From 179f86261eefc4a8602dd0c0bb92303f7fac3306 Mon Sep 17 00:00:00 2001
From: Nikhil Popli <l6u2h3d9u4z5z9s4@truefoundryworkspace.slack.com>
Date: Mon, 19 Feb 2024 11:23:31 +0530
Subject: [PATCH] complete the notebook

---
 mnist-classifaction/deploy_model.ipynb        | 336 ++++++++++++++++++
 .../deploy_model/fastapi_service.py           |   1 -
 mnist-classifaction/train_model.ipynb         |   2 +-
 3 files changed, 337 insertions(+), 2 deletions(-)
 create mode 100644 mnist-classifaction/deploy_model.ipynb

diff --git a/mnist-classifaction/deploy_model.ipynb b/mnist-classifaction/deploy_model.ipynb
new file mode 100644
index 0000000..fc7c4ec
--- /dev/null
+++ b/mnist-classifaction/deploy_model.ipynb
@@ -0,0 +1,336 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deploy a Model on Truefoundry\n",
+    "In this module we will deploy an already trained model on Truefoundry"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🛠 Setup\n",
+    "To follow along with the notebook, you will have to do the following:\n",
+    "* Install **mlfoundry** and required ML Libraries\n",
+    "* Setup logging\n",
+    "* Select the Workspace in which you want to deploy your application.\n",
+    "* Install the required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -U -q \"mlfoundry==0.10.5\" \"tensorflow==2.15.0\" \"matplotlib==3.8.2\" \"servicefoundry==0.9.28\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "[logging.root.removeHandler(h) for h in logging.root.handlers]\n",
+    "logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(name)s] %(levelname)-8s %(message)s')"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Login into truefoundry\n",
+    "In order to login run the cell below. Host can be found from the Truefoundry UI as shown below like https://app.truefoundry.com\n",
+    "\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mlfoundry login --host <Host name of Truefoundry UI. e.g. https://company.truefoundry.cloud>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the model saved in Truefoundry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_VERSION_FQN = input(\"Enter the Model Version FQN (Can be found in run details of training job)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mlfoundry as mlf\n",
+    "import os\n",
+    "\n",
+    "client = mlf.get_client()\n",
+    "model_version = client.get_model_version_by_fqn(MODEL_VERSION_FQN)\n",
+    "download_path = model_version.download('.', overwrite=True)\n",
+    "\n",
+    "model_file = os.path.join(download_path.model_dir, \"mnist_model.h5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deploy_model.predict import load_model, predict_fn\n",
+    "\n",
+    "model = load_model(model_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Try out a sample inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "test_img = tf.keras.utils.load_img('deploy_model/sample_images/0.jpg', target_size=(28, 28))\n",
+    "img_arr = tf.keras.preprocessing.image.img_to_array(test_img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predict_fn(model,img_arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploying the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inference Scripts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtensorflow\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mtf\u001b[39;49;00m\n",
+      "\n",
+      "\u001b[34mdef\u001b[39;49;00m \u001b[32mload_model\u001b[39;49;00m(model_path):\n",
+      "    \u001b[37m# Load the trained model\u001b[39;49;00m\n",
+      "    model = tf.keras.models.load_model(model_path)\n",
+      "    \u001b[34mreturn\u001b[39;49;00m model\n",
+      "\n",
+      "\u001b[34mdef\u001b[39;49;00m \u001b[32mpredict_fn\u001b[39;49;00m(model, img_arr):\n",
+      "  \u001b[37m# Preprocess the image before passing it to the model\u001b[39;49;00m\n",
+      "  img_arr = tf.expand_dims(img_arr, \u001b[34m0\u001b[39;49;00m)\n",
+      "  img_arr = img_arr[:, :, :, \u001b[34m0\u001b[39;49;00m]  \u001b[37m# Keep only the first channel (grayscale)\u001b[39;49;00m\n",
+      "\n",
+      "  \u001b[37m# Make predictions\u001b[39;49;00m\n",
+      "  predictions = model.predict(img_arr)\n",
+      "  predicted_label = tf.argmax(predictions[\u001b[34m0\u001b[39;49;00m]).numpy()\n",
+      "\n",
+      "  \u001b[34mreturn\u001b[39;49;00m \u001b[36mstr\u001b[39;49;00m(predicted_label)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pygmentize deploy_model/predict.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mpredict\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m predict_fn, load_model\n",
+      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m\n",
+      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mgradio\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mgr\u001b[39;49;00m\n",
+      "\n",
+      "model_path = os.path.join(\n",
+      "    os.environ.get(\u001b[33m\"\u001b[39;49;00m\u001b[33mMODEL_DOWNLOAD_PATH\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33m.\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m), \u001b[33m\"\u001b[39;49;00m\u001b[33mmnist_model.h5\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\n",
+      ")\n",
+      "model = load_model(model_path)\n",
+      "\n",
+      "\u001b[34mdef\u001b[39;49;00m \u001b[32mget_inference\u001b[39;49;00m(img_arr):\n",
+      "    \u001b[34mreturn\u001b[39;49;00m predict_fn(model, img_arr)\n",
+      "\n",
+      "interface = gr.Interface(\n",
+      "    fn=get_inference,\n",
+      "    inputs=\u001b[33m\"\u001b[39;49;00m\u001b[33mimage\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\n",
+      "    outputs=\u001b[33m\"\u001b[39;49;00m\u001b[33mlabel\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\n",
+      "    examples=[[\u001b[33m\"\u001b[39;49;00m\u001b[33msample_images/0.jpg\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m], [\u001b[33m\"\u001b[39;49;00m\u001b[33msample_images/1.jpg\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]]\n",
+      ")\n",
+      "\n",
+      "interface.launch(server_name=\u001b[33m\"\u001b[39;49;00m\u001b[33m0.0.0.0\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, server_port=\u001b[34m8000\u001b[39;49;00m)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pygmentize deploy_model/gradio_demo.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy on Truefoundry as Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36margparse\u001b[39;49;00m\n",
+      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mlogging\u001b[39;49;00m\n",
+      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mservicefoundry\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m (\n",
+      "    Build,\n",
+      "    PythonBuild,\n",
+      "    Service,\n",
+      "    Resources,\n",
+      "    Port,\n",
+      "    ArtifactsDownload,\n",
+      "    TruefoundryArtifactSource,\n",
+      ")\n",
+      "\n",
+      "logging.basicConfig(level=logging.INFO)\n",
+      "\n",
+      "parser = argparse.ArgumentParser()\n",
+      "parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--workspace_fqn\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, required=\u001b[34mTrue\u001b[39;49;00m)\n",
+      "parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--model_version_fqn\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, required=\u001b[34mTrue\u001b[39;49;00m)\n",
+      "parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--host\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, required=\u001b[34mTrue\u001b[39;49;00m)\n",
+      "parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--path\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, required=\u001b[34mFalse\u001b[39;49;00m)\n",
+      "args = parser.parse_args()\n",
+      "\n",
+      "service = Service(\n",
+      "    name=\u001b[33m\"\u001b[39;49;00m\u001b[33mmnist-classification-svc\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\n",
+      "    image=Build(\n",
+      "        build_spec=PythonBuild(\n",
+      "            command=\u001b[33m\"\u001b[39;49;00m\u001b[33mpython gradio_demo.py\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\n",
+      "            requirements_path=\u001b[33m\"\u001b[39;49;00m\u001b[33mrequirements.txt\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\n",
+      "        )\n",
+      "    ),\n",
+      "    ports=[Port(port=\u001b[34m8000\u001b[39;49;00m, host=args.host, path=args.path)],\n",
+      "    resources=Resources(\n",
+      "        memory_limit=\u001b[34m500\u001b[39;49;00m,\n",
+      "        memory_request=\u001b[34m500\u001b[39;49;00m,\n",
+      "        ephemeral_storage_limit=\u001b[34m600\u001b[39;49;00m,\n",
+      "        ephemeral_storage_request=\u001b[34m600\u001b[39;49;00m,\n",
+      "        cpu_limit=\u001b[34m0.3\u001b[39;49;00m,\n",
+      "        cpu_request=\u001b[34m0.3\u001b[39;49;00m,\n",
+      "    ),\n",
+      "    artifacts_download=ArtifactsDownload(\n",
+      "        artifacts=[\n",
+      "            TruefoundryArtifactSource(\n",
+      "                artifact_version_fqn=args.model_version_fqn,\n",
+      "                download_path_env_variable=\u001b[33m\"\u001b[39;49;00m\u001b[33mMODEL_DOWNLOAD_PATH\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\n",
+      "            )\n",
+      "        ]\n",
+      "    ),\n",
+      ")\n",
+      "service.deploy(workspace_fqn=args.workspace_fqn)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pygmentize deploy_model/deploy.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd deploy_model/ && python deploy.py --workspace_fqn <paste your workspace fqn> --model_version_fqn MODEL_VERSION_FQN --host <Host name of Truefoundry UI. e.g. endpoint.xyz.cloud> --path <path where you want your service to be deployed, like /gradio/app> "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "housing",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/mnist-classifaction/deploy_model/fastapi_service.py b/mnist-classifaction/deploy_model/fastapi_service.py
index bcac4a1..26bb952 100644
--- a/mnist-classifaction/deploy_model/fastapi_service.py
+++ b/mnist-classifaction/deploy_model/fastapi_service.py
@@ -18,7 +18,6 @@ def load_image(img_url):
     img_path = tf.keras.utils.get_file("image.jpg", img_url)
     img = tf.keras.preprocessing.image.load_img(img_path, target_size=(28, 28))
     img_arr = tf.keras.preprocessing.image.img_to_array(img)
-    img_arr = tf.image.rgb_to_grayscale(img_arr) / 255.0
     return img_arr 
 
 
diff --git a/mnist-classifaction/train_model.ipynb b/mnist-classifaction/train_model.ipynb
index 3092090..ceb34b2 100644
--- a/mnist-classifaction/train_model.ipynb
+++ b/mnist-classifaction/train_model.ipynb
@@ -6,7 +6,7 @@
     "id": "QOAnoPl-dlSY"
    },
    "source": [
-    "# Train and Deploy Model on Truefoundry\n",
+    "# Train Model on Truefoundry\n",
     "This notebook demonstrates a demo on how you can train an image classification model on mnist dataset and deploy the model training job on truefoundry platform."
    ]
   },