1.Fix error when pserver finishes training.

2.Optimize worker and server log.
3.Try Catch ps-lite exceptions.

mindspore/ccsrc/frontend/parallel/ps/parameter_server.h

@@ -736,7 +736,9 @@ void ParameterServer<T>::SyncEmbeddingTables() {
 
 template <typename T>
 void ParameterServer<T>::Run(const FuncGraphPtr &func_graph) {
+  MS_LOG(INFO) << "PServer starts connecting to scheduler and workers...";
   ::ps::Start(0);
+  MS_LOG(INFO) << "PServer connected successfully.";
   if (!::ps::IsServer()) {
     std::cout << "This is not ther Server" << std::endl;
     return;
@@ -744,7 +746,9 @@ void ParameterServer<T>::Run(const FuncGraphPtr &func_graph) {
   Init(func_graph);
   PSContext::instance()->SetPSRankId(rank_id_);
   thread_->join();
+  MS_LOG(INFO) << "PServer finished updating models, starts finalizing...";
   ::ps::Finalize(0, true);
+  MS_LOG(INFO) << "PServer finalized successfully.";
 }
 }  // namespace ps
 }  // namespace parallel

mindspore/ccsrc/frontend/parallel/ps/worker.h

@@ -86,7 +86,9 @@ void Worker<T>::Run() {
     MS_LOG(INFO) << "'Worker is already running.";
     return;
   }
+  MS_LOG(INFO) << "Worker starts connecting to scheduler and server...";
   ::ps::Start(0);
+  MS_LOG(INFO) << "Worker connected successfully.";
   if (!::ps::IsWorker()) {
     MS_LOG(EXCEPTION) << "The role is not worker.";
   }
@@ -176,9 +178,11 @@ void Worker<T>::DoPSEmbeddingLookup(const ::ps::SArray<::ps::Key> &keys, const :
 template <typename T>
 void Worker<T>::Finalize() {
   if (running_) {
+    MS_LOG(INFO) << "Worker starts finalizing...";
     kv_worker_->Finalize();
     kv_worker_.reset();
     running_ = false;
+    MS_LOG(INFO) << "Worker finalized successfully.";
   }
 }
 
@@ -315,7 +319,7 @@ void Worker<T>::InitPSParamAndOptim(const std::string &param_name, tensor::Tenso
 
   size_t param_key = GetParamKey(param_name);
   if (param_key == kInvalidKey) {
-    MS_LOG(INFO) << "Parameter " << param_name << " has no key assigned.";
+    MS_LOG(DEBUG) << "Parameter " << param_name << " has no key assigned.";
     return;
   }
   bool init_in_server = false;

model_zoo/official/recommend/wide_and_deep/src/callbacks.py

@@ -36,6 +36,7 @@ class LossCallBack(Callback):
 
     Note:
         If per_print_times is 0, do NOT print loss.
+        If this process is MS_PSERVER role, do not run callbacks.
 
     Args:
         per_print_times (int): Print loss every times. Default: 1.
@@ -50,6 +51,8 @@ class LossCallBack(Callback):
     def step_end(self, run_context):
         """Monitor the loss in training."""
         cb_params = run_context.original_args()
+        if cb_params.net_outputs is None:
+            return
         wide_loss, deep_loss = cb_params.net_outputs[0].asnumpy(), cb_params.net_outputs[1].asnumpy()
         cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
         cur_num = cb_params.cur_step_num