Status and known limitations#

This page describes the current repository. It is not a promise of dates or future work. Pin a release or commit for research and validate the exact model, data, and execution path you use.

Current capability#

Area

Status

sparse, variable-length 3D point-cloud inputs

primary supported modality

local single-GPU and multi-GPU DDP training

supported path

Submitit-backed Slurm batch and interactive launch

implemented; site profile must be validated locally

standard split checkpoints and same-topology loader-state resume

implemented

topology-changing model/optimizer restore

implemented with saved-epoch restart rather than exact loader-cursor continuation

portable safetensors/Hub exports

implemented; loading reconstructs the model, not a preprocessing pipeline or trainer state

FSDP2

experimental; no repository-wide model parity matrix

standalone evaluation

available through scripts/test.sh; no pimm evaluate command

2D wire-plane, optical waveform, and other detector modalities

not currently supported by the common workflow

Platform boundary#

The full locked training environment targets Linux x86-64, Python 3.10, PyTorch 2.10, CUDA 12.6, and an NVIDIA driver compatible with that runtime. macOS can use the launcher-only environment; the native sparse/CUDA stack is not installed there. Other architectures, Python/PyTorch/CUDA combinations, and non-NVIDIA accelerators are not covered by the committed lock/wheels.

Scientific boundaries#

  • A common Point representation does not standardize detector coordinates, units, calibration, feature order, labels, or selection.

  • A committed recipe is not automatically a supported benchmark. Several are active research variants without signed-off metrics or stability guarantees.

  • Published exports reconstruct architecture and weights, not the transform pipeline, class interpretation, dataset license, or evaluation protocol.

  • PILArNet-M v1 and v2 have a standard downloader. The HDF5 reader accepts v3, but pimm does not provide a standard public v3 download path.

  • The Parquet reader does not support test_mode; use the HDF5 reader for the current voxelized/augmented test path.

  • Two committed Panda/Sonata Parquet configs inherit a base file absent from this revision and cannot currently resolve; see the config catalog.

  • Exact resume depends on dataloader state plus the same world-size/worker topology and does not guarantee deterministic kernels outside pimm’s control.

User-facing gaps that should stay visible#

TODO

Replace the gaps below only with measured values or an agreed project policy. Until then, they remain visible here rather than being implied as features.

Gap

What is needed before claiming completion

canonical software citation

approved authors/ORCIDs, CITATION.cff, release archive and DOI

model chooser metrics

signed-off held-out values, exact split/protocol, uncertainty, config and checkpoint revision

data reference

authoritative sizes/counts/checksums, licenses/citations, and coordinate/unit figures per revision

evaluation artifacts

one versioned prediction/metric schema per task plus a first-class evaluation CLI

FSDP2 support level

model-by-model multi-rank train/save/resume/evaluation parity matrix

support/security policy

maintainers, expected response scope, supported versions, private reporting route

compatibility policy

documented stability guarantees and migration/deprecation process for registry names/config/checkpoint schemas

How priorities are chosen#

No formal public roadmap or release schedule is committed in this repository. Propose work through a GitHub issue with:

  1. the user/research outcome;

  2. the current blocking behavior;

  3. the data/model/API and compatibility contract;

  4. a bounded test or measurable acceptance criterion;

  5. required ownership, hardware, data access, and publication decisions.

For implementation expectations, see Contributing.