in my case using self-hosted and agent inside a docker container:
47:45 : taks foo pulled
[ git clone, pip install, check that all requirements satisfied, and nothing is downloaded]
48:16 : start training
- try with the latest RC
1.8.1rc2
, it feels like after git clone, it spend minutes without outputting anything
yeah that is odd , can you run the agent with --debug (add before the daemon command) , and then at the end of the command add --foreground
Now launch the same task on that queue, you will have a verbose log in the console.
Let us know what you see
would those containers best be started from something in services mode?
Yes as long as the machine has enough cpu/ram
Notice that the services mode will start a second parallel Task after the first one is done setting up the env, if running with CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL, with containers that have git/python/clearml-agent preinstalled it should be minimal.
or is it possible to get no-overhead with my approach of worker-inside-docker?
No do not do that, see above explanation on why CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL does not work in docker venv mode
i designed my tasks as different functions, based mostly on what metrics to report and artifacts that are best cached (and how to best leverage comparisons of tasks). they do require cpu, but not a ton.
just report a single Task as multiple "titles" then each title is it's own step, then inside the "title" they have different seriese
is there a way for me to toggle CLEARML's log level?
Try to set the python master logger base logging level
ha! yup. that was it exactly. I posted about it too None lol
im not running in docker mode though
hmmm that might be the first issue. it cannot skip venv creation, it can however use a pre-existing venv (but it will change it every time it installs a missing package)
so setting CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1 in non docker mode has no affect
def seeing some that took 7-8 mins whereas others 2-3...
i just ran a pipeline that took about 2h (more than half this time was just the DAG), with about a hundred tasks. i'm taking a look at them now to see what the logs show for runtimes.
oh it's there, before running task.
from task pick-up to "git clone" is now ~30s, much better.
though as far as I understand, the recommendation is still to not run workers-in-docker like this:
export CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1
export CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=$(which python)
(and fwiw I have this in my entrypoint.sh )
cat <<EOF > ~/clearml.conf
agent {
vcs_cache {
enabled: true
}
package_manager: {
type: pip,
system_site_packages: true,
}
}
EOF
@<1523701205467926528:profile|AgitatedDove14> About why we stay on 1.12.2 : None
what if the preexisting venv is just the system python ? my base image is python:3.10.10 and i just pip install all requirements in that image . Does that not avoid venv still?
it's good to know that in theory there's a path forward with almost zero overhead . that's what I want .
is it reasonable to expect that with sufficient workers, I can get 50 tasks to run in the same time it takes to run a single one? i cant imagine the apiserver being a noticeable bottleneck .
"regular" worker will run one job at a time, services worker will spin multiple tasks at the same time But their setup (i.e. before running the actual task) is one at a time..
I can see all the steps like git clone,
git clone has nothing to do with "env setup" this is brining the code, you cannot skip that one, that said, this is why the git itself is cached on the host machine, so it is fast
... There may be some odd package that need to be installed because one of our DS is experimenting ... But all that we can see what is happening.
even if everything is preinstalled, it Verifies the packages match, this might take a long time. It's just pip being pip (if you want the extreme try to do the same with conda, that one is even slower)
the output of that verification stage is no new packages are installed (otherwise good thing we checked 🙂 )
bottom line, if you want to skip the pip verification/installation pass CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1
btw: i'm checking regrading the GH issue
i really dont see how this provides any additional context that the timestamps + crops dont but okay.
I think a proper screenshot of the full log with some information redacted is the way to go. Otherwise we are just guessing in the dark
minute of silence between first two msgs and then two more mins until a flood of logs. Basically 3 mins total before this task (which does almost nothing - just using it for testing) starts.
Please refer to here None
The doc need to be a bit clearer: one require a path and not just true/false
from task pick-up to "git clone" is now ~30s, much better.
This is "spent" calling apt update && update install && pip install clearml-agent
if you have those preinstalled it should be quick
though as far as I understand, the recommendation is still to not run workers-in-docker like this:
if you do not want it to install anything and just use existing venv (leaving the venv as is) and if something is missing then so be it, then yes sure that the way to go
i would love some advice on that though - should I be using services mode + docker and some max # of instances to be spinning up multiple tasks instead?
my thinking was to avoid some of the docker overhead. but i did try this approach previously and found that the container limit wasn't exactly respected.
ah I see. thank you very much!
trying export CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=$(which python)
but I still see Environment setup completed successfully
(it is printed after Running task id )
it still takes a full 3 minutes between task pulled by worker until Running task id
is this normal? What is happening in these few minutes (besides a git pull / switch)?
hard to see with your croppout here an there ...
of what task? i'm running lots of them and benchmarking execution times. would you like to see a best case or worst case scenario? (ive kept some experiments for each).
and yeah, in those docs you just linked, "boolean" vars like CLEARML_AGENT_GIT_CLONE_VERBOSE explicitly say true so I ended up trying that pattern. but originally i did try 1. let me go back to that now. thank you.
overall I've seen some improvements in execution time using the suggestions in this thread (tysm!) - the preinstalled libs seem to be helping, though some things are still just unbearably slow (one of my larger pipelines took > 1 h to generate a DAG before even starting...).
i just need to understand what I should be expecting. I thought from putting it into queue in UI to "running my code remotely" (esp with packages preloaded) should be fairly fast turnaround - certainly not three minutes... i'll have to change my whole pipeline design if this is the case)
are you on clearml agent 1.8.0?
(im noticing sometimes im just missing logs such as "Running task id.." entirely)