why does it deplete so fast?
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
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}))
##...
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 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