{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deploying a MedNIST Classifier App with MONAI Deploy App SDK (Prebuilt Model)\n", "\n", "This tutorial demos the process of packaging up a trained model using MONAI Deploy App SDK into an artifact which can be run as a local program performing inference, a workflow job doing the same, and a Docker containerized workflow execution.\n", "\n", "In this tutorial, we will use a trained model and implement & package the inference application, executing the application locally.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clone the github project (the latest version of the main branch only)\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cloning into 'source'...\n", "remote: Enumerating objects: 212, done.\u001b[K\n", "remote: Counting objects: 100% (212/212), done.\u001b[K\n", "remote: Compressing objects: 100% (188/188), done.\u001b[K\n", "remote: Total 212 (delta 33), reused 79 (delta 7), pack-reused 0\u001b[K\n", "Receiving objects: 100% (212/212), 546.28 KiB | 3.50 MiB/s, done.\n", "Resolving deltas: 100% (33/33), done.\n" ] } ], "source": [ "!git clone --branch main --depth 1 https://github.com/Project-MONAI/monai-deploy-app-sdk.git source \\\n", " && rm -rf source/.git" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mednist_classifier_monaideploy.py\n" ] } ], "source": [ "!ls source/examples/apps/mednist_classifier_monaideploy/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Install monai-deploy-app-sdk package" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n", "Collecting monai-deploy-app-sdk\n", " Downloading monai_deploy_app_sdk-0.1.0rc2-py3-none-any.whl (113 kB)\n", "\u001b[K |████████████████████████████████| 113 kB 2.6 MB/s eta 0:00:01\n", "\u001b[?25hCollecting colorama>=0.4.1\n", " Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n", "Collecting networkx>=2.4\n", " Downloading networkx-2.5.1-py3-none-any.whl (1.6 MB)\n", "\u001b[K |████████████████████████████████| 1.6 MB 12.8 MB/s eta 0:00:01\n", "\u001b[?25hCollecting typeguard>=2.12.1\n", " Downloading typeguard-2.12.1-py3-none-any.whl (17 kB)\n", "Collecting numpy>=1.17\n", " Downloading numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl (14.8 MB)\n", "\u001b[K |████████████████████████████████| 14.8 MB 9.5 MB/s eta 0:00:011\n", "\u001b[?25hCollecting decorator<5,>=4.3\n", " Downloading decorator-4.4.2-py2.py3-none-any.whl (9.2 kB)\n", "Installing collected packages: decorator, typeguard, numpy, networkx, colorama, monai-deploy-app-sdk\n", " Attempting uninstall: decorator\n", " Found existing installation: decorator 5.1.0\n", " Uninstalling decorator-5.1.0:\n", " Successfully uninstalled decorator-5.1.0\n", "Successfully installed colorama-0.4.4 decorator-4.4.2 monai-deploy-app-sdk-0.1.0rc2 networkx-2.5.1 numpy-1.19.5 typeguard-2.12.1\n" ] } ], "source": [ "!pip install --upgrade monai-deploy-app-sdk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Install necessary packages for the app" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n", "Collecting monai\n", " Downloading monai-0.6.0-202107081903-py3-none-any.whl (584 kB)\n", "\u001b[K |████████████████████████████████| 584 kB 2.7 MB/s eta 0:00:01\n", "\u001b[?25hCollecting Pillow\n", " Downloading Pillow-8.3.2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB)\n", "\u001b[K |████████████████████████████████| 3.0 MB 23.2 MB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: torch>=1.5 in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from monai) (1.9.1)\n", "Requirement already satisfied: numpy>=1.17 in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from monai) (1.19.5)\n", "Requirement already satisfied: typing_extensions in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from torch>=1.5->monai) (3.10.0.0)\n", "Requirement already satisfied: dataclasses in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from torch>=1.5->monai) (0.8)\n", "Installing collected packages: Pillow, monai\n", "Successfully installed Pillow-8.3.2 monai-0.6.0\n" ] } ], "source": [ "!pip install monai Pillow # for MONAI transforms and Pillow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download/Extract mednist_classifier_data.zip from Google Drive" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n", "Collecting gdown\n", " Downloading gdown-3.13.1.tar.gz (10 kB)\n", " Installing build dependencies ... \u001b[?25ldone\n", "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", "\u001b[?25h Preparing wheel metadata ... \u001b[?25ldone\n", "\u001b[?25hCollecting filelock\n", " Downloading filelock-3.0.12-py3-none-any.whl (7.6 kB)\n", "Requirement already satisfied: requests[socks]>=2.12.0 in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from gdown) (2.26.0)\n", "Requirement already satisfied: six in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from gdown) (1.16.0)\n", "Collecting tqdm\n", " Downloading tqdm-4.62.3-py2.py3-none-any.whl (76 kB)\n", "\u001b[K |████████████████████████████████| 76 kB 2.8 MB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: certifi>=2017.4.17 in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from requests[socks]>=2.12.0->gdown) (2021.5.30)\n", "Requirement already satisfied: idna<4,>=2.5 in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from requests[socks]>=2.12.0->gdown) (3.1)\n", "Requirement already satisfied: charset-normalizer~=2.0.0 in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from requests[socks]>=2.12.0->gdown) (2.0.0)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from requests[socks]>=2.12.0->gdown) (1.26.6)\n", "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /home/gbae/miniconda3/envs/mednist/lib/python3.6/site-packages (from requests[socks]>=2.12.0->gdown) (1.7.1)\n", "Building wheels for collected packages: gdown\n", " Building wheel for gdown (PEP 517) ... \u001b[?25ldone\n", "\u001b[?25h Created wheel for gdown: filename=gdown-3.13.1-py3-none-any.whl size=9907 sha256=34f13d3a73d5f3f25f15dd69606e75b7d211bb9cc638bc47b82043612514d1f4\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-xtmpuwlo/wheels/6b/ba/3b/57c8250cc9279fb303e8bfa589361cbc58a1afb291475c4ddc\n", "Successfully built gdown\n", "Installing collected packages: tqdm, filelock, gdown\n", "Successfully installed filelock-3.0.12 gdown-3.13.1 tqdm-4.62.3\n", "Downloading...\n", "From: https://drive.google.com/uc?id=1yJ4P-xMNEfN6lIOq_u6x1eMAq1_MJu-E\n", "To: /home/gbae/mednist_app/mednist_classifier_data.zip\n", "28.6MB [00:02, 10.3MB/s]\n" ] } ], "source": [ "# Download mednist_classifier_data.zip\n", "!pip install gdown \n", "!gdown \"https://drive.google.com/uc?id=1yJ4P-xMNEfN6lIOq_u6x1eMAq1_MJu-E\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Archive: mednist_classifier_data.zip\n", " extracting: classifier.zip \n", " extracting: input/AbdomenCT_007000.jpeg \n" ] } ], "source": [ "# After downloading mednist_classifier_data.zip from the web browser or using gdown,\n", "!unzip -o \"mednist_classifier_data.zip\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Package app (creating MAP Docker image)\n", "\n", "This assumes that nvidia docker is installed in the local machine.\n", "\n", "Please see https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker to install nvidia-docker2.\n", "\n", "Use `-l DEBUG` option to see progress." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Building MONAI Application Package... Done\n", "[2021-09-21 03:07:51,614] [INFO] (app_packager) - Successfully built mednist_app:latest\n" ] } ], "source": [ "!monai-deploy package \"source/examples/apps/mednist_classifier_monaideploy/mednist_classifier_monaideploy.py\" \\\n", " --tag mednist_app:latest \\\n", " --model classifier.zip" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the app with docker image and input file locally" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking dependencies...\n", "--> Verifying if \"docker\" is installed...\n", "\n", "--> Verifying if \"mednist_app:latest\" is available...\n", "\n", "Checking for MAP \"mednist_app:latest\" locally\n", "\"mednist_app:latest\" found.\n", "\n", "Reading MONAI App Package manifest...\n", " > export '/var/run/monai/export/' detected\n", "--> Verifying if \"nvidia-docker\" is installed...\n", "\n", "\u001b[34mGoing to initiate execution of operator LoadPILOperator\u001b[39m\n", "\u001b[32mExecuting operator LoadPILOperator \u001b[33m(Process ID: 1, Operator ID: 2000b9d2-156f-4abd-8654-cf60219673ac)\u001b[39m\n", "\u001b[34mDone performing execution of operator LoadPILOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator MedNISTClassifierOperator\u001b[39m\n", "\u001b[32mExecuting operator MedNISTClassifierOperator \u001b[33m(Process ID: 1, Operator ID: 13deb10c-dd13-4af5-8a05-a72c07406c05)\u001b[39m\n", "AbdomenCT\n", "\u001b[34mDone performing execution of operator MedNISTClassifierOperator\n", "\u001b[39m\n" ] } ], "source": [ "!monai-deploy run mednist_app:latest \"input\" \"output\"" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"AbdomenCT\"" ] } ], "source": [ "!cat output/output.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementing and Packaging Application with MONAI Deploy App SDK\n", "\n", "Based on the Torchscript model(`classifier.zip`), we will implement an application that process an input Jpeg image and write the prediction(classification) result as JSON file(`output.json`).\n", "\n", "In our inference application, we will define two operators:\n", "\n", "1. `LoadPILOperator` - Load a JPEG image from the input path and pass the loaded image object to the next operator.\n", " - This Operator does similar job with `LoadImage(image_only=True)` transform in *train_transforms*, but handles only one image.\n", " - **Input**: a file path ([`DataPath`](/modules/_autosummary/monai.deploy.core.domain.DataPath))\n", " - **Output**: an image object in memory ([`Image`](/modules/_autosummary/monai.deploy.core.domain.Image))\n", "2. `MedNISTClassifierOperator` - Pre-transform the given image by using MONAI's `Compose` class, feed to the Torchscript model (`classifier.zip`), and write the prediction into JSON file(`output.json`)\n", " - Pre-transforms consist of three transforms -- `AddChannel`, `ScaleIntensity`, and `EnsureType`.\n", " - **Input**: an image object in memory ([`Image`](/modules/_autosummary/monai.deploy.core.domain.Image))\n", " - **Output**: a folder path that the prediction result(`output.json`) would be written ([`DataPath`](/modules/_autosummary/monai.deploy.core.domain.DataPath))\n", "\n", "The workflow of the application would look like this.\n", "\n", "\"Workflow\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup imports\n", "\n", "Let's import necessary classes/decorators and define `MEDNIST_CLASSES`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import monai.deploy.core as md\n", "from monai.deploy.core import (\n", " Application,\n", " DataPath,\n", " ExecutionContext,\n", " Image,\n", " InputContext,\n", " IOType,\n", " Operator,\n", " OutputContext,\n", ")\n", "from monai.transforms import AddChannel, Compose, EnsureType, ScaleIntensity\n", "\n", "MEDNIST_CLASSES = [\"AbdomenCT\", \"BreastMRI\", \"CXR\", \"ChestCT\", \"Hand\", \"HeadCT\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating Operator classes\n", "\n", "#### LoadPILOperator" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "@md.input(\"image\", DataPath, IOType.DISK)\n", "@md.output(\"image\", Image, IOType.IN_MEMORY)\n", "@md.env(pip_packages=[\"pillow\"])\n", "class LoadPILOperator(Operator):\n", " \"\"\"Load image from the given input (DataPath) and set numpy array to the output (Image).\"\"\"\n", "\n", " def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):\n", " import numpy as np\n", " from PIL import Image as PILImage\n", "\n", " input_path = op_input.get().path\n", " if input_path.is_dir():\n", " input_path = next(input_path.glob(\"*.*\")) # take the first file\n", "\n", " image = PILImage.open(input_path)\n", " image = image.convert(\"L\") # convert to greyscale image\n", " image_arr = np.asarray(image)\n", "\n", " output_image = Image(image_arr) # create Image domain object with a numpy array\n", " op_output.set(output_image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### MedNISTClassifierOperator" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "@md.input(\"image\", Image, IOType.IN_MEMORY)\n", "@md.output(\"output\", DataPath, IOType.DISK)\n", "@md.env(pip_packages=[\"monai\"])\n", "class MedNISTClassifierOperator(Operator):\n", " \"\"\"Classifies the given image and returns the class name.\"\"\"\n", "\n", " @property\n", " def transform(self):\n", " return Compose([AddChannel(), ScaleIntensity(), EnsureType()])\n", "\n", " def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):\n", " import json\n", "\n", " import torch\n", "\n", " img = op_input.get().asnumpy() # (64, 64), uint8\n", " image_tensor = self.transform(img) # (1, 64, 64), torch.float64\n", " image_tensor = image_tensor[None].float() # (1, 1, 64, 64), torch.float32\n", "\n", " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " image_tensor = image_tensor.to(device)\n", "\n", " model = context.models.get() # get a TorchScriptModel object\n", "\n", " with torch.no_grad():\n", " outputs = model(image_tensor)\n", "\n", " _, output_classes = outputs.max(dim=1)\n", "\n", " result = MEDNIST_CLASSES[output_classes[0]] # get the class name\n", " print(result)\n", "\n", " # Get output (folder) path and create the folder if not exists\n", " output_folder = op_output.get().path\n", " output_folder.mkdir(parents=True, exist_ok=True)\n", "\n", " # Write result to \"output.json\"\n", " output_path = output_folder / \"output.json\"\n", " with open(output_path, \"w\") as fp:\n", " json.dump(result, fp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating Application class\n", "\n", "Our application class would look like below.\n", "\n", "It defines `App` class inheriting `Application` class.\n", "\n", "`LoadPILOperator` is connected to `MedNISTClassifierOperator` by using `self.add_flow()` in `compose()` method of `App`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "@md.resource(cpu=1, gpu=1, memory=\"1Gi\")\n", "class App(Application):\n", " \"\"\"Application class for the MedNIST classifier.\"\"\"\n", "\n", " def compose(self):\n", " load_pil_op = LoadPILOperator()\n", " classifier_op = MedNISTClassifierOperator()\n", "\n", " self.add_flow(load_pil_op, classifier_op)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Executing app locally" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can execute the app in the Jupyter notebook." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "app = App()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34mGoing to initiate execution of operator LoadPILOperator\u001b[39m\n", "\u001b[32mExecuting operator LoadPILOperator \u001b[33m(Process ID: 7041, Operator ID: 3aa42bbd-f8dd-4374-98ee-7b614979e75a)\u001b[39m\n", "\u001b[34mDone performing execution of operator LoadPILOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator MedNISTClassifierOperator\u001b[39m\n", "\u001b[32mExecuting operator MedNISTClassifierOperator \u001b[33m(Process ID: 7041, Operator ID: 7ee7dd5e-c042-4245-bb75-15ff064bd838)\u001b[39m\n", "AbdomenCT\n", "\u001b[34mDone performing execution of operator MedNISTClassifierOperator\n", "\u001b[39m\n" ] } ], "source": [ "app.run(input=\"input/AbdomenCT_007000.jpeg\", output=\"output\", model=\"classifier.zip\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"AbdomenCT\"" ] } ], "source": [ "!cat output/output.json" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "Once the application is verified inside Jupyter notebook, we can write the whole application as a file(`mednist_classifier_monaideploy.py`) by concatenating code above, then add the following lines:\n", "\n", "```python\n", "if __name__ == \"__main__\":\n", " App(do_run=True)\n", "```\n", "\n", "The above lines are needed to execute the application code by using `python` interpreter." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing mednist_classifier_monaideploy.py\n" ] } ], "source": [ "%%writefile mednist_classifier_monaideploy.py\n", "\n", "# Copyright 2021 MONAI Consortium\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "# http://www.apache.org/licenses/LICENSE-2.0\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License.\n", "\n", "import monai.deploy.core as md # 'md' stands for MONAI Deploy (or can use 'core' instead)\n", "from monai.deploy.core import (\n", " Application,\n", " DataPath,\n", " ExecutionContext,\n", " Image,\n", " InputContext,\n", " IOType,\n", " Operator,\n", " OutputContext,\n", ")\n", "from monai.transforms import AddChannel, Compose, EnsureType, ScaleIntensity\n", "\n", "MEDNIST_CLASSES = [\"AbdomenCT\", \"BreastMRI\", \"CXR\", \"ChestCT\", \"Hand\", \"HeadCT\"]\n", "\n", "\n", "@md.input(\"image\", DataPath, IOType.DISK)\n", "@md.output(\"image\", Image, IOType.IN_MEMORY)\n", "@md.env(pip_packages=[\"pillow\"])\n", "class LoadPILOperator(Operator):\n", " \"\"\"Load image from the given input (DataPath) and set numpy array to the output (Image).\"\"\"\n", "\n", " def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):\n", " import numpy as np\n", " from PIL import Image as PILImage\n", "\n", " input_path = op_input.get().path\n", " if input_path.is_dir():\n", " input_path = next(input_path.glob(\"*.*\")) # take the first file\n", "\n", " image = PILImage.open(input_path)\n", " image = image.convert(\"L\") # convert to greyscale image\n", " image_arr = np.asarray(image)\n", "\n", " output_image = Image(image_arr) # create Image domain object with a numpy array\n", " op_output.set(output_image)\n", "\n", "\n", "@md.input(\"image\", Image, IOType.IN_MEMORY)\n", "@md.output(\"output\", DataPath, IOType.DISK)\n", "@md.env(pip_packages=[\"monai\"])\n", "class MedNISTClassifierOperator(Operator):\n", " \"\"\"Classifies the given image and returns the class name.\"\"\"\n", "\n", " @property\n", " def transform(self):\n", " return Compose([AddChannel(), ScaleIntensity(), EnsureType()])\n", "\n", " def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):\n", " import json\n", "\n", " import torch\n", "\n", " img = op_input.get().asnumpy() # (64, 64), uint8\n", " image_tensor = self.transform(img) # (1, 64, 64), torch.float64\n", " image_tensor = image_tensor[None].float() # (1, 1, 64, 64), torch.float32\n", "\n", " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " image_tensor = image_tensor.to(device)\n", "\n", " model = context.models.get() # get a TorchScriptModel object\n", "\n", " with torch.no_grad():\n", " outputs = model(image_tensor)\n", "\n", " _, output_classes = outputs.max(dim=1)\n", "\n", " result = MEDNIST_CLASSES[output_classes[0]] # get the class name\n", " print(result)\n", "\n", " # Get output (folder) path and create the folder if not exists\n", " output_folder = op_output.get().path\n", " output_folder.mkdir(parents=True, exist_ok=True)\n", "\n", " # Write result to \"output.json\"\n", " output_path = output_folder / \"output.json\"\n", " with open(output_path, \"w\") as fp:\n", " json.dump(result, fp)\n", "\n", "\n", "@md.resource(cpu=1, gpu=1, memory=\"1Gi\")\n", "class App(Application):\n", " \"\"\"Application class for the MedNIST classifier.\"\"\"\n", "\n", " def compose(self):\n", " load_pil_op = LoadPILOperator()\n", " classifier_op = MedNISTClassifierOperator()\n", "\n", " self.add_flow(load_pil_op, classifier_op)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " App(do_run=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this time, let's execute the app in the command line." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34mGoing to initiate execution of operator LoadPILOperator\u001b[39m\n", "\u001b[32mExecuting operator LoadPILOperator \u001b[33m(Process ID: 8412, Operator ID: 631a82bf-c90e-4217-a17c-831b2c74bc50)\u001b[39m\n", "\u001b[34mDone performing execution of operator LoadPILOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator MedNISTClassifierOperator\u001b[39m\n", "\u001b[32mExecuting operator MedNISTClassifierOperator \u001b[33m(Process ID: 8412, Operator ID: a8fe1121-68bb-463f-bf1c-beff38d4fe86)\u001b[39m\n", "AbdomenCT\n", "\u001b[34mDone performing execution of operator MedNISTClassifierOperator\n", "\u001b[39m\n" ] } ], "source": [ "!python \"mednist_classifier_monaideploy.py\" -i \"input/AbdomenCT_007000.jpeg\" -o output -m \"classifier.zip\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above command is same with the following command line:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34mGoing to initiate execution of operator LoadPILOperator\u001b[39m\n", "\u001b[32mExecuting operator LoadPILOperator \u001b[33m(Process ID: 8453, Operator ID: 7dec2a01-6d18-4104-b250-5b93d663ba4f)\u001b[39m\n", "\u001b[34mDone performing execution of operator LoadPILOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator MedNISTClassifierOperator\u001b[39m\n", "\u001b[32mExecuting operator MedNISTClassifierOperator \u001b[33m(Process ID: 8453, Operator ID: 5e83dd80-5b19-4c78-9382-3d181640b80c)\u001b[39m\n", "AbdomenCT\n", "\u001b[34mDone performing execution of operator MedNISTClassifierOperator\n", "\u001b[39m\n" ] } ], "source": [ "!monai-deploy exec \"mednist_classifier_monaideploy.py\" -i \"input/AbdomenCT_007000.jpeg\" -o output -m \"classifier.zip\"" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"AbdomenCT\"" ] } ], "source": [ "!cat output/output.json" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.13" } }, "nbformat": 4, "nbformat_minor": 4 }