right, seems to have worked now!

yep, setting it to -1 is still caching envs..

@<1523701205467926528:profile|AgitatedDove14> any ideas?

web_server:

I'm on Ubuntu 18.04.3

i'm guessing the cleanup_period_in_days can only actually run every day or whatever if the script is enqueued to services

if i enqueue the script to the services queue but run_as_service is false, what happens?

do I need to set the extra_index_url in clearml.conf as well?

I am tagging AgitatedDove14 since I sort of need an answer asap...!

clearml-agent 1.2.3

(base) emilio@unicorn:~$ docker version Client: Docker Engine - Community Version: 19.03.13 API version: 1.40 Go version: go1.13.15 Git commit: 4484c46d9d Built: Wed Sep 16 17:02:36 2020 OS/Arch: linux/amd64 Experimental: false (base) emilio@unicorn:~$ docker-compose --version docker-compose version 1.17.1, build unknown

um, this line is not doing anything for me 🤔
controller_clearml_task = Task.current_task() controller_clearml_task.set_resource_monitor_iteration_timeout( seconds_from_start=10 )

they are taking longer than 30 secs, but admittedly not much longer: 1-3 minutes

I have this inside my pipeline defined with decorator

hi SuccessfulKoala55 ! has the docker compose been updated with this?>

In fact I just did that yesterday. I'll let you know how it goes

i can't get some sort of response from curl

Hi CostlyOstrich36
I added this instruction at the very end of my postprocess function
shutil.rmtree("~/.clearml")

yes, I just ran steps 6-12 again from https://allegro.ai/docs/deploying_trains/trains_server_linux_mac/

https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/dataset_module/dataset_dataset.html#clearml.Dataset.create

where should I look for this folder?
I am talking about serving

Same thing SuccessfulKoala55 😞

Sent it to you via DM!

api_server:

i expected to see 2 tasks running, and then when completed the remaining 2 could start. Is this not the expected behavior?

{"detail":"Error processing request: [Errno 2] No such file or directory: '~/.clearml'"}

platform: "tensorflow_savedmodel" input [ { name: "dense_input" data_type: TYPE_FP32 dims: [-1, 784] } ] output [ { name: "activation_2" data_type: TYPE_FP32 dims: [-1, 10] } ]

right, and why can't a particular version be found? how it does it try to find python versions?

using clearML agent

sheesh, weird. I don't get the same.