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Next-Gen Docker for AI Developers: Series Intro
Welcome to my 6-part series exploring how Docker's latest features streamline AI and LLM-based application development.
Each part will show how to:
As an AI developer working with LLM apps (LangChain, Flask APIs, etc.), I often deal with repetitive edit-rebuild-restart cycles. This gets especially frustrating while:
Even small changes can become time-consuming in a Dockerized workflow.
What Docker Compose Watch Promises
Docker Compose Watch solves this by:
It sounded perfect for speeding up my LangChain + Flask API dev loop.
My Setup
I containerized a Flask API using LangChain to answer LLM queries.
Instead of manually rebuilding every time I changed a prompt or package, I enabled:
Everything was orchestrated with docker-compose.override.yml using the new develop.watch feature.
? GitHub Repo:
My Real-World Findings
Here’s what I observed after enabling Compose Watch for my LangChain + Flask setup:
Command used
docker compose -f docker-compose.yml -f docker-compose.override.yml up --build
Python code changes synced instantly
Once I saved the file, changes reflected in the running container without needing to restart. This was a big win for fast iteration.
Rebuild didn't work at first for requirements.txt
Initially, even after modifying requirements.txt, no rebuild was triggered. The console just kept showing the logs from the first run.
Fix: Enable watch support explicitly in your terminal
On Windows, I had to explicitly enable Docker’s watch support when using Git Bash or PyCharm's terminal. Once enabled, rebuilds started working as expected.
? Tip: Use a terminal that supports Docker watch triggers or set up your IDE to allow file system notifications to propagate correctly.
Lesson: Even though docker compose watch is powerful, it may require terminal or IDE-level configuration depending on your OS and environment.
Why This Matters for AI Developers
Compose Watch is ideal if you are:
Docker Compose Watch takes us closer to hot-reload for containers, especially for interpreted languages like Python.
While rebuild actions still feel a bit rough around the edges, sync-based workflows are already a productivity booster for LLM, GenAI, and Flask-based AI apps.
? Next in the Series
In Part 2, I will explore Docker Model Runner — a new way to run LLMs locally with Docker, without relying on cloud APIs or billing.
Follow me here on Dev.to to stay updated!
Welcome to my 6-part series exploring how Docker's latest features streamline AI and LLM-based application development.
Each part will show how to:
- Accelerate development
- Reduce manual steps
- Improve containerized workflows for modern AI use cases
As an AI developer working with LLM apps (LangChain, Flask APIs, etc.), I often deal with repetitive edit-rebuild-restart cycles. This gets especially frustrating while:
- Tuning prompts
- Testing temperature settings
- Tweaking model configs
- Updating dependencies
Even small changes can become time-consuming in a Dockerized workflow.
What Docker Compose Watch Promises
Docker Compose Watch solves this by:
- Watching files or directories
- Triggering live sync into the container (sync)
- Or rebuilding when key files change (rebuild)
- Supporting sync+restart for fast config reloading
- Letting you stay in your flow — no manual restart or rebuilds
It sounded perfect for speeding up my LangChain + Flask API dev loop.
My Setup
I containerized a Flask API using LangChain to answer LLM queries.
Instead of manually rebuilding every time I changed a prompt or package, I enabled:
- sync for Python code
- rebuild for requirements.txt
Everything was orchestrated with docker-compose.override.yml using the new develop.watch feature.
? GitHub Repo:
My Real-World Findings
Here’s what I observed after enabling Compose Watch for my LangChain + Flask setup:
Command used
docker compose -f docker-compose.yml -f docker-compose.override.yml up --build
Python code changes synced instantly
Once I saved the file, changes reflected in the running container without needing to restart. This was a big win for fast iteration.
Rebuild didn't work at first for requirements.txt
Initially, even after modifying requirements.txt, no rebuild was triggered. The console just kept showing the logs from the first run.
Fix: Enable watch support explicitly in your terminal
On Windows, I had to explicitly enable Docker’s watch support when using Git Bash or PyCharm's terminal. Once enabled, rebuilds started working as expected.
? Tip: Use a terminal that supports Docker watch triggers or set up your IDE to allow file system notifications to propagate correctly.
Lesson: Even though docker compose watch is powerful, it may require terminal or IDE-level configuration depending on your OS and environment.
Why This Matters for AI Developers
Compose Watch is ideal if you are:
- Experimenting rapidly with LLM APIs
- Testing prompts, chains, or model parameters
- Tired of typing docker compose down && up every few minutes
- Wanting a fast inner loop without sacrificing containerization
Docker Compose Watch takes us closer to hot-reload for containers, especially for interpreted languages like Python.
While rebuild actions still feel a bit rough around the edges, sync-based workflows are already a productivity booster for LLM, GenAI, and Flask-based AI apps.
? Next in the Series
In Part 2, I will explore Docker Model Runner — a new way to run LLMs locally with Docker, without relying on cloud APIs or billing.
Follow me here on Dev.to to stay updated!