2 projects for "gpu monitor dash" with 1 filter applied:

  • Forever Free Full-Stack Observability | Grafana Cloud Icon
    Forever Free Full-Stack Observability | Grafana Cloud

    Our generous forever free tier includes the full platform, including the AI Assistant, for 3 users with 10k metrics, 50GB logs, and 50GB traces.

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    Compliant and Reliable File Transfers Backed by Top Security Certifications

    Cerberus FTP Server delivers SOC 2 Type II certified security and FIPS 140-2 validated encryption.

    Stop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
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  • 1
    GPU Hot

    GPU Hot

    Real-time NVIDIA GPU dashboard

    GPU Hot is an open-source, lightweight monitoring dashboard designed to provide real-time visibility into NVIDIA GPU performance across single machines or entire clusters. The project offers a self-hosted web interface that streams hardware metrics directly from GPU servers, enabling developers, ML engineers, and system administrators to observe GPU utilization and system behavior in real time through a browser. The dashboard collects and displays a wide range of performance metrics...
    Downloads: 1 This Week
    Last Update:
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  • 2
    Polyaxon

    Polyaxon

    MLOps tools for managing & orchestrating the ML LifeCycle

    Polyaxon is an open-source machine learning operations (MLOps) platform built to help individuals, teams, and organizations develop, train, orchestrate, and monitor machine learning and deep learning workflows at scale with reproducibility and automation as core principles. It provides a unified solution for tracking experiments, managing datasets, scheduling jobs, and comparing results across runs, which greatly improves productivity and collaboration in data science teams. Polyaxon integrates seamlessly with Kubernetes and container orchestration so that workloads can be scheduled efficiently, GPU and CPU resources are shared, and distributed training across multiple nodes is straightforward. ...
    Downloads: 0 This Week
    Last Update:
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