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Now AFAIU it is required to run dc pull (or clone? what is the difference @ilongin ?) to be able then in code do DataChain.from_dataset("name").
Let's avoid this manual step and clone dataset automatically if token is set into the local DB (also clone the updated version). Consider remote datasets as an extension to local.
That would simplify the workflow quite a lot.
Btw how do we handle version collisions atm @ilongin ?
The text was updated successfully, but these errors were encountered:
To me it makes sense to instantiate automatically, but this needs to be communicated to the user somehow I think, with some message as it will slow down process a lot.
Currently, if dataset with same name/ version exist locally but it has different uuid (it's not the same dataset) the message is shown to the user saying something like: "Local dataset dogs@v5 already exists with different uuid, please choose different local dataset name or version"
If the following case are met, this will pull dataset from Studio.
- User should be logged in to Studio.
- The dataset or version doesn't exist in local
- User has not pass studio=False to from_dataset.
In such case, this will pull the dataset from studio before continuing
further.
The test is added to check for such behavior.
Closes#874
Description
Now AFAIU it is required to run
dc pull
(or clone? what is the difference @ilongin ?) to be able then in code doDataChain.from_dataset("name")
.Let's avoid this manual step and clone dataset automatically if token is set into the local DB (also clone the updated version). Consider remote datasets as an extension to local.
That would simplify the workflow quite a lot.
Btw how do we handle version collisions atm @ilongin ?
The text was updated successfully, but these errors were encountered: