Comments (2)
just missing an adaptation of the memory estimation here
from autofaiss.
now done
from autofaiss.
Related Issues (20)
- Support building binary vector index using Hamming distance? HOT 10
- build_index using too much RAM during training and crashes HOT 9
- build_index take much more time when decreasing max_index_memory_usage HOT 5
- autofaiss installation fails on python3.11
- augmenting embeddings with k labels to help segment searches HOT 2
- Can autofaiss take spark dataframe as input? HOT 3
- Query Result Distances Appear in Descending Order for ANN Search HOT 2
- What is the optimized way for Bulk Retrieval of Approximate Nearest Neighbors from Large 'autofaiss' Index" HOT 4
- Exact distances HOT 2
- IP and L2 distance out of range of (0,1) HOT 7
- Updating the pandas version
- PySpark cluster and session sizing
- [Bug?] Index retrieval is not self-consistent. HOT 4
- Cannot read embeddings from parquet files stored in S3
- fail to write index HOT 5
- README.md and requirements.txt should be in MANIFEST.in, not in data_files
- GPU Support HOT 7
- In-memory setup for `build_index` doesn't work HOT 7
- No embeddings found in folder HOT 2
- Create mapping between list of image files and index creates with emebddings
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from autofaiss.