WeChat Mini Program
Old Version Features

    • Sasquatch: Rubin Observatory Metrics and Telemetry Service

    Angelo Fausti Neto,Frossie Economou,Michael A. Reuter,Jonathan Sick, Russ Allbery, Adam J. Thornton

    SOFTWARE AND CYBERINFRASTRUCTURE FOR ASTRONOMY VIII（2024）

    Vera C Rubin Observ Project Off

    Cited 0|Views2
    Abstract
    At Vera C. Rubin Observatory, the need to manage metrics and telemetry data efficiently led to the creation of Sasquatch. Sasquatch consolidates our high-frequency telemetry harness, which captures the observatory engineering data, with the science performance metrics measured by the LSST Science Pipelines. Sasquatch utilizes InfluxDB, a time series database, to efficiently store and query time-series data. We combine InfluxDB Enterprise with Apache Kafka and deploy our solution on the Kubernetes platform. Our current setup at the US Data Facility enables real-time access to data mirrored from the Summit and leverages tools like Chronograf for time series data visualization, Kapacitor for alert management, and the Rubin Science Platform's notebook environment for data analysis using Python. Sasquatch is currently employed during Rubin Observatory's System Integration Testing and Commissioning phase and is an essential service as we transition into survey operations.
    More
    Translated text
    Key words
    Vera C. Rubin Observatory,LSST,Telemetry,Metrics,Time-series Databases
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    收藏
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined