def sync_readings(session, rows: iter, sensors: list, awesome_sensors: dict,
reading_types: dict):
"""
Bulk store readings
"""
# Convert list to dictionary
sensors = {sensor['name']: sensor for sensor in sensors}
def get_readings(_rows) -> iter:
for row in _rows:
# Convert rows of portal data into portal readings
yield from maps.row_to_readings(row,
sensors=sensors,
reading_types=reading_types,
awesome_sensors=awesome_sensors)
# Iterate over data chunks
for chunk in utils.iter_chunks(
get_readings(rows), chunk_size=settings.BULK_READINGS_CHUNK_SIZE):
if chunk:
try:
objects.Reading.store_bulk(session, readings=chunk)
# No more readings, so stop
except exceptions.EmptyValueError:
break
def split_test_training(positions_w_context, est_num_positions):
print("Estimated number of chunks: %s" % (est_num_positions // CHUNK_SIZE), file=sys.stderr)
desired_test_size = 10**5
if est_num_positions < 2 * desired_test_size:
positions_w_context = list(positions_w_context)
test_size = len(positions_w_context) // 3
return positions_w_context[:test_size], [positions_w_context[test_size:]]
else:
shuffled_positions = utils.shuffler(positions_w_context)
test_chunk = utils.take_n(desired_test_size, shuffled_positions)
training_chunks = utils.iter_chunks(CHUNK_SIZE, shuffled_positions)
return test_chunk, training_chunks
def split_test_training(positions_w_context, est_num_positions):
print("Estimated number of chunks: %s" % (est_num_positions // CHUNK_SIZE),
file=sys.stderr)
desired_test_size = 10**5
if est_num_positions < 2 * desired_test_size:
positions_w_context = list(positions_w_context)
test_size = len(positions_w_context) // 3
return positions_w_context[:test_size], [
positions_w_context[test_size:]
]
else:
shuffled_positions = utils.shuffler(positions_w_context)
test_chunk = utils.take_n(desired_test_size, shuffled_positions)
training_chunks = utils.iter_chunks(CHUNK_SIZE, shuffled_positions)
return test_chunk, training_chunks
def main(argv):
"""Main program.
"""
del argv # Unused
total_games = FLAGS.training_games
total_moves = FLAGS.training_moves
fresh = FLAGS.training_fresh
batch_size = FLAGS.batch_size
output_prefix = FLAGS.output_prefix
spec = bigtable_input.BigtableSpec(FLAGS.cbt_project, FLAGS.cbt_instance,
FLAGS.cbt_table)
gq_r = bigtable_input.GameQueue(spec.project, spec.instance, spec.table)
gq_c = bigtable_input.GameQueue(spec.project, spec.instance,
spec.table + '-nr')
mix = bigtable_input.mix_by_decile(total_games, total_moves, 9)
trainings = [
(spec, start_r, start_c, mix, batch_size,
'{}{:0>10}_{:0>10}.tfrecord.zz'.format(output_prefix, start_r,
start_c))
for start_r, finish_r, start_c, finish_c in reversed(
list(
training_series(gq_r.latest_game_number,
gq_c.latest_game_number, mix, fresh)))
]
if FLAGS.starting_game:
game = FLAGS.starting_game
starts = [t[1] for t in trainings]
where = bisect.bisect_left(starts, game)
trainings = trainings[where:]
if FLAGS.max_trainings:
trainings = trainings[:FLAGS.max_trainings]
if FLAGS.dry_run:
for t in trainings:
print(t)
raise SystemExit
concurrency = min(FLAGS.concurrency, multiprocessing.cpu_count() * 2)
with tqdm(desc='Training Sets', unit_scale=2,
total=len(trainings)) as pbar:
for b in utils.iter_chunks(concurrency, trainings):
with multiprocessing.Pool(processes=concurrency) as pool:
pool.map(_export_training_set, b)
pbar.update(len(b))
def delete_row_range(self, format_str, start_game, end_game):
"""Delete rows related to the given game range.
Args:
format_str: a string to `.format()` by the game numbers
in order to create the row prefixes.
start_game: the starting game number of the deletion.
end_game: the ending game number of the deletion.
"""
row_keys = make_single_array(
self.tf_table.keys_by_range_dataset(
format_str.format(start_game),
format_str.format(end_game)))
row_keys = list(row_keys)
if not row_keys:
utils.dbg('No rows left for games %d..%d' % (
start_game, end_game))
return
utils.dbg('Deleting %d rows: %s..%s' % (
len(row_keys), row_keys[0], row_keys[-1]))
# Reverse the keys so that the queue is left in a more
# sensible end state if you change your mind (say, due to a
# mistake in the timestamp) and abort the process: there will
# be a bit trimmed from the end, rather than a bit
# trimmed out of the middle.
row_keys.reverse()
total_keys = len(row_keys)
utils.dbg('Deleting total of %d keys' % total_keys)
concurrency = min(MAX_BT_CONCURRENCY,
multiprocessing.cpu_count() * 2)
with multiprocessing.Pool(processes=concurrency) as pool:
batches = []
with tqdm(desc='Keys', unit_scale=2, total=total_keys) as pbar:
for b in utils.iter_chunks(bigtable.row.MAX_MUTATIONS,
row_keys):
pbar.update(len(b))
batches.append((self.btspec, b))
if len(batches) >= concurrency:
pool.map(_delete_rows, batches)
batches = []
pool.map(_delete_rows, batches)
batches = []
