Showing 3 open source projects for "labels"

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  • Go from Code to Production URL in Seconds Icon
    Go from Code to Production URL in Seconds

    Cloud Run deploys apps in any language instantly. Scales to zero. Pay only when code runs.

    Skip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
    Try it free
  • Custom VMs From 1 to 96 vCPUs With 99.95% Uptime Icon
    Custom VMs From 1 to 96 vCPUs With 99.95% Uptime

    General-purpose, compute-optimized, or GPU/TPU-accelerated. Built to your exact specs.

    Live migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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  • 1
    KWOK

    KWOK

    Kubernetes WithOut Kubelet - Simulates thousands of Nodes and Clusters

    KWOK is a toolkit that enables setting up a cluster of thousands of Nodes in seconds. Under the scene, all Nodes are simulated to behave like real ones, so the overall approach employs a pretty low resource footprint that you can easily play around with on your laptop.
    Downloads: 7 This Week
    Last Update:
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  • 2
    kube-capacity

    kube-capacity

    A simple CLI that provides an overview of the resource requests

    This is a simple CLI that provides an overview of the resource requests, limits, and utilization in a Kubernetes cluster. It attempts to combine the best parts of the output from kubectl top and kubectl describe into an easy-to-use CLI focused on cluster resources. By default, kube-capacity will output a list of nodes with the total CPU and Memory resource requests and limits for all the pods running on them. For clusters with more than one node, the first line will also include cluster-wide totals.
    Downloads: 1 This Week
    Last Update:
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  • 3
    DeepCluster

    DeepCluster

    Deep Clustering for Unsupervised Learning of Visual Features

    DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups. This alternating “cluster & train” scheme helps the model gradually discover meaningful structure without labels. ...
    Downloads: 0 This Week
    Last Update:
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