Dask Distributed Write Fix For Zarr

anndata/_io/specs/methods.py

@@ -38,6 +38,18 @@
 H5File = h5py.File
 
 
+####################
+# Dask utils       #
+####################
+
+try:
+    from dask.utils import SerializableLock as Lock
+except ImportError:
+    from threading import Lock
+
+# to fix https://github.com/dask/distributed/issues/780
+GLOBAL_LOCK = Lock()
+
 ####################
 # Dispatch methods #
 ####################
@@ -331,9 +343,24 @@ def write_basic(f, k, elem, _writer, dataset_kwargs=MappingProxyType({})):
 
 
 @_REGISTRY.register_write(ZarrGroup, DaskArray, IOSpec("array", "0.2.0"))
+def write_basic_dask_zarr(f, k, elem, _writer, dataset_kwargs=MappingProxyType({})):
+    import dask.array as da
+
+    g = f.require_dataset(k, shape=elem.shape, dtype=elem.dtype, **dataset_kwargs)
+    da.store(elem, g, lock=GLOBAL_LOCK)
+
+
+# Adding this seperately because h5py isn't serializable
+# https://github.com/pydata/xarray/issues/4242
 @_REGISTRY.register_write(H5Group, DaskArray, IOSpec("array", "0.2.0"))
-def write_basic_dask(f, k, elem, _writer, dataset_kwargs=MappingProxyType({})):
+def write_basic_dask_h5(f, k, elem, _writer, dataset_kwargs=MappingProxyType({})):
     import dask.array as da
+    import dask.config as dc
+
+    if dc.get("scheduler", None) == "dask.distributed":
+        raise ValueError(
+            "Cannot write dask arrays to hdf5 when using distributed scheduler"
+        )
 
     g = f.require_dataset(k, shape=elem.shape, dtype=elem.dtype, **dataset_kwargs)
     da.store(elem, g)

anndata/tests/test_dask.py

@@ -11,6 +11,8 @@
     gen_adata,
     assert_equal,
 )
+from anndata.experimental import write_elem, read_elem
+from anndata.experimental.merge import as_group
 from anndata.compat import DaskArray
 
 pytest.importorskip("dask.array")
@@ -94,6 +96,44 @@ def test_dask_write(adata, tmp_path, diskfmt):
     assert isinstance(orig.varm["a"], DaskArray)
 
 
+def test_dask_distributed_write(adata, tmp_path, diskfmt):
+    import dask.array as da
+    import dask.distributed as dd
+    import numpy as np
+
+    pth = tmp_path / f"test_write.{diskfmt}"
+    g = as_group(pth, mode="w")
+
+    with dd.LocalCluster(n_workers=1, threads_per_worker=1, processes=False) as cluster:
+        with dd.Client(cluster):
+            M, N = adata.X.shape
+            adata.obsm["a"] = da.random.random((M, 10))
+            adata.obsm["b"] = da.random.random((M, 10))
+            adata.varm["a"] = da.random.random((N, 10))
+            orig = adata
+            if diskfmt == "h5ad":
+                with pytest.raises(
+                    ValueError, match="Cannot write dask arrays to hdf5"
+                ):
+                    write_elem(g, "", orig)
+                return
+            write_elem(g, "", orig)
+            curr = read_elem(g)
+
+    with pytest.raises(Exception):
+        assert_equal(curr.obsm["a"], curr.obsm["b"])
+
+    assert_equal(curr.varm["a"], orig.varm["a"])
+    assert_equal(curr.obsm["a"], orig.obsm["a"])
+
+    assert isinstance(curr.X, np.ndarray)
+    assert isinstance(curr.obsm["a"], np.ndarray)
+    assert isinstance(curr.varm["a"], np.ndarray)
+    assert isinstance(orig.X, DaskArray)
+    assert isinstance(orig.obsm["a"], DaskArray)
+    assert isinstance(orig.varm["a"], DaskArray)
+
+
 def test_dask_to_memory_check_array_types(adata, tmp_path, diskfmt):
     import dask.array as da
     import numpy as np

pyproject.toml

@@ -88,7 +88,7 @@ test = [
     "joblib",
     "boltons",
     "scanpy",
-    "dask[array]",
+    "dask[array,distributed]",
     "awkward>=2.3",
     "pytest_memray",
 ]