---

name: deployment-exchange description: "Teach NIR export/import plus Lava and Lynxi deployment exchange for SpikingJelly." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Deployment Exchange

Use this sub-skill when the task is about moving a SpikingJelly model across neuromorphic formats or deployment runtimes.

Route here for

• NIR export/import, HDF5 graph round-trips, shape metadata, and supported-module checks.
• Lava / Loihi exchange: quantizers, CubaLIFNode, synapse/block conversion, and time-layout transforms.
• Lynxi-oriented compilation: supported-module rewriting, [TN, *] flattening rules, tensor conversion, and offline model loading.

Do not handle here

• Model zoo, training loops, dataset setup, or benchmark-heavy workflows: route to ../training-and-scaleout/.
• Backend profiling, kernel performance, precision, or memory analysis: route to ../performance-and-analysis/.
• ANN2SNN recipe selection or converted-model semantics: route to ../ann2snn/.
• Dataset acquisition or preprocessing: route to ../datasets/.
• Pure step-mode, reset, or module-composition basics: route to ../core-snn/.

Operating workflow

1. Decide whether the user needs NIR, Lava, or Lynxi. If they are still choosing modules or step modes, route to core-snn first.
2. For NIR, verify that example_input is executable, that the node shapes exclude batch/time axes, and that the installed NIR release matches the exported constructor path before promising a full neuron round-trip.
3. For Lava, separate the always-available quantizer / CubaLIFNode notes from the optional Lava-DL conversion helpers.
4. For Lynxi, convert only the supported layer / neuron set, respect the explicit T contract, and keep the 4D-or-less tensor and no-inplace constraints visible.
5. If the user is actually debugging kernels or device-specific runtime failures, route to performance-and-analysis instead of deepening deployment logic here.

Read first

• references/deployment-workflows.md
• references/troubleshooting.md

Bundled script

• scripts/nir_roundtrip_smoke.py: no-download CPU smoke for NIR export/import, shape rules, file round-trip, and step-mode import.

Cross-links

• ../core-snn/ for module composition, reset, and step-mode basics.
• ../ann2snn/ for converted-model semantics when a deployment target is really a converted network.
• ../performance-and-analysis/ for runtime and backend troubleshooting, not deployment-format logic.

Evidence used

• Source files:
  • spikingjelly/activation_based/nir_exchange/to_nir.py
  • spikingjelly/activation_based/nir_exchange/from_nir.py
  • spikingjelly/activation_based/lava_exchange.py
  • spikingjelly/activation_based/lynxi_exchange.py
  • spikingjelly/activation_based/examples/lava_mnist.py
  • spikingjelly/activation_based/examples/lynxi_fmnist_inference.py
• Tutorials and API docs:
  • docs/source/tutorials/en/nir_exchange.rst
  • docs/source/tutorials/en/lava_exchange.rst
  • docs/source/tutorials/cn/inference_on_lynxi.rst
  • docs/source/APIs/spikingjelly.activation_based.nir_exchange.rst
  • docs/source/APIs/spikingjelly.activation_based.lava_exchange.rst
  • docs/source/APIs/spikingjelly.activation_based.lynxi_exchange.rst
• Tests and behavioral evidence:
  • test/activation_based/test_functional_neuron.py
  • integration notes under skills/tests/spikingjelly/reports/integration/
• Verified live signatures in the prepared inspection environment:
  • spikingjelly.activation_based.nir_exchange.export_to_nir(net, example_input, save_path=None, dt=1e-4)
  • spikingjelly.activation_based.nir_exchange.import_from_nir(graph, dt=1e-4, device='cpu', dtype=torch.float32, step_mode='s') -> fx.GraphModule
  • spikingjelly.activation_based.lava_exchange.CubaLIFNode(current_decay, voltage_decay, v_threshold=1.0, v_reset=0.0, scale=64, requires_grad=False, surrogate_function=..., norm=None, detach_reset=False, step_mode='s', backend='torch', store_v_seq=False, store_i_seq=False)
  • spikingjelly.activation_based.lynxi_exchange.IFNode(v_threshold=1.0, v_reset=0.0, step_mode='s', T=None, return_v=False)
  • spikingjelly.activation_based.lynxi_exchange.LIFNode(tau=2.0, decay_input=True, v_threshold=1.0, v_reset=0.0, step_mode='s', T=None, return_v=False)
  • spikingjelly.activation_based.lynxi_exchange.to_lynxi_supported_module(m_in, T) / to_lynxi_supported_modules(net, T)
• Current environment status:
  • spikingjelly 2.0.0.dev1 imported successfully.
  • nir 1.0.8 and nirtorch 2.6 are installed.
  • The stateless NIR round-trip smoke passes in the prepared env; the neuron-node NIR path remains environment-sensitive because the installed NIR release does not expose the shape-bearing constructor contract expected by the current to_nir.py source path.
  • Lava-DL optional helpers and Lynxi vendor compilation helpers were not importable in the prepared env; treat those branches as optional / hardware-dependent.