Search Results for "cdm-core" - Page 6
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Showing 466 open source projects for "cdm-core"
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Emissary-ingress
open source Kubernetes-native API gateway for microservices
Emissary-Ingress is the most popular API Gateway Kubernetes-native - open-source, that delivers scalability, flexibility, and simplicity for some of the world's largest Kubernetes installations. Emissary-Ingress is an open source CNCF incubating project, and it uses the ubiquitous Envoy Proxy at its core. Once your organization hits a certain scale, a reverse proxy or load balancer isn’t enough for traffic management. If you’re using Kubernetes, you’re probably already at that scale and need support for advanced ingress and API management solutions like blue-green deployment. -
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Optax
Optax is a gradient processing and optimization library for JAX
Optax is a gradient processing and optimization library for JAX. It is designed to facilitate research by providing building blocks that can be recombined in custom ways in order to optimize parametric models such as, but not limited to, deep neural networks. We favor focusing on small composable building blocks that can be effectively combined into custom solutions. Others may build upon these basic components in more complicated abstractions. Whenever reasonable, implementations prioritize... -
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SageMaker Python SDK
Training and deploying machine learning models on Amazon SageMaker
...With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker-compatible Docker containers, you can train and host models using these as well. -
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Pwntools
CTF framework and exploit development library
Pwntools is a CTF framework and exploit development library. Written in Python, it is designed for rapid prototyping and development, and intended to make exploit writing as simple as possible. Whether you’re using it to write exploits, or as part of another software project will dictate how you use it. Historically pwntools was used as a sort of exploit-writing DSL. Simply doing from pwn import in a previous version of pwntools would bring all sorts of nice side-effects. This version... -
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Nano-vLLM
A lightweight vLLM implementation built from scratch
Nano-vLLM is a lightweight implementation of the vLLM inference engine designed to run large language models efficiently while maintaining a minimal and readable codebase. The project recreates the core functionality of vLLM in a simplified architecture written in approximately a thousand lines of Python, making it easier for developers and researchers to understand how modern LLM inference systems work. Despite its compact design, nano-vllm incorporates advanced optimization techniques such as prefix caching, tensor parallelism, and CUDA graph execution to achieve high performance during model inference. ... -
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ZeusDB Vector Database
Blazing-fast vector DB with similarity search and metadata filtering
...The storage layer is designed for durability and growth, supporting sharding, replication, and background compaction while keeping query tails predictable. Developers get multiple ingestion paths—batch, streaming, and upsert—making it easy to keep embeddings synchronized as content changes. Hybrid search is a core design goal, allowing you to mix vector, keyword, and filter queries in a single request for practical relevance. Observability and safety round out the system, with metrics, tracing, and guardrails to manage recalls, deletions, and privacy-sensitive data at scale. -
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Jupyter Enterprise Gateway
Enables Jupyter Notebooks to share resources across clusters
Jupyter Enterprise Gateway is a headless web server with a pluggable framework for anyone supporting multiple notebook users in a managed-cluster environment. Some of the core functionality it provides is better optimization of compute resources, improved multi-user support, and more granular security for your Jupyter notebook environment - making it suitable for enterprise, scientific, and academic implementations. From a technical perspective, Jupyter Enterprise Gateway is a web server that enables the ability to launch kernels on behalf of remote notebooks. ... -
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Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used... -
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NoneBot
Asynchronous multi-platform robot framework written in Python
...NoneBot2 provides an easy-to-use, interactive command-line tool -- nb-cli, making it easier to get started with NoneBot2 for the first time. The plug-in system is the core of NoneBot2, through which the modularization and function expansion of the robot can be realized, which is convenient for maintenance and management. -
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Synapse
Matrix reference homeserver
Matrix is an ambitious new ecosystem for open federated Instant Messaging and VoIP. Everything in Matrix happens in a room. Rooms are distributed and do not exist on any single server. Rooms can be located using convenience aliases like #matrix:matrix.org or #test:localhost:8448. Synapse is currently in rapid development, but as of version 0.5 we believe it is sufficiently stable to be run as an internet-facing service for real usage! Create and manage fully distributed chat rooms with no... -
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any-llm
Communicate with an LLM provider using a single interface
...The project is designed to be framework-agnostic, making it easy to integrate into scripts, applications, or production systems without being tied to a specific stack. It includes a core SDK for direct usage as well as an optional gateway layer that adds enterprise features such as budget management, API key control, usage analytics, and multi-tenant support. The gateway exposes an OpenAI-compatible API, allowing existing tools and workflows to connect without modification. The system emphasizes developer experience with clear error handling, strong typing, and minimal configuration overhead. -
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Numba CUDA Target
The CUDA target for Numba
...It is also used as a foundation for accelerating higher-level libraries such as RAPIDS, where custom user-defined GPU functions are required. The repository represents the continuation of CUDA support after its deprecation in core Numba, ensuring ongoing development and optimization under NVIDIA’s ecosystem. -
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micrograd
A tiny scalar-valued autograd engine and a neural net library
...It constructs a dynamic computation graph as you perform math operations and then computes gradients by walking that graph backward, making it an approachable “from scratch” autograd reference. On top of the core autograd “Value” concept, the project includes a small neural network library that lets you define and train simple models with a PyTorch-like feel, including multilayer perceptrons. The repository is intentionally compact and readable, prioritizing clarity over performance so learners can follow every step of gradient flow and parameter updates. ... -
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Grounded-Segment-Anything
Marrying Grounding DINO with Segment Anything & Stable Diffusion
Grounded-Segment-Anything is a research-oriented project that combines powerful open-set object detection with pixel-level segmentation and subsequent creative workflows, effectively enabling detection, segmentation, and high-level vision tasks guided by free-form text prompts. The core idea behind the project is to pair Grounding DINO — a zero-shot object detector that can locate objects described by natural language — with Segment Anything Model (SAM), which can produce detailed masks for objects once they are localized. This fusion lets users provide arbitrary text descriptions (e.g., “a cat, a bicycle, or a coffee mug”), have the detection model find relevant bounding boxes, and then use SAM to generate precise segmentation masks that isolate each object in the scene. -
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Agno
Lightweight framework for building Agents with memory, knowledge, etc.
...It provides a flexible framework for modeling reasoning, memory, decision-making, and planning, aimed at long-term AI research beyond narrow learning. Agno embraces multi-agent environments and symbolic reasoning as part of its core design, enabling experiments with structured knowledge, goal-oriented behaviors, and meta-learning. It’s designed for researchers seeking an extensible platform to explore AGI components without being tied to black-box models. -
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EconML
Python Package for ML-Based Heterogeneous Treatment Effects Estimation
...This package was designed and built as part of the ALICE project at Microsoft Research with the goal of combining state-of-the-art machine learning techniques with econometrics to bring automation to complex causal inference problems. One of the biggest promises of machine learning is to automate decision-making in a multitude of domains. At the core of many data-driven personalized decision scenarios is the estimation of heterogeneous treatment effects: what is the causal effect of an intervention on an outcome of interest for a sample with a particular set of features? In a nutshell, this toolkit is designed to measure the causal effect of some treatment variable(s) T on an outcome variable Y, controlling for a set of features X, W and how does that effect vary as a function of X. -
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Django Rules
Awesome Django authorization, without the database
rules is a tiny but powerful app providing object-level permissions to Django, without requiring a database. At its core, it is a generic framework for building rule-based systems, similar to decision trees. It can also be used as a standalone library in other contexts and frameworks. Versatile. Decorate callables to build complex graphs of predicates. Predicates can be any type of callable -- simple functions, lambdas, methods, callable class objects, partial functions, decorated functions, anything really. ... -
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SaltStack
Automate the management and configuration of any infrastructure
...Salt can be used for data-driven orchestration, remote execution for any infrastructure, configuration management for any app stack, and much more. Running commands on remote systems is the core function of Salt. Salt can execute commands across thousands of systems in seconds. Salt is built around an event infrastructure that can drive reactive provisioning, configuration, and management across all systems in your infrastructure. Salt contains a robust and flexible configuration management framework that allows effortless, simultaneous configuration of tens of thousands of systems. ... -
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Kornia
Open Source Differentiable Computer Vision Library
Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. ... -
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Spring AI Alibaba Examples
Spring AI Alibaba examples for building and testing AI apps
Spring AI Alibaba Examples provides a collection of example projects that demonstrate how to use Spring AI and Spring AI Alibaba across different scenarios, from basic setups to more advanced AI applications. It is designed to help developers understand core concepts, explore practical implementations, and follow best practices when building AI-powered systems using the Spring ecosystem. Each module focuses on a specific use case such as chat, image processing, audio handling, graph workflows, and retrieval-augmented generation. The examples highlight how to integrate AI models, manage prompts, handle memory, and build multi-model or multi-agent workflows. ... -
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ModelScope
Bring the notion of Model-as-a-Service to life
...It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. In particular, with rich layers of API abstraction, the ModelScope library offers unified experience to explore state-of-the-art models spanning across domains such as CV, NLP, Speech, Multi-Modality, and Scientific-computation. ... -
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Archivematica
Free and open-source digital preservation system
...The user can monitor and control the ingestion and preservation of micro-services through the control panel. Archivematica uses standards such as METS, PREMIS, Dublin Core, and the BagIt specification. -
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Recommenders
Best practices on recommendation systems
...Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. Independent or incubating algorithms and utilities are candidates for the contrib folder. This will house contributions which may not easily fit into the core repository or need time to refactor or mature the code and add necessary tests. -
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mistletoe
A fast, extensible and spec-compliant Markdown parser in pure Python
...Apart from being the fastest CommonMark-compliant Markdown parser implementation in pure Python, mistletoe also supports easy definitions of custom tokens. Parsing Markdown into an abstract syntax tree also allows us to swap out renderers for different output formats, without touching any of the core components. -
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MLE-bench
AI multi-agent framework for automating data-driven R&D workflows
...It uses large language models and multiple collaborating agents to simulate the typical cycle of research, experimentation, and improvement that human data scientists follow. It separates the process into two core phases: a research stage that proposes hypotheses and ideas, and a development stage that implements and evaluates them through code execution and experiments. By iterating through these stages, the framework continuously refines models and strategies using feedback from previous results. RD-Agent focuses heavily on automating complex tasks such as feature engineering, model design, and experimentation, which are traditionally time-consuming in machine learning and quantitative research workflows. ...