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.