DOC: Add a smoke-test model for AzureML setup

.gitignore

@@ -130,10 +130,6 @@ venv.bak/
 .dmypy.json
 dmypy.json
 
-# other
-.vscode/
-.devcontainer/
-
 /InnerEye/ML/src/aml_config
 *.exe
 *.suo

InnerEye/ML/runner.py

@@ -12,7 +12,7 @@
 # Workaround for an issue with how AzureML and Pytorch Lightning interact: When spawning additional processes for DDP,
 # the working directory is not correctly picked up in sys.path
 print(f"Starting InnerEye runner at {sys.argv[0]}")
-innereye_root = Path(__file__).absolute().parent.parent.parent
+innereye_root = Path(__file__).resolve().parent.parent.parent
 if (innereye_root / "InnerEye").is_dir():
     innereye_root_str = str(innereye_root)
     if innereye_root_str not in sys.path:

README.md

@@ -97,8 +97,8 @@ If it fails, please check the
 Further detailed instructions, including setup in Azure, are here:
 
 1. [Setting up your environment](docs/environment.md)
-1. [Training a Hello World segmentation model](docs/hello_world_model.md)
 1. [Setting up Azure Machine Learning](docs/setting_up_aml.md)
+1. [Training a simple segmentation model in Azure ML](docs/hello_world_model.md)
 1. [Creating a dataset](docs/creating_dataset.md)
 1. [Building models in Azure ML](docs/building_models.md)
 1. [Sample Segmentation and Classification tasks](docs/sample_tasks.md)

docs/hello_world_model.md

@@ -77,5 +77,5 @@ Then execute:
 
 ```shell
 conda activate InnerEye
-python InnerEye/ML/runner.py --model=HelloWorld
+python InnerEye/ML/runner.py --model=HelloWorld --azureml
 ```

docs/setting_up_aml.md

@@ -218,5 +218,14 @@ variables:
 
 You should be all set now.
 
+You can verify that your AzureML setup works by running in a shell:
+
+```shell
+python InnerEye/ML/runner.py --model=HelloContainer --azureml
+```
+
+This will start a simple model training job in AzureML. At the end of the submission process, it will print out "Run URL: ...". Paste that
+URL into a browser, and it will take you to the AzureML portal where you can monitor that job.
+
 You can now go to the next step, [creating a dataset](creating_dataset.md), to learn how segmentation
 datasets should be structured, and how to get your data ready for use in training.