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Add configuration options for dask cluster and results timestamp #30

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Add configuration options for dask cluster and results timestamp #30

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8b62ce5
allow dask configuration via environment overrides
kevingerman Feb 17, 2022
73aa482
append a timestamp to the results
kevingerman Feb 17, 2022
50fecaf
timestamp as string
kevingerman Feb 17, 2022
6e1f08d
add more dask cluster args
kevingerman Mar 17, 2022
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19 changes: 16 additions & 3 deletions algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

from abc import ABC, abstractmethod
import os
import time
import pandas as pd
import numpy as np
Expand Down Expand Up @@ -311,9 +312,21 @@ def get_slices(self, n_slices, X, y):

def fit(self, data, args):
params = self.configure(data, args)
n_workers = None if args.gpus < 0 else args.gpus
cluster = LocalCUDACluster(n_workers=n_workers,
local_directory=args.root)
clusterargs={
'n_workers':int(os.environ.get("NUM_WORKERS", None if args.gpus < 0 else args.gpus)),
'local_directory':args.root,
'memory_limit':os.environ.get("DEVICE_MEMORY_LIMIT", None),
'device_memory_limit':os.getenv('DASK_DEVICE_MEMORY_LIMIT',None),
'protocol':"ucx" if os.environ.get("CLUSTER_MODE", "TCP")=="NVLINK" else "tcp",
'enable_tcp_over_ucx':os.environ.get("CLUSTER_MODE", "TCP")=="NVLINK",
'enable_nvlink':os.environ.get("CLUSTER_MODE", "TCP")=="NVLINK",
'enable_infiniband':os.getenv('CLUSTER_CONFIG_TYPE', "").endswith("ib"),
'enable_rdmacm':bool(os.getenv('ENABLE_RDMACM', False)),
'jit_unspill':True,
'rmm_pool_size':os.environ.get("POOL_SIZE", "29GB")
}

cluster = LocalCUDACluster( n_workers=clusterargs['n_workers'], )
client = Client(cluster)
n_partitions = len(client.scheduler_info()['workers'])
X_sliced, y_sliced = self.get_slices(n_partitions,
Expand Down
5 changes: 4 additions & 1 deletion runme.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@
import json
import ast
import psutil
import datetime
import algorithms
from metrics import get_metrics
from datasets import prepare_dataset
Expand Down Expand Up @@ -126,14 +127,16 @@ def main():
args.cpus = get_number_processors(args)
args.extra = ast.literal_eval(args.extra)
print_sys_info(args)
ts=datetime.datetime.utcnow().strftime('%Y%m%d.%H%M%S%f')
if args.warmup:
benchmark(args, os.path.join(args.root, "fraud"), "fraud")
if args.dataset == 'all':
args.dataset = 'airline,bosch,fraud,higgs,year,epsilon,covtype'
results = {}
for dataset in args.dataset.split(","):
folder = os.path.join(args.root, dataset)
results.update({dataset: benchmark(args, folder, dataset)})
results.update({ 'timestamp_utc': ts,
dataset: benchmark(args, folder, dataset)})
print(json.dumps({dataset: results[dataset]}, indent=2, sort_keys=True))
output = json.dumps(results, indent=2, sort_keys=True)
output_file = open(args.output, "w")
Expand Down