luispedro§jug

Imported from gencau/test-practices-agent-configurations (dataset/repos/luispedro§jug/AGENTS.md). Install upstream with npx skills add gencau/test-practices-agent-configurations --skill luispedro§jug. Copyright stays with the author.

AGENTS.md

This file provides guidance to agents when working with code in this repository.

Commands

Run all tests:

pytest jug/tests/

Run a single test file:

pytest jug/tests/test_tasks.py

Run a single test:

pytest jug/tests/test_tasks.py::test_function_name

Install in development mode (with dev dependencies):

pip install -e ".[dev]"

Build the package:

python -m build

Architecture

Jug is a task-based parallelization framework where a jugfile (a Python script) defines tasks, and multiple jug execute processes run those tasks concurrently using a shared backend store for coordination and result caching.

Core concepts

Task (jug/task.py): The central abstraction. A Task wraps a function call with its arguments. Tasks are identified by a content hash of their function + arguments. task.alltasks is a module-level list that accumulates all tasks as the jugfile is imported. Tasks are typically created via the @TaskGenerator decorator.

Backends (jug/backends/): Storage and locking layer. base_store (abstract) defines the interface: dump, load, can_load, list, lock, etc. Implementations:

• file_store — filesystem-based (default, NFS-safe). Results stored as files named by task hash.
• dict_store — in-memory (useful for testing).
• redis_store — Redis-based.

Backends also implement locking to prevent duplicate execution across workers. file_store uses lock files; redis_store uses Redis locks.

Hashing (jug/hash.py): Task identity is determined by hashing the function (by name/module) and all arguments recursively. Objects can implement __jug_hash__() for custom hashing. numpy arrays and polars DataFrames have special handling in file_store (stored in native format for efficiency).

Subcommands (jug/subcommands/): Each jug <cmd> maps to a subcommand module: execute, status, invalidate, cleanup, count, check, graph, shell, webstatus, pack, demo. The execute subcommand runs the core execution loop.

Barrier (jug/barrier.py): barrier() raises BarrierError if any previously defined task isn't complete yet. This stops jugfile parsing at that point, allowing dynamic task graphs where the number of tasks depends on prior results. bvalue(t) is a scoped version that only checks one task.

CompoundTask (jug/compound.py): A task whose function itself returns a Task. Used to create dynamic sub-graphs that are only instantiated after the compound task runs.

Hooks (jug/hooks/): Event system for extending jug behavior (e.g., logging task execution events). Register with register_hook(event_name, callback).

Options (jug/options.py): A chained options object where attributes fall through to the next layer. Configuration can come from CLI args, local config files, or defaults.

Execution flow

1. jug execute jugfile.py --jugdir jugdata/ is called
2. jug.init() sets up the backend store and imports the jugfile, populating task.alltasks
3. If a barrier() is hit before all prior tasks are done, a BarrierError is raised and jugfile import stops at that point (the execution loop re-imports once more tasks complete)
4. The execution loop picks tasks that are ready (all dependencies loadable), acquires a lock, executes, stores result, releases lock
5. Multiple workers run in parallel — coordination is entirely through the backend store

Test infrastructure

Tests live in jug/tests/. The tmp_file_store pytest fixture (in jug/tests/utils.py) provides a temporary file_store and handles cleanup of task.Task.store. Test jugfiles (used as fixtures for integration tests) are in jug/tests/jugfiles/.