BEP1001: App Injection

drafts/BEP1001-app-injection.md

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+---
+Author: Joongi Kim (joongi@lablup.com)
+Status: Draft
+---
+
+# App Injection
+
+## Motivation
+
+* Decouple the update cycle of container apps (e.g., Jupyter) and the container images (e.g., NGC PyTorch)
+* Allow users to pin a desired version of the apps (e.g., Jupyter Notebook v7 has many breaking changes and most nbextensions do not work)
+
+## Proposed structure
+
+### App Package
+
+- `service-def.json` (mounted as `/opt/kernel/service-defs/{app-name}.json`)
+- `app/` (mounted as `/opt/apps/{app-name}/`)
+
+### Updates to Manager API
+
+- List the available app packages 
+- Auto-populate required app packages into agent nodes when creating sessions and kernels  
+  (or, we need to assume that we have consistent app package files in all agent nodes...)
+- Specify the app packages when creating sessions
+- Perform a compatibility check by comparing some metadata  
+  (e.g., is it a static build or does it depend on a specific glibc version?)
+
+### Updates to Agent API
+
+- TODO
+
+## Migration of Existing Apps
+
+### Overall technical issues
+
+- If we mount the extracted app directories as _read-only_, some apps may not work as expected.
+- If the app depends on directories that only root can write to, it may not work as expected.
+- The delay required to load and extract the app packages
+- Managing the cached extracted app packages within the disk size limit of agents
+- Should we ship PBS (python-build-standalone) individually by each app?
+
+### Jupyter
+
+Recently Jupyter has changed its configuration format. (TODO: issue link)
+
+We need to generate version-specific `.cfg` file, by replacing the service definition
+to execute an (injected) script instead of directly writing the configuration file from the embedded template string.
+
+**Design discussion:**
+- Make a wheelhouse directory and install them when first-launching the app  
+  _vs._ Make a pre-installed site-packages directory and mount subdirs into `/opt/backend.ai`?
+- We must install `ipykernel` at least in the _target_ Python (the Python already-installed inside the container image) upon the first launch
+  and generate the metakernel spec (like what we does in [backend.ai-kernels](https://github.com/lablup/backend.ai-kernels/blob/075ca379c7db279f69d4485bf014750af0bb6fdc/python/Dockerfile.3.11-ubuntu22.04#L135-L137)).
+  We need an explicit error reporting when this fails.
+
+### VSCode
+
+- Could we make a statically built app package?
+- Could we allow users to install their own extensions in container while the app package is read-only?