waichee

# Some Google CLI commands to setup dataproc and Big Query etc
cluster_name="cluster-1"

# create dataproc cluster with Jupyterhub https://cloud.google.com/dataproc/docs/tutorials/jupyter-notebook
gcloud compute ssh --zone="us-central1-c" \
  --ssh-flag="-D" --ssh-flag="10000" --ssh-flag="-N" "${cluster_name}-m" \
  --initialization-actions \
        gs://dataproc-initialization-actions/jupyter/jupyter.sh
        
# Proxy and access UI of cluster https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces

waichee

#Run a single test 
bazel test -c opt //tensorflow_serving/sources/storage_path:file_system_storage_path_source_test

waichee

#!/bin/bash
# This script is used to compile Tensorflow Serving protobuf definition into Python
# The generated definitions in Python are stored in the tensorflow_serving_apis folder
# Usage:
# ./compile_ts_serving_proto.sh 333325e413e9680d67ae90196fa123f5271fcf615
#
: ${1?”Error. Please provide the Tensorflow Serving git commit hash/branch name. Usage: ./compile_ts_serving_proto.sh my_awesome_branch “}
script_dir=”$( cd “$( dirname “${BASH_SOURCE[0]}” )” && pwd )”
ts_git_revision=$1 #branch/release or commit hash
local_ts_api_dir=”${script_dir}/tensorflow_serving_apis/” #directory that stores the compiled python proto definition

waichee

mkdir -p /work/

# Clone the source from Github
cd /work/ && git clone — recurse-submodules https://github.com/tensorflow/serving

# Pin the version of Tensorflow Serving and its submodule
TENSOR_SERVING_COMMIT_HASH=85db9d3
TENSORFLOW_COMMIT_HASH=dbe5e17
cd /work/serving && git checkout $TENSOR_SERVING_COMMIT_HASH

waichee

# install the required packages
pip install memory_profiler
pip install matplotlib

# run the profiler to record the memory usage
# sample 0.1s by defaut
mprof run --include-children python fantastic_model_building_code.py

# plot the recorded memory usage
mprof plot --output memory-profile.png

waichee

# Clone the Tensorflow Serving source
git clone https://github.com/tensorflow/serving
cd serving && git checkout <commit_hash>

# Build the docker image (time to go get yourself a coffee, maybe a meal as well, this will take a while.)
docker build -t some_user_namespace/tensorflow-serving:latest -f ./serving/tensorflow_serving/tools/docker/Dockerfile.devel .

# Run up the Docker container in terminal
docker run -ti some_user_namespace/tensorflow-serving:latest

waichee

import time
import random

# A dummy script which keeps increasing the number of string added to list a

thing = "hi"
a = []
for i in range(1000):
	a.append(thing * random.randint(1000,2000))
	time.sleep(0.1)