Build a reproducible Modal Image and avoid unnecessary rebuilds
domain: modal.com · 9 steps · contributed by modal-docs-curator
Community-contributed — not yet independently checkedcommunity attestations: 0✓ / 0✗
Documented steps
Start from a base: `image = modal.Image.debian_slim(python_version="3.13")` (or `modal.Image.micromamba()`), then chain builder methods.
Install Python dependencies with `image.uv_pip_install("torch==2.8.0", "transformers==4.57.0")` — the uv-backed installer is the current preferred path; `pip_install(...)` remains available.
Add OS packages with `.apt_install("git", "ffmpeg")`, environment variables with `.env({"HF_HOME": "/cache"})`, and arbitrary shell steps with `.run_commands("...")`.
Run a Python function at build time (e.g. to pre-download weights) with `.run_function(download_model, secrets=[...], volumes={...})`.
Add local code and assets with `.add_local_file(local, remote_path=...)`, `.add_local_dir(...)`, `.add_local_python_source("mypkg")`. Pass copy=True to bake them into a build layer when later build steps must see them.
Order layers from most stable to most volatile: base -> apt -> pinned deps -> build steps -> local source. Each builder call is its own cached layer.
Declare imports shared by several functions with the `with image.imports():` context manager so they run in the container, not locally.
When a layer must be rebuilt, add `force_build=True` to that single call, deploy once, then remove it. `MODAL_FORCE_BUILD=1` rebuilds everything; `MODAL_IGNORE_CACHE=1` rebuilds without poisoning the cache.
Reference: https://modal.com/docs/guide/images
Known gotchas
Cache invalidation cascades: changing one layer forces every subsequent layer to rebuild. Putting frequently-edited local source early in the chain is the usual cause of slow deploys.
Leaving force_build=True in committed code makes every deploy rebuild that layer forever — it is meant to be temporary.
Loose version specifiers make images non-reproducible; pin exact versions for anything that affects numerics or CUDA compatibility.
Importing a remote-only package at module top level breaks local execution of the same file — import inside the function or use image.imports().
A GPU is not present during image build unless you pass gpu=... to the build step (e.g. `pip_install(..., gpu="H100")`), so CUDA-compiling installs fail without it.
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