Notebooks

examples/image-classifier/cortex.yaml

@@ -3,7 +3,7 @@
 
 - kind: api
   name: classifier
-  model: s3://cortex-examples/imagenet
-  request_handler: image.py
+  model: s3://cortex-examples/inception
+  request_handler: handler.py
   tracker:
     model_type: classification

examples/image-classifier/inception.ipynb

@@ -0,0 +1,208 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "inception.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n8CwINQcEBKz",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Exporting ImageNet Inception\n",
+        "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."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3221z3P69fgf",
+        "colab_type": "text"
+      },
+      "source": [
+        "First, we'll install the required packages:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_SdQpq7g9LiI",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "!pip install tensorflow==1.14.* tensorflow-hub==0.6.* boto3==1.*"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I-k0gUpxDGkU",
+        "colab_type": "text"
+      },
+      "source": [
+        "Next, we'll download the model from TensorFlow Hub and export it for serving:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "z6QLCzB4BKMe",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import tensorflow as tf\n",
+        "import tensorflow_hub as hub\n",
+        "from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def\n",
+        "\n",
+        "export_dir = \"export/1\"\n",
+        "builder = tf.saved_model.builder.SavedModelBuilder(export_dir)\n",
+        "\n",
+        "with tf.Session(graph=tf.Graph()) as sess:\n",
+        "    module = hub.Module(\"https://tfhub.dev/google/imagenet/inception_v3/classification/3\")\n",
+        "\n",
+        "    input_params = module.get_input_info_dict()\n",
+        "    image_input = tf.placeholder(\n",
+        "        name=\"images\", dtype=input_params[\"images\"].dtype, shape=input_params[\"images\"].get_shape()\n",
+        "    )\n",
+        "    \n",
+        "    sess.run([tf.global_variables_initializer(), tf.tables_initializer()])\n",
+        "\n",
+        "    classes = module(image_input)\n",
+        "    signature = predict_signature_def(inputs={\"images\": image_input}, outputs={\"classes\": classes})\n",
+        "\n",
+        "    builder.add_meta_graph_and_variables(\n",
+        "        sess, [\"serve\"], signature_def_map={\"predict\": signature}, strip_default_attrs=True\n",
+        "    )\n",
+        "\n",
+        "builder.save()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aGtJiyEnBgwl",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Upload the model to AWS\n",
+        "\n",
+        "Cortex loads models from AWS, so we need to upload the exported model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fTkjvSKBBmUB",
+        "colab_type": "text"
+      },
+      "source": [
+        "Set these variables to configure your AWS credentials and model upload path:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4xcDWxqCBPre",
+        "colab_type": "code",
+        "colab": {},
+        "cellView": "form"
+      },
+      "source": [
+        "AWS_ACCESS_KEY_ID = \"\" #@param {type:\"string\"}\n",
+        "AWS_SECRET_ACCESS_KEY = \"\" #@param {type:\"string\"}\n",
+        "S3_UPLOAD_PATH = \"s3://my-bucket/inception\" #@param {type:\"string\"}\n",
+        "\n",
+        "import sys\n",
+        "import re\n",
+        "\n",
+        "if AWS_ACCESS_KEY_ID == \"\":\n",
+        "    print(\"\\033[91m{}\\033[00m\".format(\"ERROR: Please set AWS_ACCESS_KEY_ID\"), file=sys.stderr)\n",
+        "\n",
+        "elif AWS_SECRET_ACCESS_KEY == \"\":\n",
+        "    print(\"\\033[91m{}\\033[00m\".format(\"ERROR: Please set AWS_SECRET_ACCESS_KEY\"), file=sys.stderr)\n",
+        "\n",
+        "else:\n",
+        "    try:\n",
+        "        bucket = re.search(\"s3://(.+?)/\", S3_UPLOAD_PATH).group(1)\n",
+        "        key = re.search(\"s3://.+?/(.+)\", S3_UPLOAD_PATH).group(1)\n",
+        "    except:\n",
+        "        print(\"\\033[91m{}\\033[00m\".format(\"ERROR: Invalid s3 path (should be of the form s3://my-bucket/path/to/file)\"), file=sys.stderr)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "czZkjb1IBr-f",
+        "colab_type": "text"
+      },
+      "source": [
+        "Upload the model to S3:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "M0b0IbyaBsim",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import os\n",
+        "import boto3\n",
+        "\n",
+        "s3 = boto3.client(\"s3\", aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY)\n",
+        "\n",
+        "for dirpath, _, filenames in os.walk(\"export\"):\n",
+        "    for filename in filenames:\n",
+        "        filepath = os.path.join(dirpath, filename)\n",
+        "        filekey = os.path.join(key, filepath[len(\"export/\"):])\n",
+        "        print(\"Uploading s3://{}/{}...\".format(bucket, filekey), end = '')\n",
+        "        s3.upload_file(filepath, bucket, filekey)\n",
+        "        print(\" ✓\")",
+        "\n",
+        "print(\"\\nUploaded model export directory to \" + S3_UPLOAD_PATH)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pZQWoeZbE7Wc",
+        "colab_type": "text"
+      },
+      "source": [
+        "<!-- CORTEX_VERSION_MINOR -->\n",
+        "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."
+      ]
+    }
+  ]
+}