Remove [Engine] and [EnginePoolManager] debug log messages

Cleaned up verbose engine telemetry emitted to stdout/stderr and the
Windows Event Viewer. Removes logEngineEvent/logEvent calls (and their
diagnostic-only locals) across the TensorRT engine load, build, run,
multi-GPU, and pool-manager paths, plus the now-unused logEvent helper
in EnginePoolManager.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

engines/TensorRTAPI/include/engine/EngineBuildLoadNetwork.inl

@@ -267,7 +267,6 @@ bool Engine<T>::buildLoadNetwork(std::string onnxModelPath, const std::array<flo
 
     if (FileExist(engineName)) {
         if (m_verbose) { std::cout << "Engine file found: " << engineName << std::endl; }
-        logEngineEvent("[Engine] buildLoadNetwork: Loading cached engine: " + engineName);
         bool loadOk = loadNetwork(engineName, subVals, divVals, normalize);
         if (loadOk) {
             return true;
@@ -280,10 +279,6 @@ bool Engine<T>::buildLoadNetwork(std::string onnxModelPath, const std::array<flo
         if (m_skipOnnxRebuild) {
             // Elastic growth / non-critical path — don't delete and rebuild.
             // Just fail gracefully; the pool continues with existing slots.
-            size_t freeMem = 0, totalMem = 0;
-            cudaMemGetInfo(&freeMem, &totalMem);
-            logEngineEvent("[Engine] buildLoadNetwork: Load failed (skip rebuild, "
-                          + std::to_string(freeMem >> 20) + " MiB free): " + engineName, true);
             return false;
         }
         // Check if the failure was due to VRAM exhaustion vs. corrupt file.
@@ -301,17 +296,11 @@ bool Engine<T>::buildLoadNetwork(std::string onnxModelPath, const std::array<flo
             cudaMemGetInfo(&freeCheck, &totalCheck);
             constexpr size_t kMinFreeBytes = 256ULL * 1024 * 1024;
             if (m_lastLoadFailedVRAM || freeCheck < kMinFreeBytes) {
-                logEngineEvent("[Engine] buildLoadNetwork: Load failed due to LOW VRAM ("
-                    + std::to_string(freeCheck / (1024 * 1024)) + " MiB free / "
-                    + std::to_string(totalCheck / (1024 * 1024)) + " MiB total"
-                    + ", vramFlag=" + std::to_string(m_lastLoadFailedVRAM)
-                    + "). Preserving engine file (not corrupt): " + engineName, true);
                 return false;  // Don't delete the file, don't try ONNX rebuild
             }
         }
         // Enough VRAM AND loadNetwork didn't flag VRAM as cause → file is
         // likely corrupt/incompatible.  Delete and rebuild from ONNX.
-        logEngineEvent("[Engine] buildLoadNetwork: Cached engine INVALID, deleting and rebuilding: " + engineName, true);
         try { std::filesystem::remove(engineName); } catch (...) {}
         // Fall through to ONNX build path below
     }
@@ -321,14 +310,11 @@ bool Engine<T>::buildLoadNetwork(std::string onnxModelPath, const std::array<flo
             // Demand-driven growth: if no cached engine exists, bail out rather
             // than triggering a full ONNX→TRT build (30-60s, massive VRAM).
             if (m_skipOnnxBuild) {
-                logEngineEvent("[Engine] buildLoadNetwork: Engine file NOT found, skipping ONNX build (demand growth): " + engineName);
                 return false;
             }
-            logEngineEvent("[Engine] buildLoadNetwork: Engine file NOT found, will build from ONNX: " + engineName);
         }
         if (!FileExist(onnxModelPath)) {
             // ONNX model does not exist, try to find alternative precision engine
-            logEngineEvent("[Engine] buildLoadNetwork: ONNX model also not found: " + onnxModelPath, true);
             std::cout << "Searching for alternative precision engine..." << std::endl;
 
             size_t lastDot = engineName.find_last_of('.');
@@ -411,9 +397,7 @@ bool Engine<T>::buildLoadNetwork(std::string onnxModelPath, const std::array<flo
             bool preParsed = parseOnnxModelSafe(tempParser.get(),
                 onnxBuffer.data(), onnxBuffer.size(), &sehPreAnalysis);
             if (sehPreAnalysis != 0) {
-                std::cout << "[Engine] WARNING: ONNX pre-analysis parse crashed ("
+                // Skipping pre-analysis, proceeding with build...
-                          << formatCrashCode(sehPreAnalysis)
-                          << "). Skipping pre-analysis, proceeding with build..." << std::endl;
             }
             else if (preParsed) {
                 auto numInputs = tempNetwork->getNbInputs();
@@ -718,7 +702,6 @@ bool Engine<T>::loadNetwork(std::string trtModelPath, const std::array<float, 3>
     // ============================================================================
 
     if (!Util::doesFileExist(trtModelPath)) {
-        logEngineEvent("[Engine] loadNetwork FAIL: Engine file not found: " + trtModelPath, true);
         return false;
     }
 
@@ -727,13 +710,11 @@ bool Engine<T>::loadNetwork(std::string trtModelPath, const std::array<float, 3>
     {
     std::ifstream file(trtModelPath, std::ios::binary | std::ios::ate);
     if (!file.is_open()) {
-        logEngineEvent("[Engine] loadNetwork FAIL: Cannot open engine file: " + trtModelPath, true);
         return false;
     }
 
     std::streamsize size = file.tellg();
     if (size <= 0) {
-        logEngineEvent("[Engine] loadNetwork FAIL: Engine file is empty (0 bytes): " + trtModelPath, true);
         return false;
     }
 
@@ -741,7 +722,6 @@ bool Engine<T>::loadNetwork(std::string trtModelPath, const std::array<float, 3>
 
     std::vector<char> buffer(size);
     if (!file.read(buffer.data(), size)) {
-        logEngineEvent("[Engine] loadNetwork FAIL: Read error on engine file: " + trtModelPath, true);
         return false;
     }
 
@@ -761,7 +741,6 @@ bool Engine<T>::loadNetwork(std::string trtModelPath, const std::array<float, 3>
 
     m_runtime = std::shared_ptr<nvinfer1::IRuntime>{ nvinfer1::createInferRuntime(m_logger) };
     if (!m_runtime) {
-        logEngineEvent("[Engine] loadNetwork FAIL: createInferRuntime returned null for " + trtModelPath, true);
         return false;
     }
 
@@ -830,17 +809,8 @@ bool Engine<T>::loadNetwork(std::string trtModelPath, const std::array<float, 3>
         constexpr size_t kMinFreeBytes = 256ULL * 1024 * 1024;  // 256 MiB minimum
         if (memErr != cudaSuccess) {
             // cudaMemGetInfo failed — CUDA context may not be initialized on this thread.
-            // Log but don't reject: let TRT try to deserialize (it may succeed).
+            // Don't reject: let TRT try to deserialize (it may succeed).
-            logEngineEvent("[Engine] loadNetwork WARNING: cudaMemGetInfo failed ("
-                + std::string(cudaGetErrorString(memErr)) + ") on GPU["
-                + std::to_string(m_options.deviceIndex) + "] — skipping VRAM check for "
-                + trtModelPath, true);
         } else if (freeVRAM < kMinFreeBytes) {
-            logEngineEvent("[Engine] loadNetwork FAIL: GPU[" + std::to_string(m_options.deviceIndex)
-                + "] only " + std::to_string(freeVRAM / (1024 * 1024))
-                + " MiB free / " + std::to_string(totalVRAM / (1024 * 1024))
-                + " MiB total (need " + std::to_string(kMinFreeBytes / (1024 * 1024))
-                + " MiB) for " + trtModelPath, true);
             m_lastLoadFailedVRAM = true;  // signal to buildLoadNetwork: engine file is NOT corrupt
             return false;
         }
@@ -861,13 +831,9 @@ bool Engine<T>::loadNetwork(std::string trtModelPath, const std::array<float, 3>
         deserializeCudaEngineSafe(m_runtime.get(), buffer.data(),
                                   buffer.size(), &sehCodeDeserialize));
     if (sehCodeDeserialize != 0) {
-        logEngineEvent("[Engine] loadNetwork FAIL: deserializeCudaEngine CRASHED (SEH "
-                      + formatCrashCode(sehCodeDeserialize) + ") for " + trtModelPath, true);
         return false;
     }
     if (!m_engine) {
-        logEngineEvent("[Engine] loadNetwork FAIL: deserializeCudaEngine returned null for "
-                      + trtModelPath + " (file size=" + std::to_string(size / (1024*1024)) + " MiB)", true);
         return false;
     }
 
@@ -1018,8 +984,6 @@ trt_cache_create_context:
     m_context = std::unique_ptr<nvinfer1::IExecutionContext>(m_engine->createExecutionContext());
     if (!m_context) {
         ANS_DBG("TRT_Load", "ERROR: createExecutionContext returned null");
-        logEngineEvent("[Engine] loadNetwork FAIL: createExecutionContext returned null for "
-                      + trtModelPath, true);
         return false;
     }
 
@@ -1106,9 +1070,6 @@ trt_cache_create_context:
             // Allocate GPU memory
             cudaError_t err = cudaMalloc(&m_buffers[i], requestedMemory);
             if (err != cudaSuccess) {
-                logEngineEvent("[Engine] loadNetwork FAIL: cudaMalloc input buffer ("
-                    + std::to_string(requestedMemory / (1024*1024)) + " MiB): "
-                    + cudaGetErrorString(err) + " for " + trtModelPath, true);
                 return false;
             }
 
@@ -1179,9 +1140,6 @@ trt_cache_create_context:
             // Allocate GPU memory
             cudaError_t err = cudaMalloc(&m_buffers[i], requestedMemory);
             if (err != cudaSuccess) {
-                logEngineEvent("[Engine] loadNetwork FAIL: cudaMalloc output buffer ("
-                    + std::to_string(requestedMemory / (1024*1024)) + " MiB): "
-                    + cudaGetErrorString(err) + " for " + trtModelPath, true);
                 return false;
             }
 
@@ -1534,9 +1492,6 @@ bool Engine<T>::build(std::string onnxModelPath, const std::array<float, 3>& sub
     auto parsed = parseOnnxModelSafe(parser.get(), buffer.data(),
                                      buffer.size(), &sehCodeParse);
     if (sehCodeParse != 0) {
-        std::cout << "[Engine] FATAL: ONNX parser crashed ("
-                  << formatCrashCode(sehCodeParse) << ")" << std::endl;
-        std::cout << "[Engine] This may indicate a corrupt ONNX file or driver issue." << std::endl;
         return false;
     }
     if (!parsed) {
@@ -2317,12 +2272,6 @@ bool Engine<T>::build(std::string onnxModelPath, const std::array<float, 3>& sub
     auto endTime = std::chrono::high_resolution_clock::now();
 
     if (sehCodeBuild != 0) {
-        std::cout << "\n========================================" << std::endl;
-        std::cout << "Build CRASHED!" << std::endl;
-        std::cout << "========================================" << std::endl;
-        std::cout << "[Engine] FATAL: buildSerializedNetwork crashed ("
-                  << formatCrashCode(sehCodeBuild) << ")" << std::endl;
-        std::cout << "[Engine] This typically indicates insufficient GPU memory or a driver crash." << std::endl;
         Util::checkCudaErrorCode(cudaStreamDestroy(profileStream));
         return false;
     }
@@ -2478,9 +2427,6 @@ bool Engine<T>::buildWithRetry(std::string onnxModelPath,
                 bool retryParsed = parseOnnxModelSafe(tempParser.get(),
                     onnxBuffer.data(), onnxBuffer.size(), &sehRetryParse);
                 if (sehRetryParse != 0) {
-                    std::cout << "[Engine] WARNING: ONNX pre-analysis parse crashed in "
-                              << "buildWithRetry (" << formatCrashCode(sehRetryParse)
-                              << "). Skipping spatial analysis." << std::endl;
                     // hasDynamicSpatial stays false → single build() attempt
                 }
                 else if (retryParsed && tempNetwork->getNbInputs() > 0) {
@@ -2501,8 +2447,6 @@ bool Engine<T>::buildWithRetry(std::string onnxModelPath,
         unsigned long sehBuild = 0;
         bool ok = buildSafe(onnxModelPath, subVals, divVals, normalize, &sehBuild);
         if (sehBuild != 0) {
-            std::cout << "[Engine] FATAL: build() crashed in buildWithRetry ("
-                      << formatCrashCode(sehBuild) << ")" << std::endl;
             return false;
         }
         return ok;
@@ -2557,40 +2501,17 @@ bool Engine<T>::buildWithRetry(std::string onnxModelPath,
     for (size_t attempt = 0; attempt < candidates.size(); ++attempt) {
         setCandidateOptions(candidates[attempt]);
 
-        std::cout << "[Engine] buildWithRetry attempt " << (attempt + 1)
-                  << "/" << candidates.size() << " (max "
-                  << m_options.maxInputHeight << "x"
-                  << m_options.maxInputWidth << ")" << std::endl;
-
         {
             unsigned long sehAttempt = 0;
             bool attemptOk = buildSafe(onnxModelPath, subVals, divVals, normalize, &sehAttempt);
             if (sehAttempt != 0) {
-                std::cout << "[Engine] Build crashed ("
-                          << formatCrashCode(sehAttempt) << ") at max "
-                          << m_options.maxInputHeight << "x"
-                          << m_options.maxInputWidth << std::endl;
                 // CUDA context may be corrupted — no point retrying
                 return false;
             }
             if (attemptOk) {
-                if (attempt > 0) {
-                    std::cout << "[Engine] Built with reduced max "
-                              << m_options.maxInputHeight << "x"
-                              << m_options.maxInputWidth
-                              << " (requested " << origMaxH << "x" << origMaxW
-                              << " exceeded GPU capacity)" << std::endl;
-                }
                 return true;
             }
         }
-
-        if (attempt + 1 < candidates.size()) {
-            std::cout << "[Engine] Build failed at max "
-                      << m_options.maxInputHeight << "x"
-                      << m_options.maxInputWidth
-                      << ", trying smaller..." << std::endl;
-        }
     }
 
     // All candidates exhausted — restore original options for error reporting
@@ -2601,10 +2522,6 @@ bool Engine<T>::buildWithRetry(std::string onnxModelPath,
     m_options.minInputHeight = origMinH;
     m_options.minInputWidth  = origMinW;
 
-    std::cout << "[Engine] buildWithRetry: all spatial dimension fallbacks "
-              << "exhausted (tried " << candidates.size() << " candidates from "
-              << candidates.front() << " down to " << candidates.back() << ")"
-              << std::endl;
     return false;
 }
 

engines/TensorRTAPI/include/engine/EngineMultiGpu.inl

@@ -223,9 +223,6 @@ bool Engine<T>::loadSlots(
         : probeEngine->loadNetwork     (modelPath, subVals, divVals, normalize);
 
     if (!probeOk) {
-        logEngineEvent("[Engine] loadSlots FAIL: Probe engine failed on GPU["
-            + std::to_string(probeGpuIdx) + "] for " + modelPath
-            + " (freeVRAM before=" + std::to_string(freeBefore / 1048576) + " MiB)", true);
         return false;
     }
 
@@ -686,13 +683,6 @@ bool Engine<T>::runInferenceFromPool(
     }
     if (!slot) {
         ANS_DBG("TRT_Pool", "ERROR: No slot available — all %zu slots busy, timeout", m_slots.size());
-        std::string errMsg = "[Engine] runInferenceFromPool FAIL: Capacity reached -- all "
-            + std::to_string(m_activeCount.load()) + "/" + std::to_string(m_totalCapacity)
-            + " slot(s) busy"
-            + (m_elasticMode ? " and all GPUs full" : "")
-            + ". Request rejected (2s timeout).";
-        std::cout << errMsg << std::endl;
-        logEngineEvent(errMsg, true);
         return false;
     }
 

engines/TensorRTAPI/include/engine/EnginePoolManager.h

@@ -99,8 +99,6 @@ public:
         // Note: maxSlotsPerGpu==1 is now the normal "1 slot per GPU" multi-GPU
         // round-robin mode, so it goes through the pool path below.
         if (maxSlotsPerGpu == 0) {
-            logEvent("[EnginePoolManager] BYPASS (maxSlots=0): " + key.modelPath
-                     + " — creating non-shared engine");
             auto engine = std::make_shared<Engine<T>>(options);
             bool ok = engine->buildLoadNetwork(modelPath, subVals, divVals, normalize);
             return ok ? engine : nullptr;
@@ -114,8 +112,6 @@ public:
             it->second.evictTime = TimePoint{};  // cancel pending eviction
             int refs = it->second.refcount;
             auto engine = it->second.engine;
-            logEvent("[EnginePoolManager] HIT: " + key.modelPath
-                     + " refs=" + std::to_string(refs));
 
             // Demand-driven growth: only in elastic mode (maxSlotsPerGpu <= 0
             // or > 1).  With maxSlotsPerGpu==1 (round-robin default), the pool
@@ -134,19 +130,9 @@ public:
                     constexpr size_t kMinVramForGrowth = 6ULL * 1024 * 1024 * 1024;  // 6 GB
                     if (totalVram >= kMinVramForGrowth) {
                         lock.unlock();  // release PoolManager lock before growing
-                        std::thread([engine, alive, refs, modelPath = key.modelPath]() {
+                        std::thread([engine]() {
-                            int created = engine->growPool(1);
+                            engine->growPool(1);
-                            if (created > 0) {
-                                logEngineEvent("[EnginePoolManager] DEMAND GROWTH: " + modelPath
-                                         + " grew from " + std::to_string(alive)
-                                         + " to " + std::to_string(engine->getTotalCapacity())
-                                         + " slots (refs=" + std::to_string(refs) + ")");
-                            }
                         }).detach();
-                    } else {
-                        logEvent("[EnginePoolManager] SKIP GROWTH: " + key.modelPath
-                                 + " (GPU VRAM " + std::to_string(totalVram >> 20)
-                                 + " MiB < 6 GB threshold, refs=" + std::to_string(refs) + ")");
                     }
                 }
             }
@@ -155,31 +141,12 @@ public:
         }
 
         // Cache miss — create new Engine pool
-        logEvent("[EnginePoolManager] MISS: Creating pool for " + key.modelPath + "...");
-
-        // Log VRAM before attempting to create probe
-        {
-            size_t freeMem = 0, totalMem = 0;
-            cudaSetDevice(options.deviceIndex);
-            cudaMemGetInfo(&freeMem, &totalMem);
-            logEvent("[EnginePoolManager] GPU[" + std::to_string(options.deviceIndex)
-                     + "] VRAM: " + std::to_string(freeMem >> 20) + " MiB free / "
-                     + std::to_string(totalMem >> 20) + " MiB total (before probe)");
-        }
-
         auto engine = std::make_shared<Engine<T>>(options);
         bool ok = engine->buildLoadNetwork(modelPath, subVals, divVals, normalize, maxSlotsPerGpu);
         if (!ok) {
             // Step 1: Force-evict all pools with refcount=0 to reclaim VRAM
             int evicted = forceEvictPending();
             if (evicted > 0) {
-                size_t freeMem2 = 0, totalMem2 = 0;
-                cudaSetDevice(options.deviceIndex);
-                cudaMemGetInfo(&freeMem2, &totalMem2);
-                logEvent("[EnginePoolManager] RETRY EVICT: Force-evicted " + std::to_string(evicted)
-                         + " pending pool(s), now " + std::to_string(freeMem2 >> 20)
-                         + " MiB free. Retrying " + key.modelPath + "...");
-
                 engine = std::make_shared<Engine<T>>(options);
                 ok = engine->buildLoadNetwork(modelPath, subVals, divVals, normalize, maxSlotsPerGpu);
             }
@@ -189,13 +156,6 @@ public:
             // consumes ~300-500 MB vs ~50-100 MB for a simple loadNetwork.
             // Lightweight mode: tasks queue for a single shared slot — slower but works.
             if (!ok) {
-                size_t freeMem3 = 0, totalMem3 = 0;
-                cudaSetDevice(options.deviceIndex);
-                cudaMemGetInfo(&freeMem3, &totalMem3);
-                logEvent("[EnginePoolManager] RETRY LIGHTWEIGHT: Elastic probe failed, "
-                         + std::to_string(freeMem3 >> 20) + " MiB free. "
-                         "Retrying with single-slot mode for " + key.modelPath + "...");
-
                 engine = std::make_shared<Engine<T>>(options);
                 ok = engine->buildLoadNetwork(modelPath, subVals, divVals, normalize);
             }
@@ -208,13 +168,6 @@ public:
             // Evidence: FireSmoke/detector.onnx failed at 3740 MiB free, then
             //           succeeded 4 seconds later at 3154 MiB free (less VRAM!).
             if (!ok) {
-                size_t freeMem4 = 0, totalMem4 = 0;
-                cudaSetDevice(options.deviceIndex);
-                cudaMemGetInfo(&freeMem4, &totalMem4);
-                logEvent("[EnginePoolManager] RETRY DELAYED: All attempts failed with "
-                         + std::to_string(freeMem4 >> 20) + " MiB free. "
-                         "Waiting 3s before final retry for " + key.modelPath + "...");
-
                 // Release mutex during sleep so other tasks can proceed
                 // (they may complete pool creation that resolves our issue)
                 lock.unlock();
@@ -226,29 +179,15 @@ public:
                 if (it2 != m_pools.end()) {
                     it2->second.refcount++;
                     it2->second.evictTime = TimePoint{};
-                    logEvent("[EnginePoolManager] HIT (after delay): " + key.modelPath
-                             + " refs=" + std::to_string(it2->second.refcount));
                     return it2->second.engine;
                 }
 
                 // Final retry — try lightweight again after delay
-                cudaSetDevice(options.deviceIndex);
-                cudaMemGetInfo(&freeMem4, &totalMem4);
-                logEvent("[EnginePoolManager] RETRY FINAL: " + std::to_string(freeMem4 >> 20)
-                         + " MiB free. Last attempt for " + key.modelPath + "...");
-
                 engine = std::make_shared<Engine<T>>(options);
                 ok = engine->buildLoadNetwork(modelPath, subVals, divVals, normalize);
             }
 
             if (!ok) {
-                size_t freeMem = 0, totalMem = 0;
-                cudaMemGetInfo(&freeMem, &totalMem);
-                logEvent("[EnginePoolManager] FAILED: Could not load engine for "
-                         + key.modelPath + " | GPU[" + std::to_string(options.deviceIndex)
-                         + "] VRAM: " + std::to_string(freeMem >> 20) + " MiB free / "
-                         + std::to_string(totalMem >> 20) + " MiB total"
-                         + " (after 4 attempts: elastic, evict, lightweight, delayed)", true);
                 return nullptr;
             }
         }
@@ -261,7 +200,6 @@ public:
         // Start the lazy-eviction sweeper if not already running
         startSweeperIfNeeded();
 
-        logEvent("[EnginePoolManager] CREATED: " + key.modelPath + " refs=1");
         return engine;
     }
 
@@ -280,14 +218,10 @@ public:
         if (it->second.refcount <= 0) return;
 
         it->second.refcount--;
-        logEvent("[EnginePoolManager] RELEASE: " + key.modelPath
-                 + " refs=" + std::to_string(it->second.refcount));
 
         if (it->second.refcount <= 0) {
             // Mark for lazy eviction — don't destroy yet
             it->second.evictTime = Clock::now() + std::chrono::seconds(kEvictGraceSec);
-            logEvent("[EnginePoolManager] PENDING EVICT: " + key.modelPath
-                     + " (will evict in " + std::to_string(kEvictGraceSec) + "s if not re-acquired)");
         }
     }
 
@@ -295,7 +229,6 @@ public:
     void clearAll() {
         {
             std::lock_guard<std::mutex> lock(m_mutex);
-            logEvent("[EnginePoolManager] CLEAR ALL (" + std::to_string(m_pools.size()) + " pools)");
             m_pools.clear();
         }
         stopSweeper();
@@ -361,17 +294,6 @@ private:
     using Clock     = std::chrono::steady_clock;
     using TimePoint = std::chrono::time_point<Clock>;
 
-    // Log to stdout/stderr only — no Windows Event Viewer.
-    // Event Viewer logging is handled by logEngineEvent() in engine.h for
-    // critical engine-level errors.  EnginePoolManager messages are
-    // informational (HIT/MISS/EVICT) and don't need Event Viewer entries.
-    static void logEvent(const std::string& msg, bool isError = false) {
-        if (isError)
-            std::cerr << msg << std::endl;
-        else
-            std::cout << msg << std::endl;
-    }
-
     struct PoolEntry {
         std::shared_ptr<Engine<T>> engine;
         int       refcount  = 0;
@@ -408,7 +330,6 @@ private:
         int evicted = 0;
         for (auto it = m_pools.begin(); it != m_pools.end(); ) {
             if (it->second.refcount <= 0) {
-                logEvent("[EnginePoolManager] FORCE EVICT (VRAM recovery): " + it->first.modelPath);
                 it = m_pools.erase(it);
                 evicted++;
             } else {
@@ -428,7 +349,6 @@ private:
                 && entry.evictTime != TimePoint{}
                 && now >= entry.evictTime)
             {
-                logEvent("[EnginePoolManager] EVICT (expired): " + it->first.modelPath);
                 it = m_pools.erase(it);
             } else {
                 ++it;

engines/TensorRTAPI/include/engine/EngineRunInference.inl

@@ -486,10 +486,6 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
     // valid here.  Guard against the (unlikely) edge case where runInference is
     // called before loadNetwork succeeds.
     if (!m_streamInitialized || !m_inferenceStream) {
-        std::string errMsg = "Error: Inference stream not initialised. "
-                     "Call loadNetwork() / buildLoadNetwork() before runInference().";
-        std::cout << errMsg << std::endl;
-        logEngineEvent("[Engine] runInference: " + errMsg, true);
         return false;
     }
 
@@ -902,20 +898,6 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
         }
         if (!success) {
             ANS_DBG("TRT_Engine", "ERROR: enqueueV3 FAILED batch=%d", batchSize);
-            std::string debugInfo = "[Engine] runInference FAIL: enqueue returned false, batch="
-                + std::to_string(batchSize)
-                + ", dimsSpecified=" + (m_context->allInputDimensionsSpecified() ? "YES" : "NO");
-            for (size_t i = 0; i < m_IOTensorNames.size(); ++i) {
-                auto shape = m_context->getTensorShape(m_IOTensorNames[i].c_str());
-                debugInfo += ", tensor'" + m_IOTensorNames[i] + "'=[";
-                for (int j = 0; j < shape.nbDims; ++j) {
-                    if (j > 0) debugInfo += ",";
-                    debugInfo += std::to_string(shape.d[j]);
-                }
-                debugInfo += "]";
-            }
-            std::cout << debugInfo << std::endl;
-            logEngineEvent(debugInfo, true);
             return false;
         }
 
@@ -933,11 +915,6 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
                     m_inferenceStream);
 
                 if (copyErr != cudaSuccess) {
-                    std::string errMsg = "[Engine] runInference FAIL: cudaMemcpyAsync output "
-                        + std::to_string(outputIdx) + " batch " + std::to_string(batch)
-                        + ": " + cudaGetErrorString(copyErr);
-                    std::cout << errMsg << std::endl;
-                    logEngineEvent(errMsg, true);
                     return false;
                 }
             }
@@ -953,10 +930,6 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
         if (syncErr != cudaSuccess) {
             ANS_DBG("TRT_Engine", "ERROR: cudaStreamSync FAILED err=%d (%s)",
                     (int)syncErr, cudaGetErrorString(syncErr));
-            std::string errMsg = "[Engine] runInference FAIL: cudaStreamSynchronize: "
-                + std::string(cudaGetErrorString(syncErr));
-            std::cout << errMsg << std::endl;
-            logEngineEvent(errMsg, true);
             return false;
         }
     }