221 строка
8.1 KiB
Markdown
221 строка
8.1 KiB
Markdown
# Introduction
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InnerEye-Inference is a AppService webapp in python to run inference on medical imaging models trained with the [InnerEye-DeepLearning toolkit](https://github.com/microsoft/InnerEye-Inference).
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You can also integrate this with DICOM using the [InnerEye-Gateway](https://github.com/microsoft/InnerEye-Gateway)
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## Getting Started
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### Installing Conda or Miniconda
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Download a Conda or Miniconda [installer for your platform](https://docs.conda.io/en/latest/miniconda.html)
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and run it.
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### Creating a Conda environment
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Note that in order to create the Conda environment you will need to have build tools installed on your machine. If you are running Windows, they should be already installed with Conda distribution.
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You can install build tools on Ubuntu (and Debian-based distributions) by running
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`sudo apt-get install build-essential`.
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If you are running CentOS/RHEL distributions, you can install the build tools by running
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`yum install gcc gcc-c++ kernel-devel make`.
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#### Linux Users
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Start the `conda` prompt for your platform. In that prompt, navigate to your repository root and run
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```console
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conda env create --file environment.yml
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conda activate inference
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```
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#### Windows Users
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Start the `conda` prompt for your platform. In that prompt, navigate to your repository root and run
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```console
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conda env create --file environment_win.yml
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conda activate inference
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```
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### Configuration
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Add this script with name set_environment.sh to set your env variables. This can be executed in Linux. The code will read the file if the environment variables are not present.
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```bash
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#!/bin/bash
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export CUSTOMCONNSTR_AZUREML_SERVICE_PRINCIPAL_SECRET=
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export CUSTOMCONNSTR_API_AUTH_SECRET=
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export CLUSTER=
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export WORKSPACE_NAME=
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export EXPERIMENT_NAME=
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export RESOURCE_GROUP=
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export SUBSCRIPTION_ID=
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export APPLICATION_ID=
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export TENANT_ID=
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export DATASTORE_NAME=
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export IMAGE_DATA_FOLDER=
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```
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Run with `source set_environment.sh`
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### Running flask app locally
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* `flask run` to test it locally
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### Testing flask app locally
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The app can be tested locally using [`curl`](https://curl.se/).
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#### Ping
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To check that the server is running, issue this command from a local shell:
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```console
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curl -i -H "API_AUTH_SECRET: <val of CUSTOMCONNSTR_API_AUTH_SECRET>" http://localhost:5000/v1/ping
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```
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This should produce an output similar to:
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```text
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HTTP/1.0 200 OK
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Content-Type: text/html; charset=utf-8
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Content-Length: 0
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Server: Werkzeug/1.0.1 Python/3.7.3
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Date: Wed, 18 Aug 2021 11:50:20 GMT
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```
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#### Start
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To test DICOM image segmentation of a file, first create `Tests/TestData/HN.zip` containing a zipped set of the test DICOM files in `Tests/TestData/HN`. Then assuming there is a model `PassThroughModel:4`, issue this command:
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```text
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curl -i \
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-X POST \
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-H "API_AUTH_SECRET: <val of CUSTOMCONNSTR_API_AUTH_SECRET>" \
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--data-binary @Tests/TestData/HN.zip \
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http://localhost:5000/v1/model/start/PassThroughModel:4
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```
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This should produce an output similar to:
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```text
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HTTP/1.0 201 CREATED
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Content-Type: text/plain
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Content-Length: 33
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Server: Werkzeug/1.0.1 Python/3.7.3
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Date: Wed, 18 Aug 2021 13:00:13 GMT
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api_inference_1629291609_fb5dfdf9
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```
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here `api_inference_1629291609_fb5dfdf9` is the run id for the newly submitted inference job.
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#### Results
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To monitor the progress of the previously submitted inference job, issue this command:
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```console
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curl -i \
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-H "API_AUTH_SECRET: <val of CUSTOMCONNSTR_API_AUTH_SECRET>" \
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--head \
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http://localhost:5000/v1/model/results/api_inference_1629291609_fb5dfdf9 \
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--next \
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-H "API_AUTH_SECRET: <val of CUSTOMCONNSTR_API_AUTH_SECRET>" \
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--output "HN_rt.zip" \
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http://localhost:5000/v1/model/results/api_inference_1629291609_fb5dfdf9
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```
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If the run is still in progress then this should produce output similar to:
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```text
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HTTP/1.0 202 ACCEPTED
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Content-Type: text/html; charset=utf-8
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Content-Length: 0
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Server: Werkzeug/1.0.1 Python/3.7.3
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Date: Wed, 18 Aug 2021 13:45:20 GMT
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% Total % Received % Xferd Average Speed Time Time Time Current
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Dload Upload Total Spent Left Speed
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0 0 0 0 0 0 0 0 --:--:-- 0:00:01 --:--:-- 0
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```
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If the run is complete then this should produce an output similar to:
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```text
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HTTP/1.0 200 OK
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Content-Type: application/zip
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Content-Length: 131202
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Server: Werkzeug/1.0.1 Python/3.7.3
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Date: Wed, 18 Aug 2021 14:01:27 GMT
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% Total % Received % Xferd Average Speed Time Time Time Current
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Dload Upload Total Spent Left Speed
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100 128k 100 128k 0 0 150k 0 --:--:-- --:--:-- --:--:-- 150k
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```
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and download the inference result as a zipped DICOM-RT file to `HN_rt.zip`.
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### Running flask app in Azure
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1. Install Azure CLI: `curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash`
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2. Login: `az login --use-device-code`
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3. Deploy: `az webapp up --sku S1 --name test-python12345 --subscription <your_subscription_name> -g InnerEyeInference --location <your region>`
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4. In the Azure portal go to Monitoring > Log Stream for debugging logs
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### Deployment build
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If you would like to reproduce the automatic deployment of the service for testing purposes:
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* `az ad sp create-for-rbac --name "<name>" --role contributor --scope /subscriptions/<subs>/resourceGroups/InnerEyeInference --sdk-auth`
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* The previous command will return a json object with the content for the variable `secrets.AZURE_CREDENTIALS` .github/workflows/deploy.yml
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## Images
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During inference the image data zip file is copied to the IMAGE_DATA_FOLDER in the AzureML workspace's DATASTORE_NAME datastore. At the end of inference the copied image data zip file is overwritten with a simple line of text. At present we cannot delete these. If you would like these overwritten files removed from your datastore you can [add a policy](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-lifecycle-management-concepts?tabs=azure-portal) to delete items from the datastore after a period of time. We recommend 7 days.
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## Changing Primary Dependencies
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1. Make your desired changes in `primary_deps.yml`. Make sure your package name and version are correct.
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2. To create a new environment and a valid `environment.yml`, run the following command:
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```shell
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bash -i create_and_lock_environment.sh
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```
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3. Voila! You will now have a new conda environment with your desired primary package versions, as well as a new `environment.yml` which can be ingested by AzureML to create a copy of your local environment.
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## Help and Bug Reporting
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1. [Guidelines for how to report bug.](./docs/BugReporting.md)
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## Licensing
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[MIT License](LICENSE)
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**You are responsible for the performance and any necessary testing or regulatory clearances for any models generated**
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## Contributing
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This project welcomes contributions and suggestions. Most contributions require you to agree to a
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Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
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the rights to use your contribution. For details, visit the [Microsoft CLA site](https://cla.opensource.microsoft.com).
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When you submit a pull request, a CLA bot will automatically determine whether you need to provide
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a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
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provided by the bot. You will only need to do this once across all repos using our CLA.
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
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contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
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## Microsoft Open Source Code of Conduct
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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## Resources
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* [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
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* [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
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* Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns
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