From 7282d333d2fb259b1afe6c8d1d8f716464074e4b Mon Sep 17 00:00:00 2001 From: yao_yf Date: Thu, 10 Dec 2020 19:09:41 +0800 Subject: [PATCH] fix wide&deep readme --- mindspore/ops/operations/array_ops.py | 2 +- .../recommend/wide_and_deep/README.md | 132 +++++++++++------- 2 files changed, 80 insertions(+), 54 deletions(-) diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 0ed8b0a2cb8..03f5468224d 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -1888,7 +1888,7 @@ class UnsortedSegmentSum(PrimitiveWithInfer): output_min_shape = list(num_segments['min_value']) else: if isinstance(num_segments_type, type(mstype.tensor)): - raise ValueError("In dynamic shape scene, the num_segments should contains max_value and min_value") + raise ValueError("Num_segments only support int type when it is not a dynamic value") output_max_shape = [num_segments_v] output_min_shape = [num_segments_v] if 'max_shape' in x and 'min_shape' in x: diff --git a/model_zoo/official/recommend/wide_and_deep/README.md b/model_zoo/official/recommend/wide_and_deep/README.md index f26877e6d99..1672f3c4a8c 100644 --- a/model_zoo/official/recommend/wide_and_deep/README.md +++ b/model_zoo/official/recommend/wide_and_deep/README.md @@ -1,35 +1,38 @@ # Contents + +- [Contents](#contents) - [Wide&Deep Description](#widedeep-description) - [Model Architecture](#model-architecture) - [Dataset](#dataset) - [Environment Requirements](#environment-requirements) - [Quick Start](#quick-start) - [Script Description](#script-description) - - [Script and Sample Code](#script-and-sample-code) - - [Script Parameters](#script-parameters) + - [Script and Sample Code](#script-and-sample-code) + - [Script Parameters](#script-parameters) - [Training Script Parameters](#training-script-parameters) - [Preprocess Script Parameters](#preprocess-script-parameters) - - [Dataset Preparation](#dataset-preparation) + - [Dataset Preparation](#dataset-preparation) - [Process the Real World Data](#process-the-real-world-data) - [Generate and Process the Synthetic Data](#generate-and-process-the-synthetic-data) - - [Training Process](#training-process) + - [Training Process](#training-process) - [SingleDevice](#singledevice) - [Distribute Training](#distribute-training) - [Parameter Server](#parameter-server) - - [Evaluation Process](#evaluation-process) + - [Evaluation Process](#evaluation-process) - [Model Description](#model-description) - - [Performance](#performance) + - [Performance](#performance) - [Training Performance](#training-performance) - [Evaluation Performance](#evaluation-performance) - [Description of Random Situation](#description-of-random-situation) - [ModelZoo Homepage](#modelzoo-homepage) - # [Wide&Deep Description](#contents) + Wide&Deep model is a classical model in Recommendation and Click Prediction area. This is an implementation of Wide&Deep as described in the [Wide & Deep Learning for Recommender System](https://arxiv.org/pdf/1606.07792.pdf) paper. # [Model Architecture](#contents) -Wide&Deep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems. + +Wide&Deep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems. Currently we support host-device mode with column partition and parameter server mode. @@ -38,50 +41,59 @@ Currently we support host-device mode with column partition and parameter serve - [1] A dataset used in Guo H , Tang R , Ye Y , et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[J]. 2017. # [Environment Requirements](#contents) + - Hardware（Ascend or GPU） - - Prepare hardware environment with Ascend processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. + - Prepare hardware environment with Ascend processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. - Framework - - [MindSpore](https://gitee.com/mindspore/mindspore) + - [MindSpore](https://gitee.com/mindspore/mindspore) - For more information, please check the resources below： - - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html) - - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html) - - + - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html) + - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html) # [Quick Start](#contents) 1. Clone the Code -``` + +```bash git clone https://gitee.com/mindspore/mindspore.git cd mindspore/model_zoo/official/recommend/wide_and_deep ``` + 2. Download the Dataset > Please refer to [1] to obtain the download link + ```bash mkdir -p data/origin_data && cd data/origin_data wget DATA_LINK -tar -zxvf dac.tar.gz +tar -zxvf dac.tar.gz ``` + 3. Use this script to preprocess the data. This may take about one hour and the generated mindrecord data is under data/mindrecord. + ```bash python src/preprocess_data.py --data_path=./data/ --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0 ``` 4. Start Training + Once the dataset is ready, the model can be trained and evaluated on the single device(Ascend) by the command as follows: + ```bash -python train_and_eval.py --data_path=./data/mindrecord --data_type=mindrecord -``` -To evaluate the model, command as follows: -```bash -python eval.py --data_path=./data/mindrecord --data_type=mindrecord +python train_and_eval.py --data_path=./data/mindrecord --dataset_type=mindrecord ``` +To evaluate the model, command as follows: + +```bash +python eval.py --data_path=./data/mindrecord --dataset_type=mindrecord +``` # [Script Description](#contents) + ## [Script and Sample Code](#contents) -``` + +```bash └── wide_and_deep ├── eval.py ├── README.md @@ -119,10 +131,9 @@ python eval.py --data_path=./data/mindrecord --data_type=mindrecord ### [Training Script Parameters](#contents) -The parameters is same for ``train.py``,``train_and_eval.py`` ,``train_and_eval_distribute.py`` and ``train_and_eval_auto_parallel.py`` +The parameters is same for ``train.py``,``train_and_eval.py`` ,``train_and_eval_distribute.py`` and ``train_and_eval_auto_parallel.py`` - -``` +```python usage: train.py [-h] [--device_target {Ascend,GPU}] [--data_path DATA_PATH] [--epochs EPOCHS] [--full_batch FULL_BATCH] [--batch_size BATCH_SIZE] [--eval_batch_size EVAL_BATCH_SIZE] @@ -153,8 +164,8 @@ optional arguments: --keep_prob The keep rate in dropout layer.(Default:1.0) --dropout_flag Enable dropout.(Default:0) --output_path Deprecated - --ckpt_path The location of the checkpoint file. If the checkpoint file - is a slice of weight, multiple checkpoint files need to be + --ckpt_path The location of the checkpoint file. If the checkpoint file + is a slice of weight, multiple checkpoint files need to be transferred. Use ';' to separate them and sort them in sequence like "./checkpoints/0.ckpt;./checkpoints/1.ckpt". (Defalut:./checkpoints/) @@ -164,8 +175,10 @@ optional arguments: --dataset_type The data type of the training files, chosen from tfrecord/mindrecord/hd5.(Default:tfrecord) --parameter_server Open parameter server of not.(Default:0) ``` + ### [Preprocess Script Parameters](#contents) -``` + +```python usage: generate_synthetic_data.py [-h] [--output_file OUTPUT_FILE] [--label_dim LABEL_DIM] [--number_examples NUMBER_EXAMPLES] @@ -180,11 +193,11 @@ optional arguments: --dense_dim The number of the continue feature.(Default:13) --slot_dim The number of the category features.(Default:26) --vocabulary_size The vocabulary size of the total dataset.(Default:400000000) - --random_slot_values 0 or 1. If 1, the id is generated by the random. If 0, the id is set by the row_index mod part_size, where part_size is the vocab size for each slot + --random_slot_values 0 or 1. If 1, the id is generated by the random. If 0, the id is set by the row_index mod part_size, where part_size is the vocab size for each slot ``` -``` -usage: preprocess_data.py [-h] +```python +usage: preprocess_data.py [-h] [--data_path DATA_PATH] [--dense_dim DENSE_DIM] [--slot_dim SLOT_DIM] [--threshold THRESHOLD] [--train_line_count TRAIN_LINE_COUNT] @@ -193,7 +206,7 @@ usage: preprocess_data.py [-h] --data_path The path of the data file. --dense_dim The number of your continues fields.(default: 13) --slot_dim The number of your sparse fields, it can also be called category features.(default: 26) - --threshold Word frequency below this value will be regarded as OOV. It aims to reduce the vocab size. (default: 100) + --threshold Word frequency below this value will be regarded as OOV. It aims to reduce the vocab size. (default: 100) --train_line_count The number of examples in your dataset. --skip_id_convert 0 or 1. If set 1, the code will skip the id convert, regarding the original id as the final id.(default: 0) ``` @@ -203,28 +216,35 @@ usage: preprocess_data.py [-h] ### [Process the Real World Data](#content) 1. Download the Dataset and place the raw dataset under a certain path, such as: ./data/origin_data + ```bash mkdir -p data/origin_data && cd data/origin_data wget DATA_LINK -tar -zxvf dac.tar.gz +tar -zxvf dac.tar.gz ``` + > Please refer to [1] to obtain the download link 2. Use this script to preprocess the data + ```bash python src/preprocess_data.py --data_path=./data/ --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0 ``` ### [Generate and Process the Synthetic Data](#content) -1. The following command will generate 40 million lines of click data, in the format of -> "label\tdense_feature[0]\tdense_feature[1]...\tsparse_feature[0]\tsparse_feature[1]...". -``` + +1. The following command will generate 40 million lines of click data, in the format of + +> "label\tdense_feature[0]\tdense_feature[1]...\tsparse_feature[0]\tsparse_feature[1]...". + +```bash mkdir -p syn_data/origin_data python src/generate_synthetic_data.py --output_file=syn_data/origin_data/train.txt --number_examples=40000000 --dense_dim=13 --slot_dim=51 --vocabulary_size=2000000000 --random_slot_values=0 ``` 2. Preprocess the generated data -``` + +```python python src/preprocess_data.py --data_path=./syn_data/ --dense_dim=13 --slot_dim=51 --threshold=0 --train_line_count=40000000 --skip_id_convert=1 ``` @@ -233,25 +253,30 @@ python src/preprocess_data.py --data_path=./syn_data/ --dense_dim=13 --slot_dim ### [SingleDevice](#contents) To train and evaluate the model, command as follows: -``` + +```python python train_and_eval.py ``` - ### [Distribute Training](#contents) + To train the model in data distributed training, command as follows: -``` + +```bash # configure environment path before training -bash run_multinpu_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE +bash run_multinpu_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE ``` + To train the model in model parallel training, commands as follows: -``` + +```bash # configure environment path before training -bash run_auto_parallel_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE +bash run_auto_parallel_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE ``` To train the model in clusters, command as follows:''' -``` + +```bash # deploy wide&deep script in clusters # CLUSTER_CONFIG is a json file, the sample is in script/. # EXECUTE_PATH is the scripts path after the deploy. @@ -262,9 +287,12 @@ bash deploy_cluster.sh CLUSTER_CONFIG_PATH EXECUTE_PATH bash start_cluster.sh CLUSTER_CONFIG_PATH EPOCH_SIZE VOCAB_SIZE EMB_DIM DATASET ENV_SH RANK_TABLE_FILE MODE ``` + ### [Parameter Server](#contents) + To train and evaluate the model in parameter server mode, command as follows:''' -``` + +```bash # SERVER_NUM is the number of parameter servers for this task. # SCHED_HOST is the IP address of scheduler. # SCHED_PORT is the port of scheduler. @@ -272,11 +300,11 @@ To train and evaluate the model in parameter server mode, command as follows:''' bash run_parameter_server_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE SERVER_NUM SCHED_HOST SCHED_PORT ``` - - ## [Evaluation Process](#contents) + To evaluate the model, command as follows: -``` + +```python python eval.py ``` @@ -284,7 +312,7 @@ python eval.py ## [Performance](#contents) -### Training Performance +### Training Performance | Parameters | Single
Ascend | Single
GPU | Data-Parallel-8P | Host-Device-mode-8P | | ------------------------ | ------------------------------- | ------------------------------- | ------------------------------- | ------------------------------- | @@ -301,8 +329,6 @@ python eval.py | Parms(M) | 75.84 | 75.84 | 75.84 | 75.84 | | Checkpoint for inference | 233MB(.ckpt file) | 230MB(.ckpt) | 233MB(.ckpt file) | 233MB(.ckpt file) | - - All executable scripts can be found in [here](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/recommend/wide_and_deep/script) Note: The result of GPU is tested under the master version. The parameter server mode of the Wide&Deep model is still under development. @@ -322,11 +348,11 @@ Note: The result of GPU is tested under the master version. The parameter server # [Description of Random Situation](#contents) There are three random situations: + - Shuffle of the dataset. - Initialization of some model weights. - Dropout operations. - # [ModelZoo Homepage](#contents) -Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). \ No newline at end of file +Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).