benfred
commented
2 years ago Can you try this on the latest main branch? The batch computation code has been substantially rewritten - and the function the segfault occurs in no longer exists.
Closed otosky closed 2 years ago
benfred
commented
2 years ago Can you try this on the latest main branch? The batch computation code has been substantially rewritten - and the function the segfault occurs in no longer exists.
otosky
commented
2 years ago I no longer have the Linux environment handy that generated this issue, but I tried out the new recommend api with batching from d86c4420bb219ad5621658840eff8eaa9dc53cf6 in a docker container with fairly starved resources and it worked great 👍 . Feel free to close this.
import numpy as np
from implicit.als import AlternatingLeastSquares
from implicit.datasets.lastfm import get_lastfm
def batch_generator(users, batch_size):
user_idx_array = np.arange(len(users))
for start_idx in range(0, len(user_idx_array), batch_size):
batch = user_idx_array[start_idx: start_idx + batch_size]
yield batch
if __name__ == "__main__":
artists, users, plays = get_lastfm()
model = AlternatingLeastSquares()
plays = plays.tocsr()
user_plays = plays.T.tocsr()
print(f"\nShape of user_items: {user_plays.shape}")
model.fit(user_plays)
for batch in batch_generator(users, batch_size=2000):
print(f"Generating recommendations for users {batch.min()}-{batch.max()}")
ids, _ = model.recommend(batch, user_plays[batch], N=1000)
# do extra work & write out batches to an external sink
print("Finished generating recommendations")It wasn't entirely clear to me from the lastfm example nor the docs that I needed to pass in a user_items matrix to .recommend where the num of rows matched the num of users until I started hitting this error:
I think line 154 here needs updating: https://github.com/benfred/implicit/blob/d86c4420bb219ad5621658840eff8eaa9dc53cf6/examples/lastfm.py#L150-L154
Thanks for building such a great lib!
benfred
commented
2 years ago Thanks for looking into this!
It wasn't entirely clear to me from the lastfm example nor the docs that I needed to pass in a user_items matrix to .recommend where the num of rows matched the num of users until I started hitting this error:
I've made some breaking changes to the API recently - #481 has an overview.
One of the API changes you hit is https://github.com/benfred/implicit/pull/526 . The idea here was that we don't need empty rows in the user_items matrix for users that aren't being recommended - so we need to update invocations to be something like model.recommend(userids, user_items[userids]) instead of model.recommend(userids, user_items) . This change means that if you're recommending items for a single userid=10000000 - you don't need to pass in a sparse matrix with a 10000001 entries in the indptr member , and will be slightly faster in this case.
I'm trying to batch up all the breaking API changes into the next release (0.5.0).
I think line 154 here needs updating:
Good catch! I've fixed here https://github.com/benfred/implicit/pull/532
The new batch code is quite a bit more efficient than the old code - but required some of these API changes to support this: I'm benchmarking the batch calls at about 2x faster on the CPU , and we have GPU batch support now too which is over 60x faster than the CPU version on my system . The GPU support is fast enough now that the lastfm.py example you're running is bottlenecked on writing out the results to disk - rather than actually calculating the results
I'm running a script using Implicit (ALS) on GCP with 8-cores/64gb RAM using Ubuntu 18.04 and get segmentation faults when Recommending All. This is for ~18,000 users and 5 million-ish items, getting top 2000.
Setting MKL_NUM_THREADS to 1, didn't seem to help. And surprisingly, the script doesn't present any issues when I run locally on OSX. I've gotten around the issue by lowering the batch_size on Ubuntu, but I'm curious as to why the issue isn't present on my mac?
OSX details: Cython.__version__ '0.29.6' numpy.__version__ '1.16.2' scipy.__version__ '1.2.1' implicit.__version__ '0.4.0'
Linux details: Cython.__version__ '0.29.12' numpy.__version__ '1.16.4' scipy.__version__ '1.3.0' implicit.__version_\ '0.4.0'
Both running Python 3.7.3 from Anaconda (64-bit)