[HN Gopher] Hummingbird: Compile trained ML models into tensor c...
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       Hummingbird: Compile trained ML models into tensor computation
        
       Author : polm23
       Score  : 32 points
       Date   : 2020-06-07 01:43 UTC (21 hours ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | vladf wrote:
       | This is an interesting idea, with the main non-trivial win really
       | being vectorized GBDT inference. Instead of serially going down a
       | DT, you can convert it to vectorizable GEMM code.
       | 
       | By a cursory look at
       | hummingbird/ml/operator_converters/_tree_commons.py this seems to
       | just be doing a GraphBLAS-style graph traversal with dense GEMM,
       | which strikes me as resulting in incredibly redundant
       | computation. I think this would only give you acceleration for
       | very small trees (edit: granted, I think that's what the defaults
       | are for a lot of these packages).
       | 
       | I'd be interested in seeing how this stacks up against:
       | 
       | * native GPU execution for XGB/LGBM
       | 
       | * a _sparse_ GEMM implementation of their algorithm
       | 
       | * the classical reference for DT vectorization, Quickscorer, see
       | https://github.com/hpclab/quickscorer
        
       | aasasd wrote:
       | You'd think that a machine-learning thing would be called '
       | _mocking_ bird'.
        
       | 1337shadow wrote:
       | Nothing to do with the Hummingbird notation, if anyone is
       | wondering.
       | 
       | https://www.hummingbirdnotation.com/
        
       | fxtentacle wrote:
       | How is this different from replacing numpy with cupy?
        
       | KorfmannArno wrote:
       | Is anyone else interested in reading groups for the open-source
       | book D2L.AI?
        
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       (page generated 2020-06-07 23:00 UTC)