conda install not working
Closed this issue · 5 comments
[gkraemer@orange py]$ conda create -c conda-forge -n xcube xcube
Collecting package metadata (current_repodata.json): done
Solving environment: unsuccessful attempt using repodata from current_repodata.json, retrying with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: /
just keeps on solving forever.
Having the same issue. This happens is for any version returned via conda search -c conda-forge xcube. Also maybe interesting: On pip there is a package named xcube 0.0.1 with a different purpose, which can be confusing.
@gdkrmr and @janneskruse thanks for reporting, please consider using mamba instead of conda:
https://xcube.readthedocs.io/en/latest/installation.html#installation-using-mamba
we experience way faster installation with mamba :)
@gdkrmr and @janneskruse thanks for reporting, please consider using mamba instead of conda: https://xcube.readthedocs.io/en/latest/installation.html#installation-using-mamba
we experience way faster installation with mamba :)
Thank you @AliceBalfanz !
Mamba solved the xcube installation problem but now I am having some issues with aiohttp throwing AttributeError: module 'aiohttp' has no attribute 'http_exceptions'
I am trying to open this cube:
from xcube.core.store import new_data_store
store = new_data_store("s3", root="deep-esdl-public", storage_options=dict(anon=True))
ds = store.open_data('ocean-1M-9km-1x1080x1080-1.4.0.zarr')
Do you know if I need a specific version of aiohttp?
@janneskruse with aiohttp 3.8.4 there are no issues, which version do you use?
By the way, I see you are using the deep-esdl cube, but you are not one of our deepesdl jupyterlab users yet - there you would have functioning python environments preconfigured already :) Let us know, in case you are interested in being onboarded!
@janneskruse with
aiohttp 3.8.4there are no issues, which version do you use? By the way, I see you are using the deep-esdl cube, but you are not one of our deepesdl jupyterlab users yet - there you would have functioning python environments preconfigured already :) Let us know, in case you are interested in being onboarded!
@AliceBalfanz Thank you! In the end I managed to solve it with xarray[complete] installation and an update on aiohttp and requests:D However, I decided to use xarray directly as it is far easier to load the cube in that way. Maybe you could provide extra information on that in the DeepEsdl documentation (if you are even responsible:D). For example mentioning the aws zarr link and how to access it with xr.open_dataset(). And specify when it is useful to use xcube and when xarray is sufficient.
About onboarding deepesdl jupyterlab: Thank you! For now, I am more comfortable using Leipzigs hpc cluster via vscode but maybe I'll come back to your offer later:D