!10357 [Wide&Deep] Save Strategy Files According to the Rank Id

From: @huangxinjing
Reviewed-by: @yao_yf,@stsuteng,@zhunaipan
Signed-off-by: @stsuteng

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

@@ -40,7 +40,7 @@ def argparse_init():
     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="./", help="The location of the checkpoint file.")
-    parser.add_argument("--stra_ckpt", type=str, default="./checkpoints/strategy.ckpt",
+    parser.add_argument("--stra_ckpt", type=str, default="./checkpoints",
                         help="The strategy checkpoint file.")
     parser.add_argument("--eval_file_name", type=str, default="eval.log", help="Eval output file.")
     parser.add_argument("--loss_file_name", type=str, default="loss.log", help="Loss output file.")

model_zoo/official/recommend/wide_and_deep/train_and_eval_auto_parallel.py

@@ -124,11 +124,15 @@ def train_and_eval(config):
     eval_callback = EvalCallBack(
         model, ds_eval, auc_metric, config)
 
+    # Save strategy ckpts according to the rank id, this must be done before initializing the callbacks.
+    config.stra_ckpt = os.path.join(config.stra_ckpt + "-{}".format(get_rank()), "strategy.ckpt")
+
     callback = LossCallBack(config=config, per_print_times=20)
     ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size()*epochs,
                                   keep_checkpoint_max=5, integrated_save=False)
     ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
-                                 directory=config.ckpt_path + '/ckpt_' + str(get_rank()) + '/', config=ckptconfig)
+                                 directory=os.path.join(config.ckpt_path, 'ckpt_' + str(get_rank())), config=ckptconfig)
+
     context.set_auto_parallel_context(strategy_ckpt_save_file=config.stra_ckpt)
     callback_list = [TimeMonitor(
         ds_train.get_dataset_size()), eval_callback, callback]

model_zoo/official/recommend/wide_and_deep/train_and_eval_parameter_server_distribute.py

@@ -115,6 +115,10 @@ def train_and_eval(config):
 
     model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
 
+    if cache_enable:
+        config.stra_ckpt = os.path.join(config.stra_ckpt + "-{}".format(get_rank()), "strategy.ckpt")
+        context.set_auto_parallel_context(strategy_ckpt_save_file=config.stra_ckpt)
+
     eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
 
     callback = LossCallBack(config=config)
@@ -129,9 +133,6 @@ def train_and_eval(config):
     ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
                                  directory=config.ckpt_path + '/ckpt_' + str(get_rank()) + '/',
                                  config=ckptconfig)
-    if cache_enable:
-        config.stra_ckpt = './stra_ckpt_' + str(get_rank()) + '/strategy.ckpt'
-        context.set_auto_parallel_context(strategy_ckpt_save_file=config.stra_ckpt)
     callback_list = [TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback]
     if get_rank() == 0:
         callback_list.append(ckpoint_cb)