modify the ckpt path

model_zoo/official/cv/mobilenetv2/src/config.py

@@ -36,7 +36,7 @@ def set_config(args):
         "save_checkpoint": True,
         "save_checkpoint_epochs": 1,
         "keep_checkpoint_max": 20,
-        "save_checkpoint_path": "./checkpoint",
+        "save_checkpoint_path": "./",
         "platform": args.platform,
         "run_distribute": False
     })
@@ -57,7 +57,7 @@ def set_config(args):
         "save_checkpoint": True,
         "save_checkpoint_epochs": 1,
         "keep_checkpoint_max": 200,
-        "save_checkpoint_path": "./checkpoint",
+        "save_checkpoint_path": "./",
         "platform": args.platform,
         "ccl": "nccl",
         "run_distribute": args.run_distribute
@@ -79,7 +79,7 @@ def set_config(args):
         "save_checkpoint": True,
         "save_checkpoint_epochs": 1,
         "keep_checkpoint_max": 200,
-        "save_checkpoint_path": "./checkpoint",
+        "save_checkpoint_path": "./",
         "platform": args.platform,
         "ccl": "hccl",
         "device_id": int(os.getenv('DEVICE_ID', '0')),

model_zoo/official/cv/mobilenetv2/train.py

@@ -24,6 +24,7 @@ from mindspore.nn import WithLossCell, TrainOneStepCell
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.common import dtype as mstype
+from mindspore.communication.management import get_rank
 from mindspore.train.model import Model
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import save_checkpoint
@@ -94,10 +95,6 @@ if __name__ == '__main__':
         features_path = args_opt.dataset_path + '_features'
         idx_list = list(range(step_size))
 
-        if os.path.isdir(config.save_checkpoint_path):
-            os.rename(config.save_checkpoint_path, "{}_{}".format(config.save_checkpoint_path, time.time()))
-        os.mkdir(config.save_checkpoint_path)
-
         for epoch in range(epoch_size):
             random.shuffle(idx_list)
             epoch_start = time.time()
@@ -112,7 +109,11 @@ if __name__ == '__main__':
             .format(epoch + 1, step_size, epoch_mseconds, per_step_mseconds, np.mean(np.array(losses))), \
                 end="")
             if (epoch + 1) % config.save_checkpoint_epochs == 0:
-                save_checkpoint(network, os.path.join(config.save_checkpoint_path, \
+                rank = 0
+                if config.run_distribute:
+                    rank = get_rank()
+                save_ckpt_path = os.path.join(config.save_checkpoint_path, 'ckpt_' + str(rank) + '/')
+                save_checkpoint(network, os.path.join(save_ckpt_path, \
                     f"mobilenetv2_head_{epoch+1}.ckpt"))
         print("total cost {:5.4f} s".format(time.time() - start))
 

model_zoo/official/nlp/bert/scripts/ascend_distributed_launcher/hyper_parameter_config.ini

@@ -7,6 +7,6 @@ do_shuffle=true
 enable_data_sink=true
 data_sink_steps=100
 accumulation_steps=1
-save_checkpoint_path=./checkpoint/
+save_checkpoint_path=./
 save_checkpoint_steps=10000
 save_checkpoint_num=1

model_zoo/official/nlp/transformer/train.py

@@ -131,6 +131,7 @@ def run_transformer_train():
     else:
         device_num = 1
         rank_id = 0
+        save_ckpt_path = os.path.join(args.save_checkpoint_path, 'ckpt_0/')
     dataset = create_transformer_dataset(epoch_count=1, rank_size=device_num,
                                          rank_id=rank_id, do_shuffle=args.do_shuffle,
                                          dataset_path=args.data_path,

model_zoo/official/recommend/deepfm/scripts/run_distribute_train.sh

@@ -36,7 +36,7 @@ do
     env > env.log
     python -u train.py \
     --dataset_path=$DATA_URL \
-    --ckpt_path="checkpoint" \
+    --ckpt_path="./" \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
     --do_eval=True > output.log 2>&1 &

model_zoo/official/recommend/deepfm/scripts/run_distribute_train_gpu.sh

@@ -31,7 +31,7 @@ env > env.log
 mpirun --allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
   python -u train.py \
     --dataset_path=$DATA_URL \
-    --ckpt_path="checkpoint" \
+    --ckpt_path="./" \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
     --device_target='GPU' \

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

@@ -38,7 +38,7 @@ def argparse_init():
     parser.add_argument("--keep_prob", type=float, default=1.0, help="The keep rate in dropout layer.")
     parser.add_argument("--dropout_flag", type=int, default=0, help="Enable dropout")
     parser.add_argument("--output_path", type=str, default="./output/")
-    parser.add_argument("--ckpt_path", type=str, default="./checkpoints/", help="The location of the checkpoint file.")
+    parser.add_argument("--ckpt_path", type=str, default="./", help="The location of the checkpoint file.")
     parser.add_argument("--stra_ckpt", type=str, default="./checkpoints/strategy.ckpt",
                         help="The strategy checkpoint file.")
     parser.add_argument("--eval_file_name", type=str, default="eval.log", help="Eval output file.")
@@ -77,7 +77,7 @@ class WideDeepConfig():
         self.output_path = "./output"
         self.eval_file_name = "eval.log"
         self.loss_file_name = "loss.log"
-        self.ckpt_path = "./checkpoints/"
+        self.ckpt_path = "./"
         self.stra_ckpt = './checkpoints/strategy.ckpt'
         self.host_device_mix = 0
         self.dataset_type = "tfrecord"

model_zoo/official/recommend/wide_and_deep_multitable/src/config.py

@@ -35,7 +35,7 @@ def argparse_init():
     parser.add_argument("--dropout_flag", type=int, default=1) # The dropout rate
 
     parser.add_argument("--output_path", type=str, default="./output/")  # The location of the output file.
-    parser.add_argument("--ckpt_path", type=str, default="./checkpoints/")  # The location of the checkpoints file.
+    parser.add_argument("--ckpt_path", type=str, default="./")  # The location of the checkpoints file.
     parser.add_argument("--eval_file_name", type=str, default="eval.log")  # Eval output file.
     parser.add_argument("--loss_file_name", type=str, default="loss.log")  # Loss output file.
     return parser
@@ -67,7 +67,7 @@ class WideDeepConfig():
         self.output_path = "./output/"
         self.eval_file_name = "eval.log"
         self.loss_file_name = "loss.log"
-        self.ckpt_path = "./checkpoints/"
+        self.ckpt_path = "./"
 
     def argparse_init(self):
         """