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49 × Eureka!the version of the agent (the worker that received the job was 0.14.1)
the one that created the template was 0.14.2
I've solved the first part by importing trains after parsing the arguments. Still not sure about the second part of my question.
AgitatedDove14 Well, after starting a new project it works. I guess it's a bug.
Yeah, I thought to use artifact, wondered if I can avoid using it or on the other hand, use only it just to define the "the model" as a folder.
Thanks.
SteadyFox10 ModelCheckpoint is not for pytorch I think, couldn't find anything like it.
SteadyFox10 AgitatedDove14 Thanks, I really did change the name.
AgitatedDove14 The question is whether it's done post-experiment or not.
After you conducted experiments for a few projects and you want to organize it our way of thinking works.
If you wan't subversions as you go on with the experiments that are conceptually different that they require a different project you're doing something not very organized. In that case the other option will be better, not my style of work.
I think it's either parent project or parent experiment you don't need both.
AgitatedDove14 You were right. I can get them as system tags.
I've wrote a class that wraps an training session and interaction with trains as upon loading/saving the experiment I need more than just the 'model.bin'
So I use these tags to match a specific aux files that were saved with their model.
AgitatedDove14 My solution actually works better when I want to copy the model + aux to a different s3 folder for deployment as the aux is very light and I can copy the model without downloading it. But thanks for the suggestion.
TimelyPenguin76 the tags names are 'Epoch 1', 'Step 5705'
the return value of the InputModel(<Put a string copy from the UI with the tag id>).tags
is an empty array.
TimelyPenguin76 yes, both 0.15.1
TimelyPenguin76 I see it in the web-app under the model.
AgitatedDove14 Yes, I can. I didn't delete the previous project yet.