Showing 3 open source projects for "vm"

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    Collect! is a highly configurable debt collection software

    Everything that matters to debt collection, all in one solution.

    The flexible & scalable debt collection software built to automate your workflow. From startup to enterprise, we have the solution for you.
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  • Inventory and Order Management Software for Multichannel Sellers Icon
    Inventory and Order Management Software for Multichannel Sellers

    Avoid stockouts, overselling, and losing control as your business grows.

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  • 1
    PyTorch/XLA

    PyTorch/XLA

    Enabling PyTorch on Google TPU

    ...We are also introducing new TPU VMs for more transparent and easier access to the TPU hardware. This is our recommedned way of running PyTorch/XLA on Cloud TPU. Please check out our Cloud TPU VM User Guide. Cloud TPU VM is currently on general availability and provides direct access to the TPU host. The recommended setup for running distributed training on TPU Pods uses the pairing of Compute VM Instance Groups and TPU Pods. Each of the Compute VM in the instance group drives 8 cores on the TPU Pod.
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  • 2
    PySyft

    PySyft

    Data science on data without acquiring a copy

    Most software libraries let you compute over the information you own and see inside of machines you control. However, this means that you cannot compute on information without first obtaining (at least partial) ownership of that information. It also means that you cannot compute using machines without first obtaining control over those machines. This is very limiting to human collaboration and systematically drives the centralization of data, because you cannot work with a bunch of data...
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  • 3

    Unsupervised Random Forest

    On-line Unsupervised Random Forest

    This tool uses Random Forest and PAM to cluster observations and to calculate the dissimilarity between observations. It supports on-line prediction of new observations (no need to retrain); and supports datasets that contain both continuous (e.g. CPU load) and categorical (e.g. VM instance type) features. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. ...
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