Source code for dea_tools.dask

Tools for simplifying the creation of Dask clusters for parallelised computing.

License: The code in this notebook is licensed under the Apache License,
Version 2.0 ( Digital Earth
Australia data is licensed under the Creative Commons by Attribution 4.0
license (

Contact: If you need assistance, please post a question on the Open Data
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Last modified: June 2022


from importlib.util import find_spec
import os
import dask
from aiohttp import ClientConnectionError
from datacube.utils.dask import start_local_dask
from import configure_s3_access

_HAVE_PROXY = bool(find_spec('jupyter_server_proxy'))
_IS_AWS = ('AWS_ACCESS_KEY_ID' in os.environ or
           'AWS_DEFAULT_REGION' in os.environ)

[docs] def create_local_dask_cluster(spare_mem='3Gb', display_client=True, return_client=False): """ Using the datacube utils function `start_local_dask`, generate a local dask cluster. Automatically detects if on AWS or NCI. Example use : import sys sys.path.append("../Scripts") from dea_dask import create_local_dask_cluster create_local_dask_cluster(spare_mem='4Gb') Parameters ---------- spare_mem : String, optional The amount of memory, in Gb, to leave for the notebook to run. This memory will not be used by the cluster. e.g '3Gb' display_client : Bool, optional An optional boolean indicating whether to display a summary of the dask client, including a link to monitor progress of the analysis. Set to False to hide this display. return_client : Bool, optional An optional boolean indicating whether to return the dask client object. """ if _HAVE_PROXY: # Configure dashboard link to go over proxy prefix = os.environ.get('JUPYTERHUB_SERVICE_PREFIX', '/') dask.config.set({"": prefix + "proxy/{port}/status"}) # Start up a local cluster client = start_local_dask(mem_safety_margin=spare_mem) if _IS_AWS: # Configure GDAL for s3 access configure_s3_access(aws_unsigned=True, client=client) # Show the dask cluster settings if display_client: from IPython.display import display display(client) # return the client as an object if return_client: return client
try: from dask_gateway import Gateway def create_dask_gateway_cluster(profile='r5_L', workers=2): """ Create a cluster in our internal dask cluster. Parameters ---------- profile : str Possible values are: - r5_L (2 cores, 15GB memory) - r5_XL (4 cores, 31GB memory) - r5_2XL (8 cores, 63GB memory) - r5_4XL (16 cores, 127GB memory) workers : int Number of workers in the cluster. """ try: gateway = Gateway() # Close any existing clusters cluster_names = gateway.list_clusters() if len(cluster_names) > 0: print("Cluster(s) still running:", cluster_names) for n in cluster_names: cluster = gateway.connect( cluster.shutdown() options = gateway.cluster_options() options['profile'] = profile # limit username to alphanumeric characters # kubernetes pods won't launch if labels contain anything other than [a-Z, -, _] options['jupyterhub_user'] = ''.join(c if c.isalnum() else '-' for c in os.getenv('JUPYTERHUB_USER')) cluster = gateway.new_cluster(options) cluster.scale(workers) return cluster except ClientConnectionError: raise ConnectionError("access to dask gateway cluster unauthorized") except ImportError:
[docs] def create_dask_gateway_cluster(*args, **kwargs): raise NotImplementedError