awesome martin, thanks

Okay Jake, so that basically means I don't have to touch any server configuration regarding the file-server on the trains server. It will simply get ignored and all I/O initiated by clients with the right configuration will cover for that?

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

what is the difference betwee nfalse and true?

I did, but I only find task name, I'm looking to filter by the project

Not sure I understand, if i run pipe.start_locally(run_pipeline_steps_locally=True|False) what is the difference betwee ntrue and false? assuming I want to execute locally

if I called .start_locally

we are running the agent on the same machine AgitatedDove14 , it worked before upgrading the clearml... we never set these credentials

Hahahah thanks for the help SuccessfulKoala55 & CostlyOstrich36

I really do feel it would be a nice to have the ability to easily configure the Cleanup Service to cleanup only specific projects / tasks as its a common use case to have a project dedicated for debugging and alike

the link to manual model registry doesn't work

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

💪

CostlyOstrich36 ?

For example I have a DATA_DIR environment variable which points to the directory where disk-data is stored

Martin: In your trains.conf, change the value
files_server: ' s3://ip :port/bucket'

Isn't this a client configuration ( trains-init )? Shouldn't be any change to the server configuration ( /opt/trains/config... )?

later today or tomorrow, I'll update

And yes, it makes perfect sense, thanks for the answer

I just tried setting the conf in the section Martin said, it works perfectly

👍

Continuing on this line of thought... Is it possible to call task.execute_remotely on a CPU only machine (data scientists' laptop for example) and make the agent that fetches this task to run it using GPU? I'm asking that because it is mentioned that it replicates the running environment on the task creator... which is exactly what I'm not trying to do 😄

SuccessfulKoala55 here it is

Happens on my incognito aswell

👍

Searched for "custom plotly" and "log plotly" in search, didn't thinkg about "report plotly"

I'd go for

` from trains.utilities.pyhocon import ConfigFactory

config = ConfigFactory.parse_file(CONF_FILE_PATH) `

On it

I think you are talking about separate problems - the "WARNING DIFF IS TOO LARGE" is only a UI issue, that you can't see hte diff in the UI - correct me if I'm wrong with this

Maria seems to be saying that the execution FAILS when she has uncomitted changes, which is not the expected behavior - am I right maria?

Oh I get it, I thought it is only a UI issue... but it actually doesn't send it O_O

SuccessfulKoala55 AppetizingMouse58

[ec2-user@ip-10-0-0-95 ~]$ df -h Filesystem Size Used Avail Use% Mounted on devtmpfs 3.9G 0 3.9G 0% /dev tmpfs 3.9G 0 3.9G 0% /dev/shm tmpfs 3.9G 880K 3.9G 1% /run tmpfs 3.9G 0 3.9G 0% /sys/fs/cgroup /dev/nvme0n1p1 8.0G 6.5G 1.5G 82% / tmpfs 790M 0 790M 0% /run/user/1000

We try to break up every thing into independent tasks and group them using a pipeline. The dependency on an agnet caused an unnecessary overhead since we just want to execute locally. It became a burden once new data scientists join the project and instead of just telling them "yeah, just execute this script" you have to now teach them about clearml, the role of agents, how to launch them, how they behave, how to remove them and stuff like that... things you want to avoid with data scientists

this means that you supply the machine to execute the actions specified