So "Using env ..." take minutes without any output ?

the timestamps were all that mattered in those.

fwiw - i'm starting to wonder if there's a difference between me "resetting the task" vs cloning it.

okay that's a similar setup to mine... that's interesting.
much more in line with my expectation.

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

i really dont see how this provides any additional context that the timestamps + crops dont but okay.

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

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

• 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

sometimes I get "lucky" and see something more like what I expect... total experiment time < 1 min (and I have evidence of this happening. logs start-to-finish in sub-minute). But then other times the same task will take 5-10 minutes.

same worker, same queue, just one worker serving it... I am so utterly perplexed by the variation in how long things take. my clearml API server is running on a beefy 32 core machine and not much else is happening right now...

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

@<1523701205467926528:profile|AgitatedDove14> About why we stay on 1.12.2 : None

We need to focus first on Why is it taking minutes to reach Using env.
In our case, we have a container that have all packages installed straight in the system, no venv in the container. Thus we don't use CLEARML_AGENT_SKIP_PIP_VENV_INSTALL
But then when a task is pulled, I can see all the steps like git clone, a bunch of Requirement already satisfied .... 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.
In @<1689446563463565312:profile|SmallTurkey79> case, are you saying the log don't show anything at all ? After it pull the task: 5 minutes pass and no explanation of what those 5min been used for ?

starting to . thanks for your explanation .

would those containers best be started from something in services mode? or is it possible to get no-overhead with my approach of worker-inside-docker?

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.

I'm now experimenting with lumping a lot of stuff into one big task and seeing how this goes instead . i have to be more selective in the reporting of metrics and plots though .

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

of what task? i'm running lots of them and benchmarking

If you are skipping every installation it should be the same
because if you set CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1 it will not install Anything at all
This is why it's odd to me...
wdyt?

there is almost zero overhead if your docker container alreadyt has everything (including the agent) preinstalled and you set it with CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1
it then should basically just run the code.

im not running in docker mode though - im running a clearml worker in a docker container (and then multiplying the container)

BTW: you can also just add -e " CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1" to the docker args (under the Execution tab) to override the setting of the docker.
you can also add " export; " to the docker startup bash script section (do not add "#/bin/bash" , just the actual script) to get a list of all the environment variables inside the docker, just in case

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.

I'm just working on speeding up the time from "queue experiment" to "my code actually runs remotely" - as of yesterday things would sit for many minutes at a time. trying to see if venv is the culprit .

hard to see with your croppout here an there ...

yeah, still noticing that it can be multiple minutes before something starts...
like... what is happening in this time (besides a git clone), now that I set both

export CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=true
export CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=$(which python)

update: it's now been six mins and the task still isn't done. this should have run through in like a minute total end-to-end

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 will basically create a new venv inside the container forking the existing preinistalled stuff (i.e. the new venv already has everything the python system has preinstalled)
then it will call "pip install" on all the "installed packages of the Task.
Which should just check everything is there and install nothing

If you set " CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1" it will do checks and just use the existing system python environment as is.

, 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.

50 containers on a single machine would be fine if you have enough RAM/CPU, and yes they would run concurrently.
regrading the time itself, again the spinup time of a Task should be negligible.
Pipeline tasks are not meant to be "threads" they are meant as different functions you want to run on different machines,
This means that if your pipeline is just a set of simple functions that require no cpu/gpu or IO, I'm not sure pipeline steps is the right way to go

Does that make sense?