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Release v1.3.0

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@github-actions github-actions released this 28 Apr 00:09
· 16 commits to main since this release
v1.3.0
5305613

Added

  • Image curation pipeline with semantic filtering
  • Image embedding stages (Cosmos-Embed1, InternVideo2-MM, OpenAI-compatible) and image annotate pipeline
  • OpenAI- and Gemini-compatible endpoints for image captioning, filtering, and classification
  • Artificial-text detection stage for the video filtering pipeline (PaddleOCR-based)
  • Sensor library (camera-only) with SensorGroup, mcap-based ingestion, and timestamp validation
  • SeedVR-based upscaling stage
  • Pipeline config files with NVCF-compatible JSON and YAML loading (--config for split/shard/dedup)
  • Centralized pipeline argument validation via common_pipeline_settings and shard_pipeline_settings
  • vLLM async captioning stage for higher captioning throughput (experimental — correctness
    issues are still being worked through; not recommended for production use)
  • OpenTelemetry instrumentation for vLLM captioning
  • Token-counting instrumentation to measure captioning throughput
  • Caption status fields normalized across caption backends, with status-gated metadata writing
  • Stage-replay validation that compares re-run output against the original recording
  • S3 support for stage-save and stage-replay
  • Ray Data hello-world pipeline and splitting pipeline MVP as an alternative engine alongside Xenna
  • --*-cpus-per-worker knobs documented for CPU-constrained hosts
  • Run local-launched container as the host user (including AD/SSSD/NIS UIDs) to avoid root-owned outputs
  • Slim Docker image built alongside the full image, with auto-warmup honoring --envs
  • Local Xenna build path in CI and per-pipeline Xenna overrides
  • Fixed-stride coverage in the NVCF split benchmark matrix
  • Real-inference smoke test for vLLM captioning health
  • Upgrade to CUDA 13.0
  • Upgrade vLLM to 0.19.0
  • Upgrade Ray to 2.55.0 (with the serve extra)
  • Upgrade cosmos-xenna to 0.2.3
  • Bump av to >=17,<18 and add the mcap dependency for the sensor library

Fixed

  • SamplingGrid produced incorrect windows for irregular grids
  • --execution-mode CLI flag is now honored end-to-end
  • Cosmos-Embed1 writes per-variant embedding directories
  • Symlink the host pixi path so shebangs resolve inside the local-launched container
  • Sensor library uses read-only views to avoid accidental buffer mutation
  • Add Qwen3 preprocessing logic for filtering stages
  • Use pre-built images for benchmark runs to avoid redundant builds
  • Remove external storage dependency from ImageSensor
  • Semantic filter updates and dedup pipeline input path cleanup
  • Loosen Cosmos-Reason1 caption similarity threshold to reduce flakiness

Changed

  • Replace CurationPhase / PipelineBuilder with factory functions (*_builders.py); the
    phase_interface module and per-pipeline phases.py files are removed
  • Add config: VllmConfig parameter to VllmPlugin.make_llm_input for image vs video
    modality selection; subclasses must update their signature
  • Switch CI Slurm and k8s GPU jobs to the slim image with in-container pixi install and
    pixi run --as-is
  • Change CI NVCF backend
  • Normalize the SamplingGrid API and make sampling windows explicit (no sentinel boundaries)
  • Update semantic filter stages to use VllmCaptioning
  • Add a CPU-only Paddle option for the unified env
  • Pixi lockfile refreshed for CVE coverage
  • Add notice and disclaimer to README and Docker image

Documentation

  • Speed-of-light design doc for captioning throughput, with refined SOL baseline methodology
    using vllm bench as the reference
  • Refined Ray Data runner design with the first implementation slice
  • Document --*-cpus-per-worker tuning knobs
  • Add --squash-before-merge to MR guidelines
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