diff --git a/research/recommend/Fat-DeepFFM/README.md b/research/recommend/Fat-DeepFFM/README.md
index b87a870356bc651c871508989924ce32885744a4..7574948286a5876bf6ae766e3c6d315ed8d79b6b 100644
--- a/research/recommend/Fat-DeepFFM/README.md
+++ b/research/recommend/Fat-DeepFFM/README.md
@@ -97,6 +97,31 @@ Fat - DeepFFM consists of three parts. The FFM component is a factorization mach
[hccl tools](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools).
+- running on GPU
+
+ ```shell
+ # run training example
+ python train.py \
+ --dataset_path='data/mindrecord' \
+ --ckpt_path='./checkpoint/Fat-DeepFFM' \
+ --eval_file_name='./auc.log' \
+ --loss_file_name='./loss.log' \
+ --device_target='GPU' \
+ --do_eval=True > output.log 2>&1 &
+
+ # run distributed training example
+ bash scripts/run_distribute_train_gpu.sh 8 /dataset_path
+
+ # run evaluation example
+ python eval.py \
+ --dataset_path='dataset/mindrecord' \
+ --ckpt_path='./checkpoint/Fat-DeepFFM.ckpt'\
+ --device_target = 'GPU'\
+ --device_id=0 > eval_output.log 2>&1 &
+ OR
+ bash scripts/run_eval.sh 0 GPU /dataset_path /ckpt_path
+ ```
+
# [Script Description](#contents)
## [Script and Sample Code](#contents)
@@ -281,17 +306,17 @@ Inference result is saved in current path, you can find result like this in acc.
### Inference Performance
-| Parameters | Ascend |
-| ------------------- | --------------------------- |
-| Model Version | DeepFM |
-| Resource | Ascend 910; OS Euler2.8 |
-| Uploaded Date | 06/20/2021 (month/day/year) |
-| MindSpore Version | 1.2.0 |
-| Dataset | Criteo |
-| batch_size | 1000 |
-| outputs | AUC |
-| AUC | 1pc: 80.90%; |
-| Model for inference | 87.65M (.ckpt file) |
+| Parameters | Ascend | GPU
+| ------------------- | --------------------------- | ---------------------------
+| Model Version | DeepFM | DeepFM
+| Resource | Ascend 910; OS Euler2.8 | NV SMX2 V100-32G; OS Euler3.10
+| Uploaded Date | 06/20/2021 (month/day/year) | 09/04/2021 (month/day/year)
+| MindSpore Version | 1.2.0 | 1.3.0
+| Dataset | Criteo | Criteo
+| batch_size | 1000 | 1000
+| outputs | AUC | AUC
+| AUC | 1pc: 80.90%; | 8pc: 80.90%;
+| Model for inference | 87.65M (.ckpt file) | 89.35M (.ckpt file)
# [Description of Random Situation](#contents)
diff --git a/research/recommend/Fat-DeepFFM/scripts/run_distribute_train_gpu.sh b/research/recommend/Fat-DeepFFM/scripts/run_distribute_train_gpu.sh
new file mode 100644
index 0000000000000000000000000000000000000000..af08cdedcf31748cc18319fbf5ea664ebf2d0025
--- /dev/null
+++ b/research/recommend/Fat-DeepFFM/scripts/run_distribute_train_gpu.sh
@@ -0,0 +1,59 @@
+#!/bin/bash
+# Copyright 2021 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.
+# ============================================================================
+echo "Please run the script as: "
+echo "bash scripts/run_distribute_train_gpu.sh DEVICE_NUM DATASET_PATH"
+echo "for example: sh scripts/run_distribute_train_gpu.sh 8 /dataset_path"
+echo "After running the script, the network runs in the background, The log will be generated in log/output.log"
+
+if [ $# != 2 ]; then
+ echo "Usage: bash scripts/run_distribute_train_gpu.sh [DEVICE_NUM] [DATASET_PATH]"
+ exit 1
+fi
+
+get_real_path() {
+ if [ "${1:0:1}" == "/" ]; then
+ echo "$1"
+ else
+ echo "$(realpath -m $PWD/$1)"
+ fi
+}
+
+dataset_path=$(get_real_path $2)
+echo $dataset_path
+
+if [ ! -d $dataset_path ]
+then
+ echo "error: dataset_path=$dataset_path is not a directory."
+exit 1
+fi
+
+export RANK_SIZE=$1
+export DATA_URL=$2
+
+rm -rf log
+mkdir ./log
+cp *.py ./log
+cp -r ./src ./log
+cd ./log || exit
+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="Fat-DeepFFM" \
+ --eval_file_name='auc.log' \
+ --loss_file_name='loss.log' \
+ --device_target='GPU' \
+ --do_eval=True > output.log 2>&1 &
\ No newline at end of file
diff --git a/research/recommend/Fat-DeepFFM/train.py b/research/recommend/Fat-DeepFFM/train.py
index 4c437a46721e0f5c24fa225fb64d4f82858eb1bc..23bd791fb03c080b028b0c0c824cf58dabf97ad2 100644
--- a/research/recommend/Fat-DeepFFM/train.py
+++ b/research/recommend/Fat-DeepFFM/train.py
@@ -47,22 +47,20 @@ set_seed(1)
if __name__ == '__main__':
model_config = ModelConfig()
- if rank_size > 1:
- device_id = int(os.getenv('DEVICE_ID'))
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
- device_id=device_id)
- context.reset_auto_parallel_context()
- context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True,
- all_reduce_fusion_config=[9, 11])
+ device_id = 0
+ context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=device_id)
+ if args.device_target == "Ascend":
+ device_id = int(os.getenv('DEVICE_ID', '0'))
+ if rank_size == 1 or args.device_target == "CPU":
+ rank_id = 0
+ elif rank_size > 1:
init()
rank_id = get_rank()
- else:
- device_id = int(os.getenv('DEVICE_ID'))
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
- device_id=device_id)
- rank_size = None
- rank_id = None
+ context.reset_auto_parallel_context()
+ context.set_auto_parallel_context(device_num=rank_size,
+ parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)
+ if args.device_target == "Ascend":
+ context.set_auto_parallel_context(all_reduce_fusion_config=[9, 11])
print("load dataset...")
ds_train = get_mindrecord_dataset(args.dataset_path, train_mode=True, epochs=1, batch_size=model_config.batch_size,
rank_size=rank_size, rank_id=rank_id, line_per_sample=1000)
@@ -72,12 +70,12 @@ if __name__ == '__main__':
time_callback = TimeMonitor(data_size=ds_train.get_dataset_size())
loss_callback = LossCallback(args.loss_file_name)
cb = [loss_callback, time_callback]
- if rank_size == 1 or device_id == 0:
+ if rank_id == 0:
config_ck = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size() * model_config.epoch_size,
keep_checkpoint_max=model_config.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix=args.ckpt_path, config=config_ck)
cb += [ckpoint_cb]
- if args.do_eval and device_id == 0:
+ if args.do_eval and rank_id == 0:
ds_test = get_mindrecord_dataset(args.dataset_path, train_mode=False)
eval_callback = AUCCallBack(model, ds_test, eval_file_path=args.eval_file_name)
cb.append(eval_callback)