Showing 4 open source projects for "tuning"

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    Kubeflow Training Operator

    Kubeflow Training Operator

    Distributed ML Training and Fine-Tuning on Kubernetes

    Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, TensorFlow, XGBoost, MPI, Paddle, and others.
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  • 2
    Katib

    Katib

    Automated Machine Learning on Kubernetes

    Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports Hyperparameter Tuning, Early Stopping and Neural Architecture Search. Katib is a project that is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML frameworks, such as TensorFlow, Apache MXNet, PyTorch, XGBoost, and others. Katib can perform training jobs using any Kubernetes Custom...
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  • 3
    ants

    ants

    ants is a high-performance and low-cost goroutine pool in Go

    Library ants implements a goroutine pool with fixed capacity, managing and recycling a massive number of goroutines, allowing developers to limit the number of goroutines in your concurrent programs. Managing and recycling a massive number of goroutines automatically. Purging overdue goroutines periodically. Abundant APIs: submitting tasks, getting the number of running goroutines, tuning capacity of pool dynamically, releasing pool, rebooting pool. Handle panic gracefully to prevent programs...
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  • 4
    Caire

    Caire

    Content aware image resize library

    Caire is a content aware image resize library based on Seam Carving for Content-Aware Image Resizing paper. An energy map (edge detection) is generated from the provided image. The algorithm tries to find the least important parts of the image taking into account the lowest energy values. Using a dynamic programming approach the algorithm will generate individual seams across the image from top to down, or from left to right (depending on the horizontal or vertical resizing) and will...
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