Showing 2 open source projects for "component"

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  • Context for your AI agents Icon
    Context for your AI agents

    Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.

    Build 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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  • G-P - Global EOR Solution Icon
    G-P - Global EOR Solution

    Companies searching for an Employer of Record solution to mitigate risk and manage compliance, taxes, benefits, and payroll anywhere in the world

    With G-P's industry-leading Employer of Record (EOR) and Contractor solutions, you can hire, onboard and manage teams in 180+ countries — quickly and compliantly — without setting up entities.
    Learn More
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    OnnxStream

    OnnxStream

    Lightweight inference library for ONNX files, written in C++

    ...So I decided to write a super small and hackable inference library specifically focused on minimizing memory consumption: OnnxStream. OnnxStream is based on the idea of decoupling the inference engine from the component responsible for providing the model weights, which is a class derived from WeightsProvider. A WeightsProvider specialization can implement any type of loading, caching, and prefetching of the model parameters.
    Downloads: 10 This Week
    Last Update:
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  • 2
    SSD in PyTorch 1.0

    SSD in PyTorch 1.0

    High quality, fast, modular reference implementation of SSD in PyTorch

    ...Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can replace every component with your own code without changing the code base. For example, You can add EfficientNet as the backbone, just add efficient_net.py (ALREADY ADDED) and register it, specific it in the config file, It's done! Smooth and enjoyable training procedure: we save the state of model, optimizer, scheduler, training iter, you can stop your training and resume training exactly from the save point without change your training CMD.
    Downloads: 0 This Week
    Last Update:
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