- Be able to trigger the “pure” function (e.g. train()) locally, without any
code running, while driving it from a configuration e.g. path to the data.
When you say " without any http://clear.ml code" do mean without the agent, or without using the Clearml.Dataset ?
Be able to trigger the “
decorator” (e.g. train_clearml()) while driving it from configuration e.g. dataset_id
Hmm I can think of:
` def train_clearml(local_folder=None, dataset_id=None):
if Task.current_task():
params = dict(local_folder=local_folder, dataset_id=dataset_id)
Task.current_task().connect(params, name='train section')
local_folder, dataset_id = param['local_folder'], param['dataset_id']
if dataset_id:
ds = Dataset.get(dataset_id)
self.base_dir = ds.get_local_copy()
else:
self.base_dir = local_folder
train(self.base_dir) `*actually it will be nice is we could have used "locals()" instead of creating the dict and updating it back.. anyhow...
This is just a start, but is this the direction you are after ?
