{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cUsmLBhM8m-Y"
   },
   "source": [
    "# Get Started with MLflow + Tensorflow\n",
    "\n",
    "In this guide, we will show how to train your model with Tensorflow and log your training using MLflow.\n",
    "\n",
    "We will use [Databricks Community Edition](https://community.cloud.databricks.com/) as our tracking server, which has built-in support for MLflow. Databricks CE is the free version of Databricks platform, if you haven't, please register an account via [link](https://www.databricks.com/try-databricks).\n",
    "\n",
    "You can run code in this guide from cloud-based notebooks like Databricks notebook or Google Colab, or run it on your local machine."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "<a href=\"https://raw.githubusercontent.com/mlflow/mlflow/master/docs/source/deep-learning/tensorflow/quickstart/quickstart_tensorflow.ipynb\" class=\"notebook-download-btn\"><i class=\"fas fa-download\"></i>Download this Notebook</a><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9btw8G129PH9"
   },
   "source": [
    "## Install dependencies\n",
    "\n",
    "Let's install the `mlflow` package.\n",
    "\n",
    "```\n",
    "%pip install -q mlflow\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VrVhET8SqbbC"
   },
   "source": [
    "Then let's import the packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "oHqHIk47LR2K"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-BMTIjPv9gFa"
   },
   "source": [
    "## Load the dataset\n",
    "\n",
    "We will do a simple image classification on handwritten digits with [mnist dataset](https://en.wikipedia.org/wiki/MNIST_database).\n",
    "\n",
    "Let's load the dataset using `tensorflow_datasets` (`tfds`), which returns datasets in the format of `tf.data.Dataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84,
     "referenced_widgets": [
      "566a69125ddd427d8457d7e2611f0580",
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    },
    "id": "n5yeMBEwLSUA",
    "outputId": "4eb600fe-965c-442b-837f-393691bf7a79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset 11.06 MiB (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "566a69125ddd427d8457d7e2611f0580",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dl Completed...:   0%|          | 0/5 [00:00<?, ? file/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\n"
     ]
    }
   ],
   "source": [
    "# Load the mnist dataset.\n",
    "train_ds, test_ds = tfds.load(\n",
    "    \"mnist\",\n",
    "    split=[\"train\", \"test\"],\n",
    "    shuffle_files=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s3Uv9goa-Stw"
   },
   "source": [
    "Let's preprocess our data with the following steps:\n",
    "- Scale each pixel's value to `[0, 1)`.\n",
    "- Batch the dataset.\n",
    "- Use `prefetch` to speed up the training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "7iaIqGWPRoZK"
   },
   "outputs": [],
   "source": [
    "def preprocess_fn(data):\n",
    "    image = tf.cast(data[\"image\"], tf.float32) / 255\n",
    "    label = data[\"label\"]\n",
    "    return (image, label)\n",
    "\n",
    "\n",
    "train_ds = train_ds.map(preprocess_fn).batch(128).prefetch(tf.data.AUTOTUNE)\n",
    "test_ds = test_ds.map(preprocess_fn).batch(128).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aO7CTCmO-7Ee"
   },
   "source": [
    "## Define the Model\n",
    "\n",
    "Let's define a convolutional neural network as our classifier. We can use `keras.Sequential` to stack up the layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "-la8_gKPM3LQ"
   },
   "outputs": [],
   "source": [
    "input_shape = (28, 28, 1)\n",
    "num_classes = 10\n",
    "\n",
    "model = keras.Sequential(\n",
    "    [\n",
    "        keras.Input(shape=input_shape),\n",
    "        keras.layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
    "        keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        keras.layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
    "        keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        keras.layers.Flatten(),\n",
    "        keras.layers.Dropout(0.5),\n",
    "        keras.layers.Dense(num_classes, activation=\"softmax\"),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WsByR1pF_GoQ"
   },
   "source": [
    "Set training-related configs, optimizers, loss function, metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "4IEPlHw6PvEm"
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss=keras.losses.SparseCategoricalCrossentropy(),\n",
    "    optimizer=keras.optimizers.Adam(0.001),\n",
    "    metrics=[keras.metrics.SparseCategoricalAccuracy()],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MS53qvIy_R8g"
   },
   "source": [
    "## Set up tracking/visualization tool\n",
    "\n",
    "In this tutorial, we will use Databricks CE as MLflow tracking server. For other options such as using your local MLflow server, please read the [Tracking Server Overview](https://mlflow.org/docs/latest/getting-started/tracking-server-overview/index.html).\n",
    "\n",
    "If you have not, please register an account of [Databricks community edition](https://www.databricks.com/try-databricks#account). It should take no longer than 1min to register. Databricks CE (community edition) is a free platform for users to try out Databricks features. For this guide, we need the ML experiment dashboard for us to track our training progress.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FRrSfJz8_Uj6"
   },
   "source": [
    "After successfully registering an account on Databricks CE, let's connnect MLflow to Databricks CE. You will need to enter following information:\n",
    "- **Databricks Host**: https://community.cloud.databricks.com/\n",
    "- **Username**: your signed up email\n",
    "- **Password**: your password"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TJX1dnW5aUJR",
    "outputId": "9e97d75e-c6ca-42f8-f1b5-1db4a28a3db9"
   },
   "outputs": [],
   "source": [
    "import mlflow\n",
    "\n",
    "mlflow.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I7PCn16y_bbH"
   },
   "source": [
    "Now this colab is connected to the hosted tracking server. Let's configure MLflow metadata. Two things to set up:\n",
    "- `mlflow.set_tracking_uri`: always use \"databricks\".\n",
    "- `mlflow.set_experiment`: pick up a name you like, start with `/`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j_QbNd9v_eyz"
   },
   "source": [
    "## Logging with MLflow\n",
    "\n",
    "There are two ways you can log to MLflow from your Tensorflow pipeline:\n",
    "- MLflow auto logging.\n",
    "- Use a callback.\n",
    "\n",
    "Auto logging is simple to configure, but gives you less control. Using a callback is more flexible. Let's see how each way is done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Jy-qVMNF24h"
   },
   "source": [
    "### MLflow Auto Logging\n",
    "\n",
    "All you need to do is to call `mlflow.tensorflow.autolog()` before kicking off the training, then the backend will automatically log the metrics into the server you configured earlier. In our case, Databricks CE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 327,
     "referenced_widgets": [
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      "f45b994ed77d461cbfcebc4aa67e61a7"
     ]
    },
    "id": "tY-xWJOIQArU",
    "outputId": "a8a0ae06-1e2a-4265-bfa2-eb9c12a8c868"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023/11/15 01:53:35 INFO mlflow.utils.autologging_utils: Created MLflow autologging run with ID '7c1db53e417b43f0a1d9e095c9943acb', which will track hyperparameters, performance metrics, model artifacts, and lineage information for the current tensorflow workflow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "469/469 [==============================] - 13s 7ms/step - loss: 0.3610 - sparse_categorical_accuracy: 0.8890\n",
      "Epoch 2/3\n",
      "469/469 [==============================] - 3s 6ms/step - loss: 0.1035 - sparse_categorical_accuracy: 0.9681\n",
      "Epoch 3/3\n",
      "469/469 [==============================] - 4s 8ms/step - loss: 0.0798 - sparse_categorical_accuracy: 0.9760\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023/11/15 01:54:05 WARNING mlflow.tensorflow: Failed to infer model signature: could not sample data to infer model signature: tuple index out of range\n",
      "2023/11/15 01:54:05 WARNING mlflow.models.model: Model logged without a signature. Signatures will be required for upcoming model registry features as they validate model inputs and denote the expected schema of model outputs. Please visit https://www.mlflow.org/docs/2.8.1/models.html#set-signature-on-logged-model for instructions on setting a model signature on your logged model.\n",
      "2023/11/15 01:54:05 WARNING mlflow.tensorflow: You are saving a TensorFlow Core model or Keras model without a signature. Inference with mlflow.pyfunc.spark_udf() will not work unless the model's pyfunc representation accepts pandas DataFrames as inference inputs.\n",
      "2023/11/15 01:54:13 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/usr/local/lib/python3.10/dist-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.\"\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de4eb6d3e7e144e1bc11b1aacccbba71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading artifacts:   0%|          | 0/11 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023/11/15 01:54:13 INFO mlflow.store.artifact.cloud_artifact_repo: The progress bar can be disabled by setting the environment variable MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR to false\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6f8902f7a7f443a817cd109eab67918",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7d48e6556b60>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose any name that you like.\n",
    "mlflow.set_experiment(\"/mlflow-tf-keras-mnist\")\n",
    "\n",
    "mlflow.tensorflow.autolog()\n",
    "\n",
    "model.fit(x=train_ds, epochs=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AIVCW6gP3d4d"
   },
   "source": [
    "While your training is ongoing, you can find this training in your dashboard. Log in to your [Databricks CE](https://community.cloud.databricks.com/) account, and click on top left to select machine learning in the drop down list. Then click on the experiment icon. See the screenshot below:\n",
    "![landing page](https://i.imgur.com/eQgnAcI.png)\n",
    "\n",
    "After clicking the `Experiment` button, it will bring you to the experiment page, where you can find your runs. Clicking on the most recent experiment and run, you can find your metrics there, similar to:\n",
    "![experiment page](https://i.imgur.com/uuHLttD.png)\n",
    "\n",
    "\n",
    "You can click on metrics to see the chart."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X95jT-XWGO7v"
   },
   "source": [
    "Let's evaluate the training result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m2mXAq34SbLv",
    "outputId": "6879d2d1-f49a-4037-f179-c3d10e27c4a1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 1s 12ms/step - loss: 0.0484 - sparse_categorical_accuracy: 0.9838\n",
      "Test loss: 0.05\n",
      "Test accuracy:  0.98\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(test_ds)\n",
    "\n",
    "print(f\"Test loss: {score[0]:.4f}\")\n",
    "print(f\"Test accuracy: {score[1]: .2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1g30CHoDGcry"
   },
   "source": [
    "### Log with MLflow Callback\n",
    "\n",
    "Auto logging is powerful and convenient, but if you are looking for a more native way as Tensorflow pipelines, you can use `mlflow.tensorflow.MllflowCallback` inside `model.fit()`, it will log:\n",
    "- Your model configuration, layers, hyperparameters and so on.\n",
    "- The training stats, including losses and metrics configured with `model.compile()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fbZ0S53GzkJI",
    "outputId": "726f4b18-8bca-4869-b89f-044cdc2647ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "469/469 [==============================] - 5s 10ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9851\n",
      "Epoch 2/2\n",
      "469/469 [==============================] - 4s 8ms/step - loss: 0.0432 - sparse_categorical_accuracy: 0.9866\n"
     ]
    }
   ],
   "source": [
    "from mlflow.tensorflow import MlflowCallback\n",
    "\n",
    "# Turn off autologging.\n",
    "mlflow.tensorflow.autolog(disable=True)\n",
    "\n",
    "with mlflow.start_run() as run:\n",
    "    model.fit(\n",
    "        x=train_ds,\n",
    "        epochs=2,\n",
    "        callbacks=[MlflowCallback(run)],\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WaU6fUiE5nJT"
   },
   "source": [
    "Going to the Databricks CE experiment view, you will see a similar dashboard as before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NIMvwl_o1oYk"
   },
   "source": [
    "### Customize the MLflow Callback\n",
    "\n",
    "If you want to add extra logging logic, you can customize the MLflow callback. You can either subclass from `keras.callbacks.Callback` and write everything from scratch or subclass from `mlflow.tensorflow.MllflowCallback` to add you custom logging logic.\n",
    "\n",
    "Let's look at an example that we want to replace the loss with its log value to log to MLflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "ugjFO4gZZLmW"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "\n",
    "# Create our own callback by subclassing `MlflowCallback`.\n",
    "class MlflowCustomCallback(MlflowCallback):\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        if not self.log_every_epoch:\n",
    "            return\n",
    "        loss = logs[\"loss\"]\n",
    "        logs[\"log_loss\"] = math.log(loss)\n",
    "        del logs[\"loss\"]\n",
    "        self.metrics_logger.record_metrics(logs, epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pT63tsyb3FVm"
   },
   "source": [
    "Train the model with the new callback."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "43EbF19S70Nl",
    "outputId": "2921634d-310d-40f0-ee46-0c494851f385"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "469/469 [==============================] - 5s 10ms/step - loss: 0.0537 - sparse_categorical_accuracy: 0.9834 - log_loss: -2.9237\n",
      "Epoch 2/2\n",
      "469/469 [==============================] - 4s 9ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9846 - log_loss: -3.0022\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023/11/15 01:57:50 WARNING mlflow.utils.autologging_utils: Encountered unexpected error during tensorflow autologging: MLflow autologging must be turned off if an `MllflowCallback` is explicitly added to the callback list. You are creating an `MllflowCallback` while having autologging enabled. Please either call `mlflow.tensorflow.autolog(disable=True)` to disable autologging or remove `MllflowCallback` from the callback list. \n"
     ]
    }
   ],
   "source": [
    "with mlflow.start_run() as run:\n",
    "    run_id = run.info.run_id\n",
    "    model.fit(\n",
    "        x=train_ds,\n",
    "        epochs=2,\n",
    "        callbacks=[MlflowCustomCallback(run)],\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7PVnD52Z4d-f"
   },
   "source": [
    "Going to your Databricks CE page, you should find the `log_loss` is replacing the `loss` metric, similar to what is shown in the screenshot below.\n",
    "\n",
    "![log loss screenshot](https://i.imgur.com/dncAwaP.png)"
   ]
  },
  {
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