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Convert example model generation to notebooks (#480)
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‎examples/image-classifier/cortex.yaml‎

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- kind: api
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name: classifier
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model: s3://cortex-examples/imagenet
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request_handler: image.py
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model: s3://cortex-examples/inception
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request_handler: handler.py
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tracker:
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model_type: classification

‎examples/image-classifier/image.py‎ renamed to ‎examples/image-classifier/handler.py‎

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‎examples/image-classifier/inception.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "inception.ipynb",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "n8CwINQcEBKz",
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"colab_type": "text"
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},
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"source": [
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"# Exporting ImageNet Inception\n",
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"In this notebook, we'll show how to export the [pre-trained Imagenet Inception model](https://tfhub.dev/google/imagenet/inception_v3/classification/3) for serving."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "3221z3P69fgf",
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"colab_type": "text"
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},
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"source": [
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"First, we'll install the required packages:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "_SdQpq7g9LiI",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"!pip install tensorflow==1.14.* tensorflow-hub==0.6.* boto3==1.*"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "I-k0gUpxDGkU",
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"colab_type": "text"
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},
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"source": [
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"Next, we'll download the model from TensorFlow Hub and export it for serving:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "z6QLCzB4BKMe",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"import tensorflow as tf\n",
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"import tensorflow_hub as hub\n",
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"from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def\n",
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"\n",
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"export_dir = \"export/1\"\n",
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"builder = tf.saved_model.builder.SavedModelBuilder(export_dir)\n",
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"\n",
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"with tf.Session(graph=tf.Graph()) as sess:\n",
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" module = hub.Module(\"https://tfhub.dev/google/imagenet/inception_v3/classification/3\")\n",
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"\n",
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" input_params = module.get_input_info_dict()\n",
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" image_input = tf.placeholder(\n",
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" name=\"images\", dtype=input_params[\"images\"].dtype, shape=input_params[\"images\"].get_shape()\n",
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" )\n",
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" \n",
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" sess.run([tf.global_variables_initializer(), tf.tables_initializer()])\n",
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"\n",
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" classes = module(image_input)\n",
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" signature = predict_signature_def(inputs={\"images\": image_input}, outputs={\"classes\": classes})\n",
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"\n",
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" builder.add_meta_graph_and_variables(\n",
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" sess, [\"serve\"], signature_def_map={\"predict\": signature}, strip_default_attrs=True\n",
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" )\n",
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"\n",
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"builder.save()"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "aGtJiyEnBgwl",
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"colab_type": "text"
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},
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"source": [
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"## Upload the model to AWS\n",
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"\n",
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"Cortex loads models from AWS, so we need to upload the exported model."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "fTkjvSKBBmUB",
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"colab_type": "text"
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},
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"source": [
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"Set these variables to configure your AWS credentials and model upload path:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "4xcDWxqCBPre",
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"colab_type": "code",
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"colab": {},
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"cellView": "form"
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},
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"source": [
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"AWS_ACCESS_KEY_ID = \"\" #@param {type:\"string\"}\n",
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"AWS_SECRET_ACCESS_KEY = \"\" #@param {type:\"string\"}\n",
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"S3_UPLOAD_PATH = \"s3://my-bucket/inception\" #@param {type:\"string\"}\n",
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"\n",
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"import sys\n",
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"import re\n",
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"\n",
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"if AWS_ACCESS_KEY_ID == \"\":\n",
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" print(\"\\033[91m{}\\033[00m\".format(\"ERROR: Please set AWS_ACCESS_KEY_ID\"), file=sys.stderr)\n",
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"\n",
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"elif AWS_SECRET_ACCESS_KEY == \"\":\n",
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" print(\"\\033[91m{}\\033[00m\".format(\"ERROR: Please set AWS_SECRET_ACCESS_KEY\"), file=sys.stderr)\n",
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"\n",
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"else:\n",
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" try:\n",
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" bucket = re.search(\"s3://(.+?)/\", S3_UPLOAD_PATH).group(1)\n",
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" key = re.search(\"s3://.+?/(.+)\", S3_UPLOAD_PATH).group(1)\n",
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" except:\n",
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" print(\"\\033[91m{}\\033[00m\".format(\"ERROR: Invalid s3 path (should be of the form s3://my-bucket/path/to/file)\"), file=sys.stderr)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "czZkjb1IBr-f",
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"colab_type": "text"
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},
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"source": [
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"Upload the model to S3:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "M0b0IbyaBsim",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"import os\n",
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"import boto3\n",
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"\n",
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"s3 = boto3.client(\"s3\", aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY)\n",
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"\n",
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"for dirpath, _, filenames in os.walk(\"export\"):\n",
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" for filename in filenames:\n",
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" filepath = os.path.join(dirpath, filename)\n",
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" filekey = os.path.join(key, filepath[len(\"export/\"):])\n",
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" print(\"Uploading s3://{}/{}...\".format(bucket, filekey), end = '')\n",
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" s3.upload_file(filepath, bucket, filekey)\n",
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" print(\" ✓\")",
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"\n",
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"print(\"\\nUploaded model export directory to \" + S3_UPLOAD_PATH)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "pZQWoeZbE7Wc",
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"colab_type": "text"
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},
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"source": [
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"<!-- CORTEX_VERSION_MINOR -->\n",
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"That's it! See the [example on GitHub](https://github.com/cortexlabs/cortex/tree/master/examples/image-classifier) for how to deploy the model as an API."
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]
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}
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]
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}

‎examples/image-classifier/load_inception_from_tf_hub.py‎

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