128 lines
5.2 KiB
Markdown
128 lines
5.2 KiB
Markdown
# SciMesh
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SciMesh is a local-first platform for scientific computation on molecular
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datasets. It turns a scientific run into independent tasks, dispatches them
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to Python workers, and deterministically combines the partial results into a
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checksum-protected final artifact.
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The two halves of the project:
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- **The Workload SDK (`scimesh.sdk`)** — a strict Python framework for
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authoring scientific workloads. Workloads are ordinary user scripts built
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on the SDK; they run locally, in the conformance harness, and on claimed
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coordinator tasks without touching any other part of the program.
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- **The coordinator and worker** — a Go/PostgreSQL coordinator with an
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operator UI and Python worker agents that execute SDK-built workloads over
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an HTTP contract.
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## What is implemented
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- **SDK-built workloads**: `similarity-search` (exact top-k Tanimoto search),
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`similarity-graph` (exact sparse similarity graph with pair-coverage),
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`descriptor-batch` (pinned RDKit 2D descriptors), and `molwt-filter`
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(molecular-weight filter — the minimal authoring example).
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- **`MapReduceWorkload`**: the primary authoring scaffold. A subclass
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declares identity, parameters, ports, and scientific hooks; the SDK
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assembles the manifest, map/reduce stages, the digest-pinned
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planner/runner/reducer, and the exact-artifact verifier.
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- **A local conformance runtime** (`LocalCoreBatchExecutor`): a trusted,
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in-process harness that validates scientific parity, sealed outputs,
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provenance, and limits.
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- **A distributed worker** that executes the same SDK workload handlers on
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tasks claimed from the coordinator, with digest-pinned `TaskSpec`s,
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resource reservation, and allowlist-driven workload discovery.
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- **An operator UI** served by the coordinator: the control room, a workload
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library page, a workload-agnostic "new computation" form whose controls come
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from each workload's own `UIElement` declarations, and this documentation
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site at `/ui/docs/`.
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## Quick start
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The fastest path for a scientist: install the platform with one command and
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start it. Everything — the coordinator, its databases, the userservice, and
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local workers — is embedded in a single binary; no PostgreSQL, no Docker, no
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Python setup.
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```bash
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# Linux / macOS
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curl -fsSL https://raw.githubusercontent.com/emil28092005/SciMesh/main/install.sh | bash
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coordinator serve --open
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# Windows (PowerShell)
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powershell -ExecutionPolicy Bypass -c "irm https://raw.githubusercontent.com/emil28092005/SciMesh/main/install.ps1 | iex"
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coordinator serve --open
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```
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The first start prints the admin login (also stored under `~/.scimesh`), and
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`--open` opens the UI in the browser. `coordinator serve --workers 2`
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spawns two local workers; `SCIMESH_PIP_PACKAGE` points the managed venv at
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your scimesh wheel so scientific workloads can run.
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For development, install the Python SDK and run workloads locally:
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```bash
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python -m venv .venv
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source .venv/bin/activate
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pip install -e '.[dev]'
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scimesh workload list
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scimesh workload run molwt-filter \
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--input molecules.tsv \
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--params '{"min_molwt": 40.0}' \
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-o filtered.csv
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```
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Start the full demo (PostgreSQL, coordinator, UI, two workers):
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```bash
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make demo-ui
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# open http://localhost:18080/ui (root@scimesh.local / rootpassword)
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```
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## Prebuilt binaries
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Every `v*` tag pushes a GitHub Release with static binaries for
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`coordinator` and `worker-agent` on linux/darwin/windows × amd64/arm64
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(plus SHA-256 checksums), the installer scripts above, and the `coordinator`
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image on GHCR. Download and run:
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```bash
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curl -L -o coordinator https://github.com/emil28092005/SciMesh/releases/latest/download/coordinator-linux-amd64
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chmod +x coordinator
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```
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- **worker-agent** runs anywhere with Python: it spawns
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`python -m scimesh.worker.task`, so the machine needs the `scimesh`
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package in a venv (`pip install scimesh`) and the usual environment:
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`COORDINATOR_URL`, `WORKER_AUTH_TOKEN`, `WORK_DIR`. The same binary can run
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it via `coordinator agent`.
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- **coordinator** needs no external services at all in its default mode:
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`coordinator serve` embeds SQLite (both databases), the userservice, and
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local workers. The `SCIMESH_DB=postgres` engine remains for cluster
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deployments (`DATABASE_URL`, `COORDINATOR_STORAGE_DIR`, `JWT_SECRET`); the
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binary applies its embedded schema migrations itself on startup
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(`AUTO_MIGRATE=false` opts out), and `coordinator setup` provisions a
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PostgreSQL deployment interactively. The UI login uses the userservice
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(`USERSERVICE_URL`) — embedded by `serve`, or the standalone `users/`
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service otherwise.
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`coordinator --version` / `worker-agent --version` print the build tag.
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Build and serve this documentation site:
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```bash
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make docs
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make docs-serve # http://localhost:8000
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```
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## Where to go next
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- [SDK overview](sdk/overview.md) — what the SDK is and is not.
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- [Authoring workloads](sdk/authoring-workloads.md) — write your first
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workload with `MapReduceWorkload`.
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- [Workload CLI](sdk/cli.md) — list, run, and export workloads from the
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command line.
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- [Worker integration](sdk/worker-integration.md) — how the distributed
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worker executes SDK workloads.
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- [API reference](api/index.md) — the complete `scimesh.sdk` API, generated
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from docstrings.
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- [Documentation approach](approach.md) — the rules this site is written by.
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