chore: update

use_case_examples/cifar/cifar_brevitas_finetuning/CifarQuantizationAwareTraining.ipynb

@@ -88,7 +88,6 @@
    ],
    "source": [
     "bit = 5\n",
-    "seed = 42\n",
     "\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
     "\n",

use_case_examples/cifar/cifar_brevitas_training/evaluate_torch_cml.py

@@ -1,6 +1,7 @@
 import argparse
 from pathlib import Path
 
+import concrete.compiler
 import numpy as np
 import torch
 from concrete.fhe import Configuration
@@ -74,8 +75,14 @@ def main(args):
     # observe a decrease in torch's top1 accuracy when using MPS devices
     # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3953
     device = "cuda" if torch.cuda.is_available() else "cpu"
+    compilation_device = "cuda" if concrete.compiler.check_gpu_available() else "cpu"
 
-    print("Device in use:", device)
+    print("Torch device in use:", device)
+    print(
+        "To leverage the CUDA backend, follow the GPU setup guide to install the Concrete ML compiler."
+    )
+    print("GPU Enabled:", concrete.compiler.check_gpu_enabled())
+    print("GPU Available:", concrete.compiler.check_gpu_available())
 
     # Find relative path to this file
     dir_path = Path(__file__).parent.absolute()
@@ -123,7 +130,7 @@ def main(args):
                 if rounding_threshold_bits is not None
                 else None
             ),
-            device=COMPILATION_DEVICE,
+            device=compilation_device,
         )
 
         # Print max bit-width in the circuit

use_case_examples/resnet/run_resnet18_fhe.py

@@ -276,7 +276,7 @@ def main():
         "--export_statistics", action="store_true", help="Export the circuit statistics."
     )
     parser.add_argument(
-        "--use_gpu", type=bool, action="store_true", help="Use the available GPU at FHE runtime."
+        "--use_gpu", action="store_true", help="Use the available GPU at FHE runtime."
     )
     parser.add_argument(
         "--run_experiment",