Open kanglcn opened 1 year ago
Hi @kanglcn ! I came across this issue due to the rapidsai/kvikio reference. I work on the RAPIDS team at NVIDIA.
If you're willing to share, I'd love to learn more about what potential KvikIO features and functionality would be important for your use cases.
Hi @beckernick . It looks I can't install kvikio with python 3.10.
mamba install -c rapidsai kvikio=23.02
Looking for: ['kvikio=23.02']
conda-forge/linux-64 Using cache
conda-forge/noarch Using cache
pkgs/main/linux-64 No change
pkgs/main/noarch No change
pkgs/r/linux-64 No change
rapidsai/linux-64 No change
rapidsai/noarch No change
pkgs/r/noarch No change
Pinned packages:
- python 3.10.*
Could not solve for environment specs
The following packages are incompatible
└─ kvikio 23.02** is installable with the potential options
├─ kvikio 23.02.00 would require
│ └─ python >=3.8,<3.9.0a0 , which can be installed;
└─ kvikio 23.02.00 would require
└─ python >=3.9,<3.10.0a0 , which can be installed.
We've just released v23.04, which includes Python 3.10 support. Would you be able to give that a test?
Thank you @beckernick ! I have successfully installed it.
I am working on satellite image processings. My current workflow is:
zarr.load
;cp.asarray
;cp.asnumpy
;zarr.save
.The data reading and writing is too slow. That is why I am looking for kvikio
.
After I install it, I find a problem in reading zarr:
rslc_path = '../../data/rslc.zarr'
rslc_zarr = zarr.open(rslc_path,mode='r')
rslc_cpu = rslc_zarr[:]
The data is successfully load into memory. But when I use kvikio
:
rslc_zarr = zarr.open(store=GDSStore('./rslc_gpu.zarr'),mode='r')
rslc_gpu = rslc_zarr[:]
I got an error:
Can you please help me find out how to correctly use kvikio
?
Hi @kanglcn, the GPU array support in Zarr is still in development. we just merged the final piece, which will be included in the next Zarr release v2.15
.
We need to make KvikIO use this new Zarr feature and then everything should just work hopefully :)
Are you using compression? Zarr only comes with CPU compressions but we plan to implement GPU compression using NVCOMP.
Thanks @madsbk for letting me know! I haven't used any compression now. But I definitely will try it if it can help speed up the IO.
Would you consider adding support for dask? i.e. dask.array.to_zarr
and dask.array.from_zarr
. I am scaling my code with dask. If you have that plan, that will be very helpful to me!
Yes, the plan is to support dask.
Use https://github.com/rapidsai/kvikio when it is mature.