diff --git a/README.md b/README.md index c36c978a11369392565bc317657a3aa34b28264b..96fa5164136fc187ba335910762bd4afeee3a2be 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,7 @@ For more information about `MindSpore` framework, please refer to [FAQ](https:// - **Q: What is Some *RANK_TBAL_FILE* which mentioned in many models?** - **A**: *RANK_TABLE_FILE* is the config file of cluster on Ascend while running distributed training. For more information, you could refer to the generator [hccl_tools](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools) and [Parallel Distributed Training Example](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) + **A**: *RANK_TABLE_FILE* is the config file of cluster on Ascend while running distributed training. For more information, you could refer to the generator [hccl_tools](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools) and [Parallel Distributed Training Example](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) - **Q: How to run the scripts on Windows system?** diff --git a/README_CN.md b/README_CN.md index a655cd380825e3a5f607c57a1266834322001b4f..89bbff911202ecff44434d5a3d316774bcfa835e 100644 --- a/README_CN.md +++ b/README_CN.md @@ -50,11 +50,11 @@ MindSpore已获得Apache 2.0许可，请参见LICENSE文件。 - **Q: 一些模型描述中提到的*RANK_TABLE_FILE*文件，是什么？** - **A**: *RANK_TABLE_FILE*是一个Ascend环境上用于指定分布式集群信息的文件，更多信息可以参考生成工具[hccl_toos](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)和[分布式并行训练教程](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html) + **A**: *RANK_TABLE_FILE*是一个Ascend环境上用于指定分布式集群信息的文件，更多信息可以参考生成工具[hccl_toos](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)和[分布式并行训练教程](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html) - **Q: 如何使用多机多卡运行脚本** - **A**: 本仓内所提供的分布式（distribute）运行启动默认为单机多卡，如需多机多卡启动需要在单机多卡的基础上进行一定程度的适配，可参考[多机多卡分布式教程](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html#%E5%A4%9A%E6%9C%BA%E5%A4%9A%E5%8D%A1) + **A**: 本仓内所提供的分布式（distribute）运行启动默认为单机多卡，如需多机多卡启动需要在单机多卡的基础上进行一定程度的适配，可参考[多机多卡分布式教程](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html#%E5%A4%9A%E6%9C%BA%E5%A4%9A%E5%8D%A1) - **Q: 在windows环境上要怎么运行网络脚本？** diff --git a/official/README.md b/official/README.md index cfd16e89c346eaa819325e3e84a8e99747172170..ef2841e59148c61140b647b73f222b6f090673fa 100644 --- a/official/README.md +++ b/official/README.md @@ -242,29 +242,6 @@ | retinaface_mobilenet_0.25 | WiderFace | 90.77/88.2/74.76 | [config](https://github.com/mindspore-lab/mindface/tree/main/mindface/detection) | [link](https://gitee.com/mindspore/models/tree/master/research/cv/retinaface) | | retinaface_r50 | WiderFace | 95.07/93.61/84.84 | [config](https://github.com/mindspore-lab/mindface/tree/main/mindface/detection) | [link](https://gitee.com/mindspore/models/tree/master/official/cv/RetinaFace_ResNet50) | -### NLP - -### nlp - -| model | mindformer recipe | vanilla mindspore -| :-: | :-: | :-: | -| bert_base | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/t5.md) | [link](https://gitee.com/mindspore/models/tree/master/official/nlp/Bert) | -| t5_small | [config](https://github.com/mindspore-lab/mindformers/blob/master/docs/model_cards/bert.md) | | -| gpt2_small | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/gpt2.md) | | -| gpt2_13b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/gpt2.md) | | -| gpt2_52b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/gpt2.md) | | -| pangu_alpha | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/pangualpha.md) | | -| glm_6b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/glm.md) | | -| glm_6b_lora | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/glm.md) | | -| llama_7b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/llama.md) | | -| llama_13b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/llama.md) | | -| llama_65b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/llama.md) | | -| llama_7b_lora | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/llama.md) | | -| bloom_560m | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/bloom.md) | | -| bloom_7.1b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/bloom.md) | | -| bloom_65b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/bloom.md) | | -| bloom_176b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/bloom.md) | | - ### Recommendation | model | mind_series recipe | vanilla mindspore | diff --git a/official/README_CN.md b/official/README_CN.md index 316965958cf0200f9a7a6be2571ed34626e2cf93..17037ae0d72a9b845cb06c503b59f991dec1a26d 100644 --- a/official/README_CN.md +++ b/official/README_CN.md @@ -10,7 +10,7 @@ ### 计算机视觉 -#### 图像分类（骨干类) +#### 图像分类（骨干类） | model | acc@1 | mindcv recipe | vanilla mindspore | | :-: | :-: | :-: | :-: | @@ -242,30 +242,7 @@ | retinaface_mobilenet_0.25 | WiderFace | 90.77/88.2/74.76 | [config](https://github.com/mindspore-lab/mindface/tree/main/mindface/detection) | [link](https://gitee.com/mindspore/models/tree/master/research/cv/retinaface) | | retinaface_r50 | WiderFace | 95.07/93.61/84.84 | [config](https://github.com/mindspore-lab/mindface/tree/main/mindface/detection) | [link](https://gitee.com/mindspore/models/tree/master/official/cv/RetinaFace_ResNet50) | -### 自然语言处理 - -### nlp - -| model | mindformer recipe | vanilla mindspore -| :-: | :-: | :-: | -| bert_base | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/t5.md) | [link](https://gitee.com/mindspore/models/tree/master/official/nlp/Bert) | -| t5_small | [config](https://github.com/mindspore-lab/mindformers/blob/master/docs/model_cards/bert.md) | | -| gpt2_small | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/gpt2.md) | | -| gpt2_13b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/gpt2.md) | | -| gpt2_52b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/gpt2.md) | | -| pangu_alpha | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/pangualpha.md) | | -| glm_6b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/glm.md) | | -| glm_6b_lora | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/glm.md) | | -| llama_7b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/llama.md) | | -| llama_13b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/llama.md) | | -| llama_65b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/llama.md) | | -| llama_7b_lora | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/llama.md) | | -| bloom_560m | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/bloom.md) | | -| bloom_7.1b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/bloom.md) | | -| bloom_65b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/bloom.md) | | -| bloom_176b | [config](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/bloom.md) | | - -MindSpore仅提供下载和预处理公共数据集的脚本。我们不拥有这些数据集，也不对它们的质量负责或维护。请确保您具有在数据集许可下使用该数据集的权限。在这些数据集上训练的模型仅用于非商业研究和教学目的。 +### 推荐 | model | mind_series recipe | vanilla mindspore | | :-: | :-: | :-: | diff --git a/official/cv/CRNN/README.md b/official/cv/CRNN/README.md index aebf05ab2ac2e9e5526bee727197b1f4e85838dc..70491e0946e3b01c15a91b0d9e5555b81c80f283 100644 --- a/official/cv/CRNN/README.md +++ b/official/cv/CRNN/README.md @@ -51,7 +51,7 @@ We provide 2 versions of network using different ways to transfer the hidden siz Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below. -We use five datasets mentioned in the paper.For training, we use the synthetic dataset([MJSynth](https://www.robots.ox.ac.uk/~vgg/data/text/) and [SynthText](https://github.com/ankush-me/SynthText)) released by Jaderberg etal as the training data, which contains 8 millions training images and their corresponding ground truth words.For evaluation, we use four popular benchmarks for scene text recognition, nalely ICDAR 2003([IC03](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions)),ICDAR2013([IC13](https://rrc.cvc.uab.es/?ch=2&com=downloads)),IIIT 5k-word([IIIT5k](https://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset)),and Street View Text([SVT](http://vision.ucsd.edu/~kai/grocr/)). +We use five datasets mentioned in the paper.For training, we use the synthetic dataset([MJSynth](https://www.robots.ox.ac.uk/~vgg/data/text/) and [SynthText](https://github.com/ankush-me/SynthText)) released by Jaderberg etal as the training data, which contains 8 millions training images and their corresponding ground truth words.For evaluation, we use four popular benchmarks for scene text recognition, nalely ICDAR 2003([IC03](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions)),ICDAR2013([IC13](https://rrc.cvc.uab.es/?ch=2&com=downloads)),IIIT 5k-word([IIIT5k](https://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset)). ### [Dataset Prepare](#content) @@ -237,7 +237,7 @@ Parameters for both training and evaluation can be set in default_config.yaml. ## [Training Process](#contents) -- Set options in `config.py`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `config.py`, including learning rate and other network hyperparameters. ### [Training](#contents) diff --git a/official/cv/CRNN/README_CN.md b/official/cv/CRNN/README_CN.md index c4a9e796662a92c461a1bff2eccfd57e2a6a2d0e..16fb2ec4e7903423aee53ce05038c7f396301ac9 100644 --- a/official/cv/CRNN/README_CN.md +++ b/official/cv/CRNN/README_CN.md @@ -51,7 +51,7 @@ CRNN使用vgg16结构进行特征提取，附加两层双向LSTM，最后使用C 注：可以运行原始论文中提到的数据集脚本，也可以运行在相关域/网络架构中广泛使用的脚本。下面将介绍如何使用相关数据集运行脚本。 -我们使用论文中提到的五个数据集。在训练中，使用Jederberg等人发布的合成数据集（[MJSynth](https://www.robots.ox.ac.uk/~vgg/data/text/)和[SynthText](https://github.com/ankush-me/SynthText)）作为训练数据，其中包含800万张训练图像及其对应的地面真值词。在评估中，使用四个流行的场景文本识别基准，即ICDAR 2003([IC03](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions))、ICDAR2013（[IC13](https://rrc.cvc.uab.es/?ch=2&com=downloads))、IIIT 5k-word（[IIIT5k](https://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset)）和街景文本（[SVT](http://vision.ucsd.edu/~kai/grocr/)）。 +我们使用论文中提到的五个数据集。在训练中，使用Jederberg等人发布的合成数据集（[MJSynth](https://www.robots.ox.ac.uk/~vgg/data/text/)和[SynthText](https://github.com/ankush-me/SynthText)）作为训练数据，其中包含800万张训练图像及其对应的地面真值词。在评估中，使用四个流行的场景文本识别基准，即ICDAR 2003([IC03](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions))、ICDAR2013（[IC13](https://rrc.cvc.uab.es/?ch=2&com=downloads))、IIIT 5k-word（[IIIT5k](https://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset)）。 ### [数据集准备](#目录) @@ -237,7 +237,7 @@ crnn ## [训练过程](#目录) -- 设置`config.py`中的选项，包括学习率和其他网络超参。有关数据集的更多信息，请参阅[MindSpore数据集准备教程](https://www.mindspore.cn/docs/zh-CN/master/model_train/index.html)。 +- 设置`config.py`中的选项，包括学习率和其他网络超参。 ### [训练](#目录) diff --git a/official/cv/CTPN/README.md b/official/cv/CTPN/README.md index e05fc84d6925016a2018ca89e27482f99ee2b80b..af97b366702023e40526c271ce64f7d6bfed0c61 100644 --- a/official/cv/CTPN/README.md +++ b/official/cv/CTPN/README.md @@ -246,7 +246,7 @@ imagenet_cfg = edict({ Then you can train it with ImageNet2012. > Notes: -> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. +> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. > > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh` > diff --git a/official/cv/CTPN/README_CN.md b/official/cv/CTPN/README_CN.md index bb04f79b028a9c13558f2004b38332e2e65efd7a..3770e39e6728b0f20c2379613ee029cfa73a5a6a 100644 --- a/official/cv/CTPN/README_CN.md +++ b/official/cv/CTPN/README_CN.md @@ -234,7 +234,7 @@ imagenet_cfg = edict({ 然后，您可以使用ImageNet2012训练它。 > 注： -> RANK_TABLE_FILE文件，请参考[链接](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html)。如需获取设备IP，请点击[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。对于InceptionV4等大模型，最好导出外部环境变量`export HCCL_CONNECT_TIMEOUT=600`，将hccl连接检查时间从默认的120秒延长到600秒。否则，连接可能会超时，因为随着模型增大，编译时间也会增加。 +> RANK_TABLE_FILE文件，请参考[链接](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html)。如需获取设备IP，请点击[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。对于InceptionV4等大模型，最好导出外部环境变量`export HCCL_CONNECT_TIMEOUT=600`，将hccl连接检查时间从默认的120秒延长到600秒。否则，连接可能会超时，因为随着模型增大，编译时间也会增加。 > > 处理器绑核操作取决于`device_num`和总处理器数。如果不希望这样做，请删除`scripts/run_distribute_train.sh`中的`taskset`操作。 > diff --git a/official/cv/DeepText/README.md b/official/cv/DeepText/README.md index 42fffda7ad8d0d166a5b89ebed0d6fc297e85d3a..d69a317cf0cac8c779f4efcdf77f076c98d002e1 100644 --- a/official/cv/DeepText/README.md +++ b/official/cv/DeepText/README.md @@ -143,7 +143,7 @@ Here we used 4 datasets for training, and 1 datasets for Evaluation. ``` > Notes: -> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. +> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. > > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh` > diff --git a/official/cv/DeepText/README_CN.md b/official/cv/DeepText/README_CN.md index 604a3517a24c2ae9c9b361532c91b3e395cfe787..0840a9ee49c3ddf63945704411e059625f26d521 100644 --- a/official/cv/DeepText/README_CN.md +++ b/official/cv/DeepText/README_CN.md @@ -133,7 +133,7 @@ InceptionV4的整体网络架构如下： ``` > 注： -> RANK_TABLE_FILE文件，请参考[链接](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html)。如需获取设备IP，请点击[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。对于InceptionV4等大模型，最好导出外部环境变量`export HCCL_CONNECT_TIMEOUT=600`，将hccl连接检查时间从默认的120秒延长到600秒。否则，连接可能会超时，因为随着模型增大，编译时间也会增加。 +> RANK_TABLE_FILE文件，请参考[链接](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html)。如需获取设备IP，请点击[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。对于InceptionV4等大模型，最好导出外部环境变量`export HCCL_CONNECT_TIMEOUT=600`，将hccl连接检查时间从默认的120秒延长到600秒。否则，连接可能会超时，因为随着模型增大，编译时间也会增加。 > > 处理器绑核操作取决于`device_num`和总处理器数。如果不希望这样做，请删除`scripts/run_distribute_train.sh`中的`taskset`操作。 > diff --git a/official/cv/Inception/inceptionv4/README.md b/official/cv/Inception/inceptionv4/README.md index df52f98bfd50baf3853a6db8606f55e56c6505fd..3378a3eb732879307fa2e29a5781b54088127194 100644 --- a/official/cv/Inception/inceptionv4/README.md +++ b/official/cv/Inception/inceptionv4/README.md @@ -279,7 +279,7 @@ You can start training using python or shell scripts. The usage of shell scripts ``` > Notes: -> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. +> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. > > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh` diff --git a/official/cv/Inception/inceptionv4/README_CN.md b/official/cv/Inception/inceptionv4/README_CN.md index 9502b76267b3d0f45ca0818dbc4dd807314e5388..3c481fbbb4004d553de07ef7cbb077dc8f44a326 100644 --- a/official/cv/Inception/inceptionv4/README_CN.md +++ b/official/cv/Inception/inceptionv4/README_CN.md @@ -267,7 +267,7 @@ train.py和config.py中的主要涉及如下参数： ``` > 注： -> 有关RANK_TABLE_FILE，可参考[链接](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html)。设备IP可参考[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。对于像InceptionV4这样的大型模型，最好设置外部环境变量`export HCCL_CONNECT_TIMEOUT=600`，将hccl连接检查时间从默认的120秒延长到600秒。否则，可能会连接超时，因为编译时间会随着模型增大而增加。 +> 有关RANK_TABLE_FILE，可参考[链接](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html)。设备IP可参考[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。对于像InceptionV4这样的大型模型，最好设置外部环境变量`export HCCL_CONNECT_TIMEOUT=600`，将hccl连接检查时间从默认的120秒延长到600秒。否则，可能会连接超时，因为编译时间会随着模型增大而增加。 > > 绑核操作取决于`device_num`参数值及处理器总数。如果不需要，删除`scripts/run_distribute_train.sh`脚本中的`taskset`操作任务集即可。 diff --git a/official/cv/Inception/xception/README.md b/official/cv/Inception/xception/README.md index a0181e25aed0ae6c879e090f94acda0c3d040424..8ea9743fee071a727be38b62b7601a3cbd40dd7b 100644 --- a/official/cv/Inception/xception/README.md +++ b/official/cv/Inception/xception/README.md @@ -189,7 +189,7 @@ You can start training using python or shell scripts. The usage of shell scripts bash run_infer_310.sh MINDIR_PATH DATA_PATH LABEL_FILE DEVICE_ID ``` -> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). +> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). ### Launch diff --git a/official/cv/Inception/xception/README_CN.md b/official/cv/Inception/xception/README_CN.md index f36fc15c1546940f09e10247120247e3cd7b5d5d..005e8690005487b97d2607b8384131de0111141d 100644 --- a/official/cv/Inception/xception/README_CN.md +++ b/official/cv/Inception/xception/README_CN.md @@ -189,7 +189,7 @@ Xception的整体网络架构如下： bash run_infer_310.sh MINDIR_PATH DATA_PATH LABEL_FILE DEVICE_ID ``` -> 注：RANK_TABLE_FILE可以参考[链接](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html)，device_ip可以参考[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。 +> 注：RANK_TABLE_FILE可以参考[链接](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html)，device_ip可以参考[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。 ### 启动 diff --git a/official/cv/MaskRCNN/maskrcnn_mobilenetv1/README.md b/official/cv/MaskRCNN/maskrcnn_mobilenetv1/README.md index 1659ce27f9e3fd67a3e75713d80851fa62732a7c..0943af1a42cc6ab9fa421603c80bd8c0978faecc 100644 --- a/official/cv/MaskRCNN/maskrcnn_mobilenetv1/README.md +++ b/official/cv/MaskRCNN/maskrcnn_mobilenetv1/README.md @@ -522,7 +522,7 @@ Usage: bash run_distribute_train_gpu.sh [DATA_PATH] [PRETRAINED_PATH] (optional) ## [Training Process](#contents) -- Set options in `default_config.yaml`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `default_config.yaml`, including loss_scale, learning rate and network hyperparameters. ### [Training](#content) diff --git a/official/cv/MaskRCNN/maskrcnn_mobilenetv1/README_CN.md b/official/cv/MaskRCNN/maskrcnn_mobilenetv1/README_CN.md index 6e68692184901065b84e279f66df5cd28c75c1c7..3b8941536fc216faefe227aea533cf1964601a2e 100644 --- a/official/cv/MaskRCNN/maskrcnn_mobilenetv1/README_CN.md +++ b/official/cv/MaskRCNN/maskrcnn_mobilenetv1/README_CN.md @@ -521,7 +521,7 @@ test_batch_size": 2, # ## [训练过程](#目录) -- 在`default_config.yaml`中设置选项，包括损失缩放、学习率和网络超参。有关数据集的更多信息，请单击[此处](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset.。 +- 在`default_config.yaml`中设置选项，包括损失缩放、学习率和网络超参。 ### [训练](#目录) diff --git a/official/cv/MaskRCNN/maskrcnn_resnet50/README.md b/official/cv/MaskRCNN/maskrcnn_resnet50/README.md index e7f5bcc9c7291df059e5cfefbe3fe8536a609b8a..a599ce73c401144f167af1d1fb78178f66106dd3 100644 --- a/official/cv/MaskRCNN/maskrcnn_resnet50/README.md +++ b/official/cv/MaskRCNN/maskrcnn_resnet50/README.md @@ -543,7 +543,7 @@ Usage: bash run_standalone_train.sh [PRETRAINED_MODEL] [DATA_PATH] ## [Training Process](#contents) -- Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. ### [Training](#content) diff --git a/official/cv/MaskRCNN/maskrcnn_resnet50/README_CN.md b/official/cv/MaskRCNN/maskrcnn_resnet50/README_CN.md index 2caaa62d542ee920ee0935f55a221c0ca5e3d266..447307e0159d19bd8c2b48424b9644e7e376c903 100644 --- a/official/cv/MaskRCNN/maskrcnn_resnet50/README_CN.md +++ b/official/cv/MaskRCNN/maskrcnn_resnet50/README_CN.md @@ -525,7 +525,7 @@ bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH] [DATA_PATH] ## 训练过程 -- 在`config.py`中设置配置项，包括loss_scale、学习率和网络超参。单击[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/index.html)获取更多数据集相关信息. +- 在`config.py`中设置配置项，包括loss_scale、学习率和网络超参。 ### 训练 diff --git a/official/cv/ResNet/README.md b/official/cv/ResNet/README.md index e207f981ca4cefc8b2cf078f1382279eda3ab412..0a08a869f778ccc365dd1c2d0ec2ab1464506440 100644 --- a/official/cv/ResNet/README.md +++ b/official/cv/ResNet/README.md @@ -480,7 +480,7 @@ bash run_eval_gpu_resnet_benchmark.sh [DATASET_PATH] [CKPT_PATH] [BATCH_SIZE](op For distributed training, a hostfile configuration needs to be created in advance. -Please follow the instructions in the link [GPU-Multi-Host](https://www.mindspore.cn/docs/en/master/model_train/parallel/mpirun.html). +Please follow the instructions in the link [GPU-Multi-Host](https://www.mindspore.cn/tutorials/en/master/parallel/mpirun.html). #### Running parameter server mode training @@ -1484,7 +1484,7 @@ Refer to the [ModelZoo FAQ](https://gitee.com/mindspore/models#FAQ) for some com **A**: Suggested reference:https://bbs.huaweicloud.com/forum/thread-134093-1-1.html -- **Q: How to solve the memory shortage caused by accumulation operators such as ReduceMean and BiasAddGrad on 910B?** +- **Q: How to solve the memory shortage caused by accumulation operators such as ReduceMean and BiasAddGrad on Atlas A2 training series?** **A**: Suggested adding `mindspore.set_context(ascend_config={"atomic_clean_policy": 0})` in `train.py`. If the problem still hasn't been resolved, please go to the [MindSpore community](https://gitee.com/mindspore/mindspore/issues) to submit an issue. diff --git a/official/cv/ResNet/README_CN.md b/official/cv/ResNet/README_CN.md index 5aafb7f53d0c8794a73313527212d889a4145eff..b030f3c6750167c1d14371e93cb38d42bbe11633 100644 --- a/official/cv/ResNet/README_CN.md +++ b/official/cv/ResNet/README_CN.md @@ -1425,7 +1425,7 @@ result:{'top_1_accuracy': 0.928385416666666} prune_rate=0.45 ckpt=~/resnet50_cif **A**： 建议参考https://bbs.huaweicloud.com/forum/thread-134093-1-1.html -- **Q: 如何解决910B硬件上因ReduceMean、BiasAddGrad等累加算子导致的内存不足？** +- **Q: 如何解决Atlas A2训练系列产品上因ReduceMean、BiasAddGrad等累加算子导致的内存不足？** **A**： 建议在`train.py`中添加`mindspore.set_context(ascend_config={"atomic_clean_policy": 0})`，如果还是没有解决问题，请到[MindSpore社区](https://gitee.com/mindspore/mindspore/issues)提issue。 diff --git a/official/cv/RetinaNet/README.md b/official/cv/RetinaNet/README.md index 51b9f5503117bbbbe8cb90a2612c9ba51a731156..b3a064e41dc008b8df36e75bc20f9b3d69aa24fe 100644 --- a/official/cv/RetinaNet/README.md +++ b/official/cv/RetinaNet/README.md @@ -208,7 +208,7 @@ bash scripts/run_single_train.sh DEVICE_ID MINDRECORD_DIR CONFIG_PATH PRE_TRAINE > Note: - For details about RANK_TABLE_FILE, see [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html). For details about how to obtain device IP address, see [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). + For details about RANK_TABLE_FILE, see [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html). For details about how to obtain device IP address, see [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). #### Running diff --git a/official/cv/RetinaNet/README_CN.md b/official/cv/RetinaNet/README_CN.md index ee093f3394469c87608f3d77552f4f905a72e799..d69a835cdbbc73296c470c6a8b0891cf08386f45 100644 --- a/official/cv/RetinaNet/README_CN.md +++ b/official/cv/RetinaNet/README_CN.md @@ -203,7 +203,7 @@ bash scripts/run_single_train.sh DEVICE_ID MINDRECORD_DIR CONFIG_PATH PRE_TRAINE > 注意： - RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。 + RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。 #### 运行 diff --git a/official/cv/SSD/README.md b/official/cv/SSD/README.md index 781d7852a6f2abfa240d4a080687976c712ef241..e41ad594844e9d3ea6e307b7dcc863b8077a72c8 100644 --- a/official/cv/SSD/README.md +++ b/official/cv/SSD/README.md @@ -324,7 +324,7 @@ Then you can run everything just like on ascend. ### [Training Process](#contents) -To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/en/master/model_train/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorials/en/master/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** #### Training on Ascend diff --git a/official/cv/SSD/README_CN.md b/official/cv/SSD/README_CN.md index 8b97f4e9b9bf3e7b6719ed357921e06ff32aa8a9..169ca7e8a32aaf119cc6b6e20999068673261d1f 100644 --- a/official/cv/SSD/README_CN.md +++ b/official/cv/SSD/README_CN.md @@ -275,7 +275,7 @@ bash run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH] ## 训练过程 -运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/docs/zh-CN/master/model_train/dataset/record.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。** +运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/record.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。** ### Ascend上训练 diff --git a/official/cv/Unet/README.md b/official/cv/Unet/README.md index f590c050bb71e280ffaabf8ec3ce5a704e7079c1..f5fc7a3ca891b27514c0096125cadcb7b810c666 100644 --- a/official/cv/Unet/README.md +++ b/official/cv/Unet/README.md @@ -617,7 +617,7 @@ Result on ONNX **Before inference, please refer to [MindSpore Inference with C++ Deployment Guide](https://gitee.com/mindspore/models/blob/master/utils/cpp_infer/README.md) to set environment variables.** If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you -can refer to this [Link](https://www.mindspore.cn/docs/en/master/model_infer/index.html). Following +can refer to this [Link](https://www.mindspore.cn/tutorials/en/master/model_infer/ms_infer/llm_inference_overview.html). Following the steps below, this is a simple example: ### Continue Training on the Pretrained Model diff --git a/official/cv/Unet/README_CN.md b/official/cv/Unet/README_CN.md index 2c664737b711316839b502d8afcb552047be5664..21b776db8643aa04e852613af83080ef72251f95 100644 --- a/official/cv/Unet/README_CN.md +++ b/official/cv/Unet/README_CN.md @@ -607,7 +607,7 @@ bash ./scripts/run_eval_onnx.sh [DATASET_PATH] [ONNX_MODEL] [DEVICE_TARGET] [CON **推理前需参照 [MindSpore C++推理部署指南](https://gitee.com/mindspore/models/blob/master/utils/cpp_infer/README_CN.md) 进行环境变量设置。** -如果您需要使用训练好的模型在Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/docs/zh-CN/master/model_infer/index.html)。下面是一个简单的操作步骤示例： +如果您需要使用训练好的模型在Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/tutorials/zh-CN/master/model_infer/ms_infer/llm_inference_overview.html)。下面是一个简单的操作步骤示例： ### 继续训练预训练模型 diff --git a/official/cv/VGG/vgg16/README.md b/official/cv/VGG/vgg16/README.md index 931d40a708d33c554bfb537ee34d2006b76398c8..063fe2f39e02b87c4c8a8a951a4101d8fbb62a01 100644 --- a/official/cv/VGG/vgg16/README.md +++ b/official/cv/VGG/vgg16/README.md @@ -530,7 +530,7 @@ train_parallel1/log:epcoh: 2 step: 97, loss is 1.7133579 ... ``` -> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/docs/en/master/model_train/parallel/overview.html). +> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorials/en/master/parallel/overview.html). > **Attention** This will bind the processor cores according to the `device_num` and total processor numbers. If you don't expect to run pretraining with binding processor cores, remove the operations about `taskset` in `scripts/run_distribute_train.sh` ##### Run vgg16 on GPU diff --git a/official/cv/VGG/vgg16/README_CN.md b/official/cv/VGG/vgg16/README_CN.md index 12d1fae1c6d20e22132dd14accb2a4070e0674aa..132b9f13d517f0cba665a5a88d397ec890e2f6f8 100644 --- a/official/cv/VGG/vgg16/README_CN.md +++ b/official/cv/VGG/vgg16/README_CN.md @@ -530,7 +530,7 @@ train_parallel1/log:epcoh: 2 step: 97, loss is 1.7133579 ... ``` -> 关于rank_table.json，可以参考[分布式并行训练](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/overview.html)。 +> 关于rank_table.json，可以参考[分布式并行训练](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/overview.html)。 > **注意** 将根据`device_num`和处理器总数绑定处理器核。如果您不希望预训练中绑定处理器内核，请在`scripts/run_distribute_train.sh`脚本中移除`taskset`相关操作。 ##### GPU处理器环境运行VGG16 diff --git a/official/cv/VGG/vgg19/README.md b/official/cv/VGG/vgg19/README.md index bd2a9724ebb1ae11b8b37a6f091bf9852be2198e..f3bc2c8e7aea3c1172db3849c6b3afc1b595be5a 100644 --- a/official/cv/VGG/vgg19/README.md +++ b/official/cv/VGG/vgg19/README.md @@ -453,7 +453,7 @@ train_parallel1/log:epcoh: 2 step: 97, loss is 1.7133579 ... ``` -> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/docs/en/master/model_train/parallel/overview.html). +> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorials/en/master/parallel/overview.html). > **Attention** This will bind the processor cores according to the `device_num` and total processor numbers. If you don't expect to run pretraining with binding processor cores, remove the operations about `taskset` in `scripts/run_distribute_train.sh` ##### Run vgg19 on GPU diff --git a/official/cv/VGG/vgg19/README_CN.md b/official/cv/VGG/vgg19/README_CN.md index 7d9b1710bf2c79feccd1c1d2a2b328169d04d080..d54dfe934d49519ac784d6252450f338541e3dee 100644 --- a/official/cv/VGG/vgg19/README_CN.md +++ b/official/cv/VGG/vgg19/README_CN.md @@ -466,7 +466,7 @@ train_parallel1/log:epcoh: 2 step: 97, loss is 1.7133579 ... ``` -> 关于rank_table.json，可以参考[分布式并行训练](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/overview.html)。 +> 关于rank_table.json，可以参考[分布式并行训练](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/overview.html)。 > **注意** 将根据`device_num`和处理器总数绑定处理器核。如果您不希望预训练中绑定处理器内核，请在`scripts/run_distribute_train.sh`脚本中移除`taskset`相关操作。 ##### GPU处理器环境运行VGG19 diff --git a/official/cv/VIT/README.md b/official/cv/VIT/README.md index 3fe19000a8b13686e2e14016871af156b0b2716b..e6355d369eb6a780754df515e99fc336059c791d 100644 --- a/official/cv/VIT/README.md +++ b/official/cv/VIT/README.md @@ -449,7 +449,7 @@ in acc.log. ### Inference -If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/docs/en/master/model_infer/index.html). Following the steps below, this is a simple example: +If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorials/en/master/model_infer/ms_infer/llm_inference_overview.html). Following the steps below, this is a simple example: - Running on Ascend diff --git a/official/cv/VIT/README_CN.md b/official/cv/VIT/README_CN.md index 3e33d3eed87adbb83dd05ddd3b3d195b114a2c6c..28da32b148737ac6b0fe4a181452228fcf2ab425 100644 --- a/official/cv/VIT/README_CN.md +++ b/official/cv/VIT/README_CN.md @@ -451,7 +451,7 @@ python export.py --config_path=[CONFIG_PATH] ### 推理 -如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/docs/zh-CN/master/model_infer/index.html)。下面是操作步骤示例： +如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/tutorials/zh-CN/master/model_infer/ms_infer/llm_inference_overview.html)。下面是操作步骤示例： - Ascend处理器环境运行 diff --git a/official/nlp/Pangu_alpha/README.md b/official/nlp/Pangu_alpha/README.md index 118a38c4229b2159483ac8d0b51c41d039dc20a2..e2156c592728843eb3c235c6462bc4226e628b7e 100644 --- a/official/nlp/Pangu_alpha/README.md +++ b/official/nlp/Pangu_alpha/README.md @@ -51,7 +51,7 @@ with our parallel setting. We summarized the training tricks as following: 2. Pipeline Model Parallelism 3. Optimizer Model Parallelism -The above features can be found [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/overview.html). +The above features can be found [here](https://www.mindspore.cn/tutorials/en/master/parallel/overview.html). More amazing features are still under developing. The technical report and checkpoint file can be found [here](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-AIpha). @@ -157,7 +157,7 @@ bash scripts/run_distribute_train.sh /data/pangu_30_step_ba64/ /root/hccl_8p.jso The above command involves some `args` described below: - DATASET: The path to the mindrecord files's parent directory . For example: `/home/work/mindrecord/`. -- RANK_TABLE: The details of the rank table can be found [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html). It's a json file describes the `device id`, `service ip` and `rank`. +- RANK_TABLE: The details of the rank table can be found [here](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html). It's a json file describes the `device id`, `service ip` and `rank`. - RANK_SIZE: The device number. This can be your total device numbers. For example, 8, 16, 32 ... - TYPE: The param init type. The parameters will be initialized with float32. Or you can replace it with `fp16`. This will save a little memory used on the device. - MODE: The configure mode. This mode will set the `hidden size` and `layers` to make the parameter number near 2.6 billions. The other mode can be `13B` (`hidden size` 5120 and `layers` 40, which needs at least 16 cards to train.) and `200B`. @@ -206,7 +206,7 @@ bash scripts/run_distribute_train_gpu.sh RANK_SIZE HOSTFILE DATASET PER_BATCH MO ``` - RANK_SIZE: The device number. This can be your total device numbers. For example, 8, 16, 32 ... -- HOSTFILE: It's a text file describes the host ip and its devices. Please see our [tutorial](https://www.mindspore.cn/docs/en/master/model_train/parallel/mpirun.html) or [OpenMPI](https://www.open-mpi.org/) for more details. +- HOSTFILE: It's a text file describes the host ip and its devices. Please see our [tutorial](https://www.mindspore.cn/tutorials/en/master/parallel/mpirun.html) or [OpenMPI](https://www.open-mpi.org/) for more details. - DATASET: The path to the mindrecord files's parent directory . For example: `/home/work/mindrecord/`. - PER_BATCH: The batch size for each data parallel-way. - MODE: Can be `1.3B` `2.6B`, `13B` and `200B`. @@ -228,7 +228,7 @@ bash scripts/run_distribute_train_moe_host_device.sh DATASET RANK_TABLE RANK_SIZ The above command involves some `args` described below: - DATASET: The path to the mindrecord files's parent directory . For example: `/home/work/mindrecord/`. -- RANK_TABLE: The details of the rank table can be found [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html). It's a json file describes the `device id`, `service ip` and `rank`. +- RANK_TABLE: The details of the rank table can be found [here](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html). It's a json file describes the `device id`, `service ip` and `rank`. - RANK_SIZE: The device number. This can be your total device numbers. For example, 8, 16, 32 ... - TYPE: The param init type. The parameters will be initialized with float32. Or you can replace it with `fp16`. This will save a little memory used on the device. - MODE: The configure mode. This mode will set the `hidden size` and `layers` to make the parameter number near 2.6 billions. The other mode can be `13B` (`hidden size` 5120 and `layers` 40, which needs at least 16 cards to train.) and `200B`. diff --git a/official/nlp/Pangu_alpha/README_CN.md b/official/nlp/Pangu_alpha/README_CN.md index 9e272de851f76e60136fd4f97afc9bf15fbec222..9307a1c00ef7238d661a60f3689f15ce20c6f45c 100644 --- a/official/nlp/Pangu_alpha/README_CN.md +++ b/official/nlp/Pangu_alpha/README_CN.md @@ -51,7 +51,7 @@ 2. 流水线模型并行 3. 优化器模型并行 -有关上述特性，请点击[此处](https://www.mindspore.cn/docs/en/master/model_train/parallel/overview.html)查看详情。 +有关上述特性，请点击[此处](https://www.mindspore.cn/tutorials/en/master/parallel/overview.html)查看详情。 更多特性敬请期待。 详细技术报告和检查点文件，可点击[此处](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-AIpha)查看。 @@ -156,7 +156,7 @@ bash scripts/run_distribute_train.sh /data/pangu_30_step_ba64/ /root/hccl_8p.jso 上述命令涉及以下`args`： - DATASET：mindrecord文件父目录的路径。例如：`/home/work/mindrecord/`。 -- RANK_TABLE：rank table的详细信息，请点击[此处](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html)查看。该.json文件描述了`device id`、`service ip`和`rank`。 +- RANK_TABLE：rank table的详细信息，请点击[此处](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html)查看。该.json文件描述了`device id`、`service ip`和`rank`。 - RANK_SIZE：设备编号，也可以表示设备总数。例如，8、16、32 ... - TYPE：参数初始化类型。参数使用单精度（FP32） 或半精度（FP16）初始化。可以节省设备占用内存。 - MODE：配置模式。通过设置`hidden size`和`layers`，将参数量增至26亿。还可以选择13B（`hidden size`为5120和`layers`为40，训练至少需要16卡）和200B模式。 @@ -205,7 +205,7 @@ bash scripts/run_distribute_train_gpu.sh RANK_SIZE HOSTFILE DATASET PER_BATCH MO ``` - RANK_SIZE：设备编号，也可以表示设备总数。例如，8、16、32 ... -- HOSTFILE：描述主机IP及其设备的文本文件。有关更多详细信息，请参见我们的[教程](https://www.mindspore.cn/docs/en/master/model_train/parallel/mpirun.html) or [OpenMPI](https://www.open-mpi.org/)。 +- HOSTFILE：描述主机IP及其设备的文本文件。有关更多详细信息，请参见我们的[教程](https://www.mindspore.cn/tutorials/en/master/parallel/mpirun.html) or [OpenMPI](https://www.open-mpi.org/)。 - DATASET：mindrecord文件父目录的路径。例如：`/home/work/mindrecord/`。 - PER_BATCH：每个数据并行的批处理大小， - MODE：可以是`1.3B`、`2.6B`、`13B`或`200B`。 @@ -227,7 +227,7 @@ bash scripts/run_distribute_train_moe_host_device.sh DATASET RANK_TABLE RANK_SIZ 上述命令涉及以下args： - DATASET：mindrecord文件父目录的路径。例如：`/home/work/mindrecord/`。 -- RANK_TABLE：rank table的详细信息，请点击[此处](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html)查看。该.json文件描述了device id、service ip和rank。 +- RANK_TABLE：rank table的详细信息，请点击[此处](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html)查看。该.json文件描述了device id、service ip和rank。 - RANK_SIZE：设备编号，也可以是您的设备总数。例如，8、16、32 ... - TYPE：参数初始化类型。参数使用单精度（FP32） 或半精度（FP16）初始化。可以节省设备占用内存。 - MODE：配置模式。通过设置`hidden size`和`layers`，将参数量增至26亿。还可以选择`13B`（`hidden size`为5120和`layers`为40，训练至少需要16卡）和`200B`模式。 diff --git a/official/nlp/Transformer/README.md b/official/nlp/Transformer/README.md index ec6a66aee4834a9c3cfec7fe89b0f23623f4b2da..298cf04042418ea2167b2502dcf07f7758210e11 100644 --- a/official/nlp/Transformer/README.md +++ b/official/nlp/Transformer/README.md @@ -354,7 +354,7 @@ Parameters for learning rate: ## [Training Process](#contents) -- Set options in `default_config_large.yaml`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `default_config_large.yaml`, including loss_scale, learning rate and network hyperparameters. - Run `run_standalone_train.sh` for non-distributed training of Transformer model. diff --git a/official/nlp/Transformer/README_CN.md b/official/nlp/Transformer/README_CN.md index dc9386944cc02e3d196415b81c57a8e7217edf79..150bc66f299c622741493fbe77cc9a4ebcb3ad29 100644 --- a/official/nlp/Transformer/README_CN.md +++ b/official/nlp/Transformer/README_CN.md @@ -356,7 +356,7 @@ Parameters for learning rate: ### 训练过程 -- 在`default_config_large.yaml`中设置选项，包括loss_scale、学习率和网络超参数。点击[这里](https://www.mindspore.cn/docs/zh-CN/master/model_train/index.html)查看更多数据集信息。 +- 在`default_config_large.yaml`中设置选项，包括loss_scale、学习率和网络超参数。 - 运行`run_standalone_train.sh`，进行Transformer模型的单卡训练。 diff --git a/research/audio/speech_transformer/README.md b/research/audio/speech_transformer/README.md index bf62c4616d656cbc7a0c6792e1479f9ab25a0ed5..4cb9b65753434d8329480ba70f493e8abbe58544 100644 --- a/research/audio/speech_transformer/README.md +++ b/research/audio/speech_transformer/README.md @@ -187,7 +187,7 @@ Dataset is preprocessed using `Kaldi` and converts kaldi binaries into Python pi ## [Training Process](#contents) -- Set options in `default_config.yaml`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `default_config.yaml`, including loss_scale, learning rate and network hyperparameters. - Run `run_standalone_train_gpu.sh` for non-distributed training of Transformer model. diff --git a/research/cv/3D_DenseNet/README.md b/research/cv/3D_DenseNet/README.md index 1b8a78ffef1c42b2e7adda96dc0f05a6a42a59e8..6ea1ba34942a67075346bdd7e454abccb5dac79b 100644 --- a/research/cv/3D_DenseNet/README.md +++ b/research/cv/3D_DenseNet/README.md @@ -222,7 +222,7 @@ Dice Coefficient (DC) for 9th subject (9 subjects for training and 1 subject for |-------------------|:-------------------:|:---------------------:|:-----:|:--------------:| |3D-SkipDenseSeg | 93.66| 90.80 | 90.65 | 91.70 | -Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools) For large models like InceptionV4, it's better to export an external environment variable export HCCL_CONNECT_TIMEOUT=600 to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. To avoid ops error，you should change the code like below: +Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools) For large models like InceptionV4, it's better to export an external environment variable export HCCL_CONNECT_TIMEOUT=600 to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. To avoid ops error，you should change the code like below: in train.py： diff --git a/research/cv/3D_DenseNet/README_CN.md b/research/cv/3D_DenseNet/README_CN.md index 022157600adae3fdd198ad5a1746aedc963f26e0..fe913c3783114d9d7059f958169797eaac9359b9 100644 --- a/research/cv/3D_DenseNet/README_CN.md +++ b/research/cv/3D_DenseNet/README_CN.md @@ -212,7 +212,7 @@ bash run_eval.sh 3D-DenseSeg-20000_36.ckpt data/data_val |-------------------|:-------------------:|:---------------------:|:-----:|:--------------:| |3D-SkipDenseSeg | 93.66| 90.80 | 90.65 | 91.70 | -Notes: 分布式训练需要一个RANK_TABLE_FILE，文件的删除方式可以参考该链接[Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) ,device_ip的设置参考该链接 [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools) 对于像InceptionV4这样的大模型来说, 最好导出一个外部环境变量，export HCCL_CONNECT_TIMEOUT=600，以将hccl连接检查时间从默认的120秒延长到600秒。否则，连接可能会超时，因为编译时间会随着模型大小的增加而增加。在1.3.0版本下，3D算子可能存在一些问题，您可能需要更改context.set_auto_parallel_context的部分代码: +Notes: 分布式训练需要一个RANK_TABLE_FILE，文件的删除方式可以参考该链接[Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) ,device_ip的设置参考该链接 [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools) 对于像InceptionV4这样的大模型来说, 最好导出一个外部环境变量，export HCCL_CONNECT_TIMEOUT=600，以将hccl连接检查时间从默认的120秒延长到600秒。否则，连接可能会超时，因为编译时间会随着模型大小的增加而增加。在1.3.0版本下，3D算子可能存在一些问题，您可能需要更改context.set_auto_parallel_context的部分代码: in train.py： diff --git a/research/cv/AlignedReID++/README_CN.md b/research/cv/AlignedReID++/README_CN.md index 532d291ece48fdefdd6535a4e9db577fdfa292bf..7d59efd3f1af4072a8f62d3964cb518edfd1208e 100644 --- a/research/cv/AlignedReID++/README_CN.md +++ b/research/cv/AlignedReID++/README_CN.md @@ -405,7 +405,7 @@ market1501上评估AlignedReID++ ### 推理 -如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/docs/zh-CN/master/model_infer/index.html)。下面是操作步骤示例： +如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/tutorials/zh-CN/master/model_infer/ms_infer/llm_inference_overview.html)。下面是操作步骤示例： 在进行推理之前我们需要先导出模型，mindir可以在本地环境上导出。batch_size默认为1。 diff --git a/research/cv/C3D/README.md b/research/cv/C3D/README.md index 6102a83e4a1c2658fcb1cad157cff7bf48aa8471..9a555620ebc69f922a58bfbf047146ce6ff2368f 100644 --- a/research/cv/C3D/README.md +++ b/research/cv/C3D/README.md @@ -465,7 +465,7 @@ The above shell script will run distribute training in the background. You can v #### Distributed training on Ascend > Notes: -> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. +> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. > ```text diff --git a/research/cv/C3D/README_CN.md b/research/cv/C3D/README_CN.md index 7678cd917f9f4aa3682d48bfe093fabf0e5a3005..306fe33e537bd15bd075857f8b53460e26fab4c6 100644 --- a/research/cv/C3D/README_CN.md +++ b/research/cv/C3D/README_CN.md @@ -456,7 +456,7 @@ bash run_standalone_train_gpu.sh [CONFIG_PATH] [DEVICE_ID] #### Ascend分布式训练 > 注： -> RANK_TABLE_FILE文件，请参考[链接](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html)。如需获取设备IP，请点击[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。对于InceptionV4等大模型，最好导出外部环境变量`export HCCL_CONNECT_TIMEOUT=600`，将hccl连接检查时间从默认的120秒延长到600秒。否则，连接可能会超时，因为随着模型增大，编译时间也会增加。 +> RANK_TABLE_FILE文件，请参考[链接](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html)。如需获取设备IP，请点击[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。对于InceptionV4等大模型，最好导出外部环境变量`export HCCL_CONNECT_TIMEOUT=600`，将hccl连接检查时间从默认的120秒延长到600秒。否则，连接可能会超时，因为随着模型增大，编译时间也会增加。 > ```text diff --git a/research/cv/EGnet/README_CN.md b/research/cv/EGnet/README_CN.md index e486c8f6c0023c9dd3861de373b2946dfe1f87fc..a17fbf18cce40254be3e7d2fa52afbcdb2010c4d 100644 --- a/research/cv/EGnet/README_CN.md +++ b/research/cv/EGnet/README_CN.md @@ -363,7 +363,7 @@ bash run_standalone_train_gpu.sh bash run_distribute_train.sh 8 [RANK_TABLE_FILE] ``` -线下运行分布式训练请参照[rank table启动](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html) +线下运行分布式训练请参照[rank table启动](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html) - 线上modelarts分布式训练 diff --git a/research/cv/LightCNN/README.md b/research/cv/LightCNN/README.md index c2de4a52392a28c433d1e70c91fe87af26ba6251..00c9fa6743921d4c82a812fb4f850c6139735a79 100644 --- a/research/cv/LightCNN/README.md +++ b/research/cv/LightCNN/README.md @@ -139,7 +139,7 @@ reduce precision" to view the operators with reduced precision. - Generate config json file for 8-card training - [Simple tutorial](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools) - For detailed configuration method, please refer to - the [rank table Startup](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html). + the [rank table Startup](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html). # [Quick start](#Quickstart) diff --git a/research/cv/LightCNN/README_CN.md b/research/cv/LightCNN/README_CN.md index d363114b856317c2005ca9a9e76817a7d98d99e0..6bdbae9a4a25b6c775974c9ee087994fba98e034 100644 --- a/research/cv/LightCNN/README_CN.md +++ b/research/cv/LightCNN/README_CN.md @@ -107,7 +107,7 @@ LightCNN适用于有大量噪声的人脸识别数据集，提出了maxout 的 - [MindSpore Python API](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.html) - 生成config json文件用于8卡训练。 - [简易教程](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools) - - 详细配置方法请参照[rank table启动](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html)。 + - 详细配置方法请参照[rank table启动](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html)。 # 快速入门 diff --git a/research/cv/Unet3d/README.md b/research/cv/Unet3d/README.md index 4e823c20773f66f19819452b37022e415078b50a..a47ddd087af76c821886727c321c94e4c2472f34 100644 --- a/research/cv/Unet3d/README.md +++ b/research/cv/Unet3d/README.md @@ -312,7 +312,7 @@ After training, you'll get some checkpoint files under the `train_parallel_fp[32 #### Distributed training on Ascend > Notes: -> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. +> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. > ```shell diff --git a/research/cv/Unet3d/README_CN.md b/research/cv/Unet3d/README_CN.md index 1c27edd3493e3d207f5e257b1dddfe5b2b5068a2..28a8e4bf22ce520f852af3efd4c1e05d3b8deab2 100644 --- a/research/cv/Unet3d/README_CN.md +++ b/research/cv/Unet3d/README_CN.md @@ -312,7 +312,7 @@ bash ./run_distribute_train_gpu_fp16.sh /path_prefix/LUNA16/train #### 在Ascend上进行分布式训练 > 注： -> RANK_TABLE_FILE参考[链接](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html)，device_ip参考[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。对于像InceptionV4这样的大模型，最好导出外部环境变量`export HCCL_CONNECT_TIMEOUT=600`，将HCCL连接检查时间从默认的120秒延长到600秒。否则，连接可能会超时，因为编译时间会随着模型大小的增长而增加。 +> RANK_TABLE_FILE参考[链接](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html)，device_ip参考[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。对于像InceptionV4这样的大模型，最好导出外部环境变量`export HCCL_CONNECT_TIMEOUT=600`，将HCCL连接检查时间从默认的120秒延长到600秒。否则，连接可能会超时，因为编译时间会随着模型大小的增长而增加。 > ```shell diff --git a/research/cv/cnnctc/README.md b/research/cv/cnnctc/README.md index 789b52833cb40d48615f217d147c994fed21a1dc..d14752bf20a159143101ccc0fe48255d085f58a4 100644 --- a/research/cv/cnnctc/README.md +++ b/research/cv/cnnctc/README.md @@ -542,7 +542,7 @@ accuracy: 0.8427 ### Inference -If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/docs/en/master/model_infer/index.html). Following the steps below, this is a simple example: +If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorials/en/master/model_infer/ms_infer/llm_inference_overview.html). Following the steps below, this is a simple example: - Running on Ascend diff --git a/research/cv/cnnctc/README_CN.md b/research/cv/cnnctc/README_CN.md index 1589dda88fa95caebea3dfda173c9fa8e028a16c..0dd9c823ddeef87fce33faf8a2714bbf96ca06c0 100644 --- a/research/cv/cnnctc/README_CN.md +++ b/research/cv/cnnctc/README_CN.md @@ -261,7 +261,7 @@ bash scripts/run_distribute_train_ascend.sh [RANK_TABLE_FILE] [PRETRAINED_CKPT(o > 注意: - RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). + RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). ### 训练结果 @@ -485,7 +485,7 @@ accuracy: 0.8427 ### 推理 -如果您需要在GPU、Ascend 910、Ascend 310等多个硬件平台上使用训练好的模型进行推理，请参考此[链接](https://www.mindspore.cn/docs/zh-CN/master/model_infer/index.html)。以下为简单示例： +如果您需要在GPU、Ascend 910、Ascend 310等多个硬件平台上使用训练好的模型进行推理，请参考此[链接](https://www.mindspore.cn/tutorials/zh-CN/master/model_infer/ms_infer/llm_inference_overview.html)。以下为简单示例： - Ascend处理器环境运行 diff --git a/research/cv/crnn_seq2seq_ocr/README.md b/research/cv/crnn_seq2seq_ocr/README.md index bfa509cf9f8d115fe201d7e60b7d45307b951d61..c21d9b1f3fa4db6973081c4ec27d4866ba3555fe 100644 --- a/research/cv/crnn_seq2seq_ocr/README.md +++ b/research/cv/crnn_seq2seq_ocr/README.md @@ -229,7 +229,7 @@ Parameters for both training and evaluation can be set in config.py. ## [Training Process](#contents) -- Set options in `default_config.yaml`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `default_config.yaml`, including learning rate and other network hyperparameters. ### [Training](#contents) diff --git a/research/cv/cspdarknet53/README.md b/research/cv/cspdarknet53/README.md index 5ddf567a9c0f835ce140a01a614470bf30b0bb15..b071fce85e3c00868b14876787c12b31f8712f0d 100644 --- a/research/cv/cspdarknet53/README.md +++ b/research/cv/cspdarknet53/README.md @@ -206,7 +206,7 @@ bash run_distribute_train.sh [RANK_TABLE_FILE] [DATA_DIR] (option)[PATH_CHECKPOI bash run_standalone_train.sh [DEVICE_ID] [DATA_DIR] (option)[PATH_CHECKPOINT] ``` -> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV3, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. +> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV3, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. > > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh` diff --git a/research/cv/dcgan/README.md b/research/cv/dcgan/README.md index 88986dcd815cb76f6684d13991e72ab8218447f6..a3d38ff24b671a4f1dff6e1a70bc40b57f3749f9 100644 --- a/research/cv/dcgan/README.md +++ b/research/cv/dcgan/README.md @@ -156,7 +156,7 @@ dcgan_cifar10_cfg { ## [Training Process](#contents) -- Set options in `config.py`, including learning rate, output filename and network hyperparameters. Click [here](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `config.py`, including learning rate, output filename and network hyperparameters. ### [Training](#content) diff --git a/research/cv/dlinknet/README.md b/research/cv/dlinknet/README.md index 47c9969cc1b3c7dab1ac2b23cd95f540f9f35ec0..565d149d7ff3f74ee66f45c9557fa3b648cda5a0 100644 --- a/research/cv/dlinknet/README.md +++ b/research/cv/dlinknet/README.md @@ -328,7 +328,7 @@ bash scripts/run_distribute_gpu_train.sh [DATASET] [CONFIG_PATH] [DEVICE_NUM] [C #### inference If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you -can refer to this [Link](https://www.mindspore.cn/docs/en/master/model_infer/index.html). Following +can refer to this [Link](https://www.mindspore.cn/tutorials/en/master/model_infer/ms_infer/llm_inference_overview.html). Following the steps below, this is a simple example: ##### running-on-ascend-310 diff --git a/research/cv/dlinknet/README_CN.md b/research/cv/dlinknet/README_CN.md index 3911a9e0d16df4bd8efdd40f10ae1f4d0cd01daf..1dbd95c4900ed92434b541bfdff8359aac6e111b 100644 --- a/research/cv/dlinknet/README_CN.md +++ b/research/cv/dlinknet/README_CN.md @@ -333,7 +333,7 @@ bash scripts/run_distribute_gpu_train.sh [DATASET] [CONFIG_PATH] [DEVICE_NUM] [C #### 推理 -如果您需要使用训练好的模型在Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/docs/zh-CN/master/model_infer/index.html)。下面是一个简单的操作步骤示例： +如果您需要使用训练好的模型在Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/tutorials/zh-CN/master/model_infer/ms_infer/llm_inference_overview.html)。下面是一个简单的操作步骤示例： ##### Ascend 310环境运行 diff --git a/research/cv/east/README.md b/research/cv/east/README.md index bfe3231dbaec8b0ce39a098753f6c47c011af6b4..494977be4645d6bb668db14cfa155c47337aeded 100644 --- a/research/cv/east/README.md +++ b/research/cv/east/README.md @@ -134,7 +134,7 @@ bash run_eval_gpu.sh [DATASET_PATH] [CKPT_PATH] [DEVICE_ID] ``` > Notes: -> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. +> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. > > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh` > diff --git a/research/cv/essay-recogination/README_CN.md b/research/cv/essay-recogination/README_CN.md index 707c932ec2c06f22130e658234c44eb9d8bf28ee..62246b266176db400b5088d123a0fd2f10af24c6 100644 --- a/research/cv/essay-recogination/README_CN.md +++ b/research/cv/essay-recogination/README_CN.md @@ -111,7 +111,7 @@ train.valInterval = 100 #边训练边推 ## 训练过程 -- 在`parameters/hwdb.gin`中设置选项，包括学习率和网络超参数。单击[MindSpore加载数据集教程](https://www.mindspore.cn/docs/zh-CN/master/model_train/index.html)，了解更多信息。 +- 在`parameters/hwdb.gin`中设置选项，包括学习率和网络超参数。 ### 训练 diff --git a/research/cv/googlenet/README.md b/research/cv/googlenet/README.md index de03081294f110c6c9acda23522f80327a55390f..843d7b5e12cdf46ef310232e3c3afdb3481b12fd 100644 --- a/research/cv/googlenet/README.md +++ b/research/cv/googlenet/README.md @@ -597,7 +597,7 @@ Current batch_ Size can only be set to 1. ### Inference -If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/docs/en/master/model_infer/index.html). Following the steps below, this is a simple example: +If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorials/en/master/model_infer/ms_infer/llm_inference_overview.html). Following the steps below, this is a simple example: - Running on Ascend diff --git a/research/cv/googlenet/README_CN.md b/research/cv/googlenet/README_CN.md index 3c294fce24c3ebe732ead0ad5cec7fc701c66aa2..f9f7c2ec2db03d20f6b372590b85f1475c81d549 100644 --- a/research/cv/googlenet/README_CN.md +++ b/research/cv/googlenet/README_CN.md @@ -598,7 +598,7 @@ python export.py --config_path [CONFIG_PATH] ### 推理 -如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/docs/zh-CN/master/model_infer/index.html)。下面是操作步骤示例： +如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/tutorials/zh-CN/master/model_infer/ms_infer/llm_inference_overview.html)。下面是操作步骤示例： - Ascend处理器环境运行 diff --git a/research/cv/hardnet/README_CN.md b/research/cv/hardnet/README_CN.md index 6eb181d0458d065595b4d00c51ac58ece1396b11..9e09127a4b6febd1bea4f9248ecea5b9b733c090 100644 --- a/research/cv/hardnet/README_CN.md +++ b/research/cv/hardnet/README_CN.md @@ -449,7 +449,7 @@ bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID] ### 推理 -如果您需要使用此训练模型在Ascend 910上进行推理，可参考此[链接](https://www.mindspore.cn/docs/zh-CN/master/model_infer/index.html)。下面是操作步骤示例： +如果您需要使用此训练模型在Ascend 910上进行推理，可参考此[链接](https://www.mindspore.cn/tutorials/zh-CN/master/model_infer/ms_infer/llm_inference_overview.html)。下面是操作步骤示例： - Ascend处理器环境运行 @@ -486,7 +486,7 @@ bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID] print("==============Acc: {} ==============".format(acc)) ``` -如果您需要使用此训练模型在GPU上进行推理，可参考此[链接](https://www.mindspore.cn/docs/zh-CN/master/model_infer/index.html)。下面是操作步骤示例： +如果您需要使用此训练模型在GPU上进行推理，可参考此[链接](https://www.mindspore.cn/tutorials/zh-CN/master/model_infer/ms_infer/llm_inference_overview.html)。下面是操作步骤示例： - GPU处理器环境运行 diff --git a/research/cv/inception_resnet_v2/README.md b/research/cv/inception_resnet_v2/README.md index d7a3350677c0736077c989336d3005435cf7df95..7c0f4cb22ab35f10a4beb501132452a9ee3adee8 100644 --- a/research/cv/inception_resnet_v2/README.md +++ b/research/cv/inception_resnet_v2/README.md @@ -122,7 +122,7 @@ bash scripts/run_standalone_train_ascend.sh DEVICE_ID DATA_DIR ``` > Notes: -> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. +> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. > > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh` diff --git a/research/cv/inception_resnet_v2/README_CN.md b/research/cv/inception_resnet_v2/README_CN.md index 2358f9ae41b9806f20d85e70a07948e91c3b0d1b..e20b018fcd1d4779fdac6403cb48a6c7052eb9d2 100644 --- a/research/cv/inception_resnet_v2/README_CN.md +++ b/research/cv/inception_resnet_v2/README_CN.md @@ -133,7 +133,7 @@ bash scripts/run_distribute_train_ascend.sh RANK_TABLE_FILE DATA_DIR bash scripts/run_standalone_train_ascend.sh DEVICE_ID DATA_DIR ``` -> 注：RANK_TABLE_FILE可参考[链接]( https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html)。device_ip可以通过[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)获取 +> 注：RANK_TABLE_FILE可参考[链接]( https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html)。device_ip可以通过[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)获取 - GPU: diff --git a/research/cv/nas-fpn/README_CN.md b/research/cv/nas-fpn/README_CN.md index d0a1a21fef0533f7421b3a754bc401fc79087fb6..230ab73d5bf5a380ef4e525b986588cb7118906d 100644 --- a/research/cv/nas-fpn/README_CN.md +++ b/research/cv/nas-fpn/README_CN.md @@ -161,7 +161,7 @@ bash scripts/run_single_train.sh DEVICE_ID MINDRECORD_DIR PRE_TRAINED(optional) ``` > 注意: -RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). +RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). #### 运行 diff --git a/research/cv/ntsnet/README.md b/research/cv/ntsnet/README.md index d90b27eb96131a36a2033ddca006f69ed383ebfa..915b75076cda004d0116da8c4ba9173e21cbdffa 100644 --- a/research/cv/ntsnet/README.md +++ b/research/cv/ntsnet/README.md @@ -133,7 +133,7 @@ Usage: bash run_standalone_train_ascend.sh [DATA_URL] [TRAIN_URL] ## [Training Process](#contents) -- Set options in `config.py`, including learning rate, output filename and network hyperparameters. Click [here](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `config.py`, including learning rate, output filename and network hyperparameters. - Get ResNet50 pretrained model from [Mindspore Hub](https://www.mindspore.cn/resources/hub/details?MindSpore/ascend/v1.2/resnet50_v1.2_imagenet2012) ### [Training](#content) diff --git a/research/cv/osnet/README.md b/research/cv/osnet/README.md index 6303d8f5a1d492cb19a267724c30570926ad08e2..a5674b8d2f6a7894614df746c3d5f92cfd39b78e 100644 --- a/research/cv/osnet/README.md +++ b/research/cv/osnet/README.md @@ -160,7 +160,7 @@ bash run_eval_ascend.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] ``` > Notes: -> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. +> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. > > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_train_distribute_ascend.sh` > diff --git a/research/cv/predrnn++/README.md b/research/cv/predrnn++/README.md index bb4364c74205777a35539a7c10a0e7695706134a..ff529dd966b4be7dc90e45f699a2cd90a8a3396b 100644 --- a/research/cv/predrnn++/README.md +++ b/research/cv/predrnn++/README.md @@ -161,7 +161,7 @@ input0_path: "" # export input path ## [Training Process](#contents) -- Set options in `config.py`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `config.py`, including learning rate and other network hyperparameters. ### [Training](#contents) diff --git a/research/cv/retinanet_resnet101/README.md b/research/cv/retinanet_resnet101/README.md index 5df618a2fecfe01569fd80606da8838adb6a2872..95a6191105e025057ee075e90c6d0d5f56f311ff 100644 --- a/research/cv/retinanet_resnet101/README.md +++ b/research/cv/retinanet_resnet101/README.md @@ -287,7 +287,7 @@ bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABL bash run_single_train.sh [DEVICE_ID] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional) ``` -> Note: RANK_TABLE_FILE related reference materials see in this [link](https://www.mindspore.cn/docs/en/master/model_train/parallel/rank_table.html), for details on how to get device_ip check this [link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). +> Note: RANK_TABLE_FILE related reference materials see in this [link](https://www.mindspore.cn/tutorials/en/master/parallel/rank_table.html), for details on how to get device_ip check this [link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). - GPU diff --git a/research/cv/retinanet_resnet101/README_CN.md b/research/cv/retinanet_resnet101/README_CN.md index c237d8cf03e2dcb4f33a739fdcfda42e4df4b045..8a8340f01c84fcb3da78bb2a58e54230b0fe3447 100644 --- a/research/cv/retinanet_resnet101/README_CN.md +++ b/research/cv/retinanet_resnet101/README_CN.md @@ -292,7 +292,7 @@ bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABL bash run_single_train.sh [DEVICE_ID] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional) ``` -> 注意: RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). +> 注意: RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). - GPU diff --git a/research/cv/retinanet_resnet152/README.md b/research/cv/retinanet_resnet152/README.md index 6ef8d5d6f5f7cc11d908901892ffe94705621f73..42960fd1554fcdcee6b4690b145e1d4eb1bbe1c6 100644 --- a/research/cv/retinanet_resnet152/README.md +++ b/research/cv/retinanet_resnet152/README.md @@ -291,7 +291,7 @@ bash run_distribute_train.sh DEVICE_NUM EPOCH_SIZE LR DATASET RANK_TABLE_FILE PR bash run_distribute_train.sh DEVICE_ID EPOCH_SIZE LR DATASET PRE_TRAINED(optional) PRE_TRAINED_EPOCH_SIZE(optional) ``` -> Note: RANK_TABLE_FILE related reference materials see in this [link](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html), +> Note: RANK_TABLE_FILE related reference materials see in this [link](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html), > for details on how to get device_ip check this [link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). - GPU: diff --git a/research/cv/retinanet_resnet152/README_CN.md b/research/cv/retinanet_resnet152/README_CN.md index 8d9ae8df6c9768dd3f73aee0c029ee9de0dd4735..13d27a27a3b27320e1e76b330d914b097b04b3c0 100644 --- a/research/cv/retinanet_resnet152/README_CN.md +++ b/research/cv/retinanet_resnet152/README_CN.md @@ -285,7 +285,7 @@ bash run_distribute_train.sh DEVICE_NUM EPOCH_SIZE LR DATASET RANK_TABLE_FILE PR bash run_distribute_train.sh DEVICE_ID EPOCH_SIZE LR DATASET PRE_TRAINED(optional) PRE_TRAINED_EPOCH_SIZE(optional) ``` -> 注意: RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html), +> 注意: RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html), > 获取device_ip方法详见[链接](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools). - GPU: diff --git a/research/cv/sphereface/README.md b/research/cv/sphereface/README.md index 1c22df06585df2c71bdc8f8d795957c575b8c13c..ff3c18549754dc064f2c7e5364ad79bf460c717b 100644 --- a/research/cv/sphereface/README.md +++ b/research/cv/sphereface/README.md @@ -474,7 +474,7 @@ The accuracy of evaluating DenseNet121 on the test dataset of ImageNet will be a ### Inference -If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/docs/en/master/model_infer/index.html). Following the steps below, this is a simple example: +If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorials/en/master/model_infer/ms_infer/llm_inference_overview.html). Following the steps below, this is a simple example: - Running on Ascend and GPU diff --git a/research/cv/sphereface/README_CN.md b/research/cv/sphereface/README_CN.md index 3c1a8622e287c67ee3425478f8bfcf0b3e60386f..fdad403b13af30b932a20d4e6b23c60bac7b7100 100644 --- a/research/cv/sphereface/README_CN.md +++ b/research/cv/sphereface/README_CN.md @@ -476,7 +476,7 @@ sphereface网络使用LFW推理得到的结果如下: ### 推理 -如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/docs/zh-CN/master/model_infer/index.html)。下面是操作步骤示例： +如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/tutorials/zh-CN/master/model_infer/ms_infer/llm_inference_overview.html)。下面是操作步骤示例： - Ascend、GPU处理器环境运行 diff --git a/research/cv/squeezenet/README.md b/research/cv/squeezenet/README.md index 19f13910e76308d5003b868c646134242194553b..3d58e58c527616f5ec8e5f067b76ab5dc2e8427c 100644 --- a/research/cv/squeezenet/README.md +++ b/research/cv/squeezenet/README.md @@ -720,7 +720,7 @@ Inference result is saved in current path, you can find result like this in acc. ### Inference -If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/docs/en/master/model_infer/index.html). Following the steps below, this is a simple example: +If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorials/en/master/model_infer/ms_infer/llm_inference_overview.html). Following the steps below, this is a simple example: - Running on Ascend diff --git a/research/cv/squeezenet1_1/README.md b/research/cv/squeezenet1_1/README.md index 44a0111a5cfe72d79753ccdb1fb1a61ecdcbeb47..b78dafd51b666aeb7323a687e2002c6c03633f1e 100644 --- a/research/cv/squeezenet1_1/README.md +++ b/research/cv/squeezenet1_1/README.md @@ -306,7 +306,7 @@ Inference result is saved in current path, you can find result like this in acc. ### Inference -If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/docs/en/master/model_infer/index.html). Following the steps below, this is a simple example: +If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorials/en/master/model_infer/ms_infer/llm_inference_overview.html). Following the steps below, this is a simple example: - Running on Ascend diff --git a/research/cv/ssd_ghostnet/README.md b/research/cv/ssd_ghostnet/README.md index dbc3792f0a00d06e0c6a1e47d3ffbbec3f637f44..beb2105b1c2ff42ae593b1f9060de1f5a2d8b57f 100644 --- a/research/cv/ssd_ghostnet/README.md +++ b/research/cv/ssd_ghostnet/README.md @@ -210,7 +210,7 @@ If you want to run in modelarts, please check the official documentation of [mod ### Training on Ascend -To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/en/master/model_train/dataset/record.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorials/en/master/dataset/record.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** - Distribute mode diff --git a/research/cv/ssd_inception_v2/README.md b/research/cv/ssd_inception_v2/README.md index 0139341924fadbd45db5ce823999a1a4969eed11..2e4a407a80b12663baa7eacb50a40967fdc0d40b 100644 --- a/research/cv/ssd_inception_v2/README.md +++ b/research/cv/ssd_inception_v2/README.md @@ -213,7 +213,7 @@ bash scripts/docker_start.sh ssd:20.1.0 [DATA_DIR] [MODEL_DIR] ### [Training Process](#contents) -To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/en/master/model_train/dataset/record.html) files by `coco_root`(coco dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorials/en/master/dataset/record.html) files by `coco_root`(coco dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** #### Training on GPU diff --git a/research/cv/ssd_inceptionv2/README_CN.md b/research/cv/ssd_inceptionv2/README_CN.md index 3875e7ec8827272852fae9361fac6c75e8535b80..dc07537303baae65fe4f44e265c5c90a40df1e10 100644 --- a/research/cv/ssd_inceptionv2/README_CN.md +++ b/research/cv/ssd_inceptionv2/README_CN.md @@ -171,7 +171,7 @@ bash run_eval.sh [DEVICE_ID] [DATASET] [DATASET_PATH] [CHECKPOINT_PATH] [MINDREC ## 训练过程 -运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/docs/zh-CN/master/model_train/dataset/record.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。** +运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/record.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。** ### Ascend上训练 diff --git a/research/cv/ssd_mobilenetV2/README.md b/research/cv/ssd_mobilenetV2/README.md index 8ce3f93e1f25abf5a190e1b5b198e4637b887951..93b0f196f0e1e49ce33d99870b950a8dfa14ce68 100644 --- a/research/cv/ssd_mobilenetV2/README.md +++ b/research/cv/ssd_mobilenetV2/README.md @@ -221,7 +221,7 @@ bash scripts/run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] ### [Training Process](#contents) -To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/en/master/model_train/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorials/en/master/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** #### Training on Ascend diff --git a/research/cv/ssd_mobilenetV2_FPNlite/README.md b/research/cv/ssd_mobilenetV2_FPNlite/README.md index 9f1becb77ad1badf89fce4ffdc10afbd53f5c09d..e43590a9d93be5f48a1e93f368f80e4690c2f4a9 100644 --- a/research/cv/ssd_mobilenetV2_FPNlite/README.md +++ b/research/cv/ssd_mobilenetV2_FPNlite/README.md @@ -233,7 +233,7 @@ bash run_eval_gpu.sh [CONFIG_FILE] [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] ### [Training Process](#contents) -To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/en/master/model_train/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorials/en/master/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** #### Training on Ascend diff --git a/research/cv/ssd_resnet34/README.md b/research/cv/ssd_resnet34/README.md index 968aaf4796ef2a667dd0b030dc38c722ca5e7383..067a5cf4710f361a26f678fb4ea7b162c40fc691 100644 --- a/research/cv/ssd_resnet34/README.md +++ b/research/cv/ssd_resnet34/README.md @@ -206,7 +206,7 @@ bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] ### [Training Process](#contents) -To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/zh-CN/master/model_train/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** #### Training on Ascend diff --git a/research/cv/ssd_resnet34/README_CN.md b/research/cv/ssd_resnet34/README_CN.md index 71d18431a3cf4e4c7a5e3fd6774dd64ddd664b00..f299b38293f51085b84bb057318ef9124c6b2e22 100644 --- a/research/cv/ssd_resnet34/README_CN.md +++ b/research/cv/ssd_resnet34/README_CN.md @@ -172,7 +172,7 @@ sh scripts/run_eval.sh [DEVICE_ID] [DATASET] [DATASET_PATH] [CHECKPOINT_PATH] [M ## 训练过程 -运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/docs/zh-CN/master/model_train/dataset/record.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。** +运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/record.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。** ### Ascend上训练 diff --git a/research/cv/ssd_resnet50/README.md b/research/cv/ssd_resnet50/README.md index 1063e79616a74a22d86e5e950e5ba379d94259b6..d107d4c07625184297b0bb300113ba017e6a87c2 100644 --- a/research/cv/ssd_resnet50/README.md +++ b/research/cv/ssd_resnet50/README.md @@ -204,7 +204,7 @@ Then you can run everything just like on ascend. ### [Training Process](#contents) -To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/en/master/model_train/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorials/en/master/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** #### Training on Ascend diff --git a/research/cv/ssd_resnet50/README_CN.md b/research/cv/ssd_resnet50/README_CN.md index 9fe3222adffeaad7b94d91e6a3ebcc7005f95073..816c1a774234b463f5063aba0c7c0c9f59955adb 100644 --- a/research/cv/ssd_resnet50/README_CN.md +++ b/research/cv/ssd_resnet50/README_CN.md @@ -163,7 +163,7 @@ bash run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] ## 训练过程 -运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/docs/zh-CN/master/model_train/dataset/record.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。** +运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/record.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。** ### Ascend上训练 diff --git a/research/cv/ssd_resnet_34/README.md b/research/cv/ssd_resnet_34/README.md index ae5c9b484fa3ce64a3294111c4eb0364192c644e..26ec67fc2d81141559eab39d01516600718685be 100644 --- a/research/cv/ssd_resnet_34/README.md +++ b/research/cv/ssd_resnet_34/README.md @@ -204,7 +204,7 @@ Major parameters in train.py and config.py for Multi GPU train: ### [Training Process](#contents) -To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/zh-CN/master/model_train/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/record.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** #### Training on GPU diff --git a/research/cv/textfusenet/README.md b/research/cv/textfusenet/README.md index 4eecda4be20364391a6de192232767b2f10d0968..03a9b23cbe24a30ba991bb799252314dbe7975a9 100755 --- a/research/cv/textfusenet/README.md +++ b/research/cv/textfusenet/README.md @@ -319,7 +319,7 @@ Usage: bash run_standalone_train.sh [PRETRAINED_MODEL] ## [Training Process](#contents) -- Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/docs/en/master/model_train/dataset/augment.html) for more information about dataset. +- Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/tutorials/en/master/dataset/augment.html) for more information about dataset. ### [Training](#content) diff --git a/research/cv/textfusenet/README_CN.md b/research/cv/textfusenet/README_CN.md index 635953fada28d1fbbab27055afd023557310da48..55a04213f331556a77942b2e3c9a879ce7b30f33 100755 --- a/research/cv/textfusenet/README_CN.md +++ b/research/cv/textfusenet/README_CN.md @@ -328,7 +328,7 @@ Shapely==1.5.9 ## 训练过程 -- 在`config.py`中设置配置项，包括loss_scale、学习率和网络超参。单击[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/dataset/augment.html)获取更多数据集相关信息. +- 在`config.py`中设置配置项，包括loss_scale、学习率和网络超参。单击[此处](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/augment.html)获取更多数据集相关信息. ### 训练 diff --git a/research/cv/tinydarknet/README_CN.md b/research/cv/tinydarknet/README_CN.md index e537488d0402ce2be96674891c2dfa78a8fce4f4..caf39d1c455cbc7df4a7b74133d6eb9c6e88be40 100644 --- a/research/cv/tinydarknet/README_CN.md +++ b/research/cv/tinydarknet/README_CN.md @@ -64,7 +64,7 @@ Tiny-DarkNet是Joseph Chet Redmon等人提出的一个16层的针对于经典的 - + # [环境要求](#目录) diff --git a/research/cv/vnet/README_CN.md b/research/cv/vnet/README_CN.md index 6572a68403c40e17763baff3229960baeee5648b..6b931802159624d22c4047a773f7f190185f0f2a 100644 --- a/research/cv/vnet/README_CN.md +++ b/research/cv/vnet/README_CN.md @@ -101,7 +101,7 @@ VNet适用于医学图像分割，使用3D卷积，能够处理3D MR图像数据 - [MindSpore Python API](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.html) - 生成config json文件用于多卡训练。 - [简易教程](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools) - - 详细配置方法请参照[rank table启动](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/rank_table.html)。 + - 详细配置方法请参照[rank table启动](https://www.mindspore.cn/tutorials/zh-CN/master/parallel/rank_table.html)。 # 快速入门 diff --git a/research/cv/warpctc/README.md b/research/cv/warpctc/README.md index b9b8c0106275606af05c3c1ef4150028f18851c2..3afdc681ef29f2a697babca23be4d8a41f192d9a 100644 --- a/research/cv/warpctc/README.md +++ b/research/cv/warpctc/README.md @@ -254,7 +254,7 @@ save_checkpoint_path: "./checkpoint" # path to save checkpoint ### [Training Process](#contents) -- Set options in `default_config.yaml`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `default_config.yaml`, including learning rate and other network hyperparameters. #### [Training](#contents) diff --git a/research/cv/warpctc/README_CN.md b/research/cv/warpctc/README_CN.md index eff8427d83dfa36c6ad88e057550e1d90efdcc90..3992a5e62605d0cf6dc9795d9b89a3437a1c85e8 100644 --- a/research/cv/warpctc/README_CN.md +++ b/research/cv/warpctc/README_CN.md @@ -257,7 +257,7 @@ save_checkpoint_path: "./checkpoints" # 检查点保存路径，相对于t ## 训练过程 -- 在`default_config.yaml`中设置选项，包括学习率和网络超参数。单击[MindSpore加载数据集教程](https://www.mindspore.cn/docs/zh-CN/master/model_train/index.html)，了解更多信息。 +- 在`default_config.yaml`中设置选项，包括学习率和网络超参数。 ### 训练 diff --git a/research/cv/wideresnet/README.md b/research/cv/wideresnet/README.md index ec4fea43aa19e1fcd7409577196ae4d4faed09f6..c147e51c265016597c0c5a50bda7936815228fef 100644 --- a/research/cv/wideresnet/README.md +++ b/research/cv/wideresnet/README.md @@ -208,7 +208,7 @@ bash run_standalone_train_gpu.sh [DATASET_PATH] [CONFIG_PATH] [EXPERIMENT_LABEL] For distributed training, a hostfile configuration needs to be created in advance. -Please follow the instructions in the link [GPU-Multi-Host](https://www.mindspore.cn/docs/en/master/model_train/parallel/mpirun.html). +Please follow the instructions in the link [GPU-Multi-Host](https://www.mindspore.cn/tutorials/en/master/parallel/mpirun.html). ##### Evaluation while training diff --git a/research/cv/wideresnet/README_CN.md b/research/cv/wideresnet/README_CN.md index 339c79ff4f83fba5d8d6f86c6fbdab47d802e7d0..7182c82ea5d0c9d2b515d7056d37713a407aea32 100644 --- a/research/cv/wideresnet/README_CN.md +++ b/research/cv/wideresnet/README_CN.md @@ -211,7 +211,7 @@ bash run_standalone_train_gpu.sh [DATASET_PATH] [CONFIG_PATH] [EXPERIMENT_LABEL] 对于分布式培训，需要提前创建主机文件配置。 -请按照链接中的说明操作 [GPU-Multi-Host](https://www.mindspore.cn/docs/en/master/model_train/parallel/mpirun.html). +请按照链接中的说明操作 [GPU-Multi-Host](https://www.mindspore.cn/tutorials/en/master/parallel/mpirun.html). ## 培训时的评估 diff --git a/research/cv/yolov3_resnet18/README.md b/research/cv/yolov3_resnet18/README.md index 69ec4aa5eccc9e280c67769d29df73f8d402863b..b72e14214f40cda389c752116bce16ae37fb4757 100644 --- a/research/cv/yolov3_resnet18/README.md +++ b/research/cv/yolov3_resnet18/README.md @@ -269,7 +269,7 @@ After installing MindSpore via the official website, you can start training and ### Training on Ascend -To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and `mindrecord_dir`. If the `mindrecord_dir` is empty, it wil generate [mindrecord](https://www.mindspore.cn/docs/en/master/model_train/dataset/record.html) file by `image_dir` and `anno_path`(the absolute image path is joined by the `image_dir` and the relative path in `anno_path`). **Note if `mindrecord_dir` isn't empty, it will use `mindrecord_dir` rather than `image_dir` and `anno_path`.** +To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and `mindrecord_dir`. If the `mindrecord_dir` is empty, it wil generate [mindrecord](https://www.mindspore.cn/tutorials/en/master/dataset/record.html) file by `image_dir` and `anno_path`(the absolute image path is joined by the `image_dir` and the relative path in `anno_path`). **Note if `mindrecord_dir` isn't empty, it will use `mindrecord_dir` rather than `image_dir` and `anno_path`.** - Stand alone mode diff --git a/research/cv/yolov3_resnet18/README_CN.md b/research/cv/yolov3_resnet18/README_CN.md index 94cc0bf334a42cc4f8b2a4541ad6c016e5af39c3..771f866300ee39c1bcce60197647e17cba303c93 100644 --- a/research/cv/yolov3_resnet18/README_CN.md +++ b/research/cv/yolov3_resnet18/README_CN.md @@ -268,7 +268,7 @@ YOLOv3整体网络架构如下： ### Ascend上训练 -训练模型运行`train.py`，使用数据集`image_dir`、`anno_path`和`mindrecord_dir`。如果`mindrecord_dir`为空，则通过`image_dir`和`anno_path`（图像绝对路径由`image_dir`和`anno_path`中的相对路径连接）生成[MindRecord](https://www.mindspore.cn/docs/zh-CN/master/model_train/dataset/record.html)文件。**注意，如果`mindrecord_dir`不为空，将使用`mindrecord_dir`而不是`image_dir`和`anno_path`。** +训练模型运行`train.py`，使用数据集`image_dir`、`anno_path`和`mindrecord_dir`。如果`mindrecord_dir`为空，则通过`image_dir`和`anno_path`（图像绝对路径由`image_dir`和`anno_path`中的相对路径连接）生成[MindRecord](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/record.html)文件。**注意，如果`mindrecord_dir`不为空，将使用`mindrecord_dir`而不是`image_dir`和`anno_path`。** - 单机模式 diff --git a/research/nlp/cpm/README.md b/research/nlp/cpm/README.md index ffc11e53fecd09cdb30322525124b61cdc8cb0b3..90bd85bcb8cd1747664f82c822bdde6e93f81b1a 100644 --- a/research/nlp/cpm/README.md +++ b/research/nlp/cpm/README.md @@ -309,7 +309,7 @@ After processing, the mindrecord file of training and reasoning is generated in ### Finetune Training Process -- Set options in `src/config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/docs/en/master/model_train/index.html) for more information about dataset. +- Set options in `src/config.py`, including loss_scale, learning rate and network hyperparameters. - Run `run_distribute_train_ascend_single_machine.sh` for distributed and single machine training of CPM model. diff --git a/research/nlp/cpm/README_CN.md b/research/nlp/cpm/README_CN.md index c4a0f01cd4925f27765702e779c0994abfbc7a7f..f31d352ec0d2dd74fdd8d8229b3f717ac866d029 100644 --- a/research/nlp/cpm/README_CN.md +++ b/research/nlp/cpm/README_CN.md @@ -309,7 +309,7 @@ Parameters for dataset and network (Training/Evaluation): ### Finetune训练过程 -- 在`src/config.py`中设置，包括模型并行、batchsize、学习率和网络超参数。点击[这里](https://www.mindspore.cn/docs/zh-CN/master/model_train/index.html)查看更多数据集信息。 +- 在`src/config.py`中设置，包括模型并行、batchsize、学习率和网络超参数。 - 运行`run_distribute_train_ascend_single_machine.sh`，进行CPM模型的单机8卡分布式训练。 diff --git a/research/nlp/mass/README.md b/research/nlp/mass/README.md index 979c9bcd4fcd820b0afd71a1eb09cc178b1cb347..598d943ffb91ce5aaefbd851467bf0560db5d3fe 100644 --- a/research/nlp/mass/README.md +++ b/research/nlp/mass/README.md @@ -501,7 +501,7 @@ subword-nmt rouge ``` - + # Get started @@ -563,7 +563,7 @@ Get the log and output files under the path `./train_mass_*/`, and the model fil ## Inference -If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/docs/en/master/model_infer/index.html). +If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorials/en/master/model_infer/ms_infer/llm_inference_overview.html). For inference, config the options in `default_config.yaml` firstly: - Assign the `default_config.yaml` under `data_path` node to the dataset path. diff --git a/research/nlp/mass/README_CN.md b/research/nlp/mass/README_CN.md index 0fd2da5458f20773c9ee830a36fa33c871474137..d8d954495bbe9a96c893ac25ff24d3649273ffc9 100644 --- a/research/nlp/mass/README_CN.md +++ b/research/nlp/mass/README_CN.md @@ -505,7 +505,7 @@ subword-nmt rouge ``` - + # 快速上手 @@ -567,7 +567,7 @@ bash run_gpu.sh -t t -n 1 -i 1 ## 推理 -如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/docs/zh-CN/master/model_infer/index.html)。 +如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/tutorials/zh-CN/master/model_infer/ms_infer/llm_inference_overview.html)。 推理时，请先配置`config.json`中的选项： - 将`default_config.yaml`节点下的`data_path`配置为数据集路径。 diff --git a/research/nlp/rotate/README_CN.md b/research/nlp/rotate/README_CN.md index a87a4910b4d7df31468f4ae1f7c705313bd102a5..5c92b421fdd60d4cd65b401dfec0a176ab607888 100644 --- a/research/nlp/rotate/README_CN.md +++ b/research/nlp/rotate/README_CN.md @@ -86,7 +86,7 @@ bash run_infer_310.sh [MINDIR_HEAD_PATH] [MINDIR_TAIL_PATH] [DATASET_PATH] [NEED 在裸机环境（本地有Ascend 910 AI 处理器）进行分布式训练时，需要配置当前多卡环境的组网信息文件。 请遵循一下链接中的说明创建json文件： - + - GPU处理器环境运行 diff --git a/research/recommend/ncf/README.md b/research/recommend/ncf/README.md index 078d04083977643f61c64a291e360c1e3cbbfa38..5f1e5ac89e1f14da697755f3b9f6898ab94762ef 100644 --- a/research/recommend/ncf/README.md +++ b/research/recommend/ncf/README.md @@ -356,9 +356,9 @@ Inference result is saved in current path, you can find result like this in acc. ### Inference -If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/docs/en/master/model_infer/index.html). Following the steps below, this is a simple example: +If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorials/en/master/model_infer/ms_infer/llm_inference_overview.html). Following the steps below, this is a simple example: - + ```python # Load unseen dataset for inference