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src/posts/xarray-kvikio/index.md

@@ -26,17 +26,18 @@ For Xarray, the key bit is that kvikio exposes a zarr store [kvikio.zarr.GDSStor
 ## Integrating with Xarray
 
 Getting all this to work nicely requires using three in-progress pull requests that
+
 1. [Teach Zarr to handle alternative array classes](https://github.com/zarr-developers/zarr-python/pull/934)
 2. [Rewrite a small bit of Xarray to not cast all data to a numpy array after read from disk](https://github.com/pydata/xarray/pull/6874)
 3. [Make a backend that connects Xarray to Kvikio](https://github.com/xarray-contrib/cupy-xarray/pull/10)
 
 Writing the backend for Xarray was relatively easily. Most of the code was copied over from the existing Zarr backend. Most of the effort was in ensuring that dimension coordinates could be read in directly to host memory without raising an error. This is required because Xarrays creates `pandas.Index` objects for such variables. In the future, we could consider using `cudf.Index` instead to allow a fully GPU-backed Xarray object.
 
-## Usage 
+## Usage
 
 Assuming you have all the pieces together (see [Appendix I]() and [Appendix II]() for step-by-step instructions), then using all this cool technology only requires adding `engine="kvikio"` to your `open_dataset` line (!)
 
-``` python
+```python
 import xarray as xr
 
 ds = xr.open_dataset("file.zarr", engine="kvikio", consolidated=False)
@@ -45,12 +46,14 @@ ds = xr.open_dataset("file.zarr", engine="kvikio", consolidated=False)
 With this `ds.load()` will load directly to GPU memory and `ds` will now contain CuPy arrays.
 
 At present there are a few limitations:
-1. stores cannot be read with consolidated metadata, and 
+
+1. stores cannot be read with consolidated metadata, and
 2. compression is unsupported by the backend.
 
 ## Quick demo
 
 First create an example uncompressed dataset to read from
+
 ```
 import xarray as xr
 
@@ -64,16 +67,19 @@ airt.to_zarr(store, mode="w", consolidated=True)
 ```
 
 Now read
+
 ```
 # consolidated must be False
 ds = xr.open_dataset(store, engine="kvikio", consolidated=False)
 ds
 ```
 
 Now load a small subset
-``` python
+
+```python
 type(ds["air"].isel(time=0, lat=10).load().data)
 ```
+
 ```
 cupy._core.core.ndarray
 ```
@@ -124,6 +130,7 @@ jupyter lab --no-browser
 ## Appendix II : making sure GDS is working
 
 Scott Henderson (@scottyhq) pointed out that running `python kvikio/python/benchmarks/single-node-io.py` prints nice diagnostic information that lets you check whether GDS is set up. Note that on our system, we have "compatibility mode" enabled. So we don't see the benefits now but this was enough to wire everything up.
+
 ```
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