-
Notifications
You must be signed in to change notification settings - Fork 601
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We鈥檒l occasionally send you account related emails.
Already on GitHub? Sign in to your account
[FEATURE] Simple data iterator for deeplake.Dataset #2016
Labels
enhancement
New feature or request
Comments
hey I have solved this issue can I put a pull request
this is the code I have added |
Hi @pyther-hub, absolutely! Go for it. |
sir I have put a pull request please review it |
Is something still left to be done? |
can I do work on this again? @tatevikh |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
馃毃馃毃 Feature Request
Is your feature request related to a problem?
The current implementation requires TensorFlow or PyTorch to generate the iterator on the Windows.
Of course, I could use
deplake.Dataset.dataloader
to accomplish something like this question.I would like to provide a simple method that can be done identically in all environments.
For example, I have assumed an implementation to preprocess all data in turn on the CPU using this feature.
To create data similar with the current deeplake would require some conversion process.
I assume that all series data is NumPy, and that all other data can be obtained with appropriate types such as str, int, list, etc.
Description of the possible solution
A
deeplake.Dataset.tensorflow()
includes generator function that yields dictionary of records.I guess customizing its implementation.
An alternative solution to the problem can look like
The text was updated successfully, but these errors were encountered: