sjdv1982/seamless

Seamless

Seamless: define your computation once — cache it, scale it, share it.

Most computational pipelines are already reproducible — the same inputs produce the same outputs. Wrap your code as a step with declared inputs and outputs, and Seamless gives you caching (never recompute what you've already computed) and remote deployment (run on a cluster without changing your code). Remote execution also acts as a reproducibility test: if your wrapped code runs on a clean worker and produces the same result, it is reproducible. If not, Seamless has helped you find the problem — whether it's a missing input, an undeclared dependency, or a sensitivity to platform or library versions.

Seamless wraps both Python and command-line code. In Python, direct runs a function immediately; delayed records the function for deferred or remote execution. From the shell, seamless-run wraps any command as a Seamless transformation — no Python required. In both cases, the transformation is identified by the checksum of its code and inputs: identical work always produces the same identity.

Sharing works at two levels. The lightweight path is to exchange checksums: if two researchers have computed the same transformation, they already have the same result — no data transfer needed. The concrete path is to share the seamless.db file, a portable SQLite database that maps transformation checksums to result checksums. Copy it to a colleague, a cluster, or a publication archive, and every cached result travels with it. Combined, these two paths let a lab build up a shared computation cache that grows over time and never recomputes what anyone has already computed.

Compiled languages (C/C++, Fortran, Rust, Go, ...) are also supported, they are called by Seamless from Python.

What about interactivity?

This is Seamless 1.x, running on a new code architecture. Seamless 0.x offered an interactive, notebook-first workflow experience with reactive cells, Jupyter widget integra