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SDK & CLI Guide

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CLI Commands

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agnitra optimize

agnitra optimize \
  --model tinyllama.pt \
  --input-shape 1,16,64 \
  --output dist/tinyllama_optimized.pt \
  --target A100

• --device — moves the model to a specific device (e.g. cuda:0).
• --disable-rl — skip PPO fine-tuning passes.
• --offline — disable control plane calls (requires enterprise license).
• --require-license — fail if license validation is unavailable.
• --license-seat / --license-org — override license metadata sent to the control plane.

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agnitra-api

agnitra-api --host 0.0.0.0 --port 8080

• POST /optimize — synchronous or async queued optimization.
• GET /jobs/{id} — poll async job status.
• POST /usage — convert telemetry snapshots into marketplace usage records.

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agnitra-dashboard

agnitra-dashboard --host 127.0.0.1 --port 3000

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Python SDK Highlights

• agnitra.optimize(model, input_tensor=...) returns a RuntimeOptimizationResult including the optimized model, usage event, and patch metadata.
• agnitra.sdk.resolve_input_tensor synthesizes input tensors based on shape hints or example tensors on the module.
• Usage events expose GPU hours saved, cost savings, and marketplace metadata (see agnitra/core/metering/usage_meter.py).

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Example

from agnitra import optimize

result = optimize(
    model,
    input_tensor=sample,
    project_id="demo-project",
    metadata={"source": "notebook"}
)

usage = result.usage_event
print(f"GPU hours saved: {usage.gpu_hours_saved:.6f}")

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Troubleshooting

• Missing PyTorch: install torch>=2.0 or ensure CUDA libs are discoverable.
• Control plane unavailable: pass --offline or set AGNITRA_CONTROL_PLANE_URL to the reachable endpoint.
• Stripe/NVML optional deps: install extras (agnitra[nvml], agnitra[marketplace]) to enable GPU telemetry and marketplace dispatchers.