wolf1986

# Dependencies:

from logging import Logger
from pathlib import Path
from typing import List, Union

from google.oauth2 import service_account
from googleapiclient import discovery
from oauth2client import client, tools
from oauth2client.file import Storage

wolf1986

# Adapted excerpt from: https://gist.github.com/wolf1986/1759c9b8bcfb09e92faf7e43797e4aef

import io
import traceback
from pprint import pformat
from time import perf_counter
from typing import List, Iterable, Callable, Any

from log_utils.data_logger import DataLogger
import numpy as np

wolf1986

Tensorflow Serving Tutorial - 02 - Using a Custom Model

In this tutorial:

• Assuming you have an environment that can works with the official inception-v3
• Adapt an existing model to work with TF Serving
• Query the server with images and retrieve classification results

Save a model after training

In this example we'll suppose that we want our TF Model to accept base64 strings of image files, i.e. prepared by python:

  with open(PATH_IMAGE, 'rb') as f:

wolf1986

Tensorflow Serving Tutorial - 01 - Public Inception Model

In this tutorial:

• Start with a blank Ubuntu
• Install requirements
• Download code & pre-tranied inception-v3 model from Google
• Export the downloaded "checkpoint" format to a TF Graph that is servable with Tensorflow Model Serving
• Query the server with images of a cat and a dog

General Notes

• Compiling Tensorflow Serving from source (on docker with their official instructions) produces an internal gcc error (probably specific to tensorflow commit / gcc version used when writing this: tf serving commit: c1ec43508ee57a5d6269116aba82d2a16d383c8a)