hanneshapke

#!/bin/bash
TENSORFLOW_SERVING_VERSION=2.11.0
TENSORFLOW_SERVING_HOSTNAME=serving
TENSORFLOW_SERVING_MODEL_NAME=test_model
INTRA_OP_PARALLELISM=2
INTER_OP_PARALLELISM=2

TENSORBOARD_LOGDIR="/tmp/tensorboard"
DOCKER_PROFILER_TAG=tensorboard_profiler:latest

hanneshapke

# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

hanneshapke

hanneshapke

# install job lib

from joblib import Parallel, delayed
import numpy as np

def myfun(p):
    return np.linalg.norm(p[0]-p[1])

X = [1,2,3,4]
Y = [6,4,8,1]

hanneshapke

hanneshapke

def _get_serve_tf_examples_fn(model, tf_transform_output):
    model.tft_layer = tf_transform_output.transform_features_layer()
 
    @tf.function
    def serve_tf_examples_fn(serialized_tf_examples):
        feature_spec = tf_transform_output.raw_feature_spec()
        feature_spec.pop(_LABEL_KEY)
        parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
 
        transformed_features = model.tft_layer(parsed_features)

hanneshapke

    x = tf.keras.layers.Dense(256, activation='relu')(pooled_output)
    dense = tf.keras.layers.Dense(64, activation='relu')(x)
    pred = tf.keras.layers.Dense(1, activation='sigmoid')(dense)
 
    model = tf.keras.Model(
        inputs=[inputs['input_word_ids'], 
                inputs['input_mask'], 
                inputs['input_type_ids']], 
        outputs=pred
    )

hanneshapke

    bert_layer = load_bert_layer()
    pooled_output, _ = bert_layer(
        [input_word_ids, 
         input_mask, 
         input_type_ids
        ]
    )

hanneshapke

    feature_spec = tf_transform_output.transformed_feature_spec() 
    feature_spec.pop(_LABEL_KEY)
 
    inputs = {
        key: tf.keras.layers.Input(
        shape=(max_seq_length), 
        name=key, 
        dtype=tf.int32)
        for key in feature_spec.keys()
    }

hanneshapke

tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(fn_args.train_files, tf_transform_output, 32)
eval_dataset = _input_fn(fn_args.eval_files, tf_transform_output, 32) 
     ...

model.fit(
    train_dataset,
    validation_data=eval_dataset,
    ...
)