choongng

A good way to get a taste of Swift for Tensorflow language and tools is to set it up with Jupyter with the fastai Swift notebooks. I wanted a quick setup, which the Mac install experience currently not, so instead I installed the release binaries in a Ubuntu container via Docker. The setup process for this scenario is not well documented, so here it is for you / future me.

What we're about to do is install the S4TF 0.4 release and the fastai v3 Swift notebooks on Ubuntu 18.04. Generally we follow the swift-jupyter docker file, but install cpu-only release versions of the packages.

Below are some of the references I looked at:

Rationale for S4TF and background reading

https://github.com/tensorflow/swift/blob/master/docs/WhySwiftForTensorFlow.md https://github.com/tensorflow/swift/blob/master/docs/DifferentiableFunctions.md

alexeygrigorev

pip install grpcio-tools

wget https://github.com/tensorflow/tensorflow/archive/v1.9.0.zip -O tf-190.zip
unzip tf-190.zip && rm tf-190.zip

wget https://github.com/tensorflow/serving/archive/1.9.0.zip -O tf-serving-190.zip
unzip tf-serving-190.zip && rm tf-serving-190.zip

mv serving-1.9.0/tensorflow_serving tensorflow-1.9.0

pbrumblay

from google.cloud import storage
from airflow.hooks.base_hook import BaseHook
from airflow.utils.log.logging_mixin import LoggingMixin
import random
import string


class GCSCustomHook(BaseHook, LoggingMixin):

    def __init__(self, storage_conn_id='google_cloud_storage_default'):

carlthome

W4ngatang

''' Script for downloading all GLUE data.

Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized, 
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
You should then rename and place specific files in a folder (see below for an example).

mkdir MRPC
cabextract MSRParaphraseCorpus.msi -d MRPC

moodoki

import os, argparse

import tensorflow as tf
from tensorflow.python.framework import graph_util

dir = os.path.dirname(os.path.realpath(__file__))

def freeze_graph(model_folder, output_nodes='y_hat', 
                 output_filename='frozen-graph.pb', 
                 rename_outputs=None):

mauri870

// Tensorflow Serving Go client for the inception model

// go get github.com/golang/protobuf/ptypes/wrappers google.golang.org/grpc
//
// Compile the proto files:
//
// git clone https://github.com/tensorflow/serving.git
// git clone https://github.com/tensorflow/tensorflow.git
//
// mkdir -p vendor

zombiezen

# Copyright 2017 Google Inc.
#
# 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
#
# https://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,

WillieMaddox

# -*- coding: utf-8 -*-
import numpy as np
import xml.etree.ElementTree as ET
from pycocotools.coco import COCO


def convert_coco_bbox(size, box):
    dw = 1. / size[0]
    dh = 1. / size[1]
    x = box[0] + box[2] / 2.0

alansouzati

apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: grommet-drone
  namespace: drone
  labels:
    app: grommet-drone
  annotations:
    description: drone with interface
spec: