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Showing 5 open source projects for "cpu"
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Context for your AI agentsBuild data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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1
MESHROOM
3D reconstruction software
...Automatically estimate fisheye circle or manually edit it. Take advantage of motorized-head file. Easy to integrate in your Renderfarm System. Add specific rules to select the most suitable machines regarding CPU, RAM, GPU requirements of each Node. -
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LDPartEditor
Create and edit LDraw™ parts quick and easy.
...Minimum System Requirements: - OpenGL 2.1 compatible Graphics Card - Operating System (64-bit): Windows [7 or newer], Mac OS X [>=10.6], Linux [e.g. Ubuntu Linux >=14.4] - CPU: Multicore-Processor e.g. Intel Core 2 Duo or AMD Athlon II (>2.0Ghz) - RAM: 4 GB - Video-Memory: 1 GB - Free Disk Space: 150 MB Recommended Requirements: - OpenGL 3.3 compatible Graphics Card - Operating System (64-bit): Windows 7,8,10,11, Mac OS X [>=10.6], Linux [e.g. Ubuntu Linux >=14.4] - CPU: Multicore-Processor with 4 cores (or more) - RAM: >4 GB - Video-Memory: >1 GB - Free Disk Space: 512 MB The sourcecode is now hosted on GitHub here: http://nilsschmidt1337.github.io/ldparteditor/ -
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GroIMP
Growth-grammar related Interactive Modelling Platform
Important: Groimp migrates to Gitlab. You can find the latest code at "https://gitlab.com/grogra/groimp/". The version on Sourceforge will not be updated anymore. The modelling platform GroIMP is designed as an integrated platform which incorporates modelling, visualisation and interaction. It exhibits several features which makes itself suitable for the field of biological or ALife modelling: The “modelling backbone” consists in the language XL. It is fully integrated, e.g., the... -
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3DDFA
Fast, accurate and stable 3D dense face alignment
...A simple 3D render written by c++ and cython is also included. This repo supports the onnxruntime, and the latency of regressing 3DMM parameters using the default backbone is about 1.35ms/image on CPU with a single image as input. See requirements.txt, tested on macOS and Linux platforms. The Windows users may refer to FQA for building issues. Note that this repo uses Python3. The major dependencies are PyTorch, numpy, opencv-python and onnxruntime, etc. -
Leverage AI to Automate Medical CodingAs a healthcare provider, you should be paid promptly for the services you provide to patients. Slow, inefficient, and error-prone manual coding keeps you from the financial peace you deserve. XpertDox’s autonomous coding solution accelerates the revenue cycle so you can focus on providing great healthcare.
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5
Keras.js
Run Keras models in the browser, with GPU support using WebGL
Run Keras models in the browser, with GPU support provided by WebGL 2. Models can be run in Node.js as well, but only in CPU mode. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc. Check out the demos/ directory for real examples running Keras.js in VueJS. Library version compatibility, Keras 2.1.2.
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