Davit Buniatyan

@farizrahman4u the test fails is pretty strange ``` > raise TransformError(e) from e E hub.util.exceptions.TransformError: cannot reshape array of size 100 into shape (98,1) ```

@daniel-falk indeed current implementation of `.tensorflow()` is not optimized compared to `.pytorch()`. Furthermore, our current efforts are focused on making `.pytorch()` even further optimized dataloader which you can get started...

Hi @wakanawakana, fully aligned on AI database approach and really appreciate your feedback. Can you please give us more context on Nooba's self-certification issue? Please feel free to join our...

@Phoveran thanks for raising the issue, we are reimplementing the pytorch dataloader and adding the sampler strategies as well. Please stay tuned!

yes you should be able to `ds[0:256].pytorch(...)`

@rajdeepdas2000 @h20200051 really appreciate willingness to contribute here, but just checked and this problem has been fixed. I will close it for now and can be reopened if the problem...

you can use local paths such as `./path/to/your/local/dataset` while creating or loading a dataset. ``` import deeplake ds = deeplake.empty("./path/to/your/local/dataset") ... ``` and then access it ``` ds = deeplake.load("./path/to/your/local/dataset")...

Thanks @LucasVandroux for raising the question. Leaving `htype=generic` as default and optionally specifying `dtype=np.uint8` or `dtype=int` in `create_tensor` would be suffice for optimally storing the data. If you don't specify...

@SaiNikhileshReddy the issue is good, but we need to understand what do we mean by verifying uploads. It can mean certain things. - [ ] (ultimate) Does the raw data...

@bhattbhuwan13 Thanks for bringing the solution. I have few questions here and maybe more in future. 1) Does it make easy adding optional dependancies e.g. building `pip3 install hub[distributed]` that...