kinfer-runtime
K-Scale kinfer model inference engine for deploying trained RL policies to real robots via ONNX Runtime in Rust
Best use case
kinfer-runtime is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
K-Scale kinfer model inference engine for deploying trained RL policies to real robots via ONNX Runtime in Rust
Teams using kinfer-runtime should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/kinfer-runtime/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How kinfer-runtime Compares
| Feature / Agent | kinfer-runtime | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
K-Scale kinfer model inference engine for deploying trained RL policies to real robots via ONNX Runtime in Rust
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# K-Scale kinfer Skill
> *"The K-Scale model export and inference tool"*
## Trigger Conditions
- User asks about deploying RL policies to real robots
- Questions about ONNX model inference, Rust ML runtime
- Policy execution on embedded systems
- Real-time neural network inference
## Overview
**kinfer** is K-Scale's model inference engine for deploying trained policies:
1. **Model Loading**: ONNX format support via `ort` (ONNX Runtime)
2. **Real-time Execution**: Rust implementation for low latency
3. **Logging**: NDJSON telemetry for debugging
4. **Integration**: Seamless connection with KOS firmware
## Architecture
```
┌─────────────────────────────────────────────────────────────────────────┐
│ kinfer Inference Pipeline │
│ │
│ ┌──────────────┐ load ┌──────────────┐ │
│ │ ONNX Model │───────────────▶│ Runtime │ │
│ │ (.onnx) │ │ (ort-sys) │ │
│ └──────────────┘ └──────┬───────┘ │
│ │ │
│ ┌──────────────┐ step ┌──────┴───────┐ output │
│ │ Observation │───────────────▶│ Inference │───────────────▶Action │
│ │ (sensors) │ │ Engine │ │
│ └──────────────┘ └──────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────┐ │
│ │ Logger │ │
│ │ (NDJSON) │ │
│ └──────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
```
## Key Features
### 1. Single Tokio Runtime
```rust
// Efficient async execution with GIL management
lazy_static! {
static ref RUNTIME: Runtime = Runtime::new().unwrap();
}
```
### 2. Pre-fetch Inputs
```rust
// Minimize latency by preparing inputs ahead of time
fn step_and_take_action(&mut self, observation: &[f32]) -> Vec<f32> {
// Pre-fetch next input while processing current
...
}
```
### 3. NDJSON Logging
```rust
// Async logging thread for telemetry
struct Logger {
file: File,
tx: Sender<LogEntry>,
}
```
## Language & Stack
- **Primary**: Rust (performance-critical)
- **ML Runtime**: ONNX Runtime (`ort`, `ort-sys`)
- **Async**: Tokio for non-blocking I/O
- **Bindings**: Python via PyO3
## GF(3) Trit Assignment
```
Trit: -1 (MINUS)
Role: Verification/Validation (inference must be correct)
Color: #6E5FE4
URI: skill://kscale-kinfer#6E5FE4
```
### Balanced Triads
```
kscale-kinfer (-1) ⊗ kscale-ksim (0) ⊗ onnx-export (+1) = 0 ✓
kscale-kinfer (-1) ⊗ rust-ml (0) ⊗ policy-training (+1) = 0 ✓
```
## Key Contributors
| Contributor | Focus Areas |
|------------|-------------|
| **b-vm** | Step function, command names |
| **codekansas** | Performance, refactoring |
| **WT-MM** | Logging, env variables |
| **alik-git** | NDJSON logging, plotting |
| **nfreq** | Tokio runtime, GIL management |
## Example Usage
```python
import kinfer
# Load model
model = kinfer.load_model("walking_policy.onnx")
# Get observation from sensors
obs = get_sensor_data()
# Run inference
action = model.step(obs)
# Apply to actuators
apply_action(action)
```
### Rust API
```rust
use kinfer::InferenceEngine;
let mut engine = InferenceEngine::load("policy.onnx")?;
loop {
let obs = get_observation();
let action = engine.step_and_take_action(&obs);
send_to_actuators(&action);
}
```
## References
- [kscalelabs/kinfer](https://github.com/kscalelabs/kinfer) - Main repository (17 stars)
- [kscalelabs/kinfer-sim](https://github.com/kscalelabs/kinfer-sim) - Simulation visualization
- [ONNX Runtime](https://onnxruntime.ai/) - Inference backendRelated Skills
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