yeah... still seeing variances from 1m to 10m for the same task. been testing parallel execution for hours.
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)
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
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)?
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
normally when new package need to be install, it shows up in the Console tab
Hi Guys, just curious here, what's was the final issue?
Also out of curiosity, what does that mean? "1.12.2 because some bug that make fastai lag 2x" ?
ha! yup. that was it exactly. I posted about it too None lol
are you on clearml agent 1.8.0?
(im noticing sometimes im just missing logs such as "Running task id.." entirely)
apologies - just trying to keep sensitive data out of screenshot
oh yes. Using env until the next message is 2 minutes.
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 .
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.
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
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?
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.
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...
thank you!
i'll take that design into consideration.
re: CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL in "docker venv mode" im still not quite sure I understand correctly - since the agent is running in a container, as far as it is concerned it may as well be on bare-metal.
is it just that there's no way for that worker to avoid venv? (i.e. the only way to bypass venv is to use docker-mode?)
"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..
@<1689446563463565312:profile|SmallTurkey79> could you attach the full log of the Task?
also I would recommend "export CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1" (not true )
Usually binary env vars are 0/1
(I can see that the docs here: None
never mention it, I'll ask them to add that)
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?
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
1.12.2 because some bug that make fastai lag 2x
1.8.1rc2 because it fix an annoying git clone bug
you should be able to see int the Console tab that show what is happening
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.