added dask clustering support

This commit is contained in:
Ari Brown 2023-10-02 17:26:02 -04:00
parent f4dd130266
commit 5eb2e39c69
7 changed files with 625 additions and 62 deletions

59
examples/dask_cluster.py Normal file
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from minerva.cluster import Cluster
from minerva.pier import Pier
from dask.distributed import Client
########### PREP ############################
def worker(pier, n):
mach = pier.machine(ami = "ami-05a242924e713f80a", # dask on ubuntu 22.04 x86
instance_type = "t3.medium",
username = "ubuntu",
name = f"dask-worker-{n}",
variables = {"type": "worker",
"number": n})
return mach
def scheduler(pier):
mach = pier.machine(ami = "ami-05a242924e713f80a", # dask on ubuntu 22.04 x86
instance_type = "t3.medium",
username = "ubuntu",
name = f"dask-scheduler",
variables = {"type": "scheduler"})
return mach
########## CLUSTER ##########################
pier = Pier("hay",
subnet_id = "subnet-05eb26d8649a093e1", # project-subnet-public1-us-east-1a
sg_groups = ["sg-0f9e555954e863954", # ssh
"sg-0b34a3f7398076545", # default
"sg-04cd2626d91ac093c"], # dask (8786, 8787, ping)
iam = "S3+SSM+CloudWatch+ECR",
key_pair = ("Ari-Brown-HAY", "~/.ssh/Ari-Brown-HAY.pem"))
cluster = Cluster(pier, scheduler, worker, num_workers=1)
cluster.start()
client = Client(cluster.private_location)
print(client)
########## USAGE ########################
try:
# Practice with a big array
import numpy as np
import dask.array as da
large_array = np.random.rand(10000, 10000)
print("oh boy")
dask_array = da.from_array(large_array, chunks=(100, 100))
dask_array = dask_array.persist() # non-blocking
mean = dask_array.mean().compute()
print(mean)
finally:
########## FIN #######################
cluster.terminate()