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#configs — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #configs, aggregated by home.social.

  1. Use the appropriate solutions.

    are automatically allocated and managed on chosen storage providers.

    allow you to distribute read-only configurations in complex environments, possibly with specialized tools.

    are like configs, but with extra security. It can also be integrated to secrets managers.

    [2/4]

  2. Use the appropriate solutions.

    #Volumes are automatically allocated and managed on chosen storage providers.

    #Configs allow you to distribute read-only configurations in complex environments, possibly with specialized tools.

    #Secrets are like configs, but with extra security. It can also be integrated to secrets managers.

    [2/4]

  3. Use the appropriate solutions.

    #Volumes are automatically allocated and managed on chosen storage providers.

    #Configs allow you to distribute read-only configurations in complex environments, possibly with specialized tools.

    #Secrets are like configs, but with extra security. It can also be integrated to secrets managers.

    [2/4]

  4. Use the appropriate solutions.

    #Volumes are automatically allocated and managed on chosen storage providers.

    #Configs allow you to distribute read-only configurations in complex environments, possibly with specialized tools.

    #Secrets are like configs, but with extra security. It can also be integrated to secrets managers.

    [2/4]

  5. Use the appropriate solutions.

    #Volumes are automatically allocated and managed on chosen storage providers.

    #Configs allow you to distribute read-only configurations in complex environments, possibly with specialized tools.

    #Secrets are like configs, but with extra security. It can also be integrated to secrets managers.

    [2/4]

  6. Does anyone know of any fun and/or overly-complicated methods for syncing your #git / #ssh #configs across multiple machines? I regularly switch between multiple #computers throughout the day and I’m never able to keep those configs the same across all of them (my git commit author fields are a mess in some repos thanks to this).

  7. Does anyone know of any fun and/or overly-complicated methods for syncing your #git/#ssh #configs across multiple machines? I regularly switch between multiple #computers throughout the day and I’m never able to keep those configs the same across all of them (my git commit author fields are a mess in some repos thanks to this).

  8. Does anyone know of any fun and/or overly-complicated methods for syncing your #git/#ssh #configs across multiple machines? I regularly switch between multiple #computers throughout the day and I’m never able to keep those configs the same across all of them (my git commit author fields are a mess in some repos thanks to this).

  9. Does anyone know of any fun and/or overly-complicated methods for syncing your #git / #ssh #configs across multiple machines? I regularly switch between multiple #computers throughout the day and I’m never able to keep those configs the same across all of them (my git commit author fields are a mess in some repos thanks to this).

  10. Does anyone know of any fun and/or overly-complicated methods for syncing your #git/#ssh #configs across multiple machines? I regularly switch between multiple #computers throughout the day and I’m never able to keep those configs the same across all of them (my git commit author fields are a mess in some repos thanks to this).

  11. what i love most about spark is that you can run multiple queries a day and have no results by the end because #configs

  12. what i love most about spark is that you can run multiple queries a day and have no results by the end because #configs

  13. 9/10) Our model selection #algorithm is as follows:
    Use α to filter out bad models and perform readout eval on a downstream task only on “good” models.
    Our proposal decreases the #readout evals from #linear to #logarithmic growth in #configs in a fixed #compute budget setting. 🎉🥳

    #AI #ML #deeplearning #neuroscience

  14. 9/10) Our model selection #algorithm is as follows:
    Use α to filter out bad models and perform readout eval on a downstream task only on “good” models.
    Our proposal decreases the #readout evals from #linear to #logarithmic growth in #configs in a fixed #compute budget setting. 🎉🥳

    #AI #ML #deeplearning #neuroscience

  15. 9/10) Our model selection #algorithm is as follows:
    Use α to filter out bad models and perform readout eval on a downstream task only on “good” models.
    Our proposal decreases the #readout evals from #linear to #logarithmic growth in #configs in a fixed #compute budget setting. 🎉🥳

    #AI #ML #deeplearning #neuroscience

  16. 9/10) Our model selection #algorithm is as follows:
    Use α to filter out bad models and perform readout eval on a downstream task only on “good” models.
    Our proposal decreases the #readout evals from #linear to #logarithmic growth in #configs in a fixed #compute budget setting. 🎉🥳

    #AI #ML #deeplearning #neuroscience

  17. 9/10) Our model selection #algorithm is as follows:
    Use α to filter out bad models and perform readout eval on a downstream task only on “good” models.
    Our proposal decreases the #readout evals from #linear to #logarithmic growth in #configs in a fixed #compute budget setting. 🎉🥳

    #AI #ML #deeplearning #neuroscience