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.
Please refer to here None
The doc need to be a bit clearer: one require a path and not just true/false
1.12.2 because some bug that make fastai lag 2x
1.8.1rc2 because it fix an annoying git clone bug
okay that's a similar setup to mine... that's interesting.
much more in line with my expectation.
hard to see with your croppout here an there ...
@<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)
fwiw - i'm starting to wonder if there's a difference between me "resetting the task" vs cloning it.
yeah... still seeing variances from 1m to 10m for the same task. been testing parallel execution for hours.
you should be able to see int the Console tab that show what is happening
"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..
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)?
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 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
@<1523701205467926528:profile|AgitatedDove14> About why we stay on 1.12.2 : None
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
I know that git clone and pip verify all installed is normal. But for some reason in Michael screenshot, I don't see those steps ...
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.
oh yes. Using env until the next message is 2 minutes.
i was having a ton of git clone issues - disabled caching entirely... wonder if that may help too.
tysm for your help! will report back soon.
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.
are you on clearml agent 1.8.0?
(im noticing sometimes im just missing logs such as "Running task id.." entirely)
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
but pretty reliably some proportion of tasks still just take a much longer time. 1m - 10m is a variance i'd really like to understand.
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...
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...).
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
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 .