semvec/pypi-packages huggingface dataset:
robo-orchard-lab 0.4.0
Deep Learning Algorithms for RoboOrchard
Requires Python: >=3.10

# RoboOrchardLab

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**RoboOrchardLab** is a python package that provides a modular and extensible framework for training and evaluating embodied AI algorithms.
It is part of the **RoboOrchard** project, which aims to support the full lifecycle of robotics research and development.

The goal of **RoboOrchardLab** is to create a unified and flexible architecture that allows researchers and developers to easily compose and customize their training pipelines, while also providing model components, datasets, and evaluation metrics that are specifically designed for embodied AI tasks and research.

## Core Features

- **Modular Training Framework**: We provide a highly modular training framework that allows you to easily compose and customize your training pipelines. For example, you can easily add or change the implementation of model updates, training monitors or other components to suit your specific research needs without changing the overall training pipeline.
- **Efficient Distributed Training**: This project is designed to work seamlessly with the Hugging Face ecosystem (Accelerate, Datasets, etc), enabling efficient distributed training across multiple GPUs and nodes. This allows you to scale your experiments and leverage the power of modern hardware.
- **SOTA Embodied AI Algorithms**: Access an expanding suite of state-of-the-art (SOTA) algorithms. Initial support focuses on advanced perception, with intelligent grasping policies and end-to-end Vision-Language Action (VLA) models being actively developed and planned for near-future releases. Longer-term, we aim to integrate broader manipulation and whole-body control capabilities.
- **Open Community**. **RoboOrchardLab** is an open-source project, and we warmly welcome developers and researchers from academia and industry to join us. Contribute code, share models, improve documentation, and let's advance embodied intelligence technology together.

## License

**RoboOrchardLab** is open-source and licensed under the [Apache License 2.0](https://github.com/HorizonRobotics/robo_orchard_lab/blob/master/LICENSE). If you are interested in contributing, please reach out to us.
societal-costs-pipeline-2026-02-13-1400 0.1.0
Societal costs pipeline CLI.
Requires Python: >=3.9

# societal_costs

Workspace for the societal costs study.

## Crates

- `crates/costs_core` — core domain types, aggregation, pricing, and validation.
- `crates/costs_models` — two-part, Tweedie, and log-normal models + selection tools.
- `crates/costs_matching` — matching + balance diagnostics.
- `crates/costs_cohort` — cohort assignment + diagnostics.
- `crates/costs_components` — register-derived component builders (LMDB volume so far).
- `crates/costs_population` — population files + public stats QC.
- `crates/costs_pipeline_common` — config, errors, tracing, and shared helpers.
- `crates/costs_pipeline_io` — report/Arrow/CSV/JSON writers.
- `crates/costs_registers` — register ingestion flows (BEF/LPR/LMDB/VNDS/DOD/etc).
- `crates/costs_pipeline_core` — orchestration + analysis/matching/modeling logic.
- `crates/costs_pipeline` — thin facade that re-exports common/core/io APIs.

## LMDB example

See `crates/costs_pipeline/README.md` for the LMDB ingestion example.

## Pipeline config

Start from `pipeline.example.toml` and update the register file paths to match
your local dataset layout. Most SAS7BDAT paths accept either a single file
or a directory (all `.sas7bdat` files are loaded, sorted by name). You can
also set `base_path` in the config to prefix all relative register paths.
File filtering uses `study.data_*` and optional `study.baseline_*` /
`study.followup_*` windows to avoid reading out-of-scope files.
Event registers (VNDS/DOD/DODSAARS/DODSAASG) are **not** year-filtered.
If you use contact-based SCD classification, set
`components.lpr.scd_inpatient_contact_types` (or flip
`components.lpr.scd_allow_outpatient = true` to allow outpatient contacts).

Pipeline outputs include consolidated QC under `qc_summary.csv/json`, plus
step-specific outputs such as `lpr_episode_qc.csv`, `matching_outcome_qc.csv`,
`matching_parent_qc.csv`, and `population_public_stats.csv`.

For terminal progress and timing, set:
- `pipeline.progress = true`
- `pipeline.log_level = "info"` (or `debug`)

## Additional docs

- `docs/README.md` — onboarding index and quick-start path.
- `crates/costs_core/README.md` — core domain and aggregation usage.
- `crates/costs_models/README.md` — model APIs and examples.
- `crates/costs_models/examples/README.md` — runnable model examples.
- `crates/costs_components/README.md` — LMDB note (missing price fields).
- `crates/costs_pipeline/README.md` — pipeline usage and LMDB example.
- `docs/notes/pipeline_contracts.md` — step-level inputs, outputs, invariants.