From 8db20ba017a56ee080329c30818f4559b60d7107 Mon Sep 17 00:00:00 2001 From: Alexandra Teste Date: Tue, 19 Mar 2019 18:25:50 -0700 Subject: [PATCH 01/20] First deployment notebook --- ...rvice_with_Azure_Container_Instances.ipynb | 1456 +++++++++++++++++ ..._service_with_Azure_Container_Instances.py | 542 ++++++ .../notebooks/deployment/myenv.yml | 25 + .../notebooks/deployment/score.py | 36 + .../deployment/screenshots/80_driver_log.jpg | Bin 0 -> 233198 bytes .../deployment/screenshots/ACI_diagram.png | Bin 0 -> 33330 bytes .../screenshots/AMLCompute_GPU_idel.jpg | Bin 0 -> 126531 bytes .../screenshots/AMLCompute_GPU_nodes.jpg | Bin 0 -> 85062 bytes .../screenshots/AMLCompute_GPU_running.jpg | Bin 0 -> 130749 bytes .../screenshots/aks_compute_target.jpg | Bin 0 -> 63853 bytes .../deployment/screenshots/aks_webservice.jpg | Bin 0 -> 53177 bytes .../screenshots/all_requests_line_chart.jpg | Bin 0 -> 127541 bytes .../application_insights_all_charts.jpg | Bin 0 -> 120526 bytes 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create mode 100644 image_classification/utils_ic/image_conversion.py create mode 100644 image_classification/utils_ic/windows_create_cnn.py diff --git a/image_classification/notebooks/deployment/1_Model_deployment_as_a_service_with_Azure_Container_Instances.ipynb b/image_classification/notebooks/deployment/1_Model_deployment_as_a_service_with_Azure_Container_Instances.ipynb new file mode 100644 index 0000000..6bc24cf --- /dev/null +++ b/image_classification/notebooks/deployment/1_Model_deployment_as_a_service_with_Azure_Container_Instances.ipynb @@ -0,0 +1,1456 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deployment of a model as a service with Azure Container Instances\n", + "\n", + "At the end of this notebook, we will have learned how to:\n", + "- Deploy this model as a web service using an [Azure Container Instance](https://azure.microsoft.com/en-us/services/container-instances/)\n", + "- Test that the service works well when called from the notebook\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pre-requisite\n", + "\n", + "For this notebook to run properly on your machine, the following should already be in place:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Local machine setup\n", + "- Miniconda virtual environment - Please follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python), if you do not have one yet\n", + "- Python 3.6 (Tensorboard and some Azure elements are not supported with Python 3.7)\n", + "- Azure SDK: [Steps](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python#install-the-sdk-1) to follow to install it\n", + "- Pytorch: `conda install –c pytorch pytorch torchvision`\n", + "- Fastai: `conda install fastai=1.0.44 -c fastai –c conda-forge`\n", + " - Please follow [these instructions](https://github.com/fastai/fastai/blob/master/README.md#installation), if you encounter any issues with the above" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Azure subscription setup\n", + "You also need an account on the Azure platform. If you do not have one:\n", + "- [Create an account](https://azure.microsoft.com/en-us/free/services/machine-learning/)\n", + "- [Add a subscription](https://ms.portal.azure.com/#blade/Microsoft_Azure_Billing/SubscriptionsBlade) -- _You can start with a free one_\n", + "- [Add a resource group](https://ms.portal.azure.com/#blade/HubsExtension/Resources/resourceType/Microsoft.Resources%2Fsubscriptions%2FresourceGroups)\n", + "\n", + "What is required here is:\n", + "- A subscription ID\n", + "- A resource group" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Library import\n", + "Throughout this notebook, we will be using a variety of libraries. We are listing them here for better readibility." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# For automatic reloading of modified libraries\n", + "%reload_ext autoreload\n", + "%autoreload 2\n", + "\n", + "# Regular python libraries\n", + "from io import BytesIO\n", + "import os\n", + "import requests\n", + "import sys\n", + "from sys import platform\n", + "\n", + "# fast.ai\n", + "from fastai.vision import *\n", + "import torchvision.models as models\n", + "\n", + "# Azure\n", + "import azureml.core\n", + "from azureml.core import Experiment, Workspace\n", + "from azureml.core.conda_dependencies import CondaDependencies \n", + "from azureml.core.image import ContainerImage\n", + "from azureml.core.webservice import AciWebservice, Webservice\n", + "\n", + "# Computer Vision repository\n", + "sys.path.append(\"../../\")\n", + "from utils_ic.datasets import Urls, unzip_url, data_path\n", + "from utils_ic.windows_create_cnn import custom_create_cnn\n", + "from utils_ic.image_conversion import image2json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Azure workspace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SDK version\n", + "\n", + "Before we start, let's check which version of the Azure SDK we are working with." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Azure ML SDK Version: 1.0.17\n" + ] + } + ], + "source": [ + "# Check core SDK version number\n", + "print(\"Azure ML SDK Version: {}\".format(azureml.core.VERSION))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Workspace creation\n", + "Now that we have our environment and proper libraries in place, let's load an existing workspace or create a new one on our Azure account, and save it to a local configuration file (`./aml_config/config.json`).\n", + "\n", + "If it is the first time we create a workspace, or if we are missing our `config.json` file, we need to provide the appropriate:\n", + "- subscription ID\n", + "- resource group\n", + "- location\n", + "- name of the workspace we want to create (e.g. 'myworkspace')." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found the config file in: C:\\Users\\alteste\\Repos\\ComputerVisionBestPractices\\image_classification\\notebooks\\deployment\\aml_config\\config.json\n", + "Workspace configuration succeeded.\n" + ] + } + ], + "source": [ + "# If we do not already have a ./aml_config/config.json file, we need to uncomment and run the 4 lines below\n", + "# subscription_id = \"\"\n", + "# resource_group = \"\"\n", + "# workspace_name = \"\" (e.g. \"myworkspace\")\n", + "# workspace_region = \"\" (e.g. \"westus2\")\n", + "\n", + "try:\n", + " # Load the workspace\n", + " if os.path.exists('./aml_config/config.json'):\n", + " # From a configuration file\n", + " ws = Workspace.from_config()\n", + " else:\n", + " # Or directly from Azure\n", + " ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n", + " # and generate a local configuration file\n", + " ws.write_config()\n", + " print(\"Workspace configuration succeeded.\")\n", + "except:\n", + " # Create a workspace from scratch\n", + " ws = Workspace.create(name=workspace_name,\n", + " subscription_id=subscription_id,\n", + " resource_group=resource_group,\n", + " create_resource_group=True,\n", + " location=workspace_region\n", + " )\n", + " print(\"Workspace {} is not accessible. Change your parameters or create a new workspace.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check that the workspace is properly loaded" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the workspace attributes\n", + "print('Workspace name: ' + ws.name, \n", + " 'Azure region: ' + ws.location, \n", + " 'Subscription id: ' + ws.subscription_id, \n", + " 'Resource group: ' + ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see this workspace on the Azure portal by sequentially clicking on:\n", + "- Resource groups, and clicking the one we referenced above" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- workspace_name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Experiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An experiment contains a series of trials called `Runs`. In the present case, the `run` will consist of the training of our model. Through this `run`'s methods, we will be able to log several metrics such as training and test loss and accuracy, and to tag our run. The full description of the `run` class is available [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py).\n", + "\n", + "Let's first create a new experiment. If an experiment with the same name already exists in our workspace, the run we will generate will be recorded under that already existing experiment." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new/Retrieve an existing experiment\n", + "experiment_name = 'image-classifier-webservice'\n", + "experiment = Experiment(workspace=ws, name=experiment_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New/Existing experiment:\n", + " --> Name: image-classifier-webservice\n", + " --> Workspace name: ws2_tutorials2\n" + ] + } + ], + "source": [ + "print(\"New/Existing experiment:\\n --> Name: {}\\n --> Workspace name: {}\".format(experiment.name, experiment.workspace.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While our `experiment` object is now created, it is not visible on the Azure portal yet. It will become visible once the first run under it gets triggered." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Run initialization\n", + "\n", + "With our new experiment, we can now start a run. `start_logging()` allows us to start an interactive session in our current experiment.\n", + "\n", + "*__Note:__ For those of us who have run several experiments and triggered many runs, we may start seeing a warning message related to a too big snapshot. This is not a problem (WAITING FOR INFO FROM SDK TEAM HERE). If that is the case, we can use the `snapshot_directory=None` argument to ignore this warning.*" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the run\n", + "runner = experiment.start_logging(snapshot_directory=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run e417a3a9-d4bf-4e33-a7f3-29b239851de6 was created\n" + ] + } + ], + "source": [ + "print(\"Run {} was created\".format(runner.id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have launched our run, we can see our experiment on the Azure portal, under `Experiments` (in the left-hand side list).\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From here, we will be able to get a visual representation of metrics tracked during training. For that, let's click on:\n", + "- *image-classifier-webservice*, under `Experiments`\n", + "- the run number at the top of the displayed list\n", + "\n", + "\n", + "\n", + "We are all set from the Azure point of view. Let's now focus on the training of our model, on our local machine." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data retrieval and pre-processing\n", + "\n", + "As our main purpose is to deploy our model as a service, we are going to train our model on the small fridge objects dataset, and keep our training section short. The deployment process would remain the same if we trained on more data, with a different model and for a longer time. Details on input data structure, on model misclassifications investigation, and on visualization are available in the [**01_training_introduction.ipynb**](https://github.com/Microsoft/ComputerVisionBestPractices/blob/staging/image_classification/notebooks/01_training_introduction.ipynb) notebook.\n", + "\n", + "Let's start by setting some parameters and downloading our dataset from an Azure blob storage." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_PATH = unzip_url(Urls.fridge_objects_path, exist_ok=True) # Path to downloaded data\n", + "EPOCHS = 5\n", + "LEARNING_RATE = 1e-4\n", + "IMAGE_SIZE = 299 # size to convert our input data into\n", + "BATCH_SIZE = 16\n", + "ARCHITECTURE = models.resnet50\n", + "# Any model among alexnet, resnet18/34/50/101/152, squeezenet1.0/1.1, densenet121/161/169/201, vgg16/19 should work\n", + "RANDOM_SEED = 456\n", + "VALIDATION_PCT = 0.2 # For train-validation split: 80%/20%\n", + "MODEL_NAME = 'im_classif_fridge_obj' # Name we will give our model both locally and on Azure\n", + "PICKLED_MODEL_NAME = MODEL_NAME + '.pkl'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data are now stored locally in a *data/fridge_objects/* directory." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\alteste\\Repos\\ComputerVisionBestPractices\\image_classification\\data\\fridgeObjects\n" + ] + } + ], + "source": [ + "# Path to our data\n", + "print(DATA_PATH)\n", + "im_path = Path(DATA_PATH)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now ingest them and start training our model." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data = (ImageItemList\n", + " .from_folder(im_path) # Data location: in path and its subfolders\n", + " .random_split_by_pct(valid_pct=VALIDATION_PCT, seed=RANDOM_SEED) # Train/valid split: with percentage of all data\n", + " .label_from_folder() # Folder names contain the labels\n", + " .transform(size=IMAGE_SIZE) # Size reduction of the images\n", + " .databunch(bs=BATCH_SIZE) # Conversion to ImageDataBunch: with custom batch size\n", + " .normalize(imagenet_stats))\n", + " # Normalization: (x - mean)/std with mean and std dev of the ImageNet dataset:\n", + " # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training dataset: 108 samples\n", + "Testing dataset: 26 samples\n", + "Read-in classes: ['can', 'carton', 'milk_bottle', 'water_bottle']\n" + ] + } + ], + "source": [ + "print(\"Training dataset: {} samples\\nTesting dataset: {} samples\\nRead-in classes: {}\"\n", + " .format(len(data.train_ds.x), len(data.valid_ds.x), data.classes))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data.show_batch(rows=2, figsize=(6,6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model training (locally)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We consider here a ResNet50 pre-trained model, with all layers frozen, except the last one. For this, we use fast.ai's [create_cnn](https://docs.fast.ai/vision.learner.html#create_cnn) function.\n", + "\n", + "Note: If you are working on a Windows machine, depending on your environment setup, you may observe that `create_cnn` hangs and never completes. If that is the case, use the `custom_create_cnn()` function instead. It is part of the `utils.py` file that accompanies this notebook. To use it, just run:\n", + "\n", + "`import utils\n", + "learn = custom_create_cnn(data, mdl_object, metrics=error_rate)`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model *resnet50* is using 4096 features\n" + ] + } + ], + "source": [ + "if platform == 'win32':\n", + " learn = custom_create_cnn(data, ARCHITECTURE, metrics=error_rate)\n", + "else:\n", + " learn = create_cnn(data, ARCHITECTURE, metrics=error_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We train our model over 5 epochs and over one [learning rate cycle](https://medium.com/@nachiket.tanksale/finding-good-learning-rate-and-the-one-cycle-policy-7159fe1db5d6). **This may take a few minutes, if trained on CPU.**" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Total time: 08:09

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\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "learn.fit_one_cycle(EPOCHS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we saw in the [01_training_introduction.py](https://github.com/Microsoft/ComputerVisionBestPractices/blob/staging/image_classification/notebooks/01_training_introduction.ipynb) notebook, we can use several fast.ai built-in functions to visualize training and validation metrcis, to identify misclassified images, to build a confusion matrix and to retrieve the worst predictions made by the model.\n", + "\n", + "But since we are within an experiment run, we can also capture some of these metrics on the Azure portal, by \"attaching\" them to our run. `runner.log()` and `runner.log_list()` help us do just that." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "training_losses = [x.numpy().ravel()[0] for x in learn.recorder.losses]\n", + "error_rate = [x[0].numpy().ravel()[0] for x in learn.recorder.metrics]\n", + "\n", + "runner.log_list('training_loss', training_losses)\n", + "runner.log_list('validation_loss', learn.recorder.val_losses)\n", + "runner.log_list('error_rate', error_rate)\n", + "runner.log_list('learning_rate', learn.recorder.lrs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These metrics are now part of the experiment. We can then visualize them on the Azure portal, by clicking on the latest run number or by running the `runner.get_portal_run()` command below.\n", + "\n", + "\n", + "\n", + "Note that the \"learning_rate\" plot indeed shows the variation of this metric over one cycle only." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://mlworkspace.azure.ai/portal/subscriptions/b8c23406-f9b5-4ccb-8a65-a8cb5dcd6a5a/resourceGroups/alteste-rg/providers/Microsoft.MachineLearningServices/workspaces/ws2_tutorials2/experiments/image-classifier-webservice/runs/e417a3a9-d4bf-4e33-a7f3-29b239851de6\n" + ] + } + ], + "source": [ + "print(runner.get_portal_url())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model export\n", + "\n", + "To be able to use the model we just trained, we need to export it to our local machine. We store it in an `outputs/` subfolder." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\alteste\\Repos\\ComputerVisionBestPractices\\image_classification\\notebooks\\deployment\n" + ] + } + ], + "source": [ + "current_directory = os.getcwd()\n", + "print(current_directory)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "output_folder = current_directory+'/outputs/'\n", + "if not os.path.exists(output_folder):\n", + " os.mkdir(output_folder)\n", + "\n", + "learn.export(output_folder + PICKLED_MODEL_NAME)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run termination\n", + "Now, that the model is trained, we can close the run using `runner.complete()`. Results can then be captured and finalized. `runner` returns a URL that allows us to see them on the Azure portal." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "runner.complete()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
ExperimentIdTypeStatusDetails PageDocs Page
image-classifier-webservicee417a3a9-d4bf-4e33-a7f3-29b239851de6CompletedLink to Azure PortalLink to Documentation
" + ], + "text/plain": [ + "Run(Experiment: image-classifier-webservice,\n", + "Id: e417a3a9-d4bf-4e33-a7f3-29b239851de6,\n", + "Type: None,\n", + "Status: Completed)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "runner" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model deployment on Azure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Model registration\n", + "\n", + "Our final goal is to deploy the model we trained as a web service. To do so, we need to first register it in our workspace, i.e. place it in our workspace's model registry. We do this by using `runner.upload_file()` and `runner.register_model()`, which takes:\n", + "- a `model_name` that represents well what our model does\n", + "- and the `model_path` of the model relative to the run.\n", + "\n", + "Other methods exist to load the model and register it all at once, but using `runner.upload_file()` and specifying the `outputs/` folder allow us to check the presence of the uploaded model on the Azure portal." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "runner.upload_file(name='outputs/' + PICKLED_MODEL_NAME, path_or_stream=current_directory+'/outputs/' + PICKLED_MODEL_NAME)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Register the model with the workspace\n", + "model = runner.register_model(model_name=MODEL_NAME, model_path='outputs/' + PICKLED_MODEL_NAME)\n", + "# !!! We need to make sure that the model name we use here is the same as in the scoring script below !!!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the model is uploaded and registered, we can see it on the Azure platform, under `Outputs` and `Models`\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also check that it is programatically accessible" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model:\n", + " --> Name: im_classif_fridge_obj\n", + " --> ID: im_classif_fridge_obj:7\n", + " --> Path:azureml-models\\im_classif_fridge_obj\\7\\im_classif_fridge_obj.pkl\n" + ] + } + ], + "source": [ + "print(\"Model:\\n --> Name: {}\\n --> ID: {}\\n --> Path:{}\"\n", + " .format(model.name, model.id, model._get_model_path_remote(model.name, model.version, ws)))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['outputs/im_classif_fridge_obj.pkl']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "runner.get_file_names()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we are also interested in verifying which model we uploaded, we can download it to our local machine" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'im_classif_fridge_obj.pkl'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.download()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Scoring script\n", + "For the web service to return predictions on a given input image, we need to provide it with instructions on how to use the model we just registered. These instructions are stored in the scoring script.\n", + "\n", + "This script must contain two required functions, `init()` and `run(input_data)`:\n", + "- In the `init()` function, we typically load the model into a global object. This function is executed only once when the Docker container is started.\n", + "- In the `run(input_data)` function, the model is used to predict a value based on the input data. The input and output to `run` typically use JSON as serialization and de-serialization format but we are not limited to that.\n", + "\n", + "This file must also be stored in the current directory." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "scoring_script = \"score.py\"" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting score.py\n" + ] + } + ], + "source": [ + "%%writefile $scoring_script\n", + "# Copyright (c) Microsoft. All rights reserved.\n", + "# Licensed under the MIT license.\n", + "\n", + "from base64 import b64encode, b64decode\n", + "from io import BytesIO\n", + "import json\n", + "import numpy as np\n", + "from azureml.core.model import Model\n", + "from fastai.vision import *\n", + "from fastai.vision import Image as FImage\n", + "\n", + "def init():\n", + " global model\n", + " model_path = Model.get_model_path(model_name='im_classif_fridge_obj')\n", + " # ! We cannot use MODEL_NAME here otherwise the execution on Azure will fail !\n", + " \n", + " actual_path, actual_file = os.path.split(model_path)\n", + " model = load_learner(path=actual_path, fname=actual_file)\n", + "\n", + "\n", + "def run(raw_data):\n", + "\n", + " # Expects raw_data to be a list within a json file\n", + " result = []\n", + " all_data = [b64decode(im) for im in json.loads(raw_data)['data']]\n", + " \n", + " for im_bytes in all_data:\n", + " try:\n", + " im = open_image(BytesIO(im_bytes))\n", + " pred_class, pred_idx, outputs = model.predict(im)\n", + " result_dict = {\"label\": str(pred_class), \"probability\": str(float(outputs[pred_idx]))}\n", + " result.append(result_dict)\n", + " except Exception as e:\n", + " result_dict = {\"label\": str(e), \"probability\": ''}\n", + " result.append(result_dict)\n", + " return result\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Environment setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to make predictions on the Azure platform, it is critical to create an environment similar to the one we used to train the model. For this, we use a [Docker container](https://www.docker.com/resources/what-container). We configure it via a yaml file that will contain all the conda dependencies we relied on on our local machine." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Conda environment specification. The dependencies defined in this file will\n", + "# be automatically provisioned for runs with userManagedDependencies=False.\n", + "\n", + "# Details about the Conda environment file format:\n", + "# https://conda.io/docs/user-guide/tasks/manage-environments.html#create-env-file-manually\n", + "\n", + "name: project_environment\n", + "dependencies:\n", + " # The python interpreter version.\n", + " # Currently Azure ML only supports 3.5.2 and later.\n", + "- python=3.6.2\n", + "\n", + "- pip:\n", + " # Required packages for AzureML execution, history, and data preparation.\n", + " - azureml-defaults\n", + "- scikit-learn\n", + "- pytorch\n", + "- torchvision\n", + "- spacy=2.0.18\n", + "- fastai=1.0.44\n", + "- dataclasses\n", + "channels:\n", + "- pytorch\n", + "- conda-forge\n", + "- fastai\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "'myenv.yml'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create an empty conda environment and add the scikit-learn package\n", + "conda_filename = \"myenv.yml\" # this file can only be save in the current directory\n", + "\n", + "conda_env = CondaDependencies()\n", + "conda_env.add_conda_package(\"scikit-learn\")\n", + "conda_env.add_channel(\"pytorch\")\n", + "conda_env.add_channel(\"conda-forge\")\n", + "conda_env.add_channel(\"fastai\")\n", + "conda_env.add_conda_package(\"pytorch\")\n", + "conda_env.add_conda_package(\"torchvision\")\n", + "conda_env.add_conda_package(\"spacy=2.0.18\")\n", + "conda_env.add_conda_package(\"fastai=1.0.44\")\n", + "conda_env.add_conda_package(\"dataclasses\")\n", + "\n", + "# Display the environment\n", + "print(conda_env.serialize_to_string())\n", + "\n", + "# Write the environment to disk\n", + "conda_env.save_to_file(base_directory='./', conda_file_path=conda_filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are different ways of creating a Docker image on Azure. Here, we create it separately from the service it will be used for. This way of proceeding gives us direct access to the Docker image object. Thus, if the service deployment fails, but the Docker image gets deployed successfully, we can try deploying the service again, without having to create a new image all over again." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Docker image configuration\n", + "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n", + " runtime = \"python\",\n", + " conda_file = \"myenv.yml\",\n", + " description = \"Image with fast.ai Resnet50 model (fastai 1.0.44)\",\n", + " tags = {'area': \"tableware\", 'type': \"CNN resnet50\"}\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating image\n" + ] + } + ], + "source": [ + "# Docker image creation\n", + "docker_image = ContainerImage.create(name = \"image-classif-resnet50-f44\", # this is the model object\n", + " models = [model],\n", + " image_config = image_config,\n", + " workspace = ws)\n", + "# The image name should not contain more than 32 characters, and should not contain any spaces, dots or underscores" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running.................................................................................................................................\n", + "SucceededImage creation operation finished for image image-classif-resnet50-f44:6, operation \"Succeeded\"\n", + "Wall time: 12min 12s\n" + ] + } + ], + "source": [ + "%%time\n", + "docker_image.wait_for_creation(show_output = True) # This can take up to 15 min" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://ws2tutorstoragewedqiecq.blob.core.windows.net/azureml/ImageLogs/a74f75dc-f852-4d08-bad4-8660200b3cee/build.log?sv=2017-04-17&sr=b&sig=3C8lmMK6D4DooS%2FTksBDStIFfbADrkxQXPaovUaPkpY%3D&st=2019-03-19T23%3A53%3A57Z&se=2019-04-18T23%3A58%3A57Z&sp=rl\n" + ] + } + ], + "source": [ + "print(ws.images[\"image-classif-resnet50-f44\"].image_build_log_uri)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. Computational resources" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we use [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) which are good for quick and cost-effective development/test deployment scenarios.\n", + "\n", + "To set them up properly, we need to indicate the number of cores and the amount of memory we want to allocate to our web service. Optional tags and descriptions are also available for us to identify the instances in AzureML when viewing the `Compute` tab in the Azure Portal.\n", + "\n", + "**Note:** For production workloads, it is better to use [Azure Kubernetes Service (AKS)](https://docs.microsoft.com/en-us/azure/aks/) instead. We will demonstrate how to so this in the next notebook (to be published)." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a deployment configuration with 1 CPU and 5 gigabyte of RAM\n", + "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, \n", + " memory_gb=5, \n", + " tags={'webservice': 'image classification model (fastai1.0.44)'}, \n", + " description='This service classifies fridge object images into 4 different groups.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. Web service deployment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final step to deploying our web service is to call `WebService.deploy_from_image()`. This function uses the Docker image and the deployment configuration we created above to perform the following:\n", + "\n", + "- Deploy the docker image to an Azure Container Instance\n", + "- Call the `init()` function in our scoring file\n", + "- Provide an HTTP endpoint for scoring calls\n", + "\n", + "The `deploy_from_image` method requires the following parameters:\n", + "\n", + "- workspace: the workspace containing the service\n", + "- name: a unique named used to identify the service in the workspace\n", + "- image: a docker image object that contains the environment needed for scoring/inference\n", + "- deployment_config: a configuration object describing the compute type\n", + "\n", + "Azure Container Instances have no associated ComputeTarget, so we do not specify any here. Remember, we already provided information on the number of CPUs and the amount of memory needed in the service configuration file above.\n", + "\n", + "> Note: The web service creation can take **several minutes**." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating service\n" + ] + } + ], + "source": [ + "# Creation of the Docker container image and deployment of the web service\n", + "service_name = 'im-classif-websvc'\n", + "service = Webservice.deploy_from_image(workspace=ws,\n", + " name=service_name,\n", + " image=docker_image,\n", + " deployment_config=aci_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An alternative way of deploying the service is to deploy from the model directly. In that case, we would need to provide the docker_image configuration object, not the image object itself.\n", + "The advantage of `deploy_from_image` over `deploy_from_model` is that the former allows us\n", + "to re-use the same Docker image in case the deployment of this service fails, or even for other\n", + "types of deployments, as we will see in the next notebook (to be pushlished)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # # Web service creation from registered model\n", + "# # Image configuration object indicating how our deployment container needs to be created\n", + "# image_config = ContainerImage.image_configuration(execution_script=scoring_script,\n", + "# runtime=\"python\", \n", + "# conda_file=\"myenv.yml\")\n", + "# service = Webservice.deploy_from_model(name=service_name,\n", + "# workspace=ws,\n", + "# image_config=image_config,\n", + "# deployment_config=aci_config,\n", + "# models=[model])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running.........................................\n", + "SucceededACI service creation operation finished, operation \"Succeeded\"\n" + ] + } + ], + "source": [ + "service.wait_for_deployment(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When successful, we expect to see the following:\n", + "\n", + "`Creating image\n", + "Image creation operation finished for image , operation \"Succeeded\"\n", + "Creating service\n", + "Running .....\n", + "SucceededACI service creation operation finished, operation \"Succeeded\"`\n", + "\n", + "In the case where the deployment is not successful, we can look at the image and service logs to debug. [These instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment) can also be helpful." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Access to the service logs\n", + "# print(service.get_logs())" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Service im-classif-websvc is _Healthy_ and available at http://20.42.36.116:80/score\n" + ] + } + ], + "source": [ + "print(\"Service {} is _{}_ and available at {}\".format(service.name, service.state, service.scoring_uri))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also check the presence and status of both our new Docker image and web service on the Azure portal, under the `Images` and `Deployments` tabs, respectively.\n", + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Testing of the service" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make sure that our service is working as expected, let's test it. We first need to retrieve a test image and to pre-process it into the format expected by our model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 6a. Using the `run` API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A service typically expects input data to be in a JSON serializable format, which fast.ai's image objects are not. Here, we use our own `image2json()` function to transform these images into lists of floats and then into strings." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "images_fname_list = [os.path.join('test_images', 'im_11.jpg'), os.path.join('test_images', 'im_97.jpg')]\n", + "test_samples = image2json(images_fname_list, current_directory)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Prediction using the deployed model\n", + "result = service.run(test_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plots of the results\n", + "actual_labels = ['milk_bottle', 'water_bottle']\n", + "for k in range(0, len(result)):\n", + " title = \"{}/{} - {}%\".format(actual_labels[k], result[k]['label'], \n", + " round(100.*float(result[k]['probability']), 2))\n", + " open_image(images_fname_list[k]).show(title=title)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 6b. Via a raw HTTP request" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "POST to url http://20.42.36.116:80/score\n", + "prediction: [{\"label\": \"milk_bottle\", \"probability\": \"0.9993236064910889\"}, {\"label\": \"water_bottle\", \"probability\": \"0.9746909141540527\"}]\n" + ] + } + ], + "source": [ + "# Send the same test data\n", + "headers = {'Content-Type':'application/json'}\n", + "\n", + "resp = requests.post(service.scoring_uri, test_samples, headers=headers)\n", + "\n", + "print(\"POST to url\", service.scoring_uri)\n", + "print(\"prediction:\", resp.text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 6c. Using a user interface" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we discussed above, Azure Container Instances are typically used to develop and test deployments. They are however not suited for production, or calls to the service outside of the run context. For this to be possible, we need to deploy to Azure Kubernetes Service. An example of this will be available in the next notebook (to be published), where we will show how to create a web app with a user interface." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clean up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Service termination\n", + "\n", + "In a real-life scenario, it is likely that the service we created would need to be up and running at all times. However, in the present demonstrative case, and now that we have verified that our service works, we can delete it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "service.delete()" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:light" + }, + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/image_classification/notebooks/deployment/1_Model_deployment_as_a_service_with_Azure_Container_Instances.py b/image_classification/notebooks/deployment/1_Model_deployment_as_a_service_with_Azure_Container_Instances.py new file mode 100644 index 0000000..816846e --- /dev/null +++ b/image_classification/notebooks/deployment/1_Model_deployment_as_a_service_with_Azure_Container_Instances.py @@ -0,0 +1,542 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:light +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.3' +# jupytext_version: 1.0.2 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# ## Deployment of a model as a service with Azure Container Instances +# +# At the end of this notebook, we will have learned how to: +# - Deploy this model as a web service using an [Azure Container Instance](https://azure.microsoft.com/en-us/services/container-instances/) +# - Test that the service works well when called from the notebook +# +# +# + +# ### Pre-requisite +# +# For this notebook to run properly on your machine, the following should already be in place: + +# #### Local machine setup +# - Miniconda virtual environment - Please follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python), if you do not have one yet +# - Python 3.6 (Tensorboard and some Azure elements are not supported with Python 3.7) +# - Azure SDK: [Steps](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python#install-the-sdk-1) to follow to install it +# - Pytorch: `conda install –c pytorch pytorch torchvision` +# - Fastai: `conda install fastai=1.0.44 -c fastai –c conda-forge` +# - Please follow [these instructions](https://github.com/fastai/fastai/blob/master/README.md#installation), if you encounter any issues with the above + +# #### Azure subscription setup +# You also need an account on the Azure platform. If you do not have one: +# - [Create an account](https://azure.microsoft.com/en-us/free/services/machine-learning/) +# - [Add a subscription](https://ms.portal.azure.com/#blade/Microsoft_Azure_Billing/SubscriptionsBlade) -- _You can start with a free one_ +# - [Add a resource group](https://ms.portal.azure.com/#blade/HubsExtension/Resources/resourceType/Microsoft.Resources%2Fsubscriptions%2FresourceGroups) +# +# What is required here is: +# - A subscription ID +# - A resource group + +# ### Library import +# Throughout this notebook, we will be using a variety of libraries. We are listing them here for better readibility. + +# + +# For automatic reloading of modified libraries +# %reload_ext autoreload +# %autoreload 2 + +# Regular python libraries +from io import BytesIO +import os +import requests +import sys +from sys import platform + +# fast.ai +from fastai.vision import * +import torchvision.models as models + +# Azure +import azureml.core +from azureml.core import Experiment, Workspace +from azureml.core.conda_dependencies import CondaDependencies +from azureml.core.image import ContainerImage +from azureml.core.webservice import AciWebservice, Webservice + +# Computer Vision repository +sys.path.append("../../") +from utils_ic.datasets import Urls, unzip_url, data_path +from utils_ic.windows_create_cnn import custom_create_cnn +from utils_ic.image_conversion import image2json +# - + +# ### Azure workspace + +# #### SDK version +# +# Before we start, let's check which version of the Azure SDK we are working with. + +# Check core SDK version number +print("Azure ML SDK Version: {}".format(azureml.core.VERSION)) + +# #### Workspace creation +# Now that we have our environment and proper libraries in place, let's load an existing workspace or create a new one on our Azure account, and save it to a local configuration file (`./aml_config/config.json`). +# +# If it is the first time we create a workspace, or if we are missing our `config.json` file, we need to provide the appropriate: +# - subscription ID +# - resource group +# - location +# - name of the workspace we want to create (e.g. 'myworkspace'). + +# + +# If we do not already have a ./aml_config/config.json file, we need to uncomment and run the 4 lines below +# subscription_id = "" +# resource_group = "" +# workspace_name = "" (e.g. "myworkspace") +# workspace_region = "" (e.g. "westus2") + +try: + # Load the workspace + if os.path.exists('./aml_config/config.json'): + # From a configuration file + ws = Workspace.from_config() + else: + # Or directly from Azure + ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name) + # and generate a local configuration file + ws.write_config() + print("Workspace configuration succeeded.") +except: + # Create a workspace from scratch + ws = Workspace.create(name=workspace_name, + subscription_id=subscription_id, + resource_group=resource_group, + create_resource_group=True, + location=workspace_region + ) + print("Workspace {} is not accessible. Change your parameters or create a new workspace.") +# - + +# Let's check that the workspace is properly loaded + +# Print the workspace attributes +print('Workspace name: ' + ws.name, + 'Azure region: ' + ws.location, + 'Subscription id: ' + ws.subscription_id, + 'Resource group: ' + ws.resource_group, sep = '\n') + +# We can see this workspace on the Azure portal by sequentially clicking on: +# - Resource groups, and clicking the one we referenced above + +# + +# - workspace_name + +# + +# #### Experiment + +# An experiment contains a series of trials called `Runs`. In the present case, the `run` will consist of the training of our model. Through this `run`'s methods, we will be able to log several metrics such as training and test loss and accuracy, and to tag our run. The full description of the `run` class is available [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py). +# +# Let's first create a new experiment. If an experiment with the same name already exists in our workspace, the run we will generate will be recorded under that already existing experiment. + +# Create a new/Retrieve an existing experiment +experiment_name = 'image-classifier-webservice' +experiment = Experiment(workspace=ws, name=experiment_name) + +print("New/Existing experiment:\n --> Name: {}\n --> Workspace name: {}".format(experiment.name, experiment.workspace.name)) + +# While our `experiment` object is now created, it is not visible on the Azure portal yet. It will become visible once the first run under it gets triggered. + +# #### Run initialization +# +# With our new experiment, we can now start a run. `start_logging()` allows us to start an interactive session in our current experiment. +# +# *__Note:__ For those of us who have run several experiments and triggered many runs, we may start seeing a warning message related to a too big snapshot. This is not a problem (WAITING FOR INFO FROM SDK TEAM HERE). If that is the case, we can use the `snapshot_directory=None` argument to ignore this warning.* + +# Initialize the run +runner = experiment.start_logging(snapshot_directory=None) + +print("Run {} was created".format(runner.id)) + +# Now that we have launched our run, we can see our experiment on the Azure portal, under `Experiments` (in the left-hand side list). +# +# + +# From here, we will be able to get a visual representation of metrics tracked during training. For that, let's click on: +# - *image-classifier-webservice*, under `Experiments` +# - the run number at the top of the displayed list +# +# +# +# We are all set from the Azure point of view. Let's now focus on the training of our model, on our local machine. + +# ### Data retrieval and pre-processing +# +# As our main purpose is to deploy our model as a service, we are going to train our model on the small fridge objects dataset, and keep our training section short. The deployment process would remain the same if we trained on more data, with a different model and for a longer time. Details on input data structure, on model misclassifications investigation, and on visualization are available in the [**01_training_introduction.ipynb**](https://github.com/Microsoft/ComputerVisionBestPractices/blob/staging/image_classification/notebooks/01_training_introduction.ipynb) notebook. +# +# Let's start by setting some parameters and downloading our dataset from an Azure blob storage. + +DATA_PATH = unzip_url(Urls.fridge_objects_path, exist_ok=True) # Path to downloaded data +EPOCHS = 5 +LEARNING_RATE = 1e-4 +IMAGE_SIZE = 299 # size to convert our input data into +BATCH_SIZE = 16 +ARCHITECTURE = models.resnet50 +# Any model among alexnet, resnet18/34/50/101/152, squeezenet1.0/1.1, densenet121/161/169/201, vgg16/19 should work +RANDOM_SEED = 456 +VALIDATION_PCT = 0.2 # For train-validation split: 80%/20% +MODEL_NAME = 'im_classif_fridge_obj' # Name we will give our model both locally and on Azure +PICKLED_MODEL_NAME = MODEL_NAME + '.pkl' + +# The data are now stored locally in a *data/fridge_objects/* directory. + +# Path to our data +print(DATA_PATH) +im_path = Path(DATA_PATH) + +# We can now ingest them and start training our model. + +data = (ImageItemList + .from_folder(im_path) # Data location: in path and its subfolders + .random_split_by_pct(valid_pct=VALIDATION_PCT, seed=RANDOM_SEED) # Train/valid split: with percentage of all data + .label_from_folder() # Folder names contain the labels + .transform(size=IMAGE_SIZE) # Size reduction of the images + .databunch(bs=BATCH_SIZE) # Conversion to ImageDataBunch: with custom batch size + .normalize(imagenet_stats)) + # Normalization: (x - mean)/std with mean and std dev of the ImageNet dataset: + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] + +print("Training dataset: {} samples\nTesting dataset: {} samples\nRead-in classes: {}" + .format(len(data.train_ds.x), len(data.valid_ds.x), data.classes)) + +data.show_batch(rows=2, figsize=(6,6)) + +# ### Model training (locally) + +# We consider here a ResNet50 pre-trained model, with all layers frozen, except the last one. For this, we use fast.ai's [create_cnn](https://docs.fast.ai/vision.learner.html#create_cnn) function. +# +# Note: If you are working on a Windows machine, depending on your environment setup, you may observe that `create_cnn` hangs and never completes. If that is the case, use the `custom_create_cnn()` function instead. It is part of the `utils.py` file that accompanies this notebook. To use it, just run: +# +# `import utils +# learn = custom_create_cnn(data, mdl_object, metrics=error_rate)` + +if platform == 'win32': + learn = custom_create_cnn(data, ARCHITECTURE, metrics=error_rate) +else: + learn = create_cnn(data, ARCHITECTURE, metrics=error_rate) + +# We train our model over 5 epochs and over one [learning rate cycle](https://medium.com/@nachiket.tanksale/finding-good-learning-rate-and-the-one-cycle-policy-7159fe1db5d6). **This may take a few minutes, if trained on CPU.** + +learn.fit_one_cycle(EPOCHS) + +# As we saw in the [01_training_introduction.py](https://github.com/Microsoft/ComputerVisionBestPractices/blob/staging/image_classification/notebooks/01_training_introduction.ipynb) notebook, we can use several fast.ai built-in functions to visualize training and validation metrcis, to identify misclassified images, to build a confusion matrix and to retrieve the worst predictions made by the model. +# +# But since we are within an experiment run, we can also capture some of these metrics on the Azure portal, by "attaching" them to our run. `runner.log()` and `runner.log_list()` help us do just that. + +# + +training_losses = [x.numpy().ravel()[0] for x in learn.recorder.losses] +error_rate = [x[0].numpy().ravel()[0] for x in learn.recorder.metrics] + +runner.log_list('training_loss', training_losses) +runner.log_list('validation_loss', learn.recorder.val_losses) +runner.log_list('error_rate', error_rate) +runner.log_list('learning_rate', learn.recorder.lrs) +# - + +# These metrics are now part of the experiment. We can then visualize them on the Azure portal, by clicking on the latest run number or by running the `runner.get_portal_run()` command below. +# +# +# +# Note that the "learning_rate" plot indeed shows the variation of this metric over one cycle only. + +print(runner.get_portal_url()) + +# ### Model export +# +# To be able to use the model we just trained, we need to export it to our local machine. We store it in an `outputs/` subfolder. + +current_directory = os.getcwd() +print(current_directory) + +# + +output_folder = current_directory+'/outputs/' +if not os.path.exists(output_folder): + os.mkdir(output_folder) + +learn.export(output_folder + PICKLED_MODEL_NAME) +# - + +# ### Run termination +# Now, that the model is trained, we can close the run using `runner.complete()`. Results can then be captured and finalized. `runner` returns a URL that allows us to see them on the Azure portal. + +runner.complete() + +runner + +# ### Model deployment on Azure + +# #### 1. Model registration +# +# Our final goal is to deploy the model we trained as a web service. To do so, we need to first register it in our workspace, i.e. place it in our workspace's model registry. We do this by using `runner.upload_file()` and `runner.register_model()`, which takes: +# - a `model_name` that represents well what our model does +# - and the `model_path` of the model relative to the run. +# +# Other methods exist to load the model and register it all at once, but using `runner.upload_file()` and specifying the `outputs/` folder allow us to check the presence of the uploaded model on the Azure portal. + +runner.upload_file(name='outputs/' + PICKLED_MODEL_NAME, path_or_stream=current_directory+'/outputs/' + PICKLED_MODEL_NAME) + +# Register the model with the workspace +model = runner.register_model(model_name=MODEL_NAME, model_path='outputs/' + PICKLED_MODEL_NAME) +# !!! We need to make sure that the model name we use here is the same as in the scoring script below !!! + +# Now that the model is uploaded and registered, we can see it on the Azure platform, under `Outputs` and `Models` +# +# +# + +# We can also check that it is programatically accessible + +print("Model:\n --> Name: {}\n --> ID: {}\n --> Path:{}" + .format(model.name, model.id, model._get_model_path_remote(model.name, model.version, ws))) + +runner.get_file_names() + +# If we are also interested in verifying which model we uploaded, we can download it to our local machine + +model.download() + +# #### 2. Scoring script +# For the web service to return predictions on a given input image, we need to provide it with instructions on how to use the model we just registered. These instructions are stored in the scoring script. +# +# This script must contain two required functions, `init()` and `run(input_data)`: +# - In the `init()` function, we typically load the model into a global object. This function is executed only once when the Docker container is started. +# - In the `run(input_data)` function, the model is used to predict a value based on the input data. The input and output to `run` typically use JSON as serialization and de-serialization format but we are not limited to that. +# +# This file must also be stored in the current directory. + +scoring_script = "score.py" + +# + {"magic_args": "$scoring_script", "language": "writefile"} +# # Copyright (c) Microsoft. All rights reserved. +# # Licensed under the MIT license. +# +# from base64 import b64encode, b64decode +# from io import BytesIO +# import json +# import numpy as np +# from azureml.core.model import Model +# from fastai.vision import * +# from fastai.vision import Image as FImage +# +# def init(): +# global model +# model_path = Model.get_model_path(model_name='im_classif_fridge_obj') +# # ! We cannot use MODEL_NAME here otherwise the execution on Azure will fail ! +# +# actual_path, actual_file = os.path.split(model_path) +# model = load_learner(path=actual_path, fname=actual_file) +# +# +# def run(raw_data): +# +# # Expects raw_data to be a list within a json file +# result = [] +# all_data = [b64decode(im) for im in json.loads(raw_data)['data']] +# +# for im_bytes in all_data: +# try: +# im = open_image(BytesIO(im_bytes)) +# pred_class, pred_idx, outputs = model.predict(im) +# result_dict = {"label": str(pred_class), "probability": str(float(outputs[pred_idx]))} +# result.append(result_dict) +# except Exception as e: +# result_dict = {"label": str(e), "probability": ''} +# result.append(result_dict) +# return result +# +# - + +# #### 3. Environment setup + +# In order to make predictions on the Azure platform, it is critical to create an environment similar to the one we used to train the model. For this, we use a [Docker container](https://www.docker.com/resources/what-container). We configure it via a yaml file that will contain all the conda dependencies we relied on on our local machine. + +# + +# Create an empty conda environment and add the scikit-learn package +conda_filename = "myenv.yml" # this file can only be save in the current directory + +conda_env = CondaDependencies() +conda_env.add_conda_package("scikit-learn") +conda_env.add_channel("pytorch") +conda_env.add_channel("conda-forge") +conda_env.add_channel("fastai") +conda_env.add_conda_package("pytorch") +conda_env.add_conda_package("torchvision") +conda_env.add_conda_package("spacy=2.0.18") +conda_env.add_conda_package("fastai=1.0.44") +conda_env.add_conda_package("dataclasses") + +# Display the environment +print(conda_env.serialize_to_string()) + +# Write the environment to disk +conda_env.save_to_file(base_directory='./', conda_file_path=conda_filename) +# - + +# There are different ways of creating a Docker image on Azure. Here, we create it separately from the service it will be used for. This way of proceeding gives us direct access to the Docker image object. Thus, if the service deployment fails, but the Docker image gets deployed successfully, we can try deploying the service again, without having to create a new image all over again. + +# Docker image configuration +image_config = ContainerImage.image_configuration(execution_script = "score.py", + runtime = "python", + conda_file = "myenv.yml", + description = "Image with fast.ai Resnet50 model (fastai 1.0.44)", + tags = {'area': "tableware", 'type': "CNN resnet50"} + ) + +# Docker image creation +docker_image = ContainerImage.create(name = "image-classif-resnet50-f44", # this is the model object + models = [model], + image_config = image_config, + workspace = ws) +# The image name should not contain more than 32 characters, and should not contain any spaces, dots or underscores + +# %%time +docker_image.wait_for_creation(show_output = True) # This can take up to 15 min + +print(ws.images["image-classif-resnet50-f44"].image_build_log_uri) + +# #### 4. Computational resources + +# In this notebook, we use [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) which are good for quick and cost-effective development/test deployment scenarios. +# +# To set them up properly, we need to indicate the number of cores and the amount of memory we want to allocate to our web service. Optional tags and descriptions are also available for us to identify the instances in AzureML when viewing the `Compute` tab in the Azure Portal. +# +# **Note:** For production workloads, it is better to use [Azure Kubernetes Service (AKS)](https://docs.microsoft.com/en-us/azure/aks/) instead. We will demonstrate how to so this in the next notebook (to be published). + +# Create a deployment configuration with 1 CPU and 5 gigabyte of RAM +aci_config = AciWebservice.deploy_configuration(cpu_cores=1, + memory_gb=5, + tags={'webservice': 'image classification model (fastai1.0.44)'}, + description='This service classifies fridge object images into 4 different groups.') + +# #### 5. Web service deployment + +# The final step to deploying our web service is to call `WebService.deploy_from_image()`. This function uses the Docker image and the deployment configuration we created above to perform the following: +# +# - Deploy the docker image to an Azure Container Instance +# - Call the `init()` function in our scoring file +# - Provide an HTTP endpoint for scoring calls +# +# The `deploy_from_image` method requires the following parameters: +# +# - workspace: the workspace containing the service +# - name: a unique named used to identify the service in the workspace +# - image: a docker image object that contains the environment needed for scoring/inference +# - deployment_config: a configuration object describing the compute type +# +# Azure Container Instances have no associated ComputeTarget, so we do not specify any here. Remember, we already provided information on the number of CPUs and the amount of memory needed in the service configuration file above. +# +# > Note: The web service creation can take **several minutes**. + +# Creation of the Docker container image and deployment of the web service +service_name = 'im-classif-websvc' +service = Webservice.deploy_from_image(workspace=ws, + name=service_name, + image=docker_image, + deployment_config=aci_config) + +# An alternative way of deploying the service is to deploy from the model directly. In that case, we would need to provide the docker_image configuration object, not the image object itself. +# The advantage of `deploy_from_image` over `deploy_from_model` is that the former allows us +# to re-use the same Docker image in case the deployment of this service fails, or even for other +# types of deployments, as we will see in the next notebook (to be pushlished). + +# + +# # # Web service creation from registered model +# # Image configuration object indicating how our deployment container needs to be created +# image_config = ContainerImage.image_configuration(execution_script=scoring_script, +# runtime="python", +# conda_file="myenv.yml") +# service = Webservice.deploy_from_model(name=service_name, +# workspace=ws, +# image_config=image_config, +# deployment_config=aci_config, +# models=[model]) +# - + +service.wait_for_deployment(show_output=True) + +# When successful, we expect to see the following: +# +# `Creating image +# Image creation operation finished for image , operation "Succeeded" +# Creating service +# Running ..... +# SucceededACI service creation operation finished, operation "Succeeded"` +# +# In the case where the deployment is not successful, we can look at the image and service logs to debug. [These instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment) can also be helpful. + +# + +# Access to the service logs +# print(service.get_logs()) +# - + +print("Service {} is _{}_ and available at {}".format(service.name, service.state, service.scoring_uri)) + +# We can also check the presence and status of both our new Docker image and web service on the Azure portal, under the `Images` and `Deployments` tabs, respectively. +# +# +# +# + +# #### 6. Testing of the service + +# To make sure that our service is working as expected, let's test it. We first need to retrieve a test image and to pre-process it into the format expected by our model. + +# ##### 6a. Using the `run` API + +# A service typically expects input data to be in a JSON serializable format, which fast.ai's image objects are not. Here, we use our own `image2json()` function to transform these images into lists of floats and then into strings. + +images_fname_list = [os.path.join('test_images', 'im_11.jpg'), os.path.join('test_images', 'im_97.jpg')] +test_samples = image2json(images_fname_list, current_directory) + +# Prediction using the deployed model +result = service.run(test_samples) + +# Plots of the results +actual_labels = ['milk_bottle', 'water_bottle'] +for k in range(0, len(result)): + title = "{}/{} - {}%".format(actual_labels[k], result[k]['label'], + round(100.*float(result[k]['probability']), 2)) + open_image(images_fname_list[k]).show(title=title) + +# ##### 6b. Via a raw HTTP request + +# + +# Send the same test data +headers = {'Content-Type':'application/json'} + +resp = requests.post(service.scoring_uri, test_samples, headers=headers) + +print("POST to url", service.scoring_uri) +print("prediction:", resp.text) +# - + +# ##### 6c. Using a user interface + +# As we discussed above, Azure Container Instances are typically used to develop and test deployments. They are however not suited for production, or calls to the service outside of the run context. For this to be possible, we need to deploy to Azure Kubernetes Service. An example of this will be available in the next notebook (to be published), where we will show how to create a web app with a user interface. + +# ### Clean up + +# #### Service termination +# +# In a real-life scenario, it is likely that the service we created would need to be up and running at all times. However, in the present demonstrative case, and now that we have verified that our service works, we can delete it. + +service.delete() diff --git a/image_classification/notebooks/deployment/myenv.yml b/image_classification/notebooks/deployment/myenv.yml new file mode 100644 index 0000000..0e87b61 --- /dev/null +++ b/image_classification/notebooks/deployment/myenv.yml @@ -0,0 +1,25 @@ +# Conda environment specification. The dependencies defined in this file will +# be automatically provisioned for runs with userManagedDependencies=False. + +# Details about the Conda environment file format: +# https://conda.io/docs/user-guide/tasks/manage-environments.html#create-env-file-manually + +name: project_environment +dependencies: + # The python interpreter version. + # Currently Azure ML only supports 3.5.2 and later. +- python=3.6.2 + +- pip: + # Required packages for AzureML execution, history, and data preparation. + - azureml-defaults +- scikit-learn +- pytorch +- torchvision +- spacy=2.0.18 +- fastai=1.0.44 +- dataclasses +channels: +- pytorch +- conda-forge +- fastai diff --git a/image_classification/notebooks/deployment/score.py b/image_classification/notebooks/deployment/score.py new file mode 100644 index 0000000..2e86a14 --- /dev/null +++ b/image_classification/notebooks/deployment/score.py @@ -0,0 +1,36 @@ +# Copyright (c) Microsoft. All rights reserved. +# Licensed under the MIT license. + +from base64 import b64encode, b64decode +from io import BytesIO +import json +import numpy as np +from azureml.core.model import Model +from fastai.vision import * +from fastai.vision import Image as FImage + +def init(): + global model + model_path = Model.get_model_path(model_name='im_classif_fridge_obj') + # ! We cannot use MODEL_NAME here otherwise the execution on Azure will fail ! + + actual_path, actual_file = os.path.split(model_path) + model = load_learner(path=actual_path, fname=actual_file) + + +def run(raw_data): + + # Expects raw_data to be a list within a json file + result = [] + all_data = [b64decode(im) for im in json.loads(raw_data)['data']] + + for im_bytes in all_data: + try: + im = open_image(BytesIO(im_bytes)) + pred_class, pred_idx, outputs = model.predict(im) + result_dict = {"label": str(pred_class), "probability": str(float(outputs[pred_idx]))} + result.append(result_dict) + except Exception as e: + result_dict = {"label": str(e), "probability": ''} + result.append(result_dict) + return result diff --git a/image_classification/notebooks/deployment/screenshots/80_driver_log.jpg b/image_classification/notebooks/deployment/screenshots/80_driver_log.jpg new file mode 100644 index 0000000000000000000000000000000000000000..8d869f5aeef5177676379c7a7cfd821491f69017 GIT binary 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