use GradientMachine::start and finish

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -166,12 +166,16 @@ MultiGradientMachine::MultiGradientMachine(const ModelConfig& config,
 
   outArgStream_ = HPPL_STREAM_1;
 
+  start();
+}
+
+void MultiGradientMachine::start() {
   for (auto& thread : threads_) {
     thread->start();
   }
 }
 
-MultiGradientMachine::~MultiGradientMachine() {
+void MultiGradientMachine::finish() {
   for (auto& thread : threads_) {
     thread->stop();
   }
@@ -445,7 +449,7 @@ TrainerThread::TrainerThread(const ModelConfig& config,
 
   gradStream_ = HPPL_STREAM_2;
   valueStream_ = HPPL_STREAM_3;
-  stopping_ = false;
+  stopping_ = true;
   updateCounter_ = 0;
   parameterUpdated_ = false;
 }
@@ -453,6 +457,10 @@ TrainerThread::TrainerThread(const ModelConfig& config,
 TrainerThread::~TrainerThread() { stop(); }
 
 void TrainerThread::start() {
+  if (!stopping_) return;
+
+  stopping_ = false;
+
   gradientMachine_->start();
 
   computeThread_.reset(new std::thread([this]() { computeThread(); }));

paddle/gserver/gradientmachines/MultiGradientMachine.h

@@ -176,7 +176,9 @@ public:
 
   explicit MultiGradientMachine(const ModelConfig& config, bool useGpu);
 
-  virtual ~MultiGradientMachine();
+  virtual void start();
+
+  virtual void finish();
 
   virtual void prefetch(const std::vector<Argument>& inArgs);