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 integration, filesystem mounting, and collaborative web interfaces. These features are being ported to the new architecture. If your work is primarily interactive/exploratory, you can use the legacy version today, or watch this space for updates.

Installation

pip install seamless-suite

This installs all standard Seamless components. For a minimal install, the core user-facing packages are:

Package	Import	Provides
seamless-core	import seamless	Checksum, Buffer, cell types, buffer cache
seamless-transformer	from seamless.transformer import direct, delayed, parallel	direct, delayed, parallel, parallel_async, TransformationList, seamless-run, seamless-upload, seamless-download
seamless-config	import seamless.config	seamless.config.init(), seamless.config.set_nparallel(), seamless-init

Source code

All source code is on GitHub: seamless-core, seamless-transformer, seamless-config, seamless-remote, seamless-jobserver, seamless-dask

Quick Examples

Python: direct

from seamless.transformer import direct

@direct
def add(a, b):
    return a + b

add(2, 3)   # runs the function, returns 5
add(2, 3)   # cache hit — returns 5 instantly

Command line: seamless-run

export SEAMLESS_CACHE=~/.seamless/cache     # global persistent caching

seamless-run 'seq 1 10 | tac && sleep 5'    # runs, caches result
seamless-run 'seq 1 10 | tac && sleep 5'    # cache hit — instant

Seamless mode

Automatically wrap the bash commands you type

Documentation

Full documentation — including getting-started guides, cluster setup, remote execution, compiled transformers, compression, and reference API — is at:

https://sjdv1982.github.io/seamless/

For changes, see RELEASE-NOTES.md.

Agent Skill

Seamless includes an agent skill (seamless-adoption) for AI coding assistants. It guides assessment of codebase fit and planning/executing ports — covering both the Python face (direct/delayed) and the Unix face (seamless-run). See skills/seamless-adoption/SKILL.md.