Hmmm, that actually connects with something we were thinking about: introducing sections to the hyper parameters. This way we could easily differentiate between the command line arguments and other types of parameters. DilapidatedDucks58 what do you think?
yeah, I am aware of trains-agent, we are planning to start using it soon, but still, copying original training command would be useful
this definitely would be a nice addition. number of hyperparameters in our models often goes up to 100
not necessarily, command usually stays the same irrespective of the machine
The idea is that it is not necessary, using the trains-agent you can not only launch the experiment on a remote machine, you can override the parameters, not just cmd line arguments, but any dictionary you connected with the Task or configuration...
DilapidatedDucks58 if you have so many parameters, why don't you use the
task.connect_configuration(dict)
It will put it in the artifacts, as an editable json alike string.
It's dead simple to install:
Pip install trains-agent
the.n you can simply do:
Trains-agent execute --id myexperimentid
BTW copying the cmd line assumes that you are running it in the same machine...
copy-pasting entire training command into command line 😃
You can always access the entire experiment data from python
'Task.get_task(Id).data'
It should all be there.
What's the exact use case you had in mind?