fix: requirements & formatting

dmlcloud/util/data.py

@@ -1,17 +1,13 @@
 from typing import Iterable
+
 import numpy as np
-import xarray as xr
 import torch.distributed as dist
+import xarray as xr
 
 
 def shard_indices(
-        n: int, 
-        rank: int, 
-        world_size: int, 
-        shuffle: bool=False, 
-        drop_remainder: bool=True, 
-        seed: int=0
-    ) -> list[int]:
+    n: int, rank: int, world_size: int, shuffle: bool = False, drop_remainder: bool = True, seed: int = 0
+) -> list[int]:
     indices = np.arange(n)
 
     if shuffle:
@@ -24,32 +20,27 @@ def shard_indices(
 
 
 def chunked_xr_dataset(
-        ds: xr.Dataset | xr.DataArray, 
-        chunk_size: int, 
-        dim: str, 
-        shuffle: bool=False, 
-        drop_remainder: bool=True, 
-        seed: int=0,
-        rank: int|None=None,
-        world_size: int|None=None,
-        process_group: dist.ProcessGroup|None=None,
-        load: bool = True,
-    ) -> Iterable[xr.Dataset | xr.DataArray]:
+    ds: xr.Dataset | xr.DataArray,
+    chunk_size: int,
+    dim: str,
+    shuffle: bool = False,
+    drop_remainder: bool = True,
+    seed: int = 0,
+    rank: int | None = None,
+    world_size: int | None = None,
+    process_group: dist.ProcessGroup | None = None,
+    load: bool = True,
+) -> Iterable[xr.Dataset | xr.DataArray]:
     num_total_elements = len(ds[dim])
     num_chunks = num_total_elements // chunk_size
-    
+
     if rank is None:
         rank = dist.get_rank(process_group)
     if world_size is None:
         world_size = dist.get_world_size(process_group)
 
     chunk_indices = shard_indices(
-        num_chunks, 
-        rank, 
-        world_size, 
-        shuffle=shuffle, 
-        drop_remainder=drop_remainder, 
-        seed=seed
+        num_chunks, rank, world_size, shuffle=shuffle, drop_remainder=drop_remainder, seed=seed
     )
 
     for chunk_idx in chunk_indices:
@@ -58,4 +49,4 @@ def chunked_xr_dataset(
         chunk = ds.isel({dim: slice(start, end)})
         if load:
             chunk.load()
-        yield chunk
+        yield chunk

dmlcloud/util/distributed.py

@@ -1,7 +1,6 @@
 import os
 from contextlib import contextmanager
 
-import numpy as np
 import torch.distributed as dist
 
 from .tcp import find_free_port, get_local_ips

requirements.txt

@@ -1,4 +1,5 @@
 torch
 numpy
+xarray
 progress_table>=0.1.20,<1.0.0
 omegaconf

test/test_data.py

@@ -1,14 +1,13 @@
 import sys
 
-import xarray as xr
 import numpy as np
 import pytest
-from dmlcloud.util.data import shard_indices, chunked_xr_dataset
+import xarray as xr
+from dmlcloud.util.data import chunked_xr_dataset, shard_indices
 from numpy.testing import assert_array_equal
 
 
 class TestSharding:
-
     def test_types(self):
         indices = shard_indices(10, 0, 2, shuffle=False, drop_remainder=False)
         assert isinstance(indices, list)
@@ -43,7 +42,6 @@ def test_shuffling(self):
 
 
 class TestChunking:
-
     def test_basic(self):
         ds = xr.DataArray(np.arange(100), dims=['x'], name='var').to_dataset()
         world_size = 3
@@ -73,7 +71,6 @@ def test_basic(self):
         assert_array_equal(chunks_2[1]['var'], np.arange(60, 75))
         assert_array_equal(chunks_3[1]['var'], np.arange(75, 90))
 
-
     def test_shuffled(self):
         ds = xr.DataArray(np.arange(100), dims=['x'], name='var').to_dataset()
         world_size = 3