Search Results for "project code for cloud computing" - Page 2
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Showing 58 open source projects for "project code for cloud computing"
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AWS ParallelCluster Node
Python package installed on the Amazon EC2 instances
aws-parallelcluster-node is the python package installed on the Amazon EC2 instances launched as part of AWS ParallelCluster. AWS ParallelCluster is an AWS-supported Open Source cluster management tool that makes it easy for you to deploy and manage High-Performance Computing (HPC) clusters in the AWS cloud. Built on the Open Source CfnCluster project, AWS ParallelCluster enables you to quickly build an HPC compute environment in AWS. It automatically sets up the required compute resources and a shared filesystem and offers a variety of batch schedulers such as AWS Batch and Slurm. AWS ParallelCluster facilitates both quick start proof of concepts (POCs) and production deployments. ... -
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Google Gen AI SDK
Google Gen AI Python SDK provides an interface for developers
Google Gen AI Python SDK is Google’s official Python library for integrating Google generative AI models into Python applications. It supports both the Gemini Developer API and Google’s enterprise-oriented generative AI platform APIs, giving developers one SDK for consumer-facing and cloud-based Gemini workflows. The library provides a client-based interface for generating text, working with multimodal inputs, managing chats, handling files, using tools, and accessing model capabilities from Python code. It is intended to replace older Gemini Python SDK patterns with a more unified and actively maintained API surface. ... -
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machine learning tutorials
machine learning tutorials (mainly in Python3)
machine-learning is a continuously updated repository documenting the author’s learning journey through data science and machine learning topics using practical tutorials and experiments. The project presents educational notebooks that combine mathematical explanations with code implementations using Python’s scientific computing ecosystem. Topics covered include classical machine learning algorithms, deep learning models, reinforcement learning, model deployment, and time-series analysis. The repository integrates numerous popular machine learning frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, and Hugging Face. ... -
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Materials Discovery: GNoME
AI discovers 520000 stable inorganic crystal structures for research
Materials Discovery (GNoME) is a large-scale research initiative by Google DeepMind focused on applying graph neural networks to accelerate the discovery of stable inorganic crystal materials. The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the... -
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Claude Scientific Skills
A set of ready to use Agent Skills for research, science, engineering
Claude Scientific Skills is a large open source collection of ready-to-use scientific capabilities that extend AI coding agents into full research assistants. The project provides more than 170 curated skills covering domains such as genomics, drug discovery, medical imaging, physics, and advanced data analysis. Each skill bundles documentation, examples, and tool integrations so agents can reliably execute complex multi-step scientific workflows. The framework follows the open Agent Skills standard and works with multiple AI development environments including Claude Code, Cursor, and Codex. ... -
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Generative AI for Beginners (Version 3)
21 Lessons, Get Started Building with Generative AI
Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development practices and UX for AI. ... -
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FastKoko
Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model
FastKoko is a self-hosted text-to-speech server built around the Kokoro-82M model and exposed through a FastAPI backend. It is designed to be easy to deploy via Docker, with separate CPU and GPU images so that users can choose between pure CPU inference and NVIDIA GPU acceleration. The project exposes an OpenAI-compatible speech endpoint, which means existing code that talks to the OpenAI audio API can often be pointed at a Kokoro-FastAPI instance with minimal changes. It supports multiple... -
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Klavis AI
MCP integration platforms for AI agents to use tools at any scale
Klavis AI is a Y Combinator X25-backed open-source infrastructure platform that enables AI agents to reliably connect with external tools and services at scale through Model Context Protocol (MCP). Founded by ex-Google DeepMind and ex-Lyft engineers, Klavis provides 50+ production-ready MCP servers with enterprise OAuth support for GitHub, Slack, Gmail, Salesforce, Linear, Notion, and more. The flagship product Strata solves tool overload through progressive discovery, achieving +13% higher... -
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GPU Puzzles
Solve puzzles. Learn CUDA
GPU Puzzles is an educational project designed to teach GPU programming concepts through interactive coding exercises and puzzles. Instead of presenting traditional lecture-style explanations, the project immerses learners directly in hands-on programming tasks that demonstrate how GPU computation works. The exercises are implemented using Python with the Numba CUDA interface, which allows Python code to compile into GPU kernels that run on CUDA-enabled hardware. ... -
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Nokido
Local-first runtime for an artificial organism (GitHub mirror)
Nokido is a local-first runtime for an artificial organism: the nervous system and physiology that let local models, cloud providers and coding agents work together on your machine - routing, long-term memory with provenance, regulation, deterministic gates around actions, agent messaging, one hub reached through MCP. This SourceForge project is a read-only MIRROR: source of truth, issues, contributions, releases and CI live on GitHub (https://github.com/Nokido-labs/nokido), documentation in... -
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Orbi
Issue-to-release coding agent with independent PR review
Orbi takes a GitHub Issue from label to release. When an Issue is marked ai-ready, Orbi builds the change in a dedicated worktree and opens a pull request. A reviewer agent, separate from the one that wrote the code, compares the PR with the Issue's acceptance criteria and repairs it. Orbi then merges only that reviewed head and publishes a tagged GitHub release. It is installed from the GitHub repo, with the CLI on PyPI as orbi-cli and Docker images on GHCR and Docker Hub, and can also be used as the hosted Orbi Cloud. Project site: https://orbi.build/?... -
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Spyder AI Chat Plugin
Spyder IDE plugin providing separate chat pane for AI Assistance
An OpenAI-compatible chat pane and agentic plugin for Spyder 6.1+ python IDE. Supports Open Router, OpenAI, Groq, Ollama, LM Studio, llama.cpp APIs and any other LLM inference server that exposes an OpenAI-compatible /v1/chat/completions endpoint. Installation with PyPi in the same environment as Spyder IDE: (spyder) $ pip install spyder-ai-chat Or from source: # clone / download and unzip the project source code, then: (spyder) $ cd spyder_ai_chat (spyder) $ pip install -e . -
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build nanoGPT
Video+code lecture on building nanoGPT from scratch
...Its Git history is intentionally organized so learners can follow each architectural and training improvement as it is introduced. The accompanying video lecture explains how the code develops into a reproduction of the 124-million-parameter GPT-2 model. The project covers tokenization, transformer architecture, optimization, distributed training, data loading, and performance improvements. It includes FineWeb data preparation and HellaSwag evaluation utilities. With sufficient computing resources, the same general code can scale toward larger GPT-3-style configurations. ... -
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CloudI is an open-source private cloud computing framework for efficient, secure, and internal data processing. CloudI provides scaling for previously unscalable source code with efficient fault-tolerant execution of ATS, C/C++, Erlang/Elixir, Go, Haskell, Java, JavaScript/node.js, OCaml, Perl, PHP, Python, Ruby, or Rust services. The bare essentials for efficient fault-tolerant processing on a cloud!
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Mixtral offloading
Run Mixtral-8x7B models in Colab or consumer desktops
...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. -
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dbt-re-data
re_data - fix data issues before your users & CEO would discover them
re_data is an open-source data reliability framework for the modern data stack. Currently, re_data focuses on observing the dbt project (together with underlaying data warehouse - Postgres, BigQuery, Snowflake, Redshift). Data transformations in re_data are implemented and exposed as models & macros in this dbt package. Gather all relevant outputs about your data in one place using our cloud. Invite your team and debug it easily from there. Go back in time, and see your past metadata. Set up... -
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Ubix Linux
The Pocket Datalab
Ubix stands for Universal Business Intelligence Computing System. Ubix Linux is an open-source, Debian-based Linux distribution geared towards data acquisition, transformation, analysis and presentation. Ubix Linux purpose is to offer a tiny but versatile datalab. Ubix Linux is easily accessible, resource-efficient and completely portable on a simple USB key. Ubix Linux is a perfect toolset for learning data analysis and artificial intelligence basics on small to medium... -
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Mars Framework
Mars is a tensor-based unified framework for large-scale data
Mars is a distributed computing framework designed to scale scientific computing and data science workloads across large clusters while preserving the familiar programming interfaces of common Python libraries. The project provides a tensor-based execution model that extends the capabilities of tools such as NumPy, pandas, and scikit-learn so that large datasets can be processed in parallel without rewriting code for distributed environments. -
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data-science-on-gcp
Source code accompanying book: Data Science on the GCP
The data-science-on-gcp repository is a comprehensive collection of code examples and end-to-end workflows that accompany the book Data Science on the Google Cloud Platform, designed to teach developers how to build scalable data science and machine learning systems using Google Cloud services. It provides structured, chapter-aligned implementations that guide users through the full lifecycle of a data science project, including data ingestion, storage, processing, analysis, model training, and deployment. ... -
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Unified Sessions Manager
Pioneering Private and Public Cloud Management since 2008
The UnifiedSessionsManager supports the integrated management of user sessions within Private-Clouds, comprising heterogeneous IT landscapes of various physical and virtual machines, hypervisor management, and virtual user sessions with remote desktops. Extracted documents see https://sourceforge.net/projects/ctys-doc. -
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Python Data Science Tutorials
Common data analysis and machine learning tasks using python
Python Data Science Tutorials is a curated learning repository for common data analysis, machine learning, and natural language processing tasks in Python. It combines external tutorials, courses, reference guides, notebooks, articles, and selected code examples. The collection begins with Python fundamentals and then moves into scientific computing, statistics, NumPy, pandas, data exploration, and visualization. Its machine learning sections cover practical algorithms and libraries, including regression, classification, clustering, support vector machines, and computer vision resources. ... -
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pybotlib
Python Robotic Process Automation Library
pybotlib is a python library for developing Robotic Process Automation projects easily with python primarily on Linux. It contains the code for a central object that will maintain the state and behavior of the RPA. The focus of the project is to accelerate RPA development at scale using open source technology. The package is split into two parts the pybotlib.VirtualAgent object that will control the overall behavior of your RPA as well as pybotlib.utils that contain many useful functions for... -
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Inelastica
Transport code and tools based on SIESTA and TranSIESTA (DFT-NEGF)
NOTE: The Inelastica project moved to https://github.com/tfrederiksen/inelastica/ in February 2018. Pre- and post-processing tools for SIESTA (DFT, quantum chemistry) and TranSIESTA (quantum transport): (1) Calculate phonon frequencies, e-ph couplings, and inelastic contributions to the conductance (IETS). (2) Access Hamiltonian etc from Python. Some code documentation and installation instructions are available at this mediawiki page: http://dipc.ehu.es/frederiksen/inelastica/index.php. -
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ChIP-RNA-seqPRO
ChIP-RNA-sequencing-processing (ChIP-RNA-seqPRO)
...Runnable python scripts packaged together with customized annotation libraries, demo data input and README guide. 9/26 : v1.1 Updated MAIN_IV to debug error thrown by python pandas no longer supporting 'subset'. This code will no longer be actively maintained/updated here. A cloud-based resource for comparative analysis of epigenetic, sequence variation, and expression datasets is now available. Please visit the Cloudomics, project for cloud-based resources: https://sourceforge.net/projects/cloudomics-for-aws/ -
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WARNING: Project moved to http://github.com/scemama/irpf90 IRPF90 is a Fortran90 preprocessor written in Python for programming using the Implicit Reference to Parameters (IRP) method. It simplifies the development of large fortran codes in the field of scientific high performance computing.
