From f5335f22bbd6037124d60edb3c2d1934d7673e23 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 8 Aug 2022 20:36:18 +0200 Subject: [PATCH] Capitalize YouTube (#8903) --- utils/loggers/clearml/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/loggers/clearml/README.md b/utils/loggers/clearml/README.md index cead64c..64eef6b 100644 --- a/utils/loggers/clearml/README.md +++ b/utils/loggers/clearml/README.md @@ -172,7 +172,7 @@ python utils/loggers/clearml/hpo.py Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site or you have some budget to use cloud GPUs. This is where the ClearML Agent comes into play. Check out what the agent can do here: -- [Youtube video](https://youtu.be/MX3BrXnaULs) +- [YouTube video](https://youtu.be/MX3BrXnaULs) - [Documentation](https://clear.ml/docs/latest/docs/clearml_agent) In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager.