I have great news, we finally fully implemented support for continuing previously trained models 🎉
Here is a quick example (this is torch, but any framework will work here):
Experiment A (stage 1):
from trains import Task task = Task.init(project_name='demo', task_name='train stage1', output_uri=' ` ')
Experiment B (stage 2): from trains import Task
task = Task.init(project_name='demo', task_name='train stage2', output_uri='
previous_task = Task.get_task(project_name='demo', task_name='train stage1')
local_model = previous_task.models['output'][-1].get_local_copy()
do some stuff
Notice that I used output_uri ` , and pointed it to the Trains file server. This will make sure that I will automatically have a copy of all the stored models on the file server. This also means that Experiment B can be executed on any machine (e.g. Trains-Agent) , and it will download the model from the file server and open a local copy of the model.pt .
With the next Trains release, the model files will also be cached locally 🙂
Also notice that Experiment B will automatically have the output model of experiment A as its own input model, so we can trace back the model evolution :)