Artifacts are stored per experiment, that means that storage wise every experiment uploading an artifact (even if it is the same file content as previous execution) will create a new file on the central storage (default being the trains-server)
As for the preferred way to share data / artifacts. Where do you have your trains server ? Is it local ? Cloud? Where do you access it from home? VPN?
EnviousStarfish54 regrading file server, you have one built into the trains-server, and this will be the default location to store all artifacts. You can also use external solutions like S3 GS Azure etc.
Regarding the models, any model store / load is automatically logged as long as you are using one of the supported frameworks (TF Keras PyTorch scikit learn)
If you want your model to be automatically uploaded, just add outpu_uri:
task=Task.init('examples', 'model', output_uri=' http://trains-server:8081/ ')
oh, this is a bit different from my expectation. I thought I can use artifact for dataset or model version control.
You totally can use artifacts as a way to version data (actually we will have it built in in the next versions)
Getting an artifact programmatically:
Models are logged automatically. No need to log manually