one the same or different machine !
Ok. Found the solution.
The importance is to use this:
Task.add_requirements("requirements.txt")
task = Task.init(project_name='hieutest', task_name='foo',reuse_last_task_id=False)
And not:
task = Task.init(project_name='hieutest', task_name='foo',reuse_last_task_id=False)
task.add_requirements("requirements.txt")
that format is correct as I can run pip install -r requirements.txt
using the exact same file
and in the train.py , I have task.add_requirements("requirements.txt")
there is a whole discussion about it here: None
you should be able to use as many agent as you want.
On the same or different queue
not sure how that work with Docker and machine that is not set up with ssh public key ... We will go to that path sometime in the future so I am quite interested too, on how people do it without ssh public key
Are you running within a zero-trust environment like ZScaler ?
Feels like your issue is not ClearML itself, but issue with https/SSL and certificate from your zero-trust system
while the other may need to be 1 instead of true
You are using CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL the wrong way
CLEARML_AGENT_SKIP_PIP_VENV_INSTALL need to be a path
so what was the solution/hack then ?
yup, you have the flexibility and option, that what so nice with ClearML
we are usign mmsegmentation by the way
with ssh public key, if from a terminal, I can do git clone, then so do the clearml agent, as it run on behalf of an local user. That apply to both local and VM
What should I put in there? What is the syntax for git package?
most of people probable wont even know what that do
Yes. I am investigating that route now.
I tried mounting azure storage account on that path and it worked: all files end up in the cloud storage
most of the time, "user" would expect that clearml handle the caching by itself
Onprem: User management is not "live" as you need to reboot and password are hardcoded ... No permission distinction, as everyone is admin ...
So we have 3 python package, store in github.com
On the dev machine, the datascientist (DS) will add the local ssh key to his github account as authorized ssh keys, account level.
With the DS can run git clone git@github.com:org/repo1 then install that python package via pip install -e .
Do that for all 3 python packages, each in its own repo1 , repo2 and repo3 . All 3 can be clone using the same key that the DS added to his account.
The DS run a tra...