提交 899cb645 编写于 作者: X Xiaoda Zhang

fix the wide_and_deep link

上级 61ff9e87
...@@ -19,10 +19,10 @@ the number of required accelerators is too overwhelming for people to access, re ...@@ -19,10 +19,10 @@ the number of required accelerators is too overwhelming for people to access, re
efficient method for addressing huge model problem. efficient method for addressing huge model problem.
In MindSpore, users can easily implement hybrid training by configuring trainable parameters and necessary operators to run on hosts, and other operators to run on accelerators. In MindSpore, users can easily implement hybrid training by configuring trainable parameters and necessary operators to run on hosts, and other operators to run on accelerators.
This tutorial introduces how to train [Wide&Deep](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/wide_and_deep) in the Host+Ascend 910 AI Accelerator mode. This tutorial introduces how to train [Wide&Deep](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/recommend/wide_and_deep) in the Host+Ascend 910 AI Accelerator mode.
## Preliminaries ## Preliminaries
1. Prepare the model. The Wide&Deep code can be found at: <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/wide_and_deep>, in which `train_and_eval_auto_parallel.py` is the main function for training, 1. Prepare the model. The Wide&Deep code can be found at: <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/recommend/wide_and_deep>, in which `train_and_eval_auto_parallel.py` is the main function for training,
`src/` directory contains the model definition, data processing and configuration files, `script/` directory contains the launch scripts in different modes. `src/` directory contains the model definition, data processing and configuration files, `script/` directory contains the launch scripts in different modes.
2. Prepare the dataset. The dataset can be found at: <https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz>. Use the script `/src/preprocess_data.py` to transform dataset into MindRecord format. 2. Prepare the dataset. The dataset can be found at: <https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz>. Use the script `/src/preprocess_data.py` to transform dataset into MindRecord format.
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...@@ -16,11 +16,11 @@ ...@@ -16,11 +16,11 @@
在深度学习中,工作人员时常会遇到超大模型的训练问题,即模型参数所占内存超过了设备内存上限。为高效地训练超大模型,一种方案便是[分布式并行训练](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/distributed_training.html),也就是将工作交由同构的多个加速器(如Ascend 910 AI处理器,GPU等)共同完成。但是这种方式在面对几百GB甚至几TB级别的模型时,所需的加速器过多。而当从业者实际难以获取大规模集群时,这种方式难以应用。另一种可行的方案是使用主机端(Host)和加速器(Device)的混合训练模式。此方案同时发挥了主机端内存大和加速器端计算快的优势,是一种解决超大模型训练较有效的方式。 在深度学习中,工作人员时常会遇到超大模型的训练问题,即模型参数所占内存超过了设备内存上限。为高效地训练超大模型,一种方案便是[分布式并行训练](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/distributed_training.html),也就是将工作交由同构的多个加速器(如Ascend 910 AI处理器,GPU等)共同完成。但是这种方式在面对几百GB甚至几TB级别的模型时,所需的加速器过多。而当从业者实际难以获取大规模集群时,这种方式难以应用。另一种可行的方案是使用主机端(Host)和加速器(Device)的混合训练模式。此方案同时发挥了主机端内存大和加速器端计算快的优势,是一种解决超大模型训练较有效的方式。
在MindSpore中,用户可以将待训练的参数放在主机,同时将必要算子的执行位置配置为主机,其余算子的执行位置配置为加速器,从而方便地实现混合训练。此教程以推荐模型[Wide&Deep](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/wide_and_deep)为例,讲解MindSpore在主机和Ascend 910 AI处理器的混合训练。 在MindSpore中,用户可以将待训练的参数放在主机,同时将必要算子的执行位置配置为主机,其余算子的执行位置配置为加速器,从而方便地实现混合训练。此教程以推荐模型[Wide&Deep](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/recommend/wide_and_deep)为例,讲解MindSpore在主机和Ascend 910 AI处理器的混合训练。
## 准备工作 ## 准备工作
1. 准备模型代码。Wide&Deep的代码可参见:<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/wide_and_deep>,其中,`train_and_eval_auto_parallel.py`为训练的主函数所在,`src/`目录中包含Wide&Deep模型的定义、数据处理和配置信息等,`script/`目录中包含不同配置下的训练脚本。 1. 准备模型代码。Wide&Deep的代码可参见:<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/recommend/wide_and_deep>,其中,`train_and_eval_auto_parallel.py`为训练的主函数所在,`src/`目录中包含Wide&Deep模型的定义、数据处理和配置信息等,`script/`目录中包含不同配置下的训练脚本。
2. 准备数据集。数据集下载链接:<https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz>。利用脚本`/src/preprocess_data.py`将数据集转换为MindRecord格式。 2. 准备数据集。数据集下载链接:<https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz>。利用脚本`/src/preprocess_data.py`将数据集转换为MindRecord格式。
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