Search Results for "claw-code" - Page 5
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Showing 391 open source projects for "claw-code"
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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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lightning AI
The most intuitive, flexible, way for researchers to build models
...Find templates (Lightning Apps), modify them and publish your own. Lightning Apps can even be full standalone ML products! Run on your laptop for free! Download the code and type 'lightning run app'. Feel free to ssh into any machine and run from there as well. In research, we often have multiple separate scripts to train models, finetune them, collect results and more. -
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Rubix ML
A high-level machine learning and deep learning library for PHP
...In addition, we provide tutorials and other educational content to help you get started using ML in your projects. Our intuitive interface is quick to grasp while hiding alot of power and complexity. Write less code and iterate faster leaving the hard stuff to us. Rubix ML utilizes a versatile modular architecture that is defined by a few key abstractions and their types and interfaces. Train models in a fraction of the time by installing the optional Tensor extension powered by C. Learners such as neural networks will automatically get a performance boost. -
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marimo
A reactive notebook for Python
marimo is an open-source reactive notebook for Python, reproducible, git-friendly, executable as a script, and shareable as an app. marimo notebooks are reproducible, extremely interactive, designed for collaboration (git-friendly!), deployable as scripts or apps, and fit for modern Pythonista. Run one cell and marimo reacts by automatically running affected cells, eliminating the error-prone chore of managing the notebook state. marimo's reactive UI elements, like data frame GUIs and plots,... -
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AWS Neuron
Powering Amazon custom machine learning chips
...Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. You can continue to use the same ML frameworks you use today and migrate your software onto Inf1 instances with minimal code changes and without tie-in to vendor-specific solutions. Neuron is pre-integrated into popular machine learning frameworks like TensorFlow, MXNet and Pytorch to provide a seamless training-to-inference workflow. It includes a compiler, runtime driver, as well as debug and profiling utilities with a TensorBoard plugin for visualization. -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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Python Programming Hub
Learn Python and Machine Learning from scratch
...The project contains a wide range of tutorials and exercises that cover Python fundamentals, programming concepts, and applied techniques for data analysis and machine learning. Many sections are implemented as Jupyter notebooks, allowing learners to run code interactively while reading explanations of the concepts involved. The repository emphasizes hands-on learning by demonstrating real programming tasks such as data manipulation, statistical analysis, visualization, and automation. It also includes examples of commonly used libraries such as NumPy, Pandas, and other tools used in data science workflows. -
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Computer Vision in Action
A computer vision closed-loop learning platform
...It serves as a hands-on companion for learners and engineers who want to understand not just the theory, but how computer vision is actually implemented for tasks like object detection, image classification, feature tracking, optical flow, and image segmentation. The repository includes structured code examples, scripts, and notebooks that cover pipeline construction, preprocessing, model inference, and visual output rendering, making it easy for newcomers or intermediate practitioners to adapt patterns to their own projects. It also explores how to combine classical computer vision techniques with modern neural network-based models, offering insight into when each approach is most effective. -
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Model Zoo
Please do not feed the models
FluxML Model Zoo is a collection of demonstration models built with the Flux machine learning library in Julia. The repository provides ready-to-run implementations across multiple domains, including computer vision, natural language processing, and reinforcement learning. Each model is organized into its own project folder with pinned package versions, ensuring reproducibility and stability. The examples serve both as educational tools for learning Flux and as practical starting points for... -
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TensorFlow Serving
Serving system for machine learning models
TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. It deals with the inference aspect of machine learning, taking models after training and managing their lifetimes, providing clients with versioned access via a high-performance, reference-counted lookup table. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data. The... -
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The Operator Splitting QP Solver
The Operator Splitting QP Solver
...The algorithm is absolutely division-free after the setup and it requires no assumptions on problem data (the problem only needs to be convex). It just works. OSQP has an easy interface to generate customized embeddable C code with no memory manager required. OSQP supports many interfaces including C/C++, Fortran, Matlab, Python, R, Julia, Rust. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameter tuning to find the best model. ... -
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DeepDetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch
...While the Open Source Deep Learning Server is the core element, with REST API, and multi-platform support that allows training & inference everywhere, the Deep Learning Platform allows higher level management for training neural network models and using them as if they were simple code snippets. Ready for applications of image tagging, object detection, segmentation, OCR, Audio, Video, Text classification, CSV for tabular data and time series. Neural network templates for the most effective architectures for GPU, CPU, and Embedded devices. Training in a few hours and with small data thanks to 25+ pre-trained models. ... -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep integration with the Hugging Face Hub, allowing you to easily load and share a dataset with the wider NLP community. ... -
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Llama Cookbook
Solve end to end problems using Llama model family
The Llama Cookbook is the official Meta LLaMA guide for inference, fine‑tuning, RAG, and multi-step use-cases. It offers recipes, code samples, and integration examples across provider platforms (WhatsApp, SQL, long context workflows), enabling developers to quickly harness LLaMA models -
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mlforecast
Scalable machine learning for time series forecasting
...It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates those automatically based on a simple configuration. It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. ... -
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shimmy
Python-free Rust inference server
...Written primarily in Rust, the tool provides a small standalone binary that exposes an API compatible with the OpenAI interface, allowing existing applications to interact with local models without significant code changes. This compatibility enables developers to replace remote AI services with locally hosted models while keeping their existing software architecture intact. Shimmy focuses on performance and simplicity, using efficient runtime components to minimize memory usage and startup time compared to heavier inference frameworks. It supports modern model formats such as GGUF and SafeTensors and can automatically discover models stored locally or in common directories used by other AI tools. ... -
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PyG
Graph Neural Network Library for PyTorch
...In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour). -
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TensorFlow Model Garden
Models and examples built with TensorFlow
...To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models are suitable. A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU). -
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omegaml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
omega|ml is the innovative Python-native MLOps platform that provides a scalable development and runtime environment for your Data Products. Works from laptop to cloud. -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
...Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines of code, ktrain allows you to easily and quickly. ktrain purposely pins to a lower version of transformers to include support for older versions of TensorFlow. If you need a newer version of transformers, it is usually safe for you to upgrade transformers, as long as you do it after installing ktrain. As of v0.30.x, TensorFlow installation is optional and only required if training neural networks. -
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ML.NET
Open source and cross-platform machine learning framework for .NET
With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. These tools use Automated ML (AutoML), a cutting edge technology that automates the process of building best performing models for your Machine Learning scenario. ... -
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Machine Learning Study
This repository is for helping those interested in machine learning
Machine Learning Study is an educational repository containing tutorials and study materials related to machine learning and data science using Python. The project compiles notebooks, explanatory documents, and practical code examples that illustrate common machine learning workflows. Topics covered include supervised learning algorithms, feature engineering, model training, and performance evaluation techniques. The repository is structured as a learning resource that guides readers through building machine learning models step by step. It often demonstrates how to implement algorithms using widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. ... -
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Jittor
Jittor is a high-performance deep learning framework
...The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. ... -
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MLOps Zoomcamp
Free MLOps course from DataTalks.Club
...The course is designed to teach data scientists and engineers how to move machine learning models from experimentation environments into scalable production services. The repository provides lessons, code examples, and assignments that cover the entire MLOps lifecycle, including model training, experiment tracking, deployment, monitoring, and infrastructure management. Students learn to use widely adopted tools such as MLflow, orchestration frameworks, and cloud platforms to manage machine learning pipelines. The curriculum emphasizes hands-on projects so learners gain practical experience building automated ML pipelines and maintaining deployed models. -
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AI-Job-Notes
AI algorithm position job search strategy
...The emphasis is on doing: practicing with project ideas, setting up reproducible experiments, and showcasing results that convey impact. It ties technical study (ML/DL fundamentals) to real hiring signals like problem-solving, code quality, and experiment logging. The repository’s structure encourages progressive preparation—from fundamentals to mock interviews and post-interview retrospectives. It’s designed to reduce uncertainty and decision fatigue during the often lengthy job-hunt cycle. -
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Hamilton DAGWorks
Helps scientists define testable, modular, self-documenting dataflow
...Your DAG is expressive; Hamilton has extensive features to define and modify the execution of a DAG (e.g., data validation, experiment tracking, remote execution). To create a DAG, write regular Python functions that specify their dependencies with their parameters. As shown below, it results in readable code that can always be visualized. Hamilton loads that definition and automatically builds the DAG for you. Hamilton brings modularity and structure to any Python application moving data: ETL pipelines, ML workflows, LLM applications, RAG systems, BI dashboards, and the Hamilton UI allows you to automatically visualize, catalog, and monitor execution.
