add distribute train for vgg16

example/vgg16_cifar10/dataset.py

@@ -28,7 +28,11 @@ def create_dataset(data_home, repeat_num=1, training=True):
     data_dir = os.path.join(data_home, "cifar-10-batches-bin")
     if not training:
         data_dir = os.path.join(data_home, "cifar-10-verify-bin")
-    data_set = ds.Cifar10Dataset(data_dir)
+
+    rank_size = int(os.environ.get("RANK_SIZE")) if os.environ.get("RANK_SIZE") else None
+    rank_id = int(os.environ.get("RANK_ID")) if os.environ.get("RANK_ID") else None
+    data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
+
     resize_height = cfg.image_height
     resize_width = cfg.image_width
     rescale = 1.0 / 255.0

example/vgg16_cifar10/run_distribute_train.sh

@@ -0,0 +1,53 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [ $# != 2 ]
+then 
+    echo "Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATA_PATH]"
+exit 1
+fi
+
+if [ ! -f $1 ]
+then 
+    echo "error: MINDSPORE_HCCL_CONFIG_PATH=$1 is not a file"
+exit 1
+fi 
+
+if [ ! -d $2 ]
+then 
+    echo "error: DATA_PATH=$2 is not a directory"
+exit 1
+fi 
+
+ulimit -u unlimited
+export DEVICE_NUM=8
+export RANK_SIZE=8
+export MINDSPORE_HCCL_CONFIG_PATH=$1
+
+for((i=0; i<${DEVICE_NUM}; i++))
+do
+    export DEVICE_ID=$i
+    export RANK_ID=$i
+    rm -rf ./train_parallel$i
+    mkdir ./train_parallel$i
+    cp *.py ./train_parallel$i
+    cp *.sh ./train_parallel$i
+    cd ./train_parallel$i || exit
+    echo "start training for rank $RANK_ID, device $DEVICE_ID"
+    env > env.log
+    python train.py --data_path=$2 --device_id=$i &> log &
+    cd ..
+done

example/vgg16_cifar10/train.py

@@ -17,16 +17,18 @@
 python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
 """
 import argparse
+import os
 import random
 import numpy as np
 import mindspore.nn as nn
 from mindspore import Tensor
+from mindspore.communication.management import init
 from mindspore.nn.optim.momentum import Momentum
-from mindspore.train.model import Model
+from mindspore.train.model import Model, ParallelMode
 from mindspore import context
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.model_zoo.vgg import vgg16
-import dataset
+from dataset import create_dataset
 from config import cifar_cfg as cfg
 random.seed(1)
 np.random.seed(1)
@@ -62,18 +64,31 @@ if __name__ == '__main__':
 
     context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
     context.set_context(device_id=args_opt.device_id)
+    context.set_context(enable_task_sink=True)
+    context.set_context(enable_loop_sink=True)
     context.set_context(enable_mem_reuse=True, enable_hccl=False)
 
+    device_num = int(os.environ.get("DEVICE_NUM", 1))
+    if device_num > 1:
+        context.reset_auto_parallel_context()
+        context.set_context(enable_hccl=True)
+        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          mirror_mean=True)
+        init()
+
+    dataset = create_dataset(args_opt.data_path, cfg.epoch_size)
+    batch_num = dataset.get_dataset_size()
+
     net = vgg16(num_classes=cfg.num_classes)
-    lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=50000 // cfg.batch_size)
+    lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=batch_num)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay)
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
     model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
                   amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
 
-    dataset = dataset.create_dataset(args_opt.data_path, cfg.epoch_size)
-    batch_num = dataset.get_dataset_size()
     config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max)
+    time_cb = TimeMonitor(data_size=batch_num)
     ckpoint_cb = ModelCheckpoint(prefix="train_vgg_cifar10", directory="./", config=config_ck)
     loss_cb = LossMonitor()
-    model.train(cfg.epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])
+    model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
+    print("train success")