def init(sample_count=1, force=False, debug=False):
'''
Generate synthetic datasets for testing and benchmarking.
'''
configs = sorted(CONFIGS.keys(), key=(lambda c: -get_cost(CONFIGS[c])))
parallel_map(generate_one,
[(name, sample_count, force, debug) for name in configs])
def init(sample_count=1, force=False, debug=False):
'''
Generate synthetic datasets for testing and benchmarking.
'''
configs = sorted(CONFIGS.keys(), key=(lambda c: -get_cost(CONFIGS[c])))
parallel_map(generate_one, [
(name, sample_count, force, debug) for name in configs
])
def test(sample_count=2, force=True, debug=False):
'''
Generate small synthetic datasets for testing.
'''
mkdir_p(loom.store.STORE)
configs = sorted(TEST_CONFIGS, key=(lambda c: -get_cost(CONFIGS[c])))
parallel_map(generate_one,
[(name, sample_count, force, debug) for name in configs])
def test(sample_count=2, force=True, debug=False):
'''
Generate small synthetic datasets for testing.
'''
mkdir_p(loom.store.STORE)
configs = sorted(TEST_CONFIGS, key=(lambda c: -get_cost(CONFIGS[c])))
parallel_map(generate_one, [
(name, sample_count, force, debug) for name in configs
])
def infer(name, sample_count=10, config=None, debug=False):
'''
Infer samples in parallel.
Arguments:
name A unique identifier for ingest + inference
sample_count The number of samples to draw, typically 10-100
config An optional json config file, e.g.,
{"schedule": {"extra_passes": 500.0}}
debug Whether to run debug versions of C++ code
Environment variables:
LOOM_THREADS Number of concurrent inference tasks
LOOM_VERBOSITY Verbosity level
'''
assert sample_count >= 1, 'too few samples: {}'.format(sample_count)
parallel_map(_infer_one, [
(name, seed, config, debug) for seed in xrange(sample_count)
])
def infer(name,
sample_count=DEFAULTS['sample_count'],
config=None,
debug=False):
'''
Infer samples in parallel.
Arguments:
name A unique identifier for ingest + inference
sample_count The number of samples to draw, typically 10-100
config An optional json config file, e.g.,
{"schedule": {"extra_passes": 500.0}}
debug Whether to run debug versions of C++ code
Environment variables:
LOOM_THREADS Number of concurrent inference tasks
LOOM_VERBOSITY Verbosity level
'''
if not (sample_count >= 1):
raise LoomError('Too few samples: {}'.format(sample_count))
parallel_map(_infer_one, [(name, seed, config, debug)
for seed in xrange(sample_count)])
def download(s3_url=S3_URL):
'''
Download dataset from S3 and load into loom.benchmark jig.
'''
import boto
bucket, path = s3_split(s3_url)
conn = boto.connect_s3().get_bucket(bucket)
keys = [
key.name for key in conn.list(path)
if re.match(r'.*\d\d\d\.csv\.gz$', key.name)
]
assert keys, 'nothing to download'
files = [os.path.join(ROWS_CSV, os.path.basename(key)) for key in keys]
tasks = [(bucket, source, destin) for source, destin in izip(keys, files)
if not os.path.exists(destin)]
if tasks:
print 'starting download of {} files'.format(len(tasks))
mkdir_p(ROWS_CSV)
parallel_map(s3_get, tasks)
print 'finished download of {} files'.format(len(keys))
def transform_rows(schema_in, transforms_in, rows_in, rows_out, id_field=None):
transforms = pickle_load(transforms_in)
if not transforms:
cp_ns(rows_in, rows_out)
else:
transform = TransformSequence(transforms)
transformed_header = sorted(json_load(schema_in).iterkeys())
if id_field is not None:
assert id_field not in transformed_header
transformed_header = [id_field] + transformed_header
tasks = []
if os.path.isdir(rows_in):
loom.util.mkdir_p(rows_out)
for f in os.listdir(rows_in):
tasks.append((
transform,
transformed_header,
os.path.join(rows_in, f),
os.path.join(rows_out, f),
))
else:
tasks.append((transform, transformed_header, rows_in, rows_out))
parallel_map(_transform_rows, tasks)
def transform_rows(schema_in, transforms_in, rows_in, rows_out, id_field=None):
transforms = pickle_load(transforms_in)
if not transforms:
cp_ns(rows_in, rows_out)
else:
transform = TransformSequence(transforms)
transformed_header = sorted(json_load(schema_in).iterkeys())
if id_field is not None:
assert id_field not in transformed_header
transformed_header = [id_field] + transformed_header
tasks = []
if os.path.isdir(rows_in):
loom.util.mkdir_p(rows_out)
for f in os.listdir(rows_in):
tasks.append((
transform,
transformed_header,
os.path.join(rows_in, f),
os.path.join(rows_out, f),
))
else:
tasks.append((transform, transformed_header, rows_in, rows_out))
parallel_map(_transform_rows, tasks)
def download(s3_url=S3_URL):
'''
Download dataset from S3 and load into loom.benchmark jig.
'''
import boto
bucket, path = s3_split(s3_url)
conn = boto.connect_s3().get_bucket(bucket)
keys = [
key.name
for key in conn.list(path)
if re.match(r'.*\d\d\d\.csv\.gz$', key.name)
]
assert keys, 'nothing to download'
files = [os.path.join(ROWS_CSV, os.path.basename(key)) for key in keys]
tasks = [
(bucket, source, destin)
for source, destin in izip(keys, files)
if not os.path.exists(destin)
]
if tasks:
print 'starting download of {} files'.format(len(tasks))
mkdir_p(ROWS_CSV)
parallel_map(s3_get, tasks)
print 'finished download of {} files'.format(len(keys))
def init():
'''
Generate synthetic datasets for testing and benchmarking.
'''
configs = sorted(CONFIGS.keys(), key=(lambda c: -get_cost(CONFIGS[c])))
parallel_map(load_one, configs)