onnx_tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# ONNX tutorial with TensorFlow"
   ],
   "id": "0",
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Set up the environment and install TurboML's SDK.\n",
    "We use `turboml-installer` to set up the environment for TurboML's SDK."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install -q turboml-installer\n",
    "import turboml_installer ; turboml_installer.install_on_colab()"
   ],
   "execution_count": null,
   "metadata": {},
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "The kernel should now be restarted with TurboML's SDK installed."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Login to your TurboML instance\n",
    "\n",
    "Note that you can copy and replace this snippet with one from your TurboML homepage."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "import turboml as tb",
    "\ntb.init(backend_url=BACKEND_URL, api_key=API_KEY)"
   ],
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install -U tensorflow tf2onnx onnx==1.14.1 scikit-learn"
   ],
   "execution_count": null,
   "id": "2",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Tensorflow - Standard Model Training\n",
    "\n",
    "The following blocks of code define a standard tensorflow training code. This is completely independent of TurboML. "
   ],
   "id": "3",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import tf2onnx.convert\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ],
   "execution_count": null,
   "id": "4",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "transactions = tb.datasets.FraudDetectionDatasetFeatures()\n",
    "labels = tb.datasets.FraudDetectionDatasetLabels()"
   ],
   "execution_count": null,
   "id": "5",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "joined_df = pd.merge(transactions.df, labels.df, on=\"transactionID\", how=\"right\")\n",
    "joined_df"
   ],
   "execution_count": null,
   "id": "6",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "X = joined_df.drop(\"is_fraud\", axis=1)\n",
    "numerical_fields = [\n",
    "    \"transactionAmount\",\n",
    "    \"localHour\",\n",
    "    \"isProxyIP\",\n",
    "    \"digitalItemCount\",\n",
    "    \"physicalItemCount\",\n",
    "]\n",
    "\n",
    "feats = X[numerical_fields]\n",
    "targets = joined_df[\"is_fraud\"].astype(int)"
   ],
   "execution_count": null,
   "id": "7",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    feats, targets, test_size=0.2, random_state=42\n",
    ")"
   ],
   "execution_count": null,
   "id": "8",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64, activation=\"relu\", input_shape=(X_train.shape[1],)))\n",
    "model.add(Dense(64, activation=\"relu\"))\n",
    "model.add(Dense(2, activation=\"softmax\"))"
   ],
   "execution_count": null,
   "id": "9",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model.summary()"
   ],
   "execution_count": null,
   "id": "10",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model.compile(\n",
    "    optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"]\n",
    ")\n",
    "\n",
    "num_epochs = 10\n",
    "model.fit(X_train, y_train, epochs=num_epochs, batch_size=64, verbose=1)"
   ],
   "execution_count": null,
   "id": "11",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "_, accuracy = model.evaluate(X_test, y_test)\n",
    "print(\"Accuracy:\", accuracy)"
   ],
   "execution_count": null,
   "id": "12",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Export model to ONNX format\n",
    "\n",
    "Exporting a model to ONNX format depends on the framework. Tutorials for different frameworks can be found at https://github.com/onnx/tutorials#converting-to-onnx-format"
   ],
   "id": "13",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "onnx_model_path = \"tensorflow_model.onnx\"\n",
    "input_signature = [\n",
    "    tf.TensorSpec([1, len(numerical_fields)], tf.float32, name=\"keras_tensor\")\n",
    "]\n",
    "model.output_names = [\"output\"]\n",
    "onnx_model, _ = tf2onnx.convert.from_keras(model, input_signature, opset=13)\n",
    "\n",
    "onnx_model_bytes = onnx_model.SerializeToString()"
   ],
   "execution_count": null,
   "id": "14",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Create an ONNX model with TurboML\n",
    "\n",
    "Now that we've converted the model to ONNX format, we can deploy it with TurboML. "
   ],
   "id": "15",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "transactions = transactions.to_online(id=\"transactions\", load_if_exists=True)\n",
    "labels = labels.to_online(id=\"transaction_labels\", load_if_exists=True)\n",
    "\n",
    "features = transactions.get_model_inputs(numerical_fields=numerical_fields)\n",
    "label = labels.get_model_labels(label_field=\"is_fraud\")"
   ],
   "execution_count": null,
   "id": "16",
   "metadata": {},
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "tb.set_onnx_model(\"tensorflowmodel\", onnx_model_bytes)\n",
    "onnx_model = tb.ONNX(model_save_name=\"tensorflowmodel\")"
   ],
   "execution_count": null,
   "id": "17",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "deployed_model = onnx_model.deploy(\"onnx_model_tf\", input=features, labels=label)"
   ],
   "execution_count": null,
   "id": "18",
   "metadata": {
    "tags": []
   },
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "deployed_model.add_metric(\"WindowedAUC\")"
   ],
   "execution_count": null,
   "id": "19",
   "metadata": {},
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model_auc_scores = deployed_model.get_evaluation(\"WindowedAUC\")\n",
    "plt.plot([model_auc_score.metric for model_auc_score in model_auc_scores])"
   ],
   "execution_count": null,
   "id": "20",
   "metadata": {},
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [],
   "execution_count": null,
   "id": "21",
   "metadata": {},
   "outputs": []
  }
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