def _train_and_save(algo, file, ratings, kwargs):
"Worker for subprocess model training"
_log.info('training %s on %d ratings', algo, len(ratings))
timer = Stopwatch()
algo.fit(ratings, **kwargs)
_log.info('trained %s in %s', algo, timer)
if file is None:
return persist_binpickle(algo).transfer()
else:
return persist_binpickle(algo, file=file).transfer()
def test_persist_bpk():
matrix = np.random.randn(1000, 100)
share = lks.persist_binpickle(matrix)
try:
assert share.path.exists()
m2 = share.get()
assert m2 is not matrix
assert np.all(m2 == matrix)
del m2
finally:
share.close()
def _sp_matmul_p(a1, a2, *, fail=False):
_log.info('in worker process')
return persist_binpickle(a1 @ a2).transfer()