why does it deplete so fast?

queue looks empty

yeah I know

EagerOctopus10

okay, that's acceptable

` alabaster==0.7.12
appdirs==1.4.4
apturl==0.5.2
attrs==21.2.0
Babel==2.9.1
bcrypt==3.1.7
blinker==1.4
Brlapi==0.7.0
cachetools==4.0.0
certifi==2019.11.28
chardet==3.0.4
chrome-gnome-shell==0.0.0
clearml==1.0.5
click==8.0.1
cloud-sptheme==1.10.1.post20200504175005
cloudpickle==1.6.0
colorama==0.4.3
command-not-found==0.3
cryptography==2.8
cupshelpers==1.0
cycler==0.10.0
Cython==0.29.24
dbus-python==1.2.16
decorator==4.4.2
defer==1.0.6
distlib==0.3.1
distro==1.4.0
distro-info===0.23ubuntu1
doc...

that's the only line starting with 192.168

Maybe the case is that after start / start_locally the reference to the pipeline task disappears somehow? O_O

:face_palm: 🤔 :man-tipping-hand:

I'll tr yto work with that

awesome this will help a lot with debugging

so?

ShinyLobster84

👍

CostlyOstrich36 ⬆

it seems apiserver_conf doesn't even change

SuccessfulKoala55 this actually doesn't work

` apiserver_conf = ConfigFactory.parse_file(API_SERVER_CONF_PATH)

POINT 1

conf_content = HOCONConverter.to_hocon(config=ConfigFactory.from_dict(apiserver_conf.as_plain_ordered_dict()),
compact=False,
level=0, indent=2)

apiserver_conf['auth']['fixed_users']['users'].append(
ConfigFactory.from_dict({'username': username, 'password': password, 'name': name}))

##...

👍

lets try

I don't even know where trains is coming from... While using the same environment I can't even import trains, see

Okay, so the agent automatically inherits the launching environment's variables?

logger.report_table(title="Inference Data", series="Inference Values", iteration=0, table_plot=inference_table)

In standard docker TimelyPenguin76 this quoting you mentioned is wrong, since the whole argument is being passed - hence the double tricky quotation I posted above

This is a part of a bigger process which times quite some time and resources, I hope I can try this soon if this will help get to the bottom of this

I know tasks have a name and an ID - but projects?

I assume we are talking about the IP I would find here right?
https://www.whatismyip.com/

I'm really confused, I'm not sure what is wrong and what is the relationship between the templates the agent and all of those thing

For the meantime, I'm giving up on the pipeline thing and I'll write a bash script to orchestrate the execution, because I need to deliver and I'm not feeling this is going anywhere

On an end note I'd love for this to work as expected, I'm not sure what you need from me. A fully reproducible example will be hard because obviously this is proprietary code. What ...

I'd go for

` from trains.utilities.pyhocon import ConfigFactory

config = ConfigFactory.parse_file(CONF_FILE_PATH) `

could be 192.168.1.255?

This is the pip freeze of the environment I don't know why it differs from what the agent has... the agent only has a subset of these google libs