pyattacker

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Build resumable Python workflows for model evaluations and other dataset-driven tasks. Combine task chains, shared resource pools, retries and durable checkpoints under one Runner.

Start the tutorial Python API reference

Start here

Install Python 3.11 or later, then install the library:

python -m pip install pyattacker
pyattacker demo

For YAML configurations, install pyattacker[yaml]. The base library has no runtime dependencies. This site follows the main branch; your installed release may not yet include every feature shown. To try the development version, clone the repository and follow the tutorial setup.

Choose a path

Your goal	Read
Write a task and run a dataset	Step-by-step tutorial
Share endpoints, retry failures and resume work	Resources, retry and recovery tutorial
Compare several experiments under one Runner	Experiment suites
Look up Python interfaces and behavior	API reference
Run from JSON, TOML or YAML	CLI and configuration
Understand checkpoints and scheduling guarantees	Architecture
Compare resource scheduling strategies	Benchmarking

Know what is retained

Runner defaults to an in-memory store. Choose a file-backed store to retain checkpoints across processes. concurrency limits executing attempts; max_admitted bounds queued, executing and delayed pipelines together. Payloads, input buffers and accumulated store data also consume memory. See RunConfig before scaling a workload.

Explore and contribute

Browse the runnable examples, release history, or report an issue. For local previews and publishing, see Maintaining the documentation.