Source code for xcube.util.dask

# Copyright (c) 2018-2024 by xcube team and contributors
# Permissions are hereby granted under the terms of the MIT License:

import itertools
import os
import re
import uuid
import warnings
from typing import (
from import Iterable, Mapping, Sequence

import dask.array as da
import dask.array.core as dac
import distributed
import numpy as np

IntTuple = tuple[int, ...]
SliceTuple = tuple[slice, ...]
IntIterable = Iterable[int]
IntTupleIterable = Iterable[IntTuple]
SliceTupleIterable = Iterable[SliceTuple]


def compute_array_from_func(
    func: Callable[..., np.ndarray],
    shape: IntTuple,
    chunks: IntTuple,
    dtype: Any,
    name: str = None,
    ctx_arg_names: Sequence[str] = None,
    args: Sequence[Any] = None,
    kwargs: Mapping[str, Any] = None,
) -> da.Array:
    """Compute a dask array using the provided user function
    *func*, *shape*, and chunking *chunks*.

    The user function is expected to output the array's data
    blocks using arguments specified by *ctx_arg_names*, *args*,
    and *kwargs* and is expected to return a numpy array.

    You can request array and current block context information
    by specifying the optional *ctx_arg_names* keyword argument
    that is a sequence of names of special arguments passed to
    *user_func*. The following are available:

    * ``shape``: The array's shape. A tuple of ints.
    * ``chunks``: The array's chunks. A tuple of tuple of ints.
    * ``dtype``: The array's numpy data type.
    * ``name``: The array's name. A string or ``None``.
    * ``block_id``: The block's unique ID. An integer number
        ranging from zero to number of blocks minus one.
    * ``block_index``: The block's index as a tuple of ints.
    * ``block_shape``: The block's shape as a tuple of ints.
    * ``block_slices``: The block's shape as a tuple of int pair tuples.

        func: User function that is called for each block of the
            array using arguments specified by *ctx_arg_names*,
            *args*, and *kwargs*. It must return a numpy array of
            shape "block_shape" and type *dtype*.
        shape: The array's shape. A tuple of sizes for each
            array dimension.
        chunks: The array's chunking. A tuple of chunk sizes for
            each array dimension. Must be of same length as *shape*.
        dtype: The array's numpy data type.
        name: The array's name.
        ctx_arg_names: Sequence names of arguments that are passed
            before *args* and *kwargs* to the user function.
        args: Arguments passed to the user function.
        kwargs: Keyword-arguments passed to the user function.

    Returns: A chunked dask array.
    ctx_arg_names = ctx_arg_names or []
    args = args or []
    kwargs = kwargs or {}

    chunk_sizes = tuple(get_chunk_sizes(shape, chunks))
    chunk_counts = tuple(get_chunk_counts(shape, chunks))
    block_indexes, block_shapes, block_slices = get_block_iterators(chunk_sizes)

    ctx_values = dict(

    blocks = _NestedList(shape=chunk_counts)
    block_id = 0
    for chunk_index, chunk_shape, block_slices in zip(
        block_indexes, block_shapes, block_slices
                (chunk_slice.start, chunk_slice.stop) for chunk_slice in block_slices
        ctx_args = [ctx_values[ctx_arg_name] for ctx_arg_name in ctx_arg_names]
        block_id += 1

        # We use our own name here, because dac.from_func() tokenizes args which for some reason takes forever
        block = dac.from_func(
            args=(*ctx_args, *args),

        blocks[chunk_index] = block

    return da.block(

def get_block_iterators(
    chunk_sizes: IntTupleIterable,
) -> tuple[IntTupleIterable, IntTupleIterable, SliceTupleIterable]:
    chunk_sizes = tuple(chunk_sizes)
    chunk_slices_tuples = get_chunk_slice_tuples(chunk_sizes)
    chunk_ranges = get_chunk_ranges(chunk_sizes)
    block_indexes = itertools.product(*chunk_ranges)
    block_shapes = itertools.product(*chunk_sizes)
    block_slices = itertools.product(*chunk_slices_tuples)
    return block_indexes, block_shapes, block_slices

def get_chunk_sizes(shape: IntTuple, chunks: IntTuple) -> IntTupleIterable:
    for s, c in zip(shape, chunks):
        n = s // c
        if n * c < s:
            yield (c,) * n + (s % c,)
            yield (c,) * n

def get_chunk_counts(shape: IntTuple, chunks: IntTuple) -> Iterable[int]:
    for s, c in zip(shape, chunks):
        yield (s + c - 1) // c

def get_chunk_ranges(chunk_size_tuples: IntTupleIterable) -> Iterable[range]:
    return (range(len(chunk_size_tuple)) for chunk_size_tuple in chunk_size_tuples)

def get_chunk_slice_tuples(chunk_size_tuples: IntTupleIterable) -> SliceTupleIterable:
    return (
        for chunk_size_tuple in chunk_size_tuples

def get_chunk_slices(chunk_sizes: Sequence[int]) -> Iterable[slice]:
    stop = 0
    for i in range(len(chunk_sizes)):
        start = stop
        stop = start + chunk_sizes[i]
        yield slice(start, stop)

[docs] def new_cluster( provider: str = "coiled", name: Optional[str] = None, software: Optional[str] = None, n_workers: int = 4, resource_tags: Optional[dict[str, str]] = None, account: str = None, region: str = "eu-central-1", **kwargs, ) -> distributed.deploy.Cluster: """Create a new Dask cluster. Cloud resource tags can be specified in an environment variable XCUBE_DASK_CLUSTER_TAGS in the format ``tag_1=value_1:tag_2=value_2:...:tag_n=value_n``. In case of conflicts, tags specified in ``resource_tags`` will override tags specified by the environment variable. The cluster provider account name can be specified in an environment variable ``XCUBE_DASK_CLUSTER_ACCOUNT``. If the ``account`` argument is given to ``new_cluster``, it will override the value from the environment variable. Args: provider: identifier of the provider to use. Currently, only 'coiled' is supported. name: name to use as an identifier for the cluster software: identifier for the software environment to be used. n_workers: number of workers in the cluster resource_tags: tags to apply to the cloud resources forming the cluster account: cluster provider account name **kwargs: further named arguments will be passed on to the cluster creation function region: default region where workers of the cluster will be deployed set to eu-central-1 """ if resource_tags is None: resource_tags = {} if _CLUSTER_ACCOUNT_ENV_VAR_NAME in os.environ: account_from_env_var = os.environ[_CLUSTER_ACCOUNT_ENV_VAR_NAME] else: account_from_env_var = None warnings.warn( f"Environment variable {_CLUSTER_ACCOUNT_ENV_VAR_NAME}" f" not set; cluster account name may be incorrect." ) cluster_account = ( account if account is not None else account_from_env_var if account_from_env_var is not None else "bc" ) if provider == "coiled": try: import coiled except ImportError as e: raise ImportError( f"provider 'coiled' requires package" f"'coiled' to be installed" ) from e if software is None and "JUPYTER_IMAGE" in os.environ: # If the JUPYTER_IMAGE environment variable is set, we're # presumably in a Z2JH deployment and can base a # Coiled environment on the same image. # First we construct an identifier from the user image specifier. current_image = os.environ["JUPYTER_IMAGE"] software = re.sub( "[:.]", "-","/([^/]+)$", current_image).group(1), ) # If the referenced software environment doesn't exist yet as a # Coiled environment, create it from the currently used image. available_environments = coiled.list_software_environments( account=account ).keys() if software not in available_environments: coiled.create_software_environment( name=software, container=current_image ) # If software is (still) None, Coiled will try to mirror the current # environment automagically. coiled_params = dict( n_workers=n_workers, environ=None, tags=_collate_cluster_resource_tags(resource_tags), account=cluster_account, name=name, software=software, use_best_zone=True, compute_purchase_option="spot_with_fallback", shutdown_on_close=True, region=region, ) coiled_params.update(kwargs) return coiled.Cluster(**coiled_params) raise NotImplementedError(f"Unknown provider {provider!r}")
def _collate_cluster_resource_tags(extra_tags: dict[str, str]) -> dict[str, str]: fallback_tags = { "cost-center": "unknown", "environment": "dev", "creator": "auto", "purpose": "xcube dask cluster", "user": ( os.environ.get("JUPYTERHUB_USER") # JupyterHub or os.environ.get("USER") # Unixes or os.environ.get("USERNAME") # Windows or os.getlogin() or "" ), } if _CLUSTER_TAGS_ENV_VAR_NAME in os.environ: kvps = os.environ[_CLUSTER_TAGS_ENV_VAR_NAME].split(":") env_var_tags = { (parts := kvp.split("=", maxsplit=1))[0]: parts[1] for kvp in kvps } else: warnings.warn( f"Environment variable {_CLUSTER_TAGS_ENV_VAR_NAME}" f" not set; cluster resource tags may be missing." ) env_var_tags = {} return fallback_tags | env_var_tags | extra_tags class _NestedList: """Utility class whose instances are used as input to dask.block().""" def __init__(self, shape: Sequence[int], fill_value: Any = None): self._shape = tuple(shape) self._data = self._new_data(shape, len(shape), fill_value, 0) @classmethod def _new_data( cls, shape: Sequence[int], ndim: int, fill_value: Any, dim: int ) -> Union[list[list], list[Any]]: return [ ( cls._new_data(shape, ndim, fill_value, dim + 1) if dim < ndim - 1 else fill_value ) for _ in range(shape[dim]) ] @property def shape(self) -> tuple[int, ...]: return self._shape @property def data(self) -> Union[list[list], list[Any]]: return self._data def __len__(self) -> int: return len(self._data) def __setitem__(self, index: Union[int, slice, tuple], value: Any): data = self._data if isinstance(index, tuple): n = len(index) for i in range(n - 1): data = data[index[i]] data[index[n - 1]] = value else: data[index] = value def __getitem__(self, index: Union[int, slice, tuple]) -> Any: data = self._data if isinstance(index, tuple): n = len(index) for i in range(n - 1): data = data[index[i]] return data[index[n - 1]] else: return data[index]