Thanks, Martin.

Thanks. On the second one, can I specify it my code? (I'd prefer to avoid a separate trains.conf file if possible)

yes.
Obviously when you import the offline session, you will need to set it to point to your server with the correct credentials

Related, how to I specify in my code the cache_dir where the zip is saved?

Just to clarify, where do I run the second command?

Anywhere just open a python console and import the offline task:
from trains import TaskTask.import_offline_session('./my_task_aaa.zip')

Related, how to I specify in my code the cache_dir where the zip is saved?

This is the Trains cache folder, you can set it in the trains.conf file:
https://github.com/allegroai/trains/blob/10ec4d56fb4a1f933128b35d68c727189310aae8/docs/trains.conf#L24

Is there a way to automatically deduplicate? I'm guessing tasks have some unique id in the zip (maybe even the file name)

Hi ImpressionableRaven99
Yes, it is 🙂
Call this one before task.init, and it will run offline (at the end of the execution, you will get a link to the local zip file of the execution)
Task.set_offline(True)Then later you can import it to the system with:
Task.import_offline_session('./my_task_aaa.zip')

E.g. this one https://github.com/allegroai/trains/blob/master/docs/trains.conf

copy paste the trains.conf from any machine, it just need the definition of the trains-server address.
Specifically if you run in offline mode, there is no need for the trains.conf and you can just copy the one on GitHub

Because all my config will be in one place (my training script). I'd like the zipped runs to be at a specific place on the shared disk. Otherwise I'll have to manually copy/paste them so they are visible from the other node (the one running the server)

I see.
You can get the offline folder programmatically then copy the folder content (it's the same as the zip, and you can also pass a folder instead of zip to the import function)
task.get_offline_mode_folder()You can also have a soft link of the offline folder (if you are working on a linux machine:
ln -s myoffline_folder ~/.trains/cache/offline

Seems to work. Although if I import the same run at two different stages (because I want to see how it's doing while training, not only at the end). it doesn't recognise it's the same task/run just with more iterations. It just creates a new task...

Just to clarify, where do I run the second command?

yes that will work 🙂

not really 😞
Why would you want to set it up manually ? makes sense to have it in the cache folder, no?

So copy train.conf from elsewhere to ~/train.conf?

But this will require some code changes...

I see that to create a trains.conf file I need to run trains-init which requires a browser. What if I can't open a browser on that machine?

The import process actually creates a new Task every import, that said if you take a look here:
https://github.com/allegroai/trains/blob/10ec4d56fb4a1f933128b35d68c727189310aae8/trains/task.py#L1733
you can pass a pre-existing Task ID to "import_task" https://github.com/allegroai/trains/blob/10ec4d56fb4a1f933128b35d68c727189310aae8/trains/task.py#L1653

Yes, it will always create a new Task.

Thanks. let me try it...

BTW: you can quite easily add an option to set the offline folder, check here:
https://github.com/allegroai/trains/blob/10ec4d56fb4a1f933128b35d68c727189310aae8/trains/config/init.py#L31
PRs are always appreciated :)