diff --git a/4.1.Deploy the model (optimized)/conda_dep_opti.yml b/4.1.Deploy the model (optimized)/conda_dep_opti.yml new file mode 100644 index 0000000..45d9e09 --- /dev/null +++ b/4.1.Deploy the model (optimized)/conda_dep_opti.yml @@ -0,0 +1,15 @@ +channels: + - anaconda + - defaults +dependencies: + - pip: + - azureml-defaults + - azure-ml-api-sdk + - torchxrayvision + - pydicom + - openvino-dev + - torch==1.13.1+cpu + - torchvision==0.14.1+cpu + - intel_extension_for_pytorch==1.13.100 + - "--index-url https://pypi.org/simple/" + - "--extra-index-url https://download.pytorch.org/whl/cpu" diff --git a/4.1.Deploy the model (optimized)/deploy-opti-sdk-v1.ipynb b/4.1.Deploy the model (optimized)/deploy-opti-sdk-v1.ipynb new file mode 100644 index 0000000..b71b034 --- /dev/null +++ b/4.1.Deploy the model (optimized)/deploy-opti-sdk-v1.ipynb @@ -0,0 +1,750 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Deploy the model as a web service hosted on Azure Container Instances (ACI). \n", + "\n", + "1. Create the scoring script.\n", + "1. Prepare an inference configuration.\n", + "1. Deploy the previously trained model to the cloud.\n", + "1. Consume data sample and test the web service." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Create the scoring script.\n", + "\n", + "Create the scoring script, called score.py, used by the web service call to show how to use the model. \n", + "You must include two required functions into the scoring script:\n", + "* The `init()` function, which typically loads the model into a global object. \n", + " * This function is run only once when the Docker container is started. \n", + "* The `run(input_data)` function uses the model to predict a value based on the input data. \n", + " * Inputs and outputs to the run typically use JSON for serialization and de-serialization, but other formats are supported.\n", + "\n", + "TIP: Documentation on Deploy a model to Azure Container Instances [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-container-instance/). Advanced entry script authoring [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-advanced-entry-script#binary-data/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score_opti.py\n", + "from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n", + "from azureml.contrib.services.aml_response import AMLResponse\n", + "import json, os, io\n", + "import numpy as np\n", + "import torch\n", + "import intel_extension_for_pytorch as ipex\n", + "import torchxrayvision as xrv\n", + "from torchvision import transforms\n", + "from torchxrayvision.datasets import normalize\n", + "import pydicom\n", + "\n", + "import time\n", + "from openvino.runtime import Core\n", + "from openvino.runtime import get_version\n", + "\n", + "def init():\n", + " global bench_time\n", + " bench_time = 10 # benchmark time in sec\n", + " global target_device\n", + " target_device = \"CPU\"\n", + "\n", + " # Initial PyTorch model\n", + " global modelx\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'az-register-models', 'pc-densenet-densenet-best.pt')\n", + " # print(model_path)\n", + " modelx = torch.load(model_path)\n", + " modelx.eval()\n", + "\n", + " # Initial PyTorch IPEX model\n", + " global ipex_modelx\n", + " global traced_model\n", + " ipex_modelx = ipex.optimize(modelx)\n", + "\n", + " # Initialize OpenVINO Runtime.\n", + " global ov_compiled_model\n", + " ie = Core()\n", + " ov_xml = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'az-register-models', 'pc-densenet-densenet-best.onnx')\n", + " # Load and compile the OV model\n", + " ov_model = ie.read_model(ov_xml)\n", + " ov_compiled_model = ie.compile_model(model=ov_model, device_name=target_device)\n", + "\n", + "\n", + "\n", + "# TIP: To accept raw data, use the AMLRequest class in your entry script and add the @rawhttp decorator to the run() function\n", + "# more details in: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-advanced-entry-script\n", + "# Note that despite the fact that we trained our model on PNGs, we would like to simulate\n", + "# a scenario closer to the real world here and accept DICOMs into our score script. Here's how:\n", + "@rawhttp\n", + "def run(request):\n", + "\n", + " if request.method == 'GET':\n", + " # For this example, just return the URL for GETs.\n", + " respBody = str.encode(request.full_path)\n", + " return AMLResponse(respBody, 200)\n", + "\n", + " elif request.method == 'POST':\n", + " # For a real-world solution, you would load the data from reqBody\n", + " # and send it to the model. Then return the response.\n", + " try:\n", + "\n", + " # For labels definition see file: '3.Build a model/trainingscripts/padchest_config.py'\n", + " pathologies_labels = ['Air Trapping', 'Aortic Atheromatosis', 'Aortic Elongation', 'Atelectasis',\n", + " 'Bronchiectasis', 'Cardiomegaly', 'Consolidation', 'Costophrenic Angle Blunting', 'Edema', 'Effusion',\n", + " 'Emphysema', 'Fibrosis', 'Flattened Diaphragm', 'Fracture', 'Granuloma', 'Hemidiaphragm Elevation',\n", + " 'Hernia', 'Hilar Enlargement', 'Infiltration', 'Mass', 'Nodule', 'Pleural_Thickening',\n", + " 'Pneumonia', 'Pneumothorax', 'Scoliosis', 'Tuberculosis']\n", + " def benchmark_pt(test_image):\n", + " latency_arr = []\n", + " end = time.time() + int(bench_time)\n", + "\n", + " print(f\"\\n==== Benchmarking PyTorch inference with Fake Data for {bench_time}sec on CPU ====\")\n", + " print(f\"Input shape: {test_image.shape}\")\n", + "\n", + " while time.time() < end:\n", + " start_time = time.time()\n", + " pt_result = modelx(test_image)\n", + " latency = time.time() - start_time\n", + " latency_arr.append(latency)\n", + "\n", + " # Process output\n", + " index = np.argsort( pt_result.data.cpu().numpy() )\n", + " probability = torch.nn.functional.softmax(pt_result[0], dim=0).data.cpu().numpy()\n", + " pt_result = get_top_predictions(index, probability)\n", + "\n", + " avg_latency = np.array(latency_arr).mean()\n", + " fps = 1 / avg_latency\n", + "\n", + " print(f\"PyTorch Avg Latency: {avg_latency:.4f} sec, FPS: {fps:.2f}\")\n", + "\n", + " #Return the result\n", + " pt_summary = {\n", + " \"fwk_version\": f\"PyTorch: {torch.__version__}\",\n", + " \"pt_result\": pt_result,\n", + " \"avg_latency\": avg_latency,\n", + " \"fps\": fps\n", + " }\n", + " return pt_summary\n", + "\n", + " def benchmark_ipex(test_image):\n", + " latency_arr = []\n", + " end = time.time() + int(bench_time)\n", + "\n", + " with torch.no_grad():\n", + " traced_model = torch.jit.trace(ipex_modelx, test_image)\n", + " traced_model = torch.jit.freeze(traced_model)\n", + "\n", + " print(f\"\\n==== Benchmarking IPEX inference with Fake Data for {bench_time}sec on CPU ====\")\n", + " print(f\"Input shape: {test_image.shape}\")\n", + "\n", + " while time.time() < end:\n", + " start_time = time.time()\n", + " with torch.no_grad():\n", + " ipex_result = traced_model(test_image)\n", + " latency = time.time() - start_time\n", + " latency_arr.append(latency)\n", + "\n", + " # Process output\n", + " index = np.argsort( ipex_result.data.cpu().numpy() )\n", + " probability = torch.nn.functional.softmax(ipex_result[0], dim=0).data.cpu().numpy()\n", + " ipex_result = get_top_predictions(index, probability)\n", + "\n", + " avg_latency = np.array(latency_arr).mean()\n", + " fps = 1 / avg_latency\n", + "\n", + " print(f\"PyTorch Avg Latency: {avg_latency:.4f} sec, FPS: {fps:.2f}\")\n", + "\n", + " #Return the result\n", + " ipex_summary = {\n", + " \"fwk_version\": f\"IPEX: {ipex.__version__}\",\n", + " \"ipex_result\": ipex_result,\n", + " \"avg_latency\": avg_latency,\n", + " \"fps\": fps\n", + " }\n", + " return ipex_summary\n", + "\n", + " def benchmark_ov(test_image):\n", + " # get the names of input and output layers of the model\n", + " input_layer = ov_compiled_model.input(0)\n", + " output_layer =ov_compiled_model.output(0)\n", + "\n", + " latency_arr = []\n", + " end = time.time() + int(bench_time)\n", + " print(f\"\\n==== Benchmarking OpenVINO {bench_time}sec on {target_device} ====\")\n", + " print(f\"Input shape: {test_image.shape}\")\n", + "\n", + " while time.time() < end:\n", + " start_time = time.time()\n", + " ov_output = ov_compiled_model([test_image])\n", + " latency = time.time() - start_time\n", + " latency_arr.append(latency)\n", + "\n", + " # Process output\n", + " ov_output = ov_output[output_layer]\n", + " index = np.argsort(ov_output)\n", + " probability = torch.nn.functional.softmax(torch.from_numpy(ov_output[0]), dim=0).data.cpu().numpy()\n", + " ov_result = get_top_predictions(index, probability)\n", + "\n", + " avg_latency = np.array(latency_arr).mean()\n", + " fps = 1 / avg_latency\n", + "\n", + " print(f\"OpenVINO Avg Latency: {avg_latency:.4f} sec, FPS: {fps:.2f}\")\n", + "\n", + " ov_summary = {\n", + " \"fwk_version\": f\"OpenVINO: {get_version()}\",\n", + " \"ov_result\": ov_result,\n", + " \"avg_latency\": avg_latency,\n", + " \"fps\": fps\n", + " }\n", + " return ov_summary\n", + "\n", + " # Read DICOM and apply photometric transformations\n", + " def read_and_rescale_image( filepath):\n", + " dcm = pydicom.read_file(filepath)\n", + " image = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept\n", + "\n", + " def window_image(image, wc, ww):\n", + " img_min = wc - ww // 2\n", + " img_max = wc + ww // 2\n", + " image[image < img_min] = img_min\n", + " image[image > img_max] = img_max\n", + " return image\n", + "\n", + " image = window_image(image, dcm.WindowCenter, dcm.WindowWidth)\n", + " # Scales 16bit to [-1024 1024]\n", + " image = normalize(image, maxval=65535, reshape=True)\n", + " return image\n", + "\n", + " # Decode output and get predictions\n", + " def get_top_predictions(index, probability, num_predictions=3):\n", + " # For labels definition see file: '3.Build a model/trainingscripts/padchest_config.py'\n", + " pathologies_labels = ['Air Trapping', 'Aortic Atheromatosis', 'Aortic Elongation', 'Atelectasis',\n", + " 'Bronchiectasis', 'Cardiomegaly', 'Consolidation', 'Costophrenic Angle Blunting', 'Edema', 'Effusion',\n", + " 'Emphysema', 'Fibrosis', 'Flattened Diaphragm', 'Fracture', 'Granuloma', 'Hemidiaphragm Elevation',\n", + " 'Hernia', 'Hilar Enlargement', 'Infiltration', 'Mass', 'Nodule', 'Pleural_Thickening',\n", + " 'Pneumonia', 'Pneumothorax', 'Scoliosis', 'Tuberculosis']\n", + "\n", + " top_labels = []\n", + " top_probs = []\n", + " for i in range(num_predictions):\n", + " top_labels.append(pathologies_labels[index[0][-1-i]])\n", + " top_probs.append(round(probability[index[0][-1-i]] * 100, 2))\n", + "\n", + " result = {\"top_labels\": top_labels, \"top_probabilities\": top_probs}\n", + " return result\n", + "\n", + " # Get System information\n", + " def get_system_info():\n", + " import subprocess\n", + "\n", + "\n", + " # Run lscpu command and capture output\n", + " lscpu_out = subprocess.check_output(['lscpu']).decode('utf-8')\n", + " print(lscpu_out)\n", + " # Run free -g command and capture output\n", + " mem_out = subprocess.check_output(['free', '-g']).decode('utf-8')\n", + " print(mem_out)\n", + " os_out = subprocess.check_output(['cat', '/etc/os-release']).decode('utf-8')\n", + " kernal_out = subprocess.check_output(['uname', '-a']).decode('utf-8')\n", + " pyver_out = subprocess.check_output(['which', 'python']).decode('utf-8')\n", + " os_out = os_out + \" \\n\" + kernal_out + \"\\n\" + pyver_out\n", + " print(os_out)\n", + "\n", + " return_data = {\n", + " \"lscpu_out\": lscpu_out,\n", + " \"mem_out_gb\": mem_out,\n", + " \"os\": os_out\n", + " }\n", + " return return_data\n", + "\n", + " #\n", + " # Start Processing\n", + " #\n", + " file_bytes = request.files[\"image\"]\n", + "\n", + " # Note that user can define this to be any other type of image\n", + " input_image = read_and_rescale_image(file_bytes)\n", + "\n", + " preprocess = transforms.Compose([\n", + " xrv.datasets.XRayCenterCrop(),\n", + " xrv.datasets.XRayResizer(224)\n", + " ])\n", + "\n", + " input_image = preprocess(input_image)\n", + " input_batch = torch.from_numpy( input_image[np.newaxis,...] )\n", + "\n", + " #Benchmark PyTorch\n", + " pt_summary = benchmark_pt(input_batch)\n", + " print(f\"PyTorch Output: {pt_summary}\")\n", + "\n", + " #Benchmark IPEX\n", + " ipex_summary = benchmark_ipex(input_batch)\n", + " print(f\"IPEX Output: {ipex_summary}\")\n", + "\n", + " # Benchmark OpenVINO\n", + " ov_summary = benchmark_ov(input_batch)\n", + " print(f\"OpenVINO Output: {ov_summary}\")\n", + "\n", + " sys_info = get_system_info()\n", + "\n", + " return_data = {\"pt_summary\": pt_summary,\n", + " \"ipex_summary\" : ipex_summary,\n", + " \"ov_summary\": ov_summary,\n", + " \"system_info\": sys_info}\n", + "\n", + " return return_data\n", + "\n", + " except Exception as e:\n", + " result = str(e)\n", + " # return error message back to the client\n", + " return AMLResponse(json.dumps({\"error\": result}), 200)\n", + "\n", + " else:\n", + " return AMLResponse(\"bad request\", 500)\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Prepare an inference configuration.\n", + " * Create an environment object\n", + " * Create inference configuration to deploy the model as a web service using:\n", + " * The scoring file (`score.py`)\n", + " * Use [AMLRequest](https://docs.microsoft.com/en-us/python/api/azureml-contrib-services/azureml.contrib.services.aml_request?view=azure-ml-py) and [AMLResponse](https://docs.microsoft.com/en-us/python/api/azureml-contrib-services/azureml.contrib.services.aml_response.amlresponse?view=azure-ml-py) classes to access RAW data" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create environment object from an environment specification YAML file.\n", + "See Documentation [HERE](https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py#azureml-core-environment-environment-from-conda-specification)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile conda_dep_opti.yml\n", + "channels:\n", + " - anaconda\n", + " - defaults\n", + "dependencies:\n", + " - pip:\n", + " - azureml-defaults\n", + " - azure-ml-api-sdk\n", + " - torchxrayvision\n", + " - pydicom\n", + " - openvino-dev\n", + " - torch==1.13.1+cpu\n", + " - torchvision==0.14.1+cpu\n", + " - intel_extension_for_pytorch==1.13.100\n", + " - \"--index-url https://pypi.org/simple/\"\n", + " - \"--extra-index-url https://download.pytorch.org/whl/cpu\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.environment import Environment\n", + "# # We create a light weight environment for inference \n", + "# # An Environment defines Python packages, environment variables, and Docker settings that are used in machine learning experiments,\n", + "# # including in data preparation, training, and deployment to a web service.\n", + "# # Environment Documentation: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py\n", + "# # Conda dependencies Documentation: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py\n", + "# # Conda YAML Documentation: https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py#azureml-core-environment-environment-from-conda-specification \n", + "\n", + "# # Create environment object from an environment specification YAML file.\n", + "himms_env_yml = Environment.from_conda_specification('himms_env_opti', 'conda_dep_opti.yml')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "import uuid\n", + "from azureml.core.webservice import Webservice\n", + "from azureml.core.model import InferenceConfig\n", + "from azureml.core.webservice import AciWebservice\n", + "from azureml.core.environment import Environment\n", + "from azureml.core import Workspace\n", + "from azureml.core.model import Model\n", + "from azureml.core.environment import CondaDependencies\n", + "\n", + "# Connect to workspace\n", + "from azureml.core import Workspace\n", + "# Load workspace from config file\n", + "# The workspace is the top-level resource for Azure Machine Learning, \n", + "# providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning.\n", + "# Documentation: https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace\n", + "ws = Workspace.from_config(path='../')\n", + "print(\"Workspace:\",ws.name)\n", + "\n", + "# Register model:\n", + "# A model is the result of a Azure Machine learning training Run or some other model training process outside of Azure. \n", + "# Regardless of how the model is produced, it can be registered in a workspace, where it is represented by a name and a version. \n", + "# With the Model class, you can package models for use with Docker and deploy them as a real-time endpoint that can be used for inference requests.\n", + "# Please set the version number accordingly the number of models that you have registered.\n", + "# Documentation: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py\n", + "model = Model(ws, 'padchest-pt-onnx-ov', version=1)\n", + "\n", + "# Set inference and ACI web service:\n", + "# The inference configuration describes how to configure the model to make predictions. \n", + "# It references to the scoring script (entry_script) and is used to locate all the resources required for the deployment. \n", + "# Documentation: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py\n", + "inference_config = InferenceConfig(entry_script=\"score_opti.py\", environment=himms_env_yml)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 3. Deploy in ACI\n", + " Deploy the model as ACI web service. Note that this step may take about 2-5 minutes to complete" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1681426521105 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "# Set AciWebservice:\n", + "# The AciWebservice class represents a machine learning model deployed as a web service endpoint on Azure Container Instances\n", + "# The Inference configuration (inference_config) is an input parameter for Model deployment-related actions\n", + "# Note that we trained using a GPU cluster and we set resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=2) respectively.\n", + "# This will allow us to run inference in CPU and optimize memory. \n", + "# Documentation: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py\n", + "aci_config = AciWebservice.deploy_configuration(\n", + " cpu_cores=2,\n", + " memory_gb=4)\n", + "\n", + "service_name = 'padchest-opti-sdk-v1'\n", + "# Deploy:\n", + "# The model is packaged (using Docker behind the scenes) as a real-time endpoint that is later used for inference requests.\n", + "# Documentation: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py\n", + "service = Model.deploy(workspace=ws, \n", + " name=service_name, \n", + " models=[model], \n", + " inference_config=inference_config, \n", + " deployment_config=aci_config,\n", + " overwrite=True)\n", + "\n", + "service.wait_for_deployment(show_output=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1681327961407 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "# # [Optional] Get deployment service Logs\n", + "# print(service.get_logs())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 4. Consume data sample and test the web service.\n", + "We demonstrate how to consume DICOM images:\n", + "* We trained our model from PNG files with 16 bits pixel depth. \n", + "* To test the web service, we will send a DICOM file (16 bits).\n", + " * We will apply the image normalization implemented in the scoring script.\n", + "\n", + "To try out the model you would need a sample DICOM image. In order to obtain one, we recommend that you use one of the PADCHEST images you trained on and use the provided `png2dcm.py` script to generate a DICOM file out of it. You can also try using your own DICOM!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # Assuming \"sample.png\" file exists. THe following cmd will generate \"sample_dicom.dcm\" file.\n", + "!python png2dcm.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1681425478818 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "import pydicom\n", + "import matplotlib.pylab as plt\n", + "\n", + "# Visualize converted DICOM file from the corresponding PNG file\n", + "test_file = \"./sample_dicom.dcm\"\n", + "dcm = pydicom.read_file(test_file)\n", + "print(dcm)\n", + "plt.imshow(dcm.pixel_array, cmap=plt.cm.bone)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the model is deployed we can get the scoring web service's HTTP endpoint, which accepts REST client calls. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1681425627207 + } + }, + "outputs": [], + "source": [ + "import requests\n", + "from azureml.core.webservice import Webservice\n", + "import numpy as np\n", + "\n", + "# Webservice constructor is used to retrieve a cloud representation of a Webservice\n", + "# object associated with the provided Workspace\n", + "# Documentation: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py\n", + "service = Webservice(name=service_name, workspace=ws)\n", + "\n", + "# Get the web service HTTP endpoint.\n", + "# This endpoint can be shared with anyone who wants to test the web service or integrate it into an application.\n", + "uri = service.scoring_uri\n", + "print(uri)\n", + "\n", + "files = {'image': open(test_file, 'rb').read()}\n", + "\n", + "# Send the DICOM as a raw HTTP request and obtain results from endpoint.\n", + "response = requests.post(uri, files=files)\n", + "print(\"output:\", response.content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1681425735538 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "import json\n", + "output_dict = json.loads(response.content)\n", + "\n", + "pt_metrics = output_dict['pt_summary']\n", + "ipex_metrics = output_dict['ipex_summary']\n", + "ov_metrics = output_dict['ov_summary']\n", + "\n", + "print(f\"PyTorch Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{pt_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{pt_metrics['pt_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{pt_metrics['pt_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{pt_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{pt_metrics['fps']:.2f}\")\n", + "\n", + "print(f\"\\nIPEX Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{ipex_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{ipex_metrics['ipex_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{ipex_metrics['ipex_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{ipex_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{ipex_metrics['fps']:.2f}\")\n", + "\n", + "print(f\"\\nOpenVINO Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{ov_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{ov_metrics['ov_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{ov_metrics['ov_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{ov_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{ov_metrics['fps']:.2f}\")\n", + "\n", + "# Calculate the FPS speedup with IPEX compared to PyTorch\n", + "ipex_fps_speedup = ipex_metrics['fps'] / pt_metrics['fps']\n", + "print(f\"\\nSpeedup with IPEX: {ipex_fps_speedup:.2f}x\")\n", + "\n", + "# Calculate the FPS speedup with OpenVINO compared to PyTorch\n", + "ov_fps_speedup = ov_metrics['fps'] / pt_metrics['fps']\n", + "print(f\"\\nSpeedup with OpenVINO: {ov_fps_speedup:.2f}x\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1681425579906 + } + }, + "outputs": [], + "source": [ + "lscpu_out=output_dict['system_info']['lscpu_out'].encode().decode('unicode_escape')\n", + "print(f\"\\nSystem Info:\\n{lscpu_out}\")\n", + "\n", + "mem_out_gb=output_dict['system_info']['mem_out_gb'].encode().decode('unicode_escape')\n", + "print(f\"\\nSystem Memory Info:\\n{mem_out_gb}\")\n", + "\n", + "os_out=output_dict['system_info']['os'].encode().decode('unicode_escape')\n", + "print(f\"\\nSystem OS:\\n{os_out}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Delete Service Endpoint\n", + "After testing the service, you can uncomment the following and execute the cell to delete the endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#service.delete()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "6fa67edf6d87aa13ac525a1287441ea8850f1587e23cc2fe3e03f5742d416d61" + }, + "kernel_info": { + "name": "python38-azureml" + }, + "kernelspec": { + "display_name": "Python 3.8 - AzureML", + "language": "python", + "name": "python38-azureml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "microsoft": { + "host": { + "AzureML": { + "notebookHasBeenCompleted": true + } + }, + "ms_spell_check": { + "ms_spell_check_language": "en" + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/4.1.Deploy the model (optimized)/png2dcm.py b/4.1.Deploy the model (optimized)/png2dcm.py new file mode 100644 index 0000000..06b2152 --- /dev/null +++ b/4.1.Deploy the model (optimized)/png2dcm.py @@ -0,0 +1,63 @@ +# This script uses PyDicom library (https://pydicom.github.io/) to +# generate a DICOM file from a supplied PNG image. + +import pydicom +from pydicom.dataset import Dataset, FileMetaDataset +from PIL import Image +import numpy as np +import zipfile +import io + +# Read png from zip file. The code below assumes sample.zip which is a part of +# the PADCHEST dataset +# zf = zipfile.ZipFile("./sample.zip") +# data = zf.read("255433269247415893224655601475580025849_j5s1kc.png") +data = 'sample.png' +image2d = np.array(Image.open(data)).astype(float) +image2d = (image2d/255).astype(np.uint16) + +file_meta = FileMetaDataset() +file_meta.MediaStorageSOPClassUID = "1.2.840.10008.5.1.4.1.1.1" +file_meta.MediaStorageSOPInstanceUID ='2.25.34327501276176110812231595851948283641' +file_meta.ImplementationClassUID = '1.3.6.1.4.1.30071.8' +file_meta.TransferSyntaxUID = pydicom.uid.ExplicitVRLittleEndian + +ds = Dataset() +ds.file_meta = file_meta + +ds.Rows = image2d.shape[0] +ds.Columns = image2d.shape[1] +ds.NumberOfFrames = 1 + +ds.PixelSpacing = [1, 1] # in mm +ds.SliceThickness = 1 # in mm + +ds.SeriesInstanceUID = pydicom.uid.generate_uid() +ds.StudyInstanceUID = pydicom.uid.generate_uid() + +ds.PatientName = "Demo^RSNA2021" +ds.PatientID = "123456" +ds.Modality = "CR" +ds.StudyDate = '20211204' +ds.ContentDate = '20211204' + +ds.BitsStored = 16 +ds.BitsAllocated = 16 +ds.HighBit = 15 +ds.PixelRepresentation = 0 +ds.PhotometricInterpretation = "MONOCHROME2" +ds.SamplesPerPixel = 1 + +ds.RescaleIntercept = 900 +ds.RescaleSlope = 9 +ds.WindowCenter = 2000 +ds.WindowWidth = 2000 + +ds.is_little_endian = True +ds.is_implicit_VR = False + +ds.PixelData = image2d.tobytes() + +pydicom.dataset.validate_file_meta(ds.file_meta, enforce_standard=True) +ds.save_as("sample_dicom.dcm", write_like_original=False) + diff --git a/4.1.Deploy the model (optimized)/sample.png b/4.1.Deploy the model (optimized)/sample.png new file mode 100644 index 0000000..345daf5 --- /dev/null +++ b/4.1.Deploy the model (optimized)/sample.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2f71508216405808a07bfb8854ce1a4712187221e6c9a8189d3432204cd77748 +size 4595336 diff --git a/4.2.Deploy the model(optimized)_sdk_v2/README.md b/4.2.Deploy the model(optimized)_sdk_v2/README.md new file mode 100644 index 0000000..9c5db23 --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/README.md @@ -0,0 +1,57 @@ +# Deploy the model and model explainability (bonus) +**Deployment scenario:** Submit a DICOM file (x-ray image) to the cloud and get a model prediction in real time. + +To deploy the model that was trained in the previous section ([3.Build a model](../3.Build%20a%20model/Readme.md), [training.ipynb](../3.Build%20a%20model/training.ipynb)) as a **web service hosted on Azure Container Instances (ACI)** +, you need to open the [deploy-opti-sdk-v2.ipynb](./deploy-opti-sdk-v2.ipynb) Notebook in your Azure ML workspace and follow the steps below: + +Also, if you want to deploy locally, see [deploy-local-opti-sdk-v2.ipynb](./deploy-local-opti-sdk-v2.ipynb) + +## Steps +1. Prepare an entry script. +2. Prepare an inference configuration. +3. Deploy the model you trained before to the cloud. +4. Test the resulting web service. + +To simulate a realistic scenario: +* The model to deploy was trained from 16 bit gray scale PNG images from [PadChest](https://pubmed.ncbi.nlm.nih.gov/32877839/). +* The deployed model accepts DICOM images as inputs. + +### 1. Prepare an entry script. +In order to use a model for inferencing, you need to create a scoring script first. In the notebook that sits beside this Readme we have such script embedded. +The scoring script is only required to have two functions: +* The `init()` function, which typically loads the model into a global object. +* The `run(input_data)` function uses the model to predict a value based on the input data. + * In our case, input_data will be a DICOM file forma.
+ +The output of the scoring script is the model prediction in the format of a JSON object that will be passed into an HTTP response. + + +### 2. Prepare an inference configuration. +We will create: +* A lightweight environment to deploy the model. +* Use [AMLRequest](https://docs.microsoft.com/en-us/python/api/azureml-contrib-services/azureml.contrib.services.aml_request?view=azure-ml-py) and [AMLResponse](https://docs.microsoft.com/en-us/python/api/azureml-contrib-services/azureml.contrib.services.aml_response.amlresponse?view=azure-ml-py) classes to access DICOM raw data. +* An inference configuration to deploy the model as a web service using the entry script or scoring script [score.py](./score.py). + + +### 3. Deploy the model to the cloud. +Then, we will deploy the model as an ACI web service which will be exposed as an HTTP endpoint. +* Specify the **deployment configuration** of the compute resource (i.e., CPU or GPU, amount of RAM, etc.) required for your application. +* ***Deploy*** by bringing all together: i) model, ii) environment, iii) inference configuration (script [score.py](./score.py)) and iv) deployment configuration. +* Then Azure ML, will automatically deploy the model in the cloud and you will be able to send the data to your model. + +### 4. Test the resulting web service. +We will load a DICOM file, send it to the Webservice we have deploed and display the response. + +## Bonus: eXplainable AI (XAI) +The notebook also includes a model usage scenario which we built around the use case of model explainability. + +As the adoption of AI in Healthcare translates into clinical practice, there is an unmeet need in providing clinical meaningful insights to doctors that explain how AI algorithms work. While most the AI (Deep Learning) algorithms operate as a **black-box** (i.e., do not provide explanations), here we show how to use common XAI methods (e.g., +[SHAP](https://shap-lrjball.readthedocs.io/en/latest/generated/shap.DeepExplainer.html) and [M3d-Cam](https://github.com/MECLabTUDA/M3d-Cam)) to verify that the **trained model** is using expected pixel information from the image. + +The [explain.ipynb](./explain.ipynb) Notebook demonstrates: + +* How to use integrate [SHAP](https://shap-lrjball.readthedocs.io/en/latest/generated/shap.DeepExplainer.html) and [M3d-Cam](https://github.com/MECLabTUDA/M3d-Cam) from trained Deep Learning models. +* How to load a trained model from a run directly into your code +* How to access data directly from the datastore (after the [1.Load Data](../1.Load%20Data/README.md) step). + +![Explainability](./images/explainability_shap.png) diff --git a/4.2.Deploy the model(optimized)_sdk_v2/conda_dep_opti.yml b/4.2.Deploy the model(optimized)_sdk_v2/conda_dep_opti.yml new file mode 100644 index 0000000..81563f7 --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/conda_dep_opti.yml @@ -0,0 +1,17 @@ +channels: + - anaconda + - defaults +dependencies: + - python=3.9 + - pip + - pip: + - azureml-defaults + - azure-ml-api-sdk + - torchxrayvision + - pydicom + - openvino-dev + - torch==1.13.1+cpu + - torchvision==0.14.1+cpu + - intel_extension_for_pytorch==1.13.100 + - "--index-url https://pypi.org/simple/" + - "--extra-index-url https://download.pytorch.org/whl/cpu" diff --git a/4.2.Deploy the model(optimized)_sdk_v2/deploy-local-opti-sdk-v2.ipynb b/4.2.Deploy the model(optimized)_sdk_v2/deploy-local-opti-sdk-v2.ipynb new file mode 100755 index 0000000..9e806b4 --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/deploy-local-opti-sdk-v2.ipynb @@ -0,0 +1,498 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy a model to a Local endpoint, using Azure Machine Learning Python SDK v2." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "gather": { + "logged": 1685486329865 + } + }, + "outputs": [], + "source": [ + "# ! pip install azure-ai-ml" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "gather": { + "logged": 1687967218052 + } + }, + "outputs": [], + "source": [ + "# import required libraries\n", + "from azure.ai.ml import MLClient\n", + "from azure.ai.ml.entities import (\n", + " ManagedOnlineEndpoint,\n", + " ManagedOnlineDeployment,\n", + " Model,\n", + " Environment,\n", + " CodeConfiguration,\n", + " OnlineRequestSettings\n", + ")\n", + "from azure.ai.ml.constants import AssetTypes\n", + "from azure.identity import DefaultAzureCredential" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "gather": { + "logged": 1687967221645 + } + }, + "outputs": [], + "source": [ + "# enter details of your AML workspace\n", + "subscription_id = \"\"\n", + "resource_group = \"\"\n", + "workspace = \"\"\n", + "\n", + "# get a handle to the workspace\n", + "ml_client = MLClient(\n", + " DefaultAzureCredential(), subscription_id, resource_group, workspace\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687967914375 + } + }, + "outputs": [], + "source": [ + "online_endpoint_name = \"padchest-optimized-ipex-ov-sdk-v2-local\"\n", + "# create an online endpoint\n", + "endpoint = ManagedOnlineEndpoint(\n", + " name = online_endpoint_name, \n", + " description=\"local deployment: padchest-optimized-ipex-ov-sdk-v2-local\",\n", + " auth_mode=\"key\"\n", + ")\n", + "\n", + "poller = ml_client.online_endpoints.begin_create_or_update(endpoint, local=True)\n", + "# poller.wait()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687967941258 + } + }, + "outputs": [], + "source": [ + "# configure a model\n", + "model_path=\"./outputs/az-register-models\"\n", + "\n", + "model = Model(\n", + " path=model_path,\n", + " type=\"custom_model\",\n", + " name=\"padchest-pt-onnx-ov\",\n", + " version=\"1\",\n", + " description=\"A folder az-register-models with PT, ONNX and OV models of padchest\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "gather": { + "logged": 1687970910761 + } + }, + "outputs": [], + "source": [ + "# Configure an environment\n", + "\n", + "env = Environment(\n", + " conda_file=\"conda_dep_opti.yml\",\n", + " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n", + " )\n", + "\n", + "\n", + "# configure an inference configuration with a scoring script\n", + "code_config = CodeConfiguration(\n", + " code=\"padchest_score_code\",\n", + " scoring_script=\"score_opti.py\"\n", + " ) " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Deployment\n", + "See VM SKUs that are supported for Azure Machine Learning managed online endpoints [here](https://learn.microsoft.com/en-us/azure/machine-learning/reference-managed-online-endpoints-vm-sku-list?view=azureml-api-2)\n", + "\n", + "- For LOCAL deployments, pass `local=True` parameter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687970919047 + } + }, + "outputs": [], + "source": [ + "\n", + "req_settings = OnlineRequestSettings(request_timeout_ms=90000)\n", + "\n", + "# Define a deployment\n", + "blue_deployment = ManagedOnlineDeployment(\n", + " name=\"blue\",\n", + " endpoint_name=online_endpoint_name,\n", + " model=model,\n", + " environment=env,\n", + " code_configuration=code_config,\n", + " instance_type=\"Standard_FX4mds\", #Standard_FX12mds, #Standard_FX24mds \n", + " instance_count=1,\n", + " request_settings=req_settings\n", + ")\n", + "\n", + "# create the deployment:\n", + "poller = ml_client.begin_create_or_update(blue_deployment, local=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687971166179 + } + }, + "outputs": [], + "source": [ + "# blue deployment takes 100% traffic\n", + "endpoint.traffic = {\"blue\": 100}\n", + "ml_client.begin_create_or_update(endpoint, local=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1685993900732 + } + }, + "outputs": [], + "source": [ + "deployment_logs = ml_client.online_deployments.get_logs(\n", + " name=\"blue\", endpoint_name=online_endpoint_name, lines=50, local=True\n", + ")\n", + "deployment_logs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687971197115 + } + }, + "outputs": [], + "source": [ + "# Get the details for online endpoint\n", + "deployed_endpoint = ml_client.online_endpoints.get(name=online_endpoint_name, local=True)\n", + "\n", + "# existing traffic details\n", + "print(deployed_endpoint.traffic)\n", + "\n", + "# Get the scoring URI\n", + "print(deployed_endpoint.scoring_uri)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687968719552 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "# visualize image\n", + "\n", + "import pydicom\n", + "import matplotlib.pylab as plt\n", + "\n", + "# Visualize converted DICOM file from the corresponding PNG file\n", + "test_file = \"./sample_dicom.dcm\"\n", + "dcm = pydicom.read_file(test_file)\n", + "print(dcm)\n", + "plt.imshow(dcm.pixel_array, cmap=plt.cm.bone)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687971236518 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "test_file = \"./sample_dicom.dcm\"\n", + "files = {'image': open(test_file, 'rb').read()}\n", + "\n", + "# resp = requests.post(scoring_uri, input_data, headers=headers)\n", + "scoring_uri = endpoint_deployed.scoring_uri\n", + "\n", + "# Send the DICOM as a raw HTTP request and obtain results from endpoint.\n", + "response = requests.post(scoring_uri, files=files)\n", + "print(\"output:\", response.content)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "gather": { + "logged": 1687974788821 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stock PyTorch Metrics:\n", + "\tFramework Version:\tPyTorch: 1.13.1+cpu\n", + "\tTop Labels:\t['Pneumonia', 'Infiltration', 'Effusion']\n", + "\tTop Probabilities:\t[49.63, 32.22, 3.29]\n", + "\tAvg Latency:\t0.0506 sec\n", + "\tFPS:\t19.76\n", + "PyTorch Graph Mode Metrics:\n", + "\tFramework Version:\tPyTorch: 1.13.1+cpu\n", + "\tTop Labels:\t['Pneumonia', 'Infiltration', 'Effusion']\n", + "\tTop Probabilities:\t[49.63, 32.22, 3.29]\n", + "\tAvg Latency:\t0.0429 sec\n", + "\tFPS:\t23.32\n", + "\n", + "IPEX Eager Metrics:\n", + "\tFramework Version:\tIPEX: 1.13.100\n", + "\tTop Labels:\t['Pneumonia', 'Infiltration', 'Effusion']\n", + "\tTop Probabilities:\t[49.63, 32.22, 3.29]\n", + "\tAvg Latency:\t0.0585 sec\n", + "\tFPS:\t17.09\n", + "\n", + "IPEX Graph Mode Metrics:\n", + "\tFramework Version:\tIPEX: 1.13.100\n", + "\tTop Labels:\t['Pneumonia', 'Infiltration', 'Effusion']\n", + "\tTop Probabilities:\t[49.63, 32.22, 3.29]\n", + "\tAvg Latency:\t0.0273 sec\n", + "\tFPS:\t36.69\n", + "\n", + "OpenVINO Metrics:\n", + "\tFramework Version:\tOpenVINO: 2023.0.0-10926-b4452d56304-releases/2023/0\n", + "\tTop Labels:\t['Pneumonia', 'Infiltration', 'Effusion']\n", + "\tTop Probabilities:\t[49.63, 32.22, 3.29]\n", + "\tAvg Latency:\t0.0208 sec\n", + "\tFPS:\t48.10\n", + "\n", + "Speedup with IPEX: 1.86x\n", + "\n", + "Speedup with OV: 2.43x\n", + "\n", + "Speedup with stock graph mode: 1.18x\n" + ] + } + ], + "source": [ + "import json\n", + "output_dict = json.loads(response.content)\n", + "\n", + "pt_metrics = output_dict['pt_summary']\n", + "pt_graph_metrics = output_dict['pt_graph_summary']\n", + "ipex_metrics = output_dict['ipex_eager_summary']\n", + "ipex_graph_metrics = output_dict['ipex_graph_summary']\n", + "ov_metrics = output_dict['ov_summary']\n", + "\n", + "print(f\"Stock PyTorch Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{pt_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{pt_metrics['pt_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{pt_metrics['pt_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{pt_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{pt_metrics['fps']:.2f}\")\n", + "\n", + "print(f\"PyTorch Graph Mode Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{pt_graph_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{pt_graph_metrics['pt_graph_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{pt_graph_metrics['pt_graph_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{pt_graph_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{pt_graph_metrics['fps']:.2f}\")\n", + "\n", + "print(f\"\\nIPEX Eager Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{ipex_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{ipex_metrics['ipex_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{ipex_metrics['ipex_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{ipex_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{ipex_metrics['fps']:.2f}\")\n", + "\n", + "print(f\"\\nIPEX Graph Mode Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{ipex_graph_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{ipex_graph_metrics['ipex_graph_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{ipex_graph_metrics['ipex_graph_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{ipex_graph_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{ipex_graph_metrics['fps']:.2f}\")\n", + "\n", + "print(f\"\\nOpenVINO Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{ov_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{ov_metrics['ov_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{ov_metrics['ov_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{ov_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{ov_metrics['fps']:.2f}\")\n", + "\n", + "\n", + "# Calculate the FPS speedup with IPEX compared to PyTorch\n", + "ipex_fps_speedup = ipex_graph_metrics['fps'] / pt_metrics['fps']\n", + "print(f\"\\nSpeedup with IPEX: {ipex_fps_speedup:.2f}x\")\n", + "\n", + "# Calculate the FPS speedup with OpenVINO compared to PyTorch\n", + "ov_fps_speedup = ov_metrics['fps'] / pt_metrics['fps']\n", + "print(f\"\\nSpeedup with OV: {ov_fps_speedup:.2f}x\")\n", + "\n", + "# Calculate the FPS speedup with Stock Graph Mode compared to PyTorch\n", + "stock_graph_fps_speedup = pt_graph_metrics['fps'] / pt_metrics['fps']\n", + "print(f\"\\nSpeedup with stock graph mode: {stock_graph_fps_speedup:.2f}x\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687974811038 + } + }, + "outputs": [], + "source": [ + "#Print System info\n", + "lscpu_out=output_dict['system_info']['lscpu_out'].encode().decode('unicode_escape')\n", + "print(f\"\\nSystem Info:\\n{lscpu_out}\")\n", + "\n", + "mem_out_gb=output_dict['system_info']['mem_out_gb'].encode().decode('unicode_escape')\n", + "print(f\"\\nSystem Memory Info (GB):\\n{mem_out_gb}\")\n", + "\n", + "os_out=output_dict['system_info']['os'].encode().decode('unicode_escape')\n", + "print(f\"\\nSystem OS:\\n{os_out}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Delete endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".." + ] + } + ], + "source": [ + "#ml_client.online_endpoints.begin_delete(name=online_endpoint_name, local=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernel_info": { + "name": "python38-azureml" + }, + "kernelspec": { + "display_name": "Python 3.8 - AzureML", + "language": "python", + "name": "python38-azureml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "microsoft": { + "host": { + "AzureML": { + "notebookHasBeenCompleted": true + } + }, + "ms_spell_check": { + "ms_spell_check_language": "en" + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/4.2.Deploy the model(optimized)_sdk_v2/deploy-opti-sdk-v2.ipynb b/4.2.Deploy the model(optimized)_sdk_v2/deploy-opti-sdk-v2.ipynb new file mode 100755 index 0000000..a80063e --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/deploy-opti-sdk-v2.ipynb @@ -0,0 +1,599 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy a model to an online endpoint, using Azure Machine Learning Python SDK v2." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For reference, [click here](https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-deploy-model?view=azureml-api-2)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Prerequisites " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "gather": { + "logged": 1685486329865 + } + }, + "outputs": [], + "source": [ + "# ! pip install azure-ai-ml" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "gather": { + "logged": 1687967218052 + } + }, + "outputs": [], + "source": [ + "# import required libraries\n", + "from azure.ai.ml import MLClient\n", + "from azure.ai.ml.entities import (\n", + " ManagedOnlineEndpoint,\n", + " ManagedOnlineDeployment,\n", + " Model,\n", + " Environment,\n", + " CodeConfiguration,\n", + " OnlineRequestSettings\n", + ")\n", + "from azure.ai.ml.constants import AssetTypes\n", + "from azure.identity import DefaultAzureCredential" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "gather": { + "logged": 1687967221645 + } + }, + "outputs": [], + "source": [ + "# enter details of your AML workspace\n", + "subscription_id = \"\"\n", + "resource_group = \"\"\n", + "workspace = \"\"\n", + "\n", + "# get a handle to the workspace\n", + "ml_client = MLClient(\n", + " DefaultAzureCredential(), subscription_id, resource_group, workspace\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687967914375 + } + }, + "outputs": [], + "source": [ + "online_endpoint_name = \"padchest-optimized-ipex-ov-sdk-v2\"\n", + "# create an online endpoint\n", + "endpoint = ManagedOnlineEndpoint(\n", + " name = online_endpoint_name, \n", + " description=\"online deployment of: padchest-ipex-sdk-v2-2\",\n", + " auth_mode=\"key\"\n", + ")\n", + "\n", + "poller = ml_client.online_endpoints.begin_create_or_update(endpoint)\n", + "poller.wait()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687967941258 + } + }, + "outputs": [], + "source": [ + "# Configure a model\n", + "\n", + "\n", + "folder_model_path=\"./outputs/az-register-models\"\n", + "\n", + "file_model = Model(\n", + " path=folder_model_path,\n", + " type=AssetTypes.CUSTOM_MODEL,\n", + " name=\"padchest-opti-sdk-v2-endpoint\",\n", + " version=\"1\",\n", + " description=\"SDKv2-az-register-models with PT, ONNX and OV models of padchest\"\n", + ")\n", + "ml_client.models.create_or_update(file_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "gather": { + "logged": 1687970910761 + } + }, + "outputs": [], + "source": [ + "# Configure an environment\n", + "\n", + "env = Environment(\n", + " conda_file=\"conda_dep_opti.yml\",\n", + " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n", + " )\n", + "\n", + "\n", + "# configure an inference configuration with a scoring script\n", + "code_config = CodeConfiguration(\n", + " code=\"padchest_score_code\",\n", + " scoring_script=\"score_opti.py\"\n", + " ) " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Deployment\n", + "See VM SKUs that are supported for Azure Machine Learning managed online endpoints [here](https://learn.microsoft.com/en-us/azure/machine-learning/reference-managed-online-endpoints-vm-sku-list?view=azureml-api-2)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Deployment\n", + "See VM SKUs that are supported for Azure Machine Learning managed online endpoints [here](https://learn.microsoft.com/en-us/azure/machine-learning/reference-managed-online-endpoints-vm-sku-list?view=azureml-api-2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687970919047 + } + }, + "outputs": [], + "source": [ + "\n", + "req_settings = OnlineRequestSettings(request_timeout_ms=90000)\n", + "\n", + "# Define a deployment\n", + "blue_deployment = ManagedOnlineDeployment(\n", + " name=\"blue\",\n", + " endpoint_name=online_endpoint_name,\n", + " model=file_model,\n", + " environment=env,\n", + " code_configuration=code_config,\n", + " instance_type=\"Standard_FX4mds\", #Standard_FX12mds, #Standard_FX24mds \n", + " instance_count=1,\n", + " request_settings=req_settings\n", + ")\n", + "\n", + "# create the deployment:\n", + "poller = ml_client.begin_create_or_update(blue_deployment)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687971166179 + } + }, + "outputs": [], + "source": [ + "# blue deployment takes 100% traffic\n", + "endpoint.traffic = {\"blue\": 100}\n", + "ml_client.begin_create_or_update(endpoint)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1685993900732 + } + }, + "outputs": [], + "source": [ + "deployment_logs = ml_client.online_deployments.get_logs(\n", + " name=\"blue\", endpoint_name=online_endpoint_name, lines=50\n", + ")\n", + "deployment_logs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687971197115 + } + }, + "outputs": [], + "source": [ + "# Get the details for online endpoint\n", + "deployed_endpoint = ml_client.online_endpoints.get(name=online_endpoint_name)\n", + "\n", + "# existing traffic details\n", + "print(deployed_endpoint.traffic)\n", + "\n", + "# Get the scoring URI\n", + "print(deployed_endpoint.scoring_uri)\n", + "\n", + "auth_key = ml_client.online_endpoints.get_keys(online_endpoint_name).primary_key\n", + "print(f\"Authkye:{auth_key}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687968719552 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "# visualize image\n", + "\n", + "import pydicom\n", + "import matplotlib.pylab as plt\n", + "\n", + "# Visualize converted DICOM file from the corresponding PNG file\n", + "test_file = \"./sample_dicom.dcm\"\n", + "dcm = pydicom.read_file(test_file)\n", + "print(dcm)\n", + "plt.imshow(dcm.pixel_array, cmap=plt.cm.bone)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1687971236518 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "test_file = \"./sample_dicom.dcm\"\n", + "files = {'image': open(test_file, 'rb').read()}\n", + "\n", + "# resp = requests.post(scoring_uri, input_data, headers=headers)\n", + "scoring_uri = deployed_endpoint.scoring_uri\n", + "\n", + "# Send the DICOM as a raw HTTP request and obtain results from endpoint.\n", + "response = requests.post(scoring_uri, headers={\"Authorization\": f\"Bearer {auth_key}\"},files=files, timeout=60)\n", + "print(\"output:\", response.content)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "gather": { + "logged": 1687974788821 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stock PyTorch Metrics:\n", + "\tFramework Version:\tPyTorch: 1.13.1+cpu\n", + "\tTop Labels:\t['Pneumonia', 'Infiltration', 'Effusion']\n", + "\tTop Probabilities:\t[49.63, 32.22, 3.29]\n", + "\tAvg Latency:\t0.0506 sec\n", + "\tFPS:\t19.76\n", + "PyTorch Graph Mode Metrics:\n", + "\tFramework Version:\tPyTorch: 1.13.1+cpu\n", + "\tTop Labels:\t['Pneumonia', 'Infiltration', 'Effusion']\n", + "\tTop Probabilities:\t[49.63, 32.22, 3.29]\n", + "\tAvg Latency:\t0.0429 sec\n", + "\tFPS:\t23.32\n", + "\n", + "IPEX Eager Metrics:\n", + "\tFramework Version:\tIPEX: 1.13.100\n", + "\tTop Labels:\t['Pneumonia', 'Infiltration', 'Effusion']\n", + "\tTop Probabilities:\t[49.63, 32.22, 3.29]\n", + "\tAvg Latency:\t0.0585 sec\n", + "\tFPS:\t17.09\n", + "\n", + "IPEX Graph Mode Metrics:\n", + "\tFramework Version:\tIPEX: 1.13.100\n", + "\tTop Labels:\t['Pneumonia', 'Infiltration', 'Effusion']\n", + "\tTop Probabilities:\t[49.63, 32.22, 3.29]\n", + "\tAvg Latency:\t0.0273 sec\n", + "\tFPS:\t36.69\n", + "\n", + "OpenVINO Metrics:\n", + "\tFramework Version:\tOpenVINO: 2023.0.0-10926-b4452d56304-releases/2023/0\n", + "\tTop Labels:\t['Pneumonia', 'Infiltration', 'Effusion']\n", + "\tTop Probabilities:\t[49.63, 32.22, 3.29]\n", + "\tAvg Latency:\t0.0208 sec\n", + "\tFPS:\t48.10\n", + "\n", + "Speedup with IPEX: 1.86x\n", + "\n", + "Speedup with OV: 2.43x\n", + "\n", + "Speedup with stock graph mode: 1.18x\n" + ] + } + ], + "source": [ + "import json\n", + "output_dict = json.loads(response.content)\n", + "\n", + "pt_metrics = output_dict['pt_summary']\n", + "pt_graph_metrics = output_dict['pt_graph_summary']\n", + "ipex_metrics = output_dict['ipex_eager_summary']\n", + "ipex_graph_metrics = output_dict['ipex_graph_summary']\n", + "ov_metrics = output_dict['ov_summary']\n", + "\n", + "print(f\"Stock PyTorch Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{pt_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{pt_metrics['pt_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{pt_metrics['pt_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{pt_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{pt_metrics['fps']:.2f}\")\n", + "\n", + "print(f\"PyTorch Graph Mode Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{pt_graph_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{pt_graph_metrics['pt_graph_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{pt_graph_metrics['pt_graph_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{pt_graph_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{pt_graph_metrics['fps']:.2f}\")\n", + "\n", + "print(f\"\\nIPEX Eager Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{ipex_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{ipex_metrics['ipex_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{ipex_metrics['ipex_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{ipex_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{ipex_metrics['fps']:.2f}\")\n", + "\n", + "print(f\"\\nIPEX Graph Mode Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{ipex_graph_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{ipex_graph_metrics['ipex_graph_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{ipex_graph_metrics['ipex_graph_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{ipex_graph_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{ipex_graph_metrics['fps']:.2f}\")\n", + "\n", + "print(f\"\\nOpenVINO Metrics:\")\n", + "print(f\"\\tFramework Version:\\t{ov_metrics['fwk_version']}\")\n", + "print(f\"\\tTop Labels:\\t{ov_metrics['ov_result']['top_labels']}\")\n", + "print(f\"\\tTop Probabilities:\\t{ov_metrics['ov_result']['top_probabilities']}\")\n", + "print(f\"\\tAvg Latency:\\t{ov_metrics['avg_latency']:.4f} sec\")\n", + "print(f\"\\tFPS:\\t{ov_metrics['fps']:.2f}\")\n", + "\n", + "\n", + "# Calculate the FPS speedup with IPEX compared to PyTorch\n", + "ipex_fps_speedup = ipex_graph_metrics['fps'] / pt_metrics['fps']\n", + "print(f\"\\nSpeedup with IPEX: {ipex_fps_speedup:.2f}x\")\n", + "\n", + "# Calculate the FPS speedup with OpenVINO compared to PyTorch\n", + "ov_fps_speedup = ov_metrics['fps'] / pt_metrics['fps']\n", + "print(f\"\\nSpeedup with OV: {ov_fps_speedup:.2f}x\")\n", + "\n", + "# Calculate the FPS speedup with Stock Graph Mode compared to PyTorch\n", + "stock_graph_fps_speedup = pt_graph_metrics['fps'] / pt_metrics['fps']\n", + "print(f\"\\nSpeedup with stock graph mode: {stock_graph_fps_speedup:.2f}x\")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "gather": { + "logged": 1687974811038 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "System Info:\n", + "Architecture: x86_64\n", + "CPU op-mode(s): 32-bit, 64-bit\n", + "Byte Order: Little Endian\n", + "Address sizes: 46 bits physical, 48 bits virtual\n", + "CPU(s): 4\n", + "On-line CPU(s) list: 0-3\n", + "Thread(s) per core: 2\n", + "Core(s) per socket: 2\n", + "Socket(s): 1\n", + "NUMA node(s): 1\n", + "Vendor ID: GenuineIntel\n", + "CPU family: 6\n", + "Model: 85\n", + "Model name: Intel(R) Xeon(R) Gold 6246R CPU @ 3.40GHz\n", + "Stepping: 7\n", + "CPU MHz: 3392.031\n", + "BogoMIPS: 6784.06\n", + "Virtualization: VT-x\n", + "Hypervisor vendor: Microsoft\n", + "Virtualization type: full\n", + "L1d cache: 64 KiB\n", + "L1i cache: 64 KiB\n", + "L2 cache: 2 MiB\n", + "L3 cache: 35.8 MiB\n", + "NUMA node0 CPU(s): 0-3\n", + "Vulnerability Itlb multihit: Not affected\n", + "Vulnerability L1tf: Not affected\n", + "Vulnerability Mds: Not affected\n", + "Vulnerability Meltdown: Not affected\n", + "Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\n", + "Vulnerability Retbleed: Vulnerable\n", + "Vulnerability Spec store bypass: Vulnerable\n", + "Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\n", + "Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected\n", + "Vulnerability Srbds: Not affected\n", + "Vulnerability Tsx async abort: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\n", + "Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology cpuid pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single tpr_shadow vnmi ept vpid ept_ad fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_vnni arch_capabilities\n", + "\n", + "\n", + "System Memory Info (GB):\n", + " total used free shared buff/cache available\n", + "Mem: 82 2 73 0 7 79\n", + "Swap: 0 0 0\n", + "\n", + "\n", + "System OS:\n", + "NAME=\"Ubuntu\"\n", + "VERSION=\"20.04.6 LTS (Focal Fossa)\"\n", + "ID=ubuntu\n", + "ID_LIKE=debian\n", + "PRETTY_NAME=\"Ubuntu 20.04.6 LTS\"\n", + "VERSION_ID=\"20.04\"\n", + "HOME_URL=\"https://www.ubuntu.com/\"\n", + "SUPPORT_URL=\"https://help.ubuntu.com/\"\n", + "BUG_REPORT_URL=\"https://bugs.launchpad.net/ubuntu/\"\n", + "PRIVACY_POLICY_URL=\"https://www.ubuntu.com/legal/terms-and-policies/privacy-policy\"\n", + "VERSION_CODENAME=focal\n", + "UBUNTU_CODENAME=focal\n", + " \n", + "Linux mir-user-pod-94e28fde802543ea8d41a8bf502bf8c1000001 5.15.0-1038-azure #45~20.04.1-Ubuntu SMP Tue Apr 25 18:45:15 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux\n", + "\n", + "/azureml-envs/azureml_06a7f6bafdc80fc3c451c149a3ddc83a/bin/python\n", + "\n" + ] + } + ], + "source": [ + "#Print System info\n", + "lscpu_out=output_dict['system_info']['lscpu_out'].encode().decode('unicode_escape')\n", + "print(f\"\\nSystem Info:\\n{lscpu_out}\")\n", + "\n", + "mem_out_gb=output_dict['system_info']['mem_out_gb'].encode().decode('unicode_escape')\n", + "print(f\"\\nSystem Memory Info (GB):\\n{mem_out_gb}\")\n", + "\n", + "os_out=output_dict['system_info']['os'].encode().decode('unicode_escape')\n", + "print(f\"\\nSystem OS:\\n{os_out}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Delete endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".." + ] + } + ], + "source": [ + "#ml_client.online_endpoints.begin_delete(name=online_endpoint_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernel_info": { + "name": "python38-azureml" + }, + "kernelspec": { + "display_name": "Python 3.8 - AzureML", + "language": "python", + "name": "python38-azureml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "microsoft": { + "host": { + "AzureML": { + "notebookHasBeenCompleted": true + } + }, + "ms_spell_check": { + "ms_spell_check_language": "en" + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/4.2.Deploy the model(optimized)_sdk_v2/explain.ipynb b/4.2.Deploy the model(optimized)_sdk_v2/explain.ipynb new file mode 100644 index 0000000..a53d75e --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/explain.ipynb @@ -0,0 +1,683 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "# eXplainable AI (XAI): Explain your model with [SHAP](https://shap-lrjball.readthedocs.io/en/latest/generated/shap.DeepExplainer.html) and [M3d-Cam](https://github.com/MECLabTUDA/M3d-Cam)
\n", + "\n", + "1. Mount dataset and get label dataset\n", + "2. SHAP \n", + "3. Grad-Cam++ " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "#!pip install shap\n", + "#!pip install torchxrayvision\n", + "#!pip install medcam" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "gather": { + "logged": 1639682695329 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Workspace: rsna2021amldemo\n" + ] + } + ], + "source": [ + "# Connect to workspace\n", + "from azureml.core import Workspace\n", + "# Load workspace from config file\n", + "# The workspace is the top-level resource for Azure Machine Learning, \n", + "# providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning.\n", + "# Documentation: https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace\n", + "ws = Workspace.from_config(path='../')\n", + "print(\"Workspace:\",ws.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 1. Mount dataset and get label dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "gather": { + "logged": 1639682706384 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/tmpg2nrni30\n" + ] + } + ], + "source": [ + "from azureml.core import Dataset\n", + "from azureml.contrib.dataset import FileHandlingOption\n", + "import pandas as pd\n", + "import os\n", + "os.environ[\"RSLEX_DIRECT_VOLUME_MOUNT\"] = \"true\" # IMPORTANT for performance\n", + "\n", + "# Mount and use files\n", + "dataset = Dataset.get_by_name(ws, name=\"padchest\")\n", + "mount = dataset.mount()\n", + "mount.start()\n", + "print(mount.mount_point)\n", + "\n", + "pc_csv_file = os.path.join(mount.mount_point, \"PADCHEST_chest_x_ray_images_labels_160K_01.02.19.csv\")\n", + "pc_df = pd.read_csv(pc_csv_file, low_memory=False, index_col=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Visualize a random image" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "gather": { + "logged": 1639682793374 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "file: /tmp/tmpg2nrni30/png/0/199932188422938481238592789979586445399_5tsy8k.png\n", + "exists?: True\n", + "['aortic elongation', 'cardiomegaly']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from skimage.io import imread\n", + "import matplotlib.pylab as plt\n", + "import random\n", + "\n", + "random_index = random.randint(1,1000)\n", + "pc_df.loc[random_index,\"ImageID\"]\n", + "test_file = str(mount.mount_point) + os.sep + 'png' + os.sep + str(pc_df.loc[random_index,\"ImageDir\"]) + os.sep + pc_df.loc[random_index, \"ImageID\"]\n", + "print(\"file:\", test_file)\n", + "print(f\"exists?: \", os.path.exists(test_file))\n", + "\n", + "img = imread(test_file)\n", + "plt.imshow(img, cmap=plt.cm.bone)\n", + "print(pc_df.loc[random_index,\"Labels\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 2. SHAP Explainer\n", + "Download registered model and set up image data loader from mounted directory" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "gather": { + "logged": 1639682863852 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting XRayResizer engine to cv2 could increase performance.\n", + "Setting XRayResizer engine to cv2 could increase performance.\n", + "['Air Trapping', 'Aortic Atheromatosis', 'Aortic Elongation', 'Atelectasis', 'Bronchiectasis', 'Cardiomegaly', 'Consolidation', 'Costophrenic Angle Blunting', 'Edema', 'Effusion', 'Emphysema', 'Fibrosis', 'Flattened Diaphragm', 'Fracture', 'Granuloma', 'Hemidiaphragm Elevation', 'Hernia', 'Hilar Enlargement', 'Infiltration', 'Mass', 'Nodule', 'Pleural_Thickening', 'Pneumonia', 'Pneumothorax', 'Scoliosis', 'Tuberculosis']\n" + ] + } + ], + "source": [ + "import shap\n", + "import torch\n", + "import torchvision, torchvision.transforms\n", + "import torchxrayvision as xrv\n", + "import numpy as np\n", + "from azureml.core.model import Model\n", + "import sys\n", + "\n", + "sys.path.insert(0,r'./../3.Build a model/trainingscripts')\n", + "from padchest_dataset import PC_Dataset_Custom\n", + "\n", + "# Set resolution according to Densenet\n", + "transforms = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop(),xrv.datasets.XRayResizer(224)])\n", + "\n", + "# Get register model\n", + "model_ws = Model(ws, 'padchest', version=7) # NOTE: you may have a different version here\n", + "\n", + "model_name = './pc-densenet-densenet-best.pt'\n", + "if not os.path.exists(model_name):\n", + " model_ws.download(exist_ok=True)\n", + "\n", + "model = torch.load('pc-densenet-densenet-best.pt')\n", + "\n", + "#Select CUDA or CPU support\n", + "device = 'cuda'\n", + "# device = 'cpu'\n", + "model.to(device)\n", + "\n", + "transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop(),\n", + " xrv.datasets.XRayResizer(224, engine=\"cv2\")])\n", + "\n", + "imgpath = mount.mount_point + os.sep + 'png'\n", + "dataset = PC_Dataset_Custom(\n", + " imgpath=imgpath,\n", + " csvpath=pc_csv_file,\n", + " transform=transform, unique_patients=True, views=[\"PA\"],flat_dir=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "gather": { + "logged": 1639636489608 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "# Get a uniform randomly sampled batch from the dataset. \n", + "# Use it to initialize the SHAP explainer\n", + "\n", + "from torch.utils.data import DataLoader\n", + "batch_training = DataLoader(dataset, batch_size=40, shuffle=True)\n", + "it = iter(batch_training)\n", + "batch_init = next(it)\n", + "batch_training_images = batch_init[\"img\"].float().to(device)\n", + "e = shap.GradientExplainer((model, model.features.denseblock3.denselayer24.conv2), batch_training_images)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "gather": { + "logged": 1639638753640 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "# Explains a model using expected gradients from a testing sample\n", + "\n", + "batch_testing = DataLoader(dataset, batch_size=10, shuffle=True)\n", + "it = iter(batch_testing)\n", + "batch_testing = next(it)\n", + "batch_testing_images = batch_testing[\"img\"].float().to(device)\n", + "\n", + "def visualize_shap(images):\n", + " shap_values, indexes = e.shap_values(images.reshape((1,1,224,224)), ranked_outputs=3, nsamples=200)\n", + " # plot the explanations\n", + " shap_values = [np.swapaxes(np.swapaxes(s, 2, 3), 1, -1) for s in shap_values]\n", + " original_image = images.reshape((1,224,224,1)).detach().cpu().numpy()\n", + " shap.image_plot(shap_values, original_image, show=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "gather": { + "logged": 1639638786640 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running shap... done\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the predicted top three classes and SHAP coefficients\n", + "\n", + "visualize_shap(batch_testing_images[0].float().to(device))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "gather": { + "logged": 1639638869834 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running shap... done\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "visualize_shap(batch_testing_images[1].float().to(device))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "gather": { + "logged": 1639638896905 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running shap... done\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "visualize_shap(batch_testing_images[2].float().to(device))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 3. Explain model using Grad-Cam++\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "gather": { + "logged": 1639682202626 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "# Inject model into Medcam explainer and use Grad-Cam++ as core model\n", + "from medcam import medcam\n", + "model_medcam = medcam.inject(model, output_dir=\"attention_maps\", save_maps=True, return_attention=True, label='best',backend='gcampp')\n", + "\n", + "def show_medcam_image(img_raw, img_attention ):\n", + " img_attention = img_attention.reshape((224, 224))\n", + " img_raw =img_raw.reshape((224, 224)).detach().cpu().numpy()\n", + " plt.subplot(1, 2, 1)\n", + " plt.axis('off')\n", + " plt.imshow(img_raw,cmap=\"Greys_r\")\n", + " plt.subplot(1, 2, 2)\n", + " plt.axis('off')\n", + " plt.imshow(img_raw,cmap=\"Greys_r\") \n", + " plt.imshow(img_attention, alpha=0.4,cmap='Reds')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Visualize Grad-Cam++" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "gather": { + "logged": 1639682145144 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "__ , img_attention = model_medcam( batch_testing_images[0].float().to(device).reshape((1,1,224,224)) )\n", + "img_attention = img_attention.reshape((224, 224)).detach().cpu().numpy()\n", + "show_medcam_image(batch_testing_images[0], img_attention )" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "gather": { + "logged": 1639682157107 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "__ , img_attention = model_medcam( batch_testing_images[1].float().to(device).reshape((1,1,224,224)) )\n", + "img_attention = img_attention.reshape((224, 224)).detach().cpu().numpy()\n", + "show_medcam_image(batch_testing_images[1], img_attention )" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "gather": { + "logged": 1639682160690 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "__ , img_attention = model_medcam( batch_testing_images[2].float().to(device).reshape((1,1,224,224)) )\n", + "img_attention = img_attention.reshape((224, 224)).detach().cpu().numpy()\n", + "show_medcam_image(batch_testing_images[2], img_attention )" + ] + } + ], + "metadata": { + "kernel_info": { + "name": "python3-azureml" + }, + "kernelspec": { + "display_name": "Python 3.6 - AzureML", + "language": "python", + "name": "python3-azureml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "microsoft": { + "host": { + "AzureML": { + "notebookHasBeenCompleted": true + } + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/4.2.Deploy the model(optimized)_sdk_v2/images/explainability_shap.png b/4.2.Deploy the model(optimized)_sdk_v2/images/explainability_shap.png new file mode 100644 index 0000000..f619a96 --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/images/explainability_shap.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e4772626c2f9bc3273f50ec384fc48700739748dbfc7576b4221f61b012c2c9 +size 131 diff --git a/4.2.Deploy the model(optimized)_sdk_v2/invoke_endpoint.py b/4.2.Deploy the model(optimized)_sdk_v2/invoke_endpoint.py new file mode 100644 index 0000000..f24ae29 --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/invoke_endpoint.py @@ -0,0 +1,38 @@ +import requests + +from azure.ai.ml import MLClient +from azure.ai.ml.entities import ( + ManagedOnlineEndpoint, + ManagedOnlineDeployment, + Model, + Environment, + CodeConfiguration, +) +from azure.identity import DefaultAzureCredential + +# enter details of your AML workspace +subscription_id = "" +resource_group = "" +workspace_name = "" + +# get a handle to the workspace +ml_client = MLClient( + DefaultAzureCredential(), subscription_id, resource_group, workspace +) + +online_endpoint_name = "padchest-pt-ipex-ov-sdk-v2" +endpoint_deployed = ml_client.online_endpoints.get(name=online_endpoint_name) + + +test_file = "./sample_dicom.dcm" +files = {'image': open(test_file, 'rb').read()} + +# resp = requests.post(scoring_uri, input_data, headers=headers) +scoring_uri = endpoint_deployed.scoring_uri +auth_key = ml_client.online_endpoints.get_keys(online_endpoint_name).primary_key +print(f"Authkye:{auth_key}") + +print(f"Sending request {test_file} to {scoring_uri}") +# Send the DICOM as a raw HTTP request and obtain results from endpoint. +response = requests.post(scoring_uri, headers={"Authorization": f"Bearer {auth_key}"},files=files, timeout=60) +print("output:", response.content) diff --git a/4.2.Deploy the model(optimized)_sdk_v2/padchest_score_code/score_opti.py b/4.2.Deploy the model(optimized)_sdk_v2/padchest_score_code/score_opti.py new file mode 100755 index 0000000..dc1da24 --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/padchest_score_code/score_opti.py @@ -0,0 +1,347 @@ +from azureml.contrib.services.aml_request import AMLRequest, rawhttp +from azureml.contrib.services.aml_response import AMLResponse +import json, os, io +import numpy as np +import torch +import torchxrayvision as xrv +from torchvision import transforms +from torchxrayvision.datasets import normalize +import pydicom + +import time +from openvino.runtime import Core +from openvino.runtime import get_version + +def init(): + global target_device + target_device = "CPU" + + # Initial PyTorch model + global model + # AZUREML_MODEL_DIR is an environment variable created during deployment. + # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION) + # For multiple models, it points to the folder containing all deployed models (./azureml-models) + # Load PyTorch model + model = xrv.models.DenseNet(num_classes=26, in_channels=1, **xrv.models.get_densenet_params('densenet') ) + model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'az-register-models', 'pc-densenet-densenet-best.pt') + # model_path='./pc-densenet-densenet-best.pt' + model.load_state_dict(torch.load(model_path).state_dict() ) + + model.eval() + + # Initialize OpenVINO Runtime. + global ov_compiled_model + ie = Core() + ov_xml = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'az-register-models', 'pc-densenet-densenet-best.onnx') + # ov_xml = 'pc-densenet-densenet-best.onnx' + # Load and compile the OV model + ov_model = ie.read_model(ov_xml) + ov_compiled_model = ie.compile_model(model=ov_model, device_name=target_device) + + + +# TIP: To accept raw data, use the AMLRequest class in your entry script and add the @rawhttp decorator to the run() function +# more details in: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-advanced-entry-script +# Note that despite the fact that we trained our model on PNGs, we would like to simulate +# a scenario closer to the real world here and accept DICOMs into our score script. Here's how: +@rawhttp +def run(request): + + if request.method == 'GET': + # For this example, just return the URL for GETs. + respBody = str.encode(request.full_path) + return AMLResponse(respBody, 200) + + elif request.method == 'POST': + # For a real-world solution, you would load the data from reqBody + # and send it to the model. Then return the response. + try: + # For labels definition see file: '3.Build a model/trainingscripts/padchest_config.py' + pathologies_labels = ['Air Trapping', 'Aortic Atheromatosis', 'Aortic Elongation', 'Atelectasis', + 'Bronchiectasis', 'Cardiomegaly', 'Consolidation', 'Costophrenic Angle Blunting', 'Edema', 'Effusion', + 'Emphysema', 'Fibrosis', 'Flattened Diaphragm', 'Fracture', 'Granuloma', 'Hemidiaphragm Elevation', + 'Hernia', 'Hilar Enlargement', 'Infiltration', 'Mass', 'Nodule', 'Pleural_Thickening', + 'Pneumonia', 'Pneumothorax', 'Scoliosis', 'Tuberculosis'] + + def benchmark_pt(data): + + print(f"\n==== Benchmarking PyTorch inference with sample data for 10 warmup + 90 iters on CPU ====") + print(f"Input shape: {data.shape}") + + durs = [] + with torch.no_grad(): + for _ in range(100): + start_time = time.time() + pt_result = model(data) + latency = time.time() - start_time + durs.append(latency) + + # Process output + index = np.argsort( pt_result.data.cpu().numpy() ) + probability = torch.nn.functional.softmax(pt_result[0], dim=0).data.cpu().numpy() + pt_result = get_top_predictions(index, probability) + + avg_latency = np.mean(durs[10:]) + fps = 1 / avg_latency + + print(f"Stock PyTorch Avg Latency: {avg_latency:.4f} sec, FPS: {fps:.2f}") + + # summarize the results + pt_summary = { + "fwk_version": f"PyTorch: {torch.__version__}", + "pt_result": pt_result, + "avg_latency": avg_latency, + "fps": fps + } + + # torchscript + with torch.no_grad(): + traced_model = torch.jit.trace(model, data) + traced_model = torch.jit.freeze(traced_model) + + durs = [] + with torch.no_grad(): + for _ in range(100): + start_time = time.time() + pt_graph_result = traced_model(input_batch) + latency = time.time() - start_time + durs.append(latency) + + # Process output + index = np.argsort( pt_graph_result.data.cpu().numpy() ) + probability = torch.nn.functional.softmax(pt_graph_result[0], dim=0).data.cpu().numpy() + pt_graph_result = get_top_predictions(index, probability) + + avg_latency = np.mean(durs[10:]) + fps = 1 / avg_latency + + print(f"Stock PyTorch + TorchScript Avg Latency: {avg_latency:.4f} sec, FPS: {fps:.2f}") + + # summarize the results + pt_graph_summary = { + "fwk_version": f"PyTorch: {torch.__version__}", + "pt_graph_result": pt_graph_result, + "avg_latency": avg_latency, + "fps": fps + } + + return pt_summary, pt_graph_summary + + def benchmark_ipex(data): + + print(f"\n==== Benchmarking IPEX inference with sample data for 10 warmup + 90 iters on CPU ====") + print(f"Input shape: {data.shape}") + + # import ipex and optimize model + import intel_extension_for_pytorch as ipex + model_ipex = ipex.optimize(model) + + durs = [] + with torch.no_grad(): + for _ in range(100): + start_time = time.time() + ipex_result = model_ipex(data) + latency = time.time() - start_time + durs.append(latency) + + # Process output + index = np.argsort( ipex_result.data.cpu().numpy() ) + probability = torch.nn.functional.softmax(ipex_result[0], dim=0).data.cpu().numpy() + ipex_result = get_top_predictions(index, probability) + + avg_latency = np.mean(durs[10:]) + fps = 1 / avg_latency + + print(f"IPEX Avg Latency: {avg_latency:.4f} sec, FPS: {fps:.2f}") + + # summarize the results + ipex_summary = { + "fwk_version": f"IPEX: {ipex.__version__}", + "ipex_result": ipex_result, + "avg_latency": avg_latency, + "fps": fps + } + + # torchscript + with torch.no_grad(): + traced_model = torch.jit.trace(model_ipex, data) + traced_model = torch.jit.freeze(traced_model) + + durs = [] + with torch.no_grad(): + for _ in range(100): + start_time = time.time() + ipex_graph_result = traced_model(data) + latency = time.time() - start_time + durs.append(latency) + + # Process output + index = np.argsort( ipex_graph_result.data.cpu().numpy() ) + probability = torch.nn.functional.softmax(ipex_graph_result[0], dim=0).data.cpu().numpy() + ipex_graph_result = get_top_predictions(index, probability) + + avg_latency = np.mean(durs[10:]) + fps = 1 / avg_latency + + print(f"IPEX graph mode Avg Latency: {avg_latency:.4f} sec, FPS: {fps:.2f}") + + # summarize the results + ipex_graph_summary = { + "fwk_version": f"IPEX: {ipex.__version__}", + "ipex_graph_result": ipex_graph_result, + "avg_latency": avg_latency, + "fps": fps + } + + return ipex_summary, ipex_graph_summary + + def benchmark_ov(data): + + print(f"\n==== Benchmarking OpenVINO inference with sample data for 10 warmup + 90 iters on CPU ====") + print(f"Input shape: {data.shape}") + + # get the names of input and output layers of the model + input_layer = ov_compiled_model.input(0) + output_layer =ov_compiled_model.output(0) + + durs = [] + with torch.no_grad(): + for _ in range(100): + start_time = time.time() + ov_output = ov_compiled_model(data) + latency = time.time() - start_time + durs.append(latency) + + # Process output + ov_output = ov_output[output_layer] + index = np.argsort(ov_output) + probability = torch.nn.functional.softmax(torch.from_numpy(ov_output[0]), dim=0).data.cpu().numpy() + ov_result = get_top_predictions(index, probability) + + avg_latency = np.mean(durs[10:]) + fps = 1 / avg_latency + + print(f"OpenVINO Avg Latency: {avg_latency:.4f} sec, FPS: {fps:.2f}") + + # summarize the results + ov_summary = { + "fwk_version": f"OpenVINO: {get_version()}", + "ov_result": ov_result, + "avg_latency": avg_latency, + "fps": fps + } + + return ov_summary + + + # Get System information + def get_system_info(): + import subprocess + + # Run lscpu command and capture output + lscpu_out = subprocess.check_output(["lscpu"]).decode("utf-8") + print(lscpu_out) + # Run free -g command and capture output + mem_out = subprocess.check_output(["free", "-g"]).decode("utf-8") + print(mem_out) + os_out = subprocess.check_output(["cat", "/etc/os-release"]).decode( + "utf-8" + ) + kernal_out = subprocess.check_output(["uname", "-a"]).decode("utf-8") + pyver_out = subprocess.check_output(["which", "python"]).decode("utf-8") + os_out = os_out + " \n" + kernal_out + "\n" + pyver_out + print(os_out) + + return_data = { + "lscpu_out": lscpu_out, + "mem_out_gb": mem_out, + "os": os_out, + } + return return_data + + + # Read DICOM and apply photometric transformations + def read_and_rescale_image( filepath): + dcm = pydicom.read_file(filepath) + image = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept + + def window_image(image, wc, ww): + img_min = wc - ww // 2 + img_max = wc + ww // 2 + image[image < img_min] = img_min + image[image > img_max] = img_max + return image + + image = window_image(image, dcm.WindowCenter, dcm.WindowWidth) + # Scales 16bit to [-1024 1024] + image = normalize(image, maxval=65535, reshape=True) + return image + + # Decode output and get predictions + def get_top_predictions(index, probability, num_predictions=3): + # For labels definition see file: '3.Build a model/trainingscripts/padchest_config.py' + pathologies_labels = ['Air Trapping', 'Aortic Atheromatosis', 'Aortic Elongation', 'Atelectasis', + 'Bronchiectasis', 'Cardiomegaly', 'Consolidation', 'Costophrenic Angle Blunting', 'Edema', 'Effusion', + 'Emphysema', 'Fibrosis', 'Flattened Diaphragm', 'Fracture', 'Granuloma', 'Hemidiaphragm Elevation', + 'Hernia', 'Hilar Enlargement', 'Infiltration', 'Mass', 'Nodule', 'Pleural_Thickening', + 'Pneumonia', 'Pneumothorax', 'Scoliosis', 'Tuberculosis'] + + top_labels = [] + top_probs = [] + for i in range(num_predictions): + top_labels.append(pathologies_labels[index[0][-1-i]]) + top_probs.append(round(probability[index[0][-1-i]] * 100, 2)) + + result = {"top_labels": top_labels, "top_probabilities": top_probs} + return result + + ###################################### + # Begin processing request + ###################################### + + file_bytes = request.files["image"] + + # Note that user can define this to be any other type of image + input_image = read_and_rescale_image(file_bytes) + + preprocess = transforms.Compose([ + xrv.datasets.XRayCenterCrop(), + xrv.datasets.XRayResizer(224) + ]) + + input_image = preprocess(input_image) + input_batch = torch.from_numpy( input_image[np.newaxis,...] ) + + #Benchmark PyTorch + pt_summary, pt_graph_summary = benchmark_pt(input_batch) + print(f"PyTorch Output: {pt_summary}") + print(f"PyTorch Graph Output: {pt_graph_summary}") + + #Benchmark IPEX + ipex_summary, ipex_graph_summary = benchmark_ipex(input_batch) + print(f"IPEX Eager Output: {ipex_summary}") + print(f"IPEX Graph Output: {ipex_graph_summary}") + + # Benchmark OpenVINO + ov_summary = benchmark_ov(input_batch) + print(f"OpenVINO Output: {ov_summary}") + + sys_info = get_system_info() + + return_data = {"pt_summary": pt_summary, + "pt_graph_summary" : pt_graph_summary, + "ipex_eager_summary" : ipex_summary, + "ipex_graph_summary" : ipex_graph_summary, + "ov_summary": ov_summary, + "system_info": sys_info} + + return return_data + + except Exception as e: + result = str(e) + # return error message back to the client + return AMLResponse(json.dumps({"error": result}), 200) + + else: + return AMLResponse("bad request", 500) + diff --git a/4.2.Deploy the model(optimized)_sdk_v2/png2dcm.py b/4.2.Deploy the model(optimized)_sdk_v2/png2dcm.py new file mode 100644 index 0000000..06b2152 --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/png2dcm.py @@ -0,0 +1,63 @@ +# This script uses PyDicom library (https://pydicom.github.io/) to +# generate a DICOM file from a supplied PNG image. + +import pydicom +from pydicom.dataset import Dataset, FileMetaDataset +from PIL import Image +import numpy as np +import zipfile +import io + +# Read png from zip file. The code below assumes sample.zip which is a part of +# the PADCHEST dataset +# zf = zipfile.ZipFile("./sample.zip") +# data = zf.read("255433269247415893224655601475580025849_j5s1kc.png") +data = 'sample.png' +image2d = np.array(Image.open(data)).astype(float) +image2d = (image2d/255).astype(np.uint16) + +file_meta = FileMetaDataset() +file_meta.MediaStorageSOPClassUID = "1.2.840.10008.5.1.4.1.1.1" +file_meta.MediaStorageSOPInstanceUID ='2.25.34327501276176110812231595851948283641' +file_meta.ImplementationClassUID = '1.3.6.1.4.1.30071.8' +file_meta.TransferSyntaxUID = pydicom.uid.ExplicitVRLittleEndian + +ds = Dataset() +ds.file_meta = file_meta + +ds.Rows = image2d.shape[0] +ds.Columns = image2d.shape[1] +ds.NumberOfFrames = 1 + +ds.PixelSpacing = [1, 1] # in mm +ds.SliceThickness = 1 # in mm + +ds.SeriesInstanceUID = pydicom.uid.generate_uid() +ds.StudyInstanceUID = pydicom.uid.generate_uid() + +ds.PatientName = "Demo^RSNA2021" +ds.PatientID = "123456" +ds.Modality = "CR" +ds.StudyDate = '20211204' +ds.ContentDate = '20211204' + +ds.BitsStored = 16 +ds.BitsAllocated = 16 +ds.HighBit = 15 +ds.PixelRepresentation = 0 +ds.PhotometricInterpretation = "MONOCHROME2" +ds.SamplesPerPixel = 1 + +ds.RescaleIntercept = 900 +ds.RescaleSlope = 9 +ds.WindowCenter = 2000 +ds.WindowWidth = 2000 + +ds.is_little_endian = True +ds.is_implicit_VR = False + +ds.PixelData = image2d.tobytes() + +pydicom.dataset.validate_file_meta(ds.file_meta, enforce_standard=True) +ds.save_as("sample_dicom.dcm", write_like_original=False) + diff --git a/4.2.Deploy the model(optimized)_sdk_v2/sample.png b/4.2.Deploy the model(optimized)_sdk_v2/sample.png new file mode 100644 index 0000000..345daf5 --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/sample.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2f71508216405808a07bfb8854ce1a4712187221e6c9a8189d3432204cd77748 +size 4595336 diff --git a/4.2.Deploy the model(optimized)_sdk_v2/sample_dicom.dcm b/4.2.Deploy the model(optimized)_sdk_v2/sample_dicom.dcm new file mode 100644 index 0000000..790e2c0 --- /dev/null +++ b/4.2.Deploy the model(optimized)_sdk_v2/sample_dicom.dcm @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de542fd919cf1ffbee63524365d69864f6f8b3bfbac7a34e558fbf6cd72f890c +size 6987998