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Dataiku Individual Contributor License Agreement

Thank you for your interest in contributing to open source projects managed or sponsored by Dataiku (“Dataiku”). By submitting a Contribution to a Dataiku open source project, you agree to the terms of this Individual Contributor License Agreement (“Agreement”). This Agreement is intended to protect both you as a contributor and Dataiku as the project steward, without limiting your rights to use your own Contributions for any other purpose.

1. Definitions

“You” or “Your” means the individual copyright owner or the legal entity authorized by the copyright owner that is entering into this Agreement with Dataiku. If You are acting on behalf of a legal entity, “You” includes that entity and all entities that control, are controlled by, or are under common control with it. For purposes of this definition, “control” means direct or indirect ownership

Dataiku Individual Contributor License Agreement

Thank you for your interest in contributing to open source projects managed or sponsored by Dataiku (“Dataiku”). By submitting a Contribution to a Dataiku open source project, you agree to the terms of this Individual Contributor License Agreement (“Agreement”). This Agreement is intended to protect both you as a contributor and Dataiku as the project steward, without limiting your rights to use your own Contributions for any other purpose.

1. Definitions

“You” or “Your” means the individual copyright owner or the legal entity authorized by the copyright owner that is entering into this Agreement with Dataiku. If You are acting on behalf of a legal entity, “You” includes that entity and all entities that control, are controlled by, or are under common control with it. For purposes of this definition, “control” means direct or indirect ownership of more than fifty perce

	#!/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

	# 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,

	# 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]

	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)

	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
	)

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