Use fixed-size inputs

movies.py

@@ -7,13 +7,16 @@ import argparse
 import collections
 import json
 import os
-from pprint import pprint
 import random
 import re
 import numpy as np
 import pandas as pd
 import tensorflow as tf
+from tensorflow import keras
 import tensorflow_recommenders as tfrs
+import mlflow
+from mlflow.models.signature import ModelSignature
+from mlflow.types.schema import Schema, TensorSpec
 
 
 def load_movielense(basedir):
@@ -189,7 +192,10 @@ def load_dataset(basedir):
 
     titles = {int(k): v for k, v in titles.items()}
 
-    movies_tensor = tf.constant([[x] for x in titles.keys()])
+    movies_tensor = tf.constant(
+        [[x] for x in titles.keys()],
+        dtype=tf.uint64,
+    )
     movie_ds = tf.data.Dataset.from_tensor_slices(movies_tensor)
 
     # Cache and batch
@@ -220,7 +226,11 @@ class RecommenderModel(tfrs.Model):
         embedded_cand = self.cand_model(label_movie_id)
 
 
-        return self.retrieval_task(embedded_query, embedded_cand, compute_metrics=not training)
+        return self.retrieval_task(
+            embedded_query,
+            embedded_cand,
+            compute_metrics=not training
+        )
 
 
 def build_embedding_model(movies, embedding_dimension):
@@ -235,7 +245,10 @@ def build_embedding_model(movies, embedding_dimension):
     cand_model = tf.keras.Sequential([
         tf.keras.layers.IntegerLookup(vocabulary=vocab, mask_token=None, input_shape=(1,)),
         tf.keras.layers.Embedding(len(movies) + 1, embedding_dimension),
-        tf.keras.layers.Reshape((32,), input_shape=(1, 32))
+        tf.keras.layers.Reshape(
+            (embedding_dimension,),
+            input_shape=(1, embedding_dimension)
+        )
     ])
 
     return query_model, cand_model
@@ -245,6 +258,9 @@ def build_model(movies,
                 learning_rate=0.1,
                 embedding_dimension=32):
     """Return the full keras/tensorflow model"""
+    mlflow.log_param("learning_rate", learning_rate)
+    mlflow.log_param("embedding_dimension", embedding_dimension)
+
     query_model, cand_model = build_embedding_model(
         movies, embedding_dimension=embedding_dimension
     )
@@ -264,10 +280,26 @@ def build_model(movies,
 
     return model
 
+# pylint: disable=R0903
+class MLflowKerasCallback(keras.callbacks.Callback):
+    """Callback to record loss"""
+
+    @staticmethod
+    def on_epoch_end(epoch, logs=None):
+        """Log non-empty metrics after each step"""
+        if not logs:
+            return
+
+        # Remove empty logs
+        logs = {k: v for k, v in logs.items() if v != 0}
+        mlflow.log_metrics(logs, epoch)
 
 def fit_model(model, training_ds, epochs=3):
     """Fit the model to the training dataset"""
-    model.fit(training_ds, epochs=epochs)
+    mlflow.log_param("epochs", epochs)
+    model.fit(training_ds,
+              epochs=epochs,
+              callbacks=MLflowKerasCallback())
 
 
 def lookup(titles, movie_ids):
@@ -277,19 +309,62 @@ def lookup(titles, movie_ids):
 
 def eval_model(model, test_ds):
     """Print the test set performance"""
-    result = model.evaluate(test_ds, return_dict=True)
-    pprint(result)
+    result = model.evaluate(test_ds, return_dict=True, verbose=0)
+    val_result = {"val_" + k: v for k, v in result.items()}
+    mlflow.log_metrics(val_result)
+
+
+def build_prediction_model(model, movies):
+    """Build and return into recommend movies"""
+    index = tfrs.layers.factorized_top_k.BruteForce(model.query_model)
+    index.index_from_dataset(tf.data.Dataset.zip(
+        (movies.batch(100), movies.batch(100).map(model.cand_model))
+    ))
+
+    return index
+
+    # Use layer-model for now, with fixed-size shape
+    # inputs = tf.keras.Input(shape=(None, ))
+    # outputs = index(inputs)
+
+    # model = tf.keras.Model(inputs=inputs, outputs=outputs)
+    # return model
+
+
+def log_preduction_model(model):
+    """Log the prediction model to mlflow"""
+    # The model does not know its input shape. Set the shape to
+    # a fixed size for how
+    model(tf.constant([[1, 2, 3, 4]]))
+
+    input_schema = Schema([
+        TensorSpec(np.dtype(np.int32), (-1, -1))
+    ])
+    output_schema = Schema([
+        TensorSpec(np.dtype(np.uint64), (-1, ), name="recommendations"),
+    ])
+
+    signature = ModelSignature(inputs=input_schema, outputs=output_schema)
+    mlflow.keras.log_model(model, "models",
+                           signature=signature,
+                           pip_requirements="requirements.txt")
 
 
 def train(args):
     """Load the dataset, train and evaluate the model"""
+    mlflow.set_experiment(args.mlflow_experiment)
+
     train_ds, test_ds, _, movies = load_dataset(args.input)
 
     with tf.device("cpu:0"):
         model = build_model(movies)
         fit_model(model, train_ds)
-        eval_model(model, test_ds)
 
+        if not args.debug:
+            eval_model(model, test_ds)
+
+    index = build_prediction_model(model, movies)
+    log_preduction_model(index)
 
 commands = {
     "prepare": prepare_dataset,
@@ -301,6 +376,8 @@ def get_default_parser():
     parser = argparse.ArgumentParser()
     parser.add_argument("command", metavar="CMD", choices=commands,
                         help="Operation to execute")
+    parser.add_argument("--mlflow-experiment", "-E", default="movie-recommender",
+                        help="URI to the mlflow tracking backend")
     parser.add_argument("-i", "--input", metavar="PATH",
                         help="Path to input file")
     parser.add_argument("-o", "--output", metavar="PATH",

requirements.txt

@@ -2,3 +2,4 @@ numpy~=1.23.2
 pandas~=1.4.3
 tensorflow-recommenders~=0.7.0
 tensorflow~=2.9.1
+mlflow~=1.27.0