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March 2, 2023 19:50
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import json | |
import os | |
import resource | |
import time | |
from typing import List | |
import traceback | |
import numpy as np | |
import psutil | |
import ray | |
from ray._private.internal_api import memory_summary | |
from ray.data._internal.arrow_block import ArrowRow | |
from ray.data._internal.util import _check_pyarrow_version | |
from ray.data.block import Block, BlockMetadata | |
from ray.data.context import DatasetContext | |
from ray.data.datasource import Datasource, ReadTask | |
class RandomIntRowDatasource(Datasource[ArrowRow]): | |
"""An example datasource that generates rows with random int64 columns. | |
Examples: | |
>>> source = RandomIntRowDatasource() | |
>>> ray.data.read_datasource(source, n=10, num_columns=2).take() | |
... {'c_0': 1717767200176864416, 'c_1': 999657309586757214} | |
... {'c_0': 4983608804013926748, 'c_1': 1160140066899844087} | |
""" | |
def prepare_read( | |
self, parallelism: int, n: int, num_columns: int | |
) -> List[ReadTask]: | |
_check_pyarrow_version() | |
import pyarrow | |
read_tasks: List[ReadTask] = [] | |
block_size = max(1, n // parallelism) | |
def make_block(count: int, num_columns: int) -> Block: | |
return pyarrow.Table.from_arrays( | |
np.random.randint( | |
np.iinfo(np.int64).max, size=(num_columns, count), dtype=np.int64 | |
), | |
names=[f"c_{i}" for i in range(num_columns)], | |
) | |
schema = pyarrow.Table.from_pydict( | |
{f"c_{i}": [0] for i in range(num_columns)} | |
).schema | |
i = 0 | |
while i < n: | |
count = min(block_size, n - i) | |
meta = BlockMetadata( | |
num_rows=count, | |
size_bytes=8 * count * num_columns, | |
schema=schema, | |
input_files=None, | |
exec_stats=None, | |
) | |
read_tasks.append( | |
ReadTask( | |
lambda count=count, num_columns=num_columns: [ | |
make_block(count, num_columns) | |
], | |
meta, | |
) | |
) | |
i += block_size | |
return read_tasks | |
if __name__ == "__main__": | |
import argparse | |
parser = argparse.ArgumentParser() | |
parser.add_argument( | |
"--num-partitions", help="number of partitions", default="50", type=str | |
) | |
parser.add_argument( | |
"--partition-size", | |
help="partition size (bytes)", | |
default="200e6", | |
type=str, | |
) | |
parser.add_argument( | |
"--shuffle", help="shuffle instead of sort", action="store_true" | |
) | |
parser.add_argument("--use-polars", action="store_true") | |
args = parser.parse_args() | |
if args.use_polars and not args.shuffle: | |
print("Using polars for sort") | |
ctx = DatasetContext.get_current() | |
ctx.use_polars = True | |
num_partitions = int(args.num_partitions) | |
partition_size = int(float(args.partition_size)) | |
print( | |
f"Dataset size: {num_partitions} partitions, " | |
f"{partition_size / 1e9}GB partition size, " | |
f"{num_partitions * partition_size / 1e9}GB total" | |
) | |
start_time = time.time() | |
source = RandomIntRowDatasource() | |
num_rows_per_partition = partition_size // 8 | |
ds = ray.data.read_datasource( | |
source, | |
parallelism=num_partitions, | |
n=num_rows_per_partition * num_partitions, | |
num_columns=1, | |
) | |
exc = None | |
ds_stats = None | |
try: | |
if args.shuffle: | |
ds = ds.random_shuffle() | |
else: | |
ds = ds.sort(key="c_0") | |
ds.fully_executed() | |
ds_stats = ds.stats() | |
except Exception as e: | |
exc = e | |
pass | |
end_time = time.time() | |
duration = end_time - start_time | |
print("Finished in", duration) | |
print("") | |
print("==== Driver memory summary ====") | |
maxrss = int(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss * 1e3) | |
print(f"max: {maxrss / 1e9}/GB") | |
process = psutil.Process(os.getpid()) | |
rss = int(process.memory_info().rss) | |
print(f"rss: {rss / 1e9}/GB") | |
try: | |
print(memory_summary(stats_only=True)) | |
except Exception: | |
print("Failed to retrieve memory summary") | |
print(traceback.format_exc()) | |
print("") | |
if ds_stats is not None: | |
print(ds_stats) | |
if "TEST_OUTPUT_JSON" in os.environ: | |
out_file = open(os.environ["TEST_OUTPUT_JSON"], "w") | |
results = { | |
"time": duration, | |
"success": "1" if exc is None else "0", | |
"num_partitions": num_partitions, | |
"partition_size": partition_size, | |
"perf_metrics": [ | |
{ | |
"perf_metric_name": "peak_driver_memory", | |
"perf_metric_value": maxrss, | |
"perf_metric_type": "MEMORY", | |
}, | |
{ | |
"perf_metric_name": "runtime", | |
"perf_metric_value": duration, | |
"perf_metric_type": "LATENCY", | |
}, | |
], | |
} | |
json.dump(results, out_file) | |
if exc: | |
raise exc |
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