Comments (10)
@eyalto How about this one (it will work in tandem with ArgParse, absl etc.):
(it is basically another set of parameters dictionary, we connect with the experiment/task)
import os
from trains import Task
task = Task.init(project_name='examples', task_name='os environment example')
task.connect({k: os.environ.get(k) for k in ['PATH', 'PWD', 'PYTHONPATH']})
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This indeed results in the same effect but I was looking for a mechanism that is external to the code that can be configured somehow externally and minimises the footprint in the code
.
This would be a way to track instrumentation of the external environment to the training job.
Things like dedicated links to additional tools (like job-id or link to the container running the training job - or other resources generated by the external environment)
I will take your advise and implement such mechanism inside our code - but it may be useful for other users / developers as well - if this is indeed the case ....
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@eyalto thank you for the feature suggestion.
Here is what I was thinking:
$ export TRAINS_LOG_ENVIRONMENT=
(default) TRAINS does not log OS environment variables
$ export TRAINS_LOG_ENVIRONMENT="PWD,PYTHONPATH"
Will result in TRAINS logging values of both PWD and PYTHONPATH
$ export TRAINS_LOG_ENVIRONMENT="*"
Will result in TRAINS logging values of ALL environment variables
Is that what you had in mind?
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@eyalto, the feature is included in the latest release v0.10.2 exactly as suggested.
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Hey!
Thanks for the feature!
I already start using it, but I think that this is a little bit strange that the env params are under HYPER PARAMETERS
Maybe we can find other place for it?
Env params?
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@shaulimaya great to hear you are already using this feature.
Yes, in the next version we will have a separate section for Environment Variables, until then they are under Hyper-Parameters.
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Thanks a lot!
Another small thing - if you can render links "http://blabla" as link
:)
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@shaulimaya not sure security wise, this is recommended :)
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Closing, feature is now available in 0.10.2
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