try 8 threads

main.py

@@ -54,7 +54,7 @@ class PredictRequest(BaseModel):
     modelName: str
 
 
-torch.set_num_threads(6)
+torch.set_num_threads(8)
 
 # Dictionnaire pour stocker les pipelines de modèles
 model_pipelines = {}
@@ -128,44 +128,44 @@ class BatchPredictRequest(BaseModel):
     modelName: str
 
 
-@app.post("/batch_predict")
-async def batch_predict(request: BatchPredictRequest):
-    model_name = request.modelName
-    results = []
+# @app.post("/batch_predict")
+# async def batch_predict(request: BatchPredictRequest):
+#     model_name = request.modelName
+#     results = []
 
-    # Verify if the model is loaded
-    if model_name not in model_pipelines:
-        raise HTTPException(status_code=404, detail="Model not found")
+#     # Verify if the model is loaded
+#     if model_name not in model_pipelines:
+#         raise HTTPException(status_code=404, detail="Model not found")
 
-    model = model_pipelines[model_name]
+#     model = model_pipelines[model_name]
 
-    # Asynchronously process each image
-    async with httpx.AsyncClient() as client:
-        for image_url in request.imageUrls:
-            try:
-                response = await client.get(image_url)
-                image = Image.open(BytesIO(response.content))
-            except Exception as e:
-                results.append({"imageUrl": image_url, "error": "Invalid image URL"})
-                continue
+#     # Asynchronously process each image
+#     async with httpx.AsyncClient() as client:
+#         for image_url in request.imageUrls:
+#             try:
+#                 response = await client.get(image_url)
+#                 image = Image.open(BytesIO(response.content))
+#             except Exception as e:
+#                 results.append({"imageUrl": image_url, "error": "Invalid image URL"})
+#                 continue
 
-            # Preprocess the image
-            processed_image = process_image(image, size=image_size)
+#             # Preprocess the image
+#             processed_image = process_image(image, size=image_size)
 
-            # Convert to tensor
-            image_tensor = transforms.ToTensor()(processed_image).unsqueeze(0)
+#             # Convert to tensor
+#             image_tensor = transforms.ToTensor()(processed_image).unsqueeze(0)
 
-            # Perform inference
-            with torch.no_grad():
-                outputs = model(image_tensor)
-                probabilities = torch.nn.functional.softmax(outputs, dim=1)
-                predicted_probabilities = probabilities.numpy().tolist()
-                confidence = round(predicted_probabilities[0][1], 2)
+#             # Perform inference
+#             with torch.no_grad():
+#                 outputs = model(image_tensor)
+#                 probabilities = torch.nn.functional.softmax(outputs, dim=1)
+#                 predicted_probabilities = probabilities.numpy().tolist()
+#                 confidence = round(predicted_probabilities[0][1], 2)
 
-            results.append({"imageUrl": image_url, "confidence": confidence})
+#             results.append({"imageUrl": image_url, "confidence": confidence})
 
-    # Return the results as JSON
-    return JSONResponse(content={"results": results})
+#     # Return the results as JSON
+#     return JSONResponse(content={"results": results})
 
 
 @app.post("/batch_predict")
@@ -178,7 +178,7 @@ async def batch_predict(request: BatchPredictRequest):
 
     model = model_pipelines[model_name]
     semaphore = asyncio.Semaphore(
-        6
+        8
     )  # Limiter à 8 tâches simultanées pour éviter de surcharger la machine
 
     async def process_single_image(image_url):