Showing 4 open source projects for "squid-graph"

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    BentoML

    BentoML

    Unified Model Serving Framework

    ...Parallelize compute-intense model inference workloads to scale separately from the serving logic. Adaptive batching dynamically groups inference requests for optimal performance. Orchestrate distributed inference graph with multiple models via Yatai on Kubernetes. Easily configure CUDA dependencies for running inference with GPU. Automatically generate docker images for production deployment.
    Downloads: 1 This Week
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  • 2
    DoWhy

    DoWhy

    DoWhy is a Python library for causal inference

    ...DoWhy provides a wide variety of algorithms for effect estimation, causal structure learning, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes.
    Downloads: 0 This Week
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  • 3
    OpenFieldAI - AI Open Field Test Tracker

    OpenFieldAI - AI Open Field Test Tracker

    OpenFieldAI is an AI based Open Field Test Rodent Tracker

    OpenFieldAI use AI-CNN to track rodents movement with pretrained OFAI models , or user could create their own model with YOLOv8 for inferencing. The software generates Centroid graph, Heat map and Line path and a spreadsheet containing all calculated parameters like - Speed - Time in and out of ROI - Distance - Entries/Exits for single/multiple pre-recorded videos or live webcam video. The ROI is assigned automatically in multiple video input , and can be manually given in single input. - For Queries/ Reporting Bugs, contact: kabeermuzammil614@gmail.com - Available on WIndows OS - Software Authorship - Muzammil Kabier and Shamili Mariya Varghese ( Sole Authors )
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    Downloads: 10 This Week
    Last Update:
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  • 4
    Lightning Bolts

    Lightning Bolts

    Toolbox of models, callbacks, and datasets for AI/ML researchers

    Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production. Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. We can introduce sparsity during fine-tuning with SparseML, which ultimately allows us to leverage the DeepSparse engine to see performance improvements at inference time.
    Downloads: 0 This Week
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