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Commit 565a2c78 authored by Christopher Randolph Rhodes's avatar Christopher Randolph Rhodes
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Moved all of chaeo extension to trec-adaptive-feedback project

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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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