update Tensorflow test method

TensorFlow/Accuracy_Validation/README.md

 # 简介
 
-该测试用例可用于ResNet50/Vgg16等网络的性能测试及精度验证。 
+  该测试用例可用于ResNet50/Vgg16等网络的性能测试及精度验证。 
 
 # 单卡测试 (单精度)
 
@@ -19,7 +19,14 @@
 
 ## 运行
 
-    mpirun -np 4 --hostfile hostfile --bind-to none scripts-run/single_process.sh 
+    mpirun -np 4 --hostfile hostfile  -mca btl self,tcp  --bind-to none scripts-run/single_process.sh 
+
+# 多卡测试 (混合精度)
+
+##
+  
+   修改scripts-run/single_process.sh中的--use_fp16=True
+   mpirun -np 4 --hostfile hostfile  -mca btl self,tcp  --bind-to none scripts-run/single_process.sh 
     
 # 参考资料
 [https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks](https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks)

TensorFlow/Accuracy_Validation/benchmarks-master/scripts/tf_cnn_benchmarks/benchmark_cnn.py

@@ -1555,7 +1555,7 @@ class BenchmarkCNN(object):
       n_epochs = self.params.eval_during_training_every_n_epochs
       self.eval_during_training_at_specified_steps = {
           (int(e * num_train_examples_per_epoch + self.batch_size - 1) //
-           self.batch_size)
+           (self.batch_size * self.num_workers ))
           for e in np.arange(n_epochs, self.num_epochs, n_epochs)}
 
     if self.params.eval_during_training_at_specified_steps:
@@ -1577,7 +1577,7 @@ class BenchmarkCNN(object):
         mlperf.logger.log(key=mlperf.tags.EVAL_EPOCH_OFFSET, value=offset)
         self.eval_during_training_at_specified_steps = {
             (int(e * num_train_examples_per_epoch + self.batch_size - 1) //
-             self.batch_size)
+             (self.batch_size * self.num_workers ))
             for e in n_epochs}
       except ValueError:
         raise ValueError('Param eval_during_training_at_specified_epochs value '

TensorFlow/ComputeVision/Classification/README.md

 # 简介
 
-该测试用例用于TensorFlow分类模型性能测试，使用的数据集是imagenet。
+  该测试用例用于TensorFlow分类模型性能测试，使用的数据集是imagenet。
  
-* 该脚本支持horovod等分布式通信库方式
+  * 该脚本支持horovod等分布式通信库方式
 
 # 运行
 
@@ -16,7 +16,7 @@
 
 ## 分布式多卡
 
-    mpirun -np ${num_gpu} --hostfile hostfile --bind-to none scripts-run/single_process.sh 
+    mpirun -np ${num_gpu} --hostfile hostfile  -mca btl self,tcp  --bind-to none scripts-run/single_process.sh 
 
 hostfile格式参考：
    

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/benchmark_cnn.py

@@ -1555,7 +1555,7 @@ class BenchmarkCNN(object):
       n_epochs = self.params.eval_during_training_every_n_epochs
       self.eval_during_training_at_specified_steps = {
           (int(e * num_train_examples_per_epoch + self.batch_size - 1) //
-           self.batch_size)
+           (self.batch_size * self.num_workers ))
           for e in np.arange(n_epochs, self.num_epochs, n_epochs)}
 
     if self.params.eval_during_training_at_specified_steps:
@@ -1577,7 +1577,7 @@ class BenchmarkCNN(object):
         mlperf.logger.log(key=mlperf.tags.EVAL_EPOCH_OFFSET, value=offset)
         self.eval_during_training_at_specified_steps = {
             (int(e * num_train_examples_per_epoch + self.batch_size - 1) //
-             self.batch_size)
+             (self.batch_size * self.num_workers ))
             for e in n_epochs}
       except ValueError:
         raise ValueError('Param eval_during_training_at_specified_epochs value '