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 # model_server
+model_server implement image analysis jobs for online use (e.g. in feedback microscopy), including adapters to ilastik
 
-# How to extend service
-Add sub-package to extensions
-Add models that inherit from model_server.Model 
-In workflows, implement pipelines with File I/O via accessors.GenericImageDataAccessor
-(to decouple model logic from image data source)
-Set extensions-specific folders, etc. in conf relative to overall package root (set by user)
-As much as possible, set pipeline and model parameters with defaults and support overrides by optional API arguments; 
-this helps non-coding users control their jobs
-Set up API endpoints in router, following as much as possible existing conventions with load, infer, etc. keyword
+# Installation on Windows
 
-decouple data access from processing
+1. Install Git:<br>https://git-scm.com/download/win
+2. Install Miniforge for environment management:<br>https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge-pypy3-Windows-x86_64.exe
+3. Under the Start menu, open `Miniforge3 > Miniforge Prompt`
+4. In the new terminal, run:<br>
+   `cd %userprofile%`<br>
+   `git clone https://almf-staff:KJmFvyPRbpzoVZDqfMzV@git.embl.de/rhodes/model_server.git`
+5. Open the newly created project root: `cd model_server`
+6. Create the environment: `mamba env create --file requirements.yml --name model_server_env`
+7. Activate the environment: `mamba activate model_server_env`
+8. Add the project source as a Python package: `pip install -e .`
 
-control either via batch runners or API (serial)
+# Start the server
+Simply click "start_server command" in the model_server directory.  This should open a terminal that reports server requests, as well as a browser with a status confirmation page.  To stop the server, type "stop" in the terminal.
 
-workflow: combines data access with processing via models, produces primary outputs
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