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https://github.com/open-jarvis/OpenJarvis.git
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Jon Saad-FalconandClaude Opus 4.6
24972e3e52
Phase 12 — Energy Measurement Upgrade: - EnergyMonitor ABC with multi-vendor support (NVIDIA hw counters, AMD amdsmi, Apple zeus-ml, CPU RAPL sysfs) - EnergyBatch batch-level energy-per-token accounting - SteadyStateDetector CV-based thermal equilibrium detection - EnergyBenchmark with warmup phase - InstrumentedEngine prefers EnergyMonitor over legacy GpuMonitor - Telemetry store/aggregator extended with energy fields Phase 13 — Install, Hosting, Cross-Hardware: - jarvis doctor diagnostic command (8 checks, --json output) - jarvis init post-setup guidance with engine-specific next steps - README Quick Start section - MLX engine backend (Apple Silicon → mlx recommendation) - AMD VRAM/multi-GPU detection via rocm-smi - PyTorch MPS device selection in orchestrator trainers - PWA support (vite-plugin-pwa, service worker, manifest, icons) - Server static file serving fix for PWA files - Dockerfile.gpu.rocm + docker-compose.gpu.rocm.yml for ROCm - Eval framework display module and efficiency metrics 2244 tests pass, 37 skipped. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
18 lines
466 B
Python
18 lines
466 B
Python
"""MLX inference engine backend (OpenAI-compatible API)."""
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from __future__ import annotations
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from openjarvis.core.registry import EngineRegistry
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from openjarvis.engine._openai_compat import _OpenAICompatibleEngine
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@EngineRegistry.register("mlx")
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class MLXEngine(_OpenAICompatibleEngine):
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"""MLX backend — thin wrapper over the shared OpenAI-compatible base."""
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engine_id = "mlx"
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_default_host = "http://localhost:8080"
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__all__ = ["MLXEngine"]
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