Hannes Hapke hanneshapke

## tensorboard.sh
#!/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

## track_celery.py
import time
from memory_profiler import memory_usage

import logging
celery_logger = logging.getLogger('celery')


def track_celery(method):

    """

## install_gpu_driver_for_tf27.py
# 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,

## redis.py
import bz2
import pickle

from django.conf import settings
from djang_redis import get_redis_connection
from tqdm import tqdm

from .constants import GOOGLE_WORD2VEC_MODEL_NAME


## example-convolutional-neural-net-with-tf-keras.ipynb

      
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                / example-convolutional-neural-net-with-tf-keras.ipynb
            
            
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                Example Convolutional Neural Net with tf.keras
              
          
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## tfx-pipeline-for-bert-preprocessing.ipynb

      
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                / tfx-pipeline-for-bert-preprocessing.ipynb
            
            
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                TFX Pipeline for Bert Preprocessing.ipynb
              
          
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## parallelize.py
# 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]

## tfx_pipeline_for_bert_preprocessing_wo_tf-example.ipynb

      
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                / tfx_pipeline_for_bert_preprocessing_wo_tf-example.ipynb
            
            
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              June 4, 2020 22:48
            
              
                TFX_Pipeline_for_Bert_Preprocessing_wo_tf.Example.ipynb
              
          
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## _get_serve_tf_examples_fn.py
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)

## partial_setup_of_berttokenizer.py
import tensorflow_hub as hub

BERT_TFHUB_URL = "https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/2"

bert_layer = hub.KerasLayer(handle=BERT_TFHUB_URL, trainable=True)
vocab_file_path = bert_layer.resolved_object.vocab_file.asset_path.numpy()
	#!/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
	import time
	from memory_profiler import memory_usage

	import logging
	celery_logger = logging.getLogger('celery')


	def track_celery(method):

	"""
	# 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,
	import bz2
	import pickle

	from django.conf import settings
	from djang_redis import get_redis_connection
	from tqdm import tqdm

	from .constants import GOOGLE_WORD2VEC_MODEL_NAME
	# 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)
	import tensorflow_hub as hub

	BERT_TFHUB_URL = "https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/2"

	bert_layer = hub.KerasLayer(handle=BERT_TFHUB_URL, trainable=True)
	vocab_file_path = bert_layer.resolved_object.vocab_file.asset_path.numpy()