Actualización del Workflow

Author: Shuuida

.github/workflows/benchmarks.yml

@@ -472,59 +472,4 @@ jobs:
           name: firmware-build-${{ inputs.target_arch || 'arm' }}
           path: build/generated/
           retention-days: 7
-
-  # ------------------------------------------------------------------
-  # JOB 3: Benchmark & Performance Metrics (Opcional)
-  benchmark:
-    name: 📊 Performance Benchmarks
-    needs: build-firmware
-    runs-on: ubuntu-latest
-    if: github.event_name == 'workflow_dispatch' || contains(github.event.head_commit.message, '[benchmark]')
-    
-    steps:
-      - uses: actions/checkout@v4
-      
-      - name: Setup Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: '3.10'
-      
-      - run: pip install .
-      
-      - name: Run Embedded Benchmarks
-        run: |
-          cat <<'PYEOF' > benchmark_embedded.py
-          import miniml
-          import time
-          
-          # Benchmark: Tiempo de entrenamiento y tamaño de modelo
-          xor_data = [[0.0, 0.0, 0], [0.0, 1.0, 1], [1.0, 0.0, 1], [1.0, 1.0, 0]]
-          
-          print("=== Benchmark: Neural Network Embebido ===\n")
-          
-          # Entrenamiento
-          start = time.time()
-          result = miniml.train_pipeline(
-              "bench_nn", xor_data, "neural_network",
-              params={"n_inputs": 2, "n_hidden": 4, "n_outputs": 1, "epochs": 2000},
-              scaling="minmax"
-          )
-          train_time = time.time() - start
-          
-          # Exportación
-          start = time.time()
-          c_code = miniml.export_to_c("bench_nn")
-          export_time = time.time() - start
-          
-          # Métricas
-          model = result['model']
-          code_size = len(c_code)
-          
-          print(f"Tiempo de entrenamiento: {train_time:.3f}s")
-          print(f"Tiempo de exportación: {export_time:.3f}s")
-          print(f"Tamaño del código C: {code_size} bytes")
-          print(f"act_scales: {getattr(model, 'act_scales', 'N/A')}")
-          print(f"Cuantificado: {getattr(model, 'quantized', False)}")
-          PYEOF
-          
-          python benchmark_embedded.py
+#-----------------------------------------------------------------