def make_cluster_model(
cluster_id: str,
cluster_name: str,
cluster: Cluster,
adaptive: Union[Adaptive, None],
) -> ClusterModel:
"""
Make a cluster model. This is a JSON-serializable representation
of the information about a cluster that can be sent over the wire.
Parameters
----------
cluster_id: string
A unique string for the cluster.
cluster_name: string
A display name for the cluster.
cluster: Cluster
The cluster out of which to make the cluster model.
adaptive: Adaptive
The adaptive controller for the number of workers for the cluster, or
none if the cluster is not scaled adaptively.
"""
# This would be a great target for a dataclass
# once python 3.7 is in wider use.
try:
info = cluster.scheduler_info
except AttributeError:
info = cluster.scheduler.identity()
try:
cores = sum(d["nthreads"] for d in info["workers"].values())
except KeyError: # dask.__version__ < 2.0
cores = sum(d["ncores"] for d in info["workers"].values())
assert isinstance(info, dict)
model = dict(
id=cluster_id,
name=cluster_name,
scheduler_address=cluster.scheduler_address,
dashboard_link=cluster.dashboard_link or "",
workers=len(info["workers"]),
memory=utils.format_bytes(
sum(d["memory_limit"] for d in info["workers"].values())),
cores=cores,
)
if adaptive:
model["adapt"] = {
"minimum": adaptive.minimum,
"maximum": adaptive.maximum
}
return model
def make_cluster_model(
cluster_id: str,
cluster_name: str,
cluster: Cluster,
adaptive: Union[Adaptive, None],
) -> ClusterModel:
"""
Make a cluster model. This is a JSON-serializable representation
of the information about a cluster that can be sent over the wire.
Parameters
----------
cluster_id: string
A unique string for the cluster.
cluster_name: string
A display name for the cluster.
cluster: Cluster
The cluster out of which to make the cluster model.
adaptive: Adaptive
The adaptive controller for the number of workers for the cluster, or
none if the cluster is not scaled adaptively.
"""
# This would be a great target for a dataclass
# once python 3.7 is in wider use.
model = dict(
id=cluster_id,
name=cluster_name,
scheduler_address=cluster.scheduler_address,
dashboard_link=cluster.dashboard_link or "",
workers=len(cluster.scheduler.workers),
memory=utils.format_bytes(
sum(ws.memory_limit for ws in cluster.scheduler.workers.values())
),
cores=sum(ws.ncores for ws in cluster.scheduler.workers.values()),
)
if adaptive:
model['adapt'] = {
'minimum': adaptive.minimum,
'maximum': adaptive.maximum,
}
return model