fix s2t gpu training hang

examples/aishell/asr0/local/train.sh

@@ -26,6 +26,10 @@ if [ ${seed} != 0 ]; then
     export FLAGS_cudnn_deterministic=True
 fi
 
+# default memeory allocator strategy may case gpu training hang
+# for no OOM raised when memory exhaused
+export FLAGS_allocator_strategy=naive_best_fit
+
 if [ ${ngpu} == 0 ]; then
 python3 -u ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \

examples/aishell/asr1/local/train.sh

@@ -35,6 +35,10 @@ echo ${ips_config}
 
 mkdir -p exp
 
+# default memeory allocator strategy may case gpu training hang
+# for no OOM raised when memory exhaused
+export FLAGS_allocator_strategy=naive_best_fit
+
 if [ ${ngpu} == 0 ]; then
 python3 -u ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \

examples/librispeech/asr0/local/train.sh

@@ -26,6 +26,10 @@ if [ ${seed} != 0 ]; then
     export FLAGS_cudnn_deterministic=True
 fi
 
+# default memeory allocator strategy may case gpu training hang
+# for no OOM raised when memory exhaused
+export FLAGS_allocator_strategy=naive_best_fit
+
 if [ ${ngpu} == 0 ]; then
 python3 -u ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \

examples/librispeech/asr1/local/train.sh

@@ -29,6 +29,10 @@ fi
 # export FLAGS_cudnn_exhaustive_search=true
 # export FLAGS_conv_workspace_size_limit=4000
 
+# default memeory allocator strategy may case gpu training hang
+# for no OOM raised when memory exhaused
+export FLAGS_allocator_strategy=naive_best_fit
+
 if [ ${ngpu} == 0 ]; then
 python3 -u ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \

examples/librispeech/asr2/local/train.sh

@@ -26,6 +26,10 @@ if [ ${seed} != 0 ]; then
     export FLAGS_cudnn_deterministic=True
 fi
 
+# default memeory allocator strategy may case gpu training hang
+# for no OOM raised when memory exhaused
+export FLAGS_allocator_strategy=naive_best_fit
+
 if [ ${ngpu} == 0 ]; then
 python3 -u ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \

examples/timit/asr1/local/train.sh

@@ -19,6 +19,10 @@ if [ ${seed} != 0  ]; then
     export FLAGS_cudnn_deterministic=True
 fi
 
+# default memeory allocator strategy may case gpu training hang
+# for no OOM raised when memory exhaused
+export FLAGS_allocator_strategy=naive_best_fit
+
 if [ ${ngpu} == 0 ]; then
 python3 -u ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \

examples/tiny/asr0/local/train.sh

@@ -32,6 +32,10 @@ fi
 
 mkdir -p exp
 
+# default memeory allocator strategy may case gpu training hang
+# for no OOM raised when memory exhaused
+export FLAGS_allocator_strategy=naive_best_fit
+
 if [ ${ngpu} == 0 ]; then
 python3 -u ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \

examples/tiny/asr1/local/train.sh

@@ -34,6 +34,10 @@ fi
 
 mkdir -p exp
 
+# default memeory allocator strategy may case gpu training hang
+# for no OOM raised when memory exhaused
+export FLAGS_allocator_strategy=naive_best_fit
+
 if [ ${ngpu} == 0 ]; then
 python3 -u ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \

examples/wenetspeech/asr1/local/train.sh

@@ -35,6 +35,10 @@ echo ${ips_config}
 
 mkdir -p exp
 
+# default memeory allocator strategy may case gpu training hang
+# for no OOM raised when memory exhaused
+export FLAGS_allocator_strategy=naive_best_fit
+
 if [ ${ngpu} == 0 ]; then
 python3 -u ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \