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A Clojure library for dynamic neural nets (e.g., PyTorch, DynNet). Mostly for learning purposes, but totally usable and pretty performant (see Performance below). See introductory blog post here. Current features:


At the moment, the only supported tensor backend is via Neanderthal, which currently only uses Intel MKL. You'll need to install this library (although if you're doing a lot of neural net work locally, you probably already have). Note that if you're on a Mac, you'll need to disable System Integrity Protection, in order for Neanderthal to load the native libs from the DYLD_LIBRARY_PATH environment variable.


See some examples in the src/flare/examples directory for usage examples. Single best entry is probably the LSTM example. But here are some simple ones:

(ns example
  (:require [flare.core :as flare]
            [flare.node :as node]
            [flare.computation-graph :as cg]))

;; Z = X + Y example
(def X (node/const [2 3]))
(def Y (node/const [3 4]))
(def Z (cg/+ X Y))
(:value Z)
returns [5.0 7.0] tensor

Notice that the computation happens eagerly, you don't need to call forward on any node, the operation happens as soon great the graph node. The numerical computations use a pluggable backend, but these can use native hardware. While the above example is slightly verbose compared to PyTorch, for longer pieces of code you get more expressiveness. Here's an example of building a simple bidirectional LSTM sentiment classifier for a given sentence. It uses the concept of a module which is a protocol for building graphs given an input. Typically, you can make modules that close over new parameters and other modules in a module:

(defn lstm-sent-classifier [model word-emb lstm-size num-classes]
      ;; let-scope so the parameters get smart nesting of names
      [emb-size (embeddings/embedding-size word-emb)
       num-dirs 2
       input-size (* num-dirs emb-size)
       hidden-size (* num-dirs lstm-size)
       lstm (rnn/lstm-cell model input-size hidden-size)
       hidden->logits (module/affine model num-classes [hidden-size])]
      (graph [this sent]
        ;; build logits
        (let [inputs (embeddings/sent-nodes word-emb sent)
              [outputs _] (rnn/build-seq lstm inputs (= num-dirs 2))
              train? (:train? (meta this))
              hidden (last outputs)
              hidden (if train? (cg/dropout 0.5 hidden) hidden)]
          (module/graph hidden->logits hidden))))))


The big surprise is that Flare is relatively performant so far, no slower than PyTorch overall and for many cases 2x-3x faster than PyTorch. The actual tensor computations both utilize Intel MPK so differences in performance mostly stem from using Clojure vs Python for graph construction.

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Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.