That makes sense. But doesn't that also hold true for dataset.get_local_mutable_copy()?

To me it still looks like the only difference is that the non mutable copy is downloaded to the cache folder while mutable copy downloads to the directory I want. I could delete files from both sets so it seems like it's up to the user to make sure not to mutate the non mutable download in the cache folder.

VexedCat68 , in the screenshot you provided it looks like the location is being printed. Did you check to see if something is there?

In case of non mutable copy you get a direct link to the cache folder, you should not change the content. In case of mutable it's a local copy for you to change or play around with, you can make any changes you want there 🙂

I hope what I said was clear. Basically in reality they both seem mutable, with just the directory downloaded to being optional in one and in the other it's downloaded to the cache folder always.

os.listdir(location) shows nothing

How would the two be different? Other than I can pass the directory to local mutable copy

I think that's about that 🙂

Anyway I could apparently delete things in the dataset from the local copy. Isn't it supposed to be immutable?

VexedCat68 Hi 🙂

Can you please provide snippets of how you're saving and retrieving the files?

You can delete locally but it should not affect the remote data.

The data itself is stored in the fileserver. Whatever you do locally does not affect the remote storage, only when creating a new version the changes should be stored (Like when using 'clearml-data sync').

There's data when I manually went there. The directory was originally hidden my bad.

Here they are. I've created and published the dataset. Then when I try to get a local copy, the code works but i'm not sure how to proceed to be able to use that data.