optimizes inference by activating only a subset of experts. It applies reinforcement learning/reasoning optimization techniques. Its architectures and training approaches are tuned to enable efficient and capable reasoning performance.... -
4
NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers
...The package includes its own high-performance nonlinear solvers which include the ability to swap out to fast direct and iterative linear solvers, along with the ability to use sparse automatic differentiation for Jacobian construction and Jacobian-vector products. NonlinearSolve.jl interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
5
DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
...With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Low-order Extractor learns feature interaction through product between vectors. Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc. -
6
DeepSpeed
Deep learning optimization library: makes distributed training easy
DeepSpeed is an easy-to-use deep learning optimization software suite that enables unprecedented scale and speed for Deep Learning Training and Inference. With DeepSpeed you can: 1. Train/Inference dense or sparse models with billions or trillions of parameters 2. Achieve excellent system throughput and efficiently scale to thousands of GPUs 3. Train/Inference on resource constrained GPU systems 4. Achieve unprecedented low latency and high throughput for inference 5. Achieve extreme compression for an unparalleled inference latency and model size reduction with low costs DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. ... -
7
kg-gen
Knowledge Graph Generation from Any Text
kg-gen is an open-source framework developed by the STAIR Lab that automatically generates knowledge graphs from unstructured text using large language models. The system is designed to transform plain text sources such as documents, articles, or conversation transcripts into structured graphs composed of entities and relationships. Instead of relying on traditional rule-based extraction techniques, KG-Gen uses language models to identify entities and their relationships, producing... -
8
Map-Anything
MapAnything: Universal Feed-Forward Metric 3D Reconstruction
...Instead of stitching together many task-specific models, it uses a single architecture that supports a wide range of 3D tasks—multi-image structure-from-motion, multi-view stereo, monocular metric depth, registration, depth completion, and more. The model flexibly accepts different input combinations (images, intrinsics, poses, sparse or dense depth) and produces a rich set of outputs including per-pixel 3D points, camera intrinsics, camera poses, ray directions, confidence maps, and validity masks. Its inference path is fully feed-forward with optional mixed-precision and memory-efficient modes, making it practical to scale to long image sequences while keeping latency predictable. -
9
Vulcain
Fast and idiomatic client-driven REST APIs
...A reference, production-grade, implementation gateway server is also available in this repository. It's free software (AGPL) written in Go. A Docker image is provided. Current solutions for these problems (GraphQL, JSON:API's embedded resources and sparse fieldsets, etc.) are smart network hacks for HTTP/1. But these hacks come with (too) many drawbacks when it comes to HTTP cache, logs and even security. Fortunately, thanks to the new features introduced in HTTP/2, it's now possible to create true REST APIs fixing these problems with ease. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
10
...Its primary goal is to eliminate the complexities of the original build system, offering a straightforward "clone and compile" experience. The library supports any OpenCL 1.2+ capable device and is designed for users who need sparse linear algebra operations without the overhead of managing complex build dependencies.
-
11
-
12
OpenReco
Open-source photogrammetry for 3D reconstruction.
...The project focuses on ease of use, performance, and reproducible workflows while embracing the open-source ecosystem. OpenReco supports image alignment, camera calibration, sparse and dense reconstruction, mesh generation, texturing, and export to common formats. Whether you're reconstructing archaeological sites, creating digital twins, generating terrain models, or building assets for games and visualization, OpenReco provides a powerful foundation without licensing restrictions. -
13
Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads:... -
14
Rotations.jl
Julia implementations for different rotation parameterizations
...At their heart, each rotation parameterization is a 3×3 unitary (orthogonal) matrix (based on the StaticArrays.jl package), and acts to rotate a 3-vector about the origin through matrix-vector multiplication. While the RotMatrix type is a dense representation of a 3×3 matrix, we also have sparse (or computed, rather) representations such as quaternions, angle-axis parameterizations, and Euler angles. All rotation types support one(R) to construct the identity rotation for the desired parameterization. -
15
DAR - Disk ARchive
For full, incremental, compressed and encrypted backups or archives
DAR is a command-line backup and archiving tool that uses selective compression (not compressing already compressed files), strong encryption, may split an archive in different files of given size and provides on-fly hashing, supports differential backup with or without binary delta, ftp and sftp protocols to remote cloud storage Archive internal's catalog, allows very quick restoration even a single file from a huge, eventually sliced, compressed, encrypted archive eventually located on a remote cloud storage, by only reading/fetching the necessary data to perform the operation. Dar saves *all* UNIX inode types, takes care of hard links, sparse files as well as Extended Attributes (MacOS X file forks, Linux ACL, SELinux tags, user attributes) and some Filesystem Specific Attributes (Linux ext2/3/4, Mac OS X HFS+) more details at: http://dar.linux.free.fr/doc/Features.html -
16
gopkg
Example for the go pkg's function
gopkg is a large community-driven repository of examples for Go’s standard library packages, created to fill the gap left by the relatively sparse official examples. The project organizes content so that each package has its own directory and each function within that package gets its own Markdown file with code examples. The idea is that developers can quickly look up “how do I actually use this function?” without digging through source code or scattered blog posts. The maintainer provides conventions for naming and formatting, and requires that submitted examples compile and run locally or on the Go Playground, which helps keep the collection reliable. ... -
17
Mixtral offloading
Run Mixtral-8x7B models in Colab or consumer desktops
...The project implements techniques that allow model components to be dynamically moved between CPU memory and GPU memory during inference, significantly reducing the amount of GPU VRAM required to run the model. This approach takes advantage of the sparse activation properties of mixture-of-experts architectures, where only a subset of expert networks are used for each token during generation. By selectively loading and caching the required experts, the system avoids keeping the entire model in GPU memory at once. The repository includes notebooks and code examples that demonstrate how to run large language models on consumer hardware such as personal GPUs or cloud notebook environments. -
18
LLaMA-MoE
Building Mixture-of-Experts from LLaMA with Continual Pre-training
...The repository is centered on making MoE research more accessible by offering smaller and more affordable models with only about 3.0 to 3.5 billion activated parameters, which helps reduce deployment and experimentation costs. Its architecture works by splitting LLaMA feed-forward networks into sparse experts and adding gating mechanisms so that only selected experts are activated during inference and training. The project is not just a model release, but also a research framework that includes multiple expert construction methods, several gating strategies, and tooling for continual pre-training on filtered SlimPajama-based datasets. ... -
19
libCEED
CEED Library: Code for Efficient Extensible Discretizations
...While our focus is on high-order finite elements, the approach is mostly algebraic and thus applicable to other discretizations in factored form, as explained in the user manual and API implementation portion of the documentation. One of the challenges with high-order methods is that a global sparse matrix is no longer a good representation of a high-order linear operator, both with respect to the FLOPs needed for its evaluation, as well as the memory transfer needed for a matvec. Thus, high-order methods require a new "format" that still represents a linear (or more generally non-linear) operator, but not through a sparse matrix. -
20
CoTracker
CoTracker is a model for tracking any point (pixel) on a video
...By reasoning about all tracks together, it can maintain temporal consistency, handle mutual occlusions, and reduce identity swaps when trajectories cross. The model takes sparse point queries on one frame and predicts their sub-pixel locations and a visibility score for every subsequent frame, producing long, coherent trajectories. Its transformer-style architecture aggregates information both along time and across points, allowing it to recover tracks even after brief disappearances. The repository ships with inference scripts, pretrained weights, and simple interfaces to seed points, run tracking, and export trajectories for downstream tasks. ... -
21
IndexedTables.jl
Flexible tables with ordered indices
IndexedTables provides tabular data structures where some of the columns form a sorted index. It provides the backend to JuliaDB, but can be used on its own for efficient in-memory data processing and analytics. -
22
RedisGraph
A graph database as a Redis module
A high-performance graph database module for Redis that enables fast graph processing and analytics using a query engine based on Cypher. -
23
ekho
Chinese text-to-speech engine
ekho is a project with relatively sparse documentation, but from the repository it appears to be a small-scale tool for audio processing and playback, possibly with features for speech synthesis or manipulation. The repo includes scripts and configuration files suggesting interactions with media/audio handling libraries. Because of limited README detail, it seems targeted at users comfortable reading and modifying code, rather than end users expecting polished UIs. -
24
A sparse matrix solver for electric power systems, based on the KLU library from University of Florida.
-
25
hloc
Visual localization made easy with hloc
...It's much simpler since a 3D SfM model is not needed. We show in pipeline_SfM.ipynb how to run 3D reconstruction for an unordered set of images. This generates reference poses, and a nice sparse 3D model suitable for localization with the same pipeline as Aachen.
