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Jetson / Thor Deployment

Strands Sapiens is developed on NVIDIA Thor (JetPack 6, aarch64) - notes from that journey.

Baseline verified config

  • Hardware: NVIDIA Jetson AGX Thor
  • OS: JetPack 6 (Ubuntu 22.04, aarch64)
  • Python: 3.10 (JetPack default)
  • PyTorch: 2.7+ CUDA wheels (from NVIDIA's JetPack download page)
  • Checkpoints tested:
    • sapiens2_0.1b_pretrain.safetensors
    • sapiens2_0.4b_seg.safetensors

End-to-end seg inference runs cleanly at 1024×768 on this config.

Install checklist

# 0) Confirm JetPack + CUDA
python -c "import torch; print(torch.version.cuda, torch.cuda.is_available())"

# 1) Install sapiens2 from source
pip install git+https://github.com/facebookresearch/sapiens2.git

# 2) Install strands-sapiens
pip install -e .

# 3) Put at least one checkpoint in place
mkdir -p ~/sapiens2_host/seg
# copy sapiens2_0.4b_seg.safetensors there

# 4) Sanity-check
python -c "from strands_sapiens import sapiens_info; print(sapiens_info())"

Python version

JetPack 6 ships Python 3.10 by default. strands-sapiens requires Python ≥ 3.10, so no pyenv gymnastics needed.

If you're running newer Python, nothing breaks - tests pass on 3.12 too.

Performance

Size Resolution Single-image latency (Thor)
0.4b 1024×768 ~250ms
0.8b 1024×768 ~450ms
1b 1024×768 ~700ms

(Informal; no TensorRT conversion applied yet. Expect 2–3× with TRT.)

Running as a service

Use devduck's service tool to persist a sapiens inference server:

service(
    action='install',
    name='sapiens-worker',
    tools='strands_sapiens:sapiens_seg,sapiens_pose',
    env_vars={'SAPIENS_CHECKPOINT_ROOT': '/data/sapiens2_host'},
    startup_prompt='Wait for requests on port 9010 and run inference.'
)

Gotchas on aarch64

  • Install opencv-python (the wheel exists for aarch64) - don't rebuild opencv unless you need GPU decoding.
  • open3d (optional [pointmap] extra) may need a source build on some JetPack versions.
  • If you see Illegal instruction (core dumped) on a fresh install, you're on an older safetensors - upgrade.