def main(_):
with logger.benchmark_context(FLAGS), \
mlperf_helper.LOGGER(FLAGS.output_ml_perf_compliance_logging):
mlperf_helper.set_ncf_root(os.path.split(os.path.abspath(__file__))[0])
if FLAGS.tpu:
raise ValueError("NCF in Keras does not support TPU for now")
run_ncf(FLAGS)
def main(_):
with logger.benchmark_context(FLAGS), \
mlperf_helper.LOGGER(FLAGS.output_ml_perf_compliance_logging):
mlperf_helper.set_ncf_root(os.path.split(os.path.abspath(__file__))[0])
if FLAGS.tpu:
raise ValueError("NCF in Keras does not support TPU for now")
run_ncf(FLAGS)
def main(_):
with logger.benchmark_context(FLAGS), \
mlperf_helper.LOGGER(FLAGS.output_ml_perf_compliance_logging):
mlperf_helper.set_ncf_root(os.path.split(os.path.abspath(__file__))[0])
run_ncf(FLAGS)
def main(_):
with logger.benchmark_context(FLAGS), \
mlperf_helper.LOGGER(FLAGS.output_ml_perf_compliance_logging):
mlperf_helper.set_ncf_root(os.path.split(os.path.abspath(__file__))[0])
run_ncf(FLAGS)
mlperf_helper.stitch_ncf()
def main(_):
with logger.benchmark_context(FLAGS), mlperf_helper.LOGGER(FLAGS.ml_perf):
mlperf_helper.set_ncf_root(os.path.split(os.path.abspath(__file__))[0])
run_ncf(FLAGS)
mlperf_helper.stitch_ncf()
def main(_):
# Note: The async process must execute the following two steps in the
# following order BEFORE doing anything else:
# 1) Write the alive file
# 2) Wait for the flagfile to be written.
global _log_file
cache_paths = rconst.Paths(data_dir=flags.FLAGS.data_dir,
cache_id=flags.FLAGS.cache_id)
write_alive_file(cache_paths=cache_paths)
flagfile = os.path.join(cache_paths.cache_root, rconst.FLAGFILE)
_parse_flagfile(flagfile)
redirect_logs = flags.FLAGS.redirect_logs
log_file_name = "data_gen_proc_{}.log".format(cache_paths.cache_id)
log_path = os.path.join(cache_paths.data_dir, log_file_name)
if log_path.startswith("gs://") and redirect_logs:
fallback_log_file = os.path.join(tempfile.gettempdir(), log_file_name)
print("Unable to log to {}. Falling back to {}".format(
log_path, fallback_log_file))
log_path = fallback_log_file
# This server is generally run in a subprocess.
if redirect_logs:
print("Redirecting output of data_async_generation.py process to {}".
format(log_path))
_log_file = open(log_path, "wt") # Note: not tf.gfile.Open().
try:
log_msg("sys.argv: {}".format(" ".join(sys.argv)))
if flags.FLAGS.seed is not None:
np.random.seed(flags.FLAGS.seed)
with mlperf_helper.LOGGER(enable=flags.FLAGS.ml_perf):
mlperf_helper.set_ncf_root(
os.path.split(os.path.abspath(__file__))[0])
_generation_loop(
num_workers=flags.FLAGS.num_workers,
cache_paths=cache_paths,
num_readers=flags.FLAGS.num_readers,
num_neg=flags.FLAGS.num_neg,
num_train_positives=flags.FLAGS.num_train_positives,
num_items=flags.FLAGS.num_items,
num_users=flags.FLAGS.num_users,
epochs_per_cycle=flags.FLAGS.epochs_per_cycle,
train_batch_size=flags.FLAGS.train_batch_size,
eval_batch_size=flags.FLAGS.eval_batch_size,
deterministic=flags.FLAGS.seed is not None,
match_mlperf=flags.FLAGS.ml_perf,
)
except KeyboardInterrupt:
log_msg("KeyboardInterrupt registered.")
except:
traceback.print_exc(file=_log_file)
raise
finally:
log_msg("Shutting down generation subprocess.")
sys.stdout.flush()
sys.stderr.flush()
if redirect_logs:
_log_file.close()
def main(_):
# Note: The async process must execute the following two steps in the
# following order BEFORE doing anything else:
# 1) Write the alive file
# 2) Wait for the flagfile to be written.
global _log_file
cache_paths = rconst.Paths(
data_dir=flags.FLAGS.data_dir, cache_id=flags.FLAGS.cache_id)
write_alive_file(cache_paths=cache_paths)
flagfile = os.path.join(cache_paths.cache_root, rconst.FLAGFILE)
_parse_flagfile(flagfile)
redirect_logs = flags.FLAGS.redirect_logs
log_file_name = "data_gen_proc_{}.log".format(cache_paths.cache_id)
log_path = os.path.join(cache_paths.data_dir, log_file_name)
if log_path.startswith("gs://") and redirect_logs:
fallback_log_file = os.path.join(tempfile.gettempdir(), log_file_name)
print("Unable to log to {}. Falling back to {}"
.format(log_path, fallback_log_file))
log_path = fallback_log_file
# This server is generally run in a subprocess.
if redirect_logs:
print("Redirecting output of data_async_generation.py process to {}"
.format(log_path))
_log_file = open(log_path, "wt") # Note: not tf.gfile.Open().
try:
log_msg("sys.argv: {}".format(" ".join(sys.argv)))
if flags.FLAGS.seed is not None:
np.random.seed(flags.FLAGS.seed)
with mlperf_helper.LOGGER(
enable=flags.FLAGS.output_ml_perf_compliance_logging):
mlperf_helper.set_ncf_root(os.path.split(os.path.abspath(__file__))[0])
_generation_loop(
num_workers=flags.FLAGS.num_workers,
cache_paths=cache_paths,
num_readers=flags.FLAGS.num_readers,
num_neg=flags.FLAGS.num_neg,
num_train_positives=flags.FLAGS.num_train_positives,
num_items=flags.FLAGS.num_items,
num_users=flags.FLAGS.num_users,
epochs_per_cycle=flags.FLAGS.epochs_per_cycle,
num_cycles=flags.FLAGS.num_cycles,
train_batch_size=flags.FLAGS.train_batch_size,
eval_batch_size=flags.FLAGS.eval_batch_size,
deterministic=flags.FLAGS.seed is not None,
match_mlperf=flags.FLAGS.ml_perf,
)
except KeyboardInterrupt:
log_msg("KeyboardInterrupt registered.")
except:
traceback.print_exc(file=_log_file)
raise
finally:
log_msg("Shutting down generation subprocess.")
sys.stdout.flush()
sys.stderr.flush()
if redirect_logs:
_log_file.close()
