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May 15, 2024 18:45
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#!/usr/bin/env python | |
import sys | |
import os | |
import shutil | |
import time | |
import pyarrow as pa | |
import tiledb | |
import tiledbsoma | |
# ---------------------------------------------------------------- | |
input_path = 'norm4' | |
output_path = 'bpout' | |
compression_level = 3 | |
batch_stop = 1 | |
cpu_count = 16 | |
soma_init_buffer_bytes = 1024**3 | |
do_stats = False | |
if len(sys.argv) >= 2: | |
batch_stop = int(sys.argv[1]) | |
if len(sys.argv) >= 3: | |
soma_init_buffer_bytes = int(sys.argv[2]) | |
if len(sys.argv) >= 4: | |
output_path = sys.argv[3] | |
print(f"INPUT_PATH {input_path}") | |
print(f"OUTPUT_PATH {output_path}") | |
print(f"COMPRESSION_LEVEL {compression_level}") | |
print(f"BUFFER_SIZE {soma_init_buffer_bytes}") | |
print(f"BATCH_OUT {batch_stop}") | |
if os.path.exists(output_path): | |
print() | |
print(f"REMOVING {output_path}") | |
shutil.rmtree(output_path) | |
print(f"REMOVED {output_path}") | |
print() | |
# ---------------------------------------------------------------- | |
context = tiledbsoma.SOMATileDBContext(tiledb_config = { | |
"py.init_buffer_bytes": 4 * 1024**3, | |
"py.deduplicate": "true", | |
"soma.init_buffer_bytes": soma_init_buffer_bytes, | |
"sm.mem.reader.sparse_global_order.ratio_array_data": 0.3, | |
"sm.consolidation.total_buffer_size": 4 * 1024**3, | |
"sm.compute_concurrency_level": cpu_count, | |
"sm.io_concurrency_level": cpu_count, | |
}) | |
platform_config = { | |
"tiledb": { | |
"create": { | |
"capacity": 2**16, | |
"dims": { | |
"soma_dim_0": {"tile": 2048, "filters": [{"_type": "ZstdFilter", "level": compression_level}]}, | |
"soma_dim_1": {"tile": 2048, "filters": ["ByteShuffleFilter", {"_type": "ZstdFilter", "level": compression_level}]}, | |
}, | |
"attrs": {"soma_data": {"filters": ["ByteShuffleFilter", {"_type": "ZstdFilter", "level": compression_level}]}}, | |
"cell_order": "row-major", | |
"tile_order": "row-major", | |
"allows_duplicates": True, | |
"sort_coords": True, | |
#"sort_coords": False, | |
}, | |
} | |
} | |
print() | |
print(f"OPENING {input_path}") | |
t1 = time.time() | |
input = tiledbsoma.SparseNDArray.open(input_path, context=context, platform_config=platform_config) | |
t2 = time.time() | |
print(f"OPENED {input_path} SECONDS = %.3f" % (t2-t1)) | |
print() | |
print(f"CREATING {output_path}") | |
t1 = time.time() | |
output = tiledbsoma.SparseNDArray.create( | |
output_path, | |
type = pa.float32(), | |
shape = input.shape, | |
platform_config=platform_config, | |
context=context, | |
) | |
t2 = time.time() | |
print(f"CREATED {output_path} SECONDS = %.3f" % (t2-t1)) | |
# With the memory buffer sizes I've set above, | |
# I observe ~300 batches of ~500M entries each | |
i = 0 | |
t1 = time.time() | |
print() | |
print(f"READING {input_path} batch {i}") | |
for batch in input.read().tables(): | |
t2 = time.time() | |
print(f"READ {input_path} batch {i} SECONDS = %.3f" % (t2-t1)) | |
if do_stats: | |
tiledbsoma.pytiledbsoma.tiledbsoma_stats_enable() | |
tiledbsoma.pytiledbsoma.tiledbsoma_stats_reset() | |
tiledb.stats_enable() | |
tiledb.stats_reset() | |
print() | |
print(f"WRITING {output_path} batch {i}") | |
t1 = time.time() | |
_ = output.write(batch, platform_config=platform_config) | |
t2 = time.time() | |
print(f"WROTE {output_path} batch {i} SECONDS = %.3f" % (t2-t1)) | |
print() | |
print("----------------------------------------------------------------") | |
print("STATS FROM WRITE ONLY") | |
if do_stats: | |
tiledbsoma.pytiledbsoma.tiledbsoma_stats_dump() | |
tiledb.stats_dump() | |
i += 1 | |
if batch_stop is not None and i >= batch_stop: | |
print(f"BREAKING BATCH") | |
break | |
print() | |
print(f"READING {input_path} batch {i}") | |
t1 = time.time() |
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