From 1064cf6b28e66da38a9acb844725c1c05f52dd30 Mon Sep 17 00:00:00 2001 From: JunYuLiu Date: Fri, 11 Sep 2020 11:36:45 +0800 Subject: [PATCH] Modify the format of markdown --- docs/source_en/FAQ.md | 2 +- docs/source_en/architecture.md | 2 +- docs/source_en/benchmark.md | 2 +- .../constraints_on_network_construction.md | 2 +- .../design/mindinsight/graph_visual_design.md | 2 +- .../mindinsight/tensor_visual_design.md | 2 +- .../mindinsight/training_visual_design.md | 2 +- docs/source_en/glossary.md | 2 +- docs/source_en/help_seeking_path.md | 2 +- docs/source_en/network_list.md | 2 +- docs/source_en/operator_list.md | 2 +- docs/source_en/roadmap.md | 2 +- docs/source_zh_cn/FAQ.md | 2 +- docs/source_zh_cn/architecture.md | 2 +- docs/source_zh_cn/benchmark.md | 2 +- .../constraints_on_network_construction.md | 2 +- .../mindarmour/differential_privacy_design.md | 2 +- .../design/mindarmour/fuzzer_design.md | 3 + .../design/mindinsight/graph_visual_design.md | 2 +- .../design/mindinsight/profiler_design.md | 2 +- .../mindinsight/tensor_visual_design.md | 2 +- .../mindinsight/training_visual_design.md | 2 +- .../mindspore/distributed_training_design.md | 2 +- docs/source_zh_cn/design/mindspore/ir.md | 2 +- docs/source_zh_cn/glossary.md | 2 +- docs/source_zh_cn/help_seeking_path.md | 2 +- docs/source_zh_cn/network_list.md | 2 +- docs/source_zh_cn/operator_list.md | 2 +- docs/source_zh_cn/roadmap.md | 2 +- docs/source_zh_cn/technical_white_paper.md | 2 +- .../checkpoint_for_hybrid_parallel.md | 2 +- .../computer_vision_application.md | 2 +- .../customized_debugging_information.md | 32 +- .../debugging_in_pynative_mode.md | 2 +- .../advanced_use/differential_privacy.md | 2 +- .../distributed_training_ascend.md | 2 +- .../advanced_use/graph_kernel_fusion.md | 2 +- .../advanced_use/host_device_training.md | 2 +- .../images/cifar10_c_transforms.png | Bin 0 -> 46693 bytes .../source_en/advanced_use/images/compose.png | Bin 0 -> 18941 bytes 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.../data_preparation/loading_the_datasets.md | 2 +- .../source_en/use/multi_platform_inference.md | 2 +- .../saving_and_loading_model_parameters.md | 2 +- .../source_zh_cn/advanced_use/bert_poetry.md | 150 +++---- .../checkpoint_for_hybrid_parallel.md | 6 +- .../computer_vision_application.md | 25 +- .../customized_debugging_information.md | 85 ++-- .../source_zh_cn/advanced_use/dashboard.md | 5 +- .../debugging_in_pynative_mode.md | 2 +- .../advanced_use/differential_privacy.md | 6 +- .../distributed_training_ascend.md | 7 +- .../advanced_use/distributed_training_gpu.md | 8 +- tutorials/source_zh_cn/advanced_use/fuzzer.md | 32 +- .../advanced_use/gradient_accumulation.md | 18 +- .../advanced_use/graph_kernel_fusion.md | 2 +- .../advanced_use/host_device_training.md | 2 +- .../advanced_use/mixed_precision.md | 4 +- .../mobilenetv2_incremental_learning.md | 2 +- .../advanced_use/model_security.md | 2 +- .../advanced_use/network_migration.md | 2 +- .../advanced_use/nlp_application.md | 2 +- ...ize_the_performance_of_data_preparation.md | 2 +- .../advanced_use/parameter_server_training.md | 2 +- .../advanced_use/quantization_aware.md | 2 +- ...rder_optimizer_for_resnet50_application.md | 2 +- .../source_zh_cn/advanced_use/serving.md | 2 +- ...synchronization_training_and_evaluation.md | 2 +- .../advanced_use/use_on_the_cloud.md | 2 +- .../quick_start/linear_regression.md | 2 +- .../source_zh_cn/quick_start/quick_start.md | 2 +- tutorials/source_zh_cn/use/custom_operator.md | 2 +- .../data_preparation/converting_datasets.md | 2 +- .../data_processing_and_augmentation.md | 2 +- .../data_preparation/loading_the_datasets.md | 2 +- .../use/multi_platform_inference.md | 2 +- .../saving_and_loading_model_parameters.md | 2 +- 94 files changed, 674 insertions(+), 241 deletions(-) create mode 100644 tutorials/source_en/advanced_use/images/cifar10_c_transforms.png create mode 100644 tutorials/source_en/advanced_use/images/compose.png create mode 100644 tutorials/source_en/advanced_use/images/data_enhancement_performance_scheme.png create mode 100644 tutorials/source_en/advanced_use/images/data_loading_performance_scheme.png create mode 100644 tutorials/source_en/advanced_use/images/operator_fusion.png create mode 100644 tutorials/source_en/advanced_use/images/pipeline.png create mode 100644 tutorials/source_en/advanced_use/images/shuffle_performance_scheme.png create mode 100644 tutorials/source_en/advanced_use/optimize_the_performance_of_data_preparation.md diff --git a/docs/source_en/FAQ.md b/docs/source_en/FAQ.md index 8424f899d0..bc9dd1bd6f 100644 --- a/docs/source_en/FAQ.md +++ b/docs/source_en/FAQ.md @@ -1,6 +1,6 @@ # FAQ -`Ascend` `GPU` `CPU` `Environmental Setup` `Model Export` `Model Training` `Beginner` `Intermediate` `Expert` +`Linux` `Windows` `Ascend` `GPU` `CPU` `Environmental Setup` `Model Export` `Model Training` `Beginner` `Intermediate` `Expert` diff --git a/docs/source_en/architecture.md b/docs/source_en/architecture.md index ee7e34332e..43cf28ef2f 100644 --- a/docs/source_en/architecture.md +++ b/docs/source_en/architecture.md @@ -1,6 +1,6 @@ # Overall Architecture -`Ascend` `GPU` `CPU` `On Device` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` +`Linux` `Windows` `Ascend` `GPU` `CPU` `On Device` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` diff --git a/docs/source_en/benchmark.md b/docs/source_en/benchmark.md index cc19eeaaad..da19dcb1a4 100644 --- a/docs/source_en/benchmark.md +++ b/docs/source_en/benchmark.md @@ -1,6 +1,6 @@ # Benchmarks -`Ascend` `Model Training` `Intermediae` `Expert` +`Linux` `Ascend` `Model Training` `Intermediae` `Expert` diff --git a/docs/source_en/constraints_on_network_construction.md b/docs/source_en/constraints_on_network_construction.md index c166071953..2da31582ec 100644 --- a/docs/source_en/constraints_on_network_construction.md +++ b/docs/source_en/constraints_on_network_construction.md @@ -1,6 +1,6 @@ # Constraints on Network Construction Using Python -`Ascend` `GPU` `CPU` `Model Development` `Beginner` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Model Development` `Beginner` `Intermediate` `Expert` diff --git a/docs/source_en/design/mindinsight/graph_visual_design.md b/docs/source_en/design/mindinsight/graph_visual_design.md index 1aa1ab694d..64f6b32d45 100644 --- a/docs/source_en/design/mindinsight/graph_visual_design.md +++ b/docs/source_en/design/mindinsight/graph_visual_design.md @@ -1,6 +1,6 @@ # Computational Graph Visualization Design -`Ascend` `GPU` `CPU` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` +`Linux` `Ascend` `GPU` `CPU` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` diff --git a/docs/source_en/design/mindinsight/tensor_visual_design.md b/docs/source_en/design/mindinsight/tensor_visual_design.md index 8117ef8e0e..ce3839d5b9 100644 --- a/docs/source_en/design/mindinsight/tensor_visual_design.md +++ b/docs/source_en/design/mindinsight/tensor_visual_design.md @@ -1,6 +1,6 @@ # Tensor Visualization Design -`Ascend` `GPU` `CPU` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` +`Linux` `Ascend` `GPU` `CPU` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` diff --git a/docs/source_en/design/mindinsight/training_visual_design.md b/docs/source_en/design/mindinsight/training_visual_design.md index 0b19c78cff..599a44df3b 100644 --- a/docs/source_en/design/mindinsight/training_visual_design.md +++ b/docs/source_en/design/mindinsight/training_visual_design.md @@ -1,6 +1,6 @@ # Overall Design of Training Visualization -`Ascend` `GPU` `CPU` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` +`Linux` `Ascend` `GPU` `CPU` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` diff --git a/docs/source_en/glossary.md b/docs/source_en/glossary.md index 951360de4e..ae1fb21e91 100644 --- a/docs/source_en/glossary.md +++ b/docs/source_en/glossary.md @@ -1,6 +1,6 @@ # Glossary -`Ascend` `GPU` `CPU` `Whole Process` `Beginner` `Intermediate` `Expert` +`Linux` `Windows` `Ascend` `GPU` `CPU` `Whole Process` `Beginner` `Intermediate` `Expert` diff --git a/docs/source_en/help_seeking_path.md b/docs/source_en/help_seeking_path.md index 2b45cad1bb..ac924fcfb0 100644 --- a/docs/source_en/help_seeking_path.md +++ b/docs/source_en/help_seeking_path.md @@ -1,6 +1,6 @@ # Seeking Help and Support -`Ascend` `GPU` `CPU` `Whole Process` `Beginner` `Intermediate` `Expert` +`Linux` `Windows` `Ascend` `GPU` `CPU` `Whole Process` `Beginner` `Intermediate` `Expert` diff --git a/docs/source_en/network_list.md b/docs/source_en/network_list.md index 91b99cd6a3..59f2ad236c 100644 --- a/docs/source_en/network_list.md +++ b/docs/source_en/network_list.md @@ -1,6 +1,6 @@ # Network List -`Ascend` `GPU` `CPU` `Model Development` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Model Development` `Intermediate` `Expert` diff --git a/docs/source_en/operator_list.md b/docs/source_en/operator_list.md index cf548e1774..064eb717c9 100644 --- a/docs/source_en/operator_list.md +++ b/docs/source_en/operator_list.md @@ -1,6 +1,6 @@ # Operator List -`Ascend` `GPU` `CPU` `Model Development` `Beginner` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Model Development` `Beginner` `Intermediate` `Expert` diff --git a/docs/source_en/roadmap.md b/docs/source_en/roadmap.md index befe13ad85..774456c99e 100644 --- a/docs/source_en/roadmap.md +++ b/docs/source_en/roadmap.md @@ -1,6 +1,6 @@ # RoadMap -`Ascend` `GPU` `CPU` `Whole Process` `Framework Development` `Intermediate` `Expert` `Contributor` +`Linux` `Windows` `Ascend` `GPU` `CPU` `Whole Process` `Framework Development` `Intermediate` `Expert` `Contributor` diff --git a/docs/source_zh_cn/FAQ.md b/docs/source_zh_cn/FAQ.md index a27a12cbfe..903a3daa4e 100644 --- a/docs/source_zh_cn/FAQ.md +++ b/docs/source_zh_cn/FAQ.md @@ -1,6 +1,6 @@ # FAQ -`Ascend` `GPU` `CPU` `环境准备` `模型导出` `模型训练` `初级` `中级` `高级` +`Linux` `Windows` `Ascend` `GPU` `CPU` `环境准备` `模型导出` `模型训练` `初级` `中级` `高级` diff --git a/docs/source_zh_cn/architecture.md b/docs/source_zh_cn/architecture.md index 34766b7eb6..091f5ca131 100644 --- a/docs/source_zh_cn/architecture.md +++ b/docs/source_zh_cn/architecture.md @@ -1,6 +1,6 @@ # 总体架构 -`Ascend` `GPU` `CPU` `端侧` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` +`Linux` `Windows` `Ascend` `GPU` `CPU` `端侧` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` diff --git a/docs/source_zh_cn/benchmark.md b/docs/source_zh_cn/benchmark.md index e5fb7b4468..ef06735f46 100644 --- a/docs/source_zh_cn/benchmark.md +++ b/docs/source_zh_cn/benchmark.md @@ -1,6 +1,6 @@ # 基准性能 -`Ascend` `模型训练` `中级` `高级` +`Linux` `Ascend` `模型训练` `中级` `高级` diff --git a/docs/source_zh_cn/constraints_on_network_construction.md b/docs/source_zh_cn/constraints_on_network_construction.md index a0c85bb82a..8b352b2625 100644 --- a/docs/source_zh_cn/constraints_on_network_construction.md +++ b/docs/source_zh_cn/constraints_on_network_construction.md @@ -1,6 +1,6 @@ # Python源码构造网络约束 -`Ascend` `GPU` `CPU` `模型开发` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `模型开发` `初级` `中级` `高级` diff --git a/docs/source_zh_cn/design/mindarmour/differential_privacy_design.md b/docs/source_zh_cn/design/mindarmour/differential_privacy_design.md index b4a2b2704f..2762196556 100644 --- a/docs/source_zh_cn/design/mindarmour/differential_privacy_design.md +++ b/docs/source_zh_cn/design/mindarmour/differential_privacy_design.md @@ -1,6 +1,6 @@ # 差分隐私 -`Ascend` `模型开发` `模型调优` `框架开发` `企业` `高级` `贡献者` +`Linux` `Ascend` `模型开发` `模型调优` `框架开发` `企业` `高级` `贡献者` diff --git a/docs/source_zh_cn/design/mindarmour/fuzzer_design.md b/docs/source_zh_cn/design/mindarmour/fuzzer_design.md index 17e0ebc7b1..81a730c30a 100644 --- a/docs/source_zh_cn/design/mindarmour/fuzzer_design.md +++ b/docs/source_zh_cn/design/mindarmour/fuzzer_design.md @@ -1,5 +1,8 @@ # AI模型安全测试 +`Linux` `Ascend` `GPU` `CPU` `数据准备` `模型开发` `模型训练` `模型调优` `企业` `高级` + + - [AI模型安全测试](#ai模型安全测试) - [背景](#背景) diff --git a/docs/source_zh_cn/design/mindinsight/graph_visual_design.md b/docs/source_zh_cn/design/mindinsight/graph_visual_design.md index 9d0b32d6c4..f84aa65eba 100644 --- a/docs/source_zh_cn/design/mindinsight/graph_visual_design.md +++ b/docs/source_zh_cn/design/mindinsight/graph_visual_design.md @@ -1,6 +1,6 @@ # 计算图可视设计 -`Ascend` `GPU` `CPU` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` +`Linux` `Ascend` `GPU` `CPU` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` diff --git a/docs/source_zh_cn/design/mindinsight/profiler_design.md b/docs/source_zh_cn/design/mindinsight/profiler_design.md index 8bfd003978..0171bd3f9b 100644 --- a/docs/source_zh_cn/design/mindinsight/profiler_design.md +++ b/docs/source_zh_cn/design/mindinsight/profiler_design.md @@ -1,6 +1,6 @@ # Profiler设计文档 -`Ascend` `GPU` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` +`Linux` `Ascend` `GPU` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` diff --git a/docs/source_zh_cn/design/mindinsight/tensor_visual_design.md b/docs/source_zh_cn/design/mindinsight/tensor_visual_design.md index eca40e518c..e3af486df5 100644 --- a/docs/source_zh_cn/design/mindinsight/tensor_visual_design.md +++ b/docs/source_zh_cn/design/mindinsight/tensor_visual_design.md @@ -1,6 +1,6 @@ # 张量可视设计 -`Ascend` `GPU` `CPU` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` +`Linux` `Ascend` `GPU` `CPU` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` diff --git a/docs/source_zh_cn/design/mindinsight/training_visual_design.md b/docs/source_zh_cn/design/mindinsight/training_visual_design.md index 8dae35eef0..8e5c5071ab 100644 --- a/docs/source_zh_cn/design/mindinsight/training_visual_design.md +++ b/docs/source_zh_cn/design/mindinsight/training_visual_design.md @@ -1,6 +1,6 @@ # 训练可视总体设计 -`Ascend` `GPU` `CPU` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` +`Linux` `Ascend` `GPU` `CPU` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` diff --git a/docs/source_zh_cn/design/mindspore/distributed_training_design.md b/docs/source_zh_cn/design/mindspore/distributed_training_design.md index ab026a6526..cb9558314b 100644 --- a/docs/source_zh_cn/design/mindspore/distributed_training_design.md +++ b/docs/source_zh_cn/design/mindspore/distributed_training_design.md @@ -1,6 +1,6 @@ # 分布式训练设计 -`Ascend` `GPU` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` +`Linux` `Ascend` `GPU` `模型开发` `模型调优` `框架开发` `中级` `高级` `贡献者` diff --git a/docs/source_zh_cn/design/mindspore/ir.md b/docs/source_zh_cn/design/mindspore/ir.md index be9b62d0a1..362544c7f1 100644 --- a/docs/source_zh_cn/design/mindspore/ir.md +++ b/docs/source_zh_cn/design/mindspore/ir.md @@ -1,6 +1,6 @@ # MindSpore IR（MindIR） -`框架开发` `中级` `高级` `贡献者` +`Linux` `Windows` `框架开发` `中级` `高级` `贡献者` diff --git a/docs/source_zh_cn/glossary.md b/docs/source_zh_cn/glossary.md index f3e5142b6f..647c9076f9 100644 --- a/docs/source_zh_cn/glossary.md +++ b/docs/source_zh_cn/glossary.md @@ -1,6 +1,6 @@ # 术语 -`Ascend` `GPU` `CPU` `全流程` `初级` `中级` `高级` +`Linux` `Windows` `Ascend` `GPU` `CPU` `全流程` `初级` `中级` `高级` diff --git a/docs/source_zh_cn/help_seeking_path.md b/docs/source_zh_cn/help_seeking_path.md index 3c365a8bc4..0c469483bb 100644 --- a/docs/source_zh_cn/help_seeking_path.md +++ b/docs/source_zh_cn/help_seeking_path.md @@ -1,6 +1,6 @@ # 问题求助路径 -`Ascend` `GPU` `CPU` `全流程` `初级` `中级` `高级` +`Linux` `Windows` `Ascend` `GPU` `CPU` `全流程` `初级` `中级` `高级` diff --git a/docs/source_zh_cn/network_list.md b/docs/source_zh_cn/network_list.md index 06c400157e..386ce5e463 100644 --- a/docs/source_zh_cn/network_list.md +++ b/docs/source_zh_cn/network_list.md @@ -1,6 +1,6 @@ # 网络支持 -`Ascend` `GPU` `CPU` `模型开发` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `模型开发` `中级` `高级` diff --git a/docs/source_zh_cn/operator_list.md b/docs/source_zh_cn/operator_list.md index 2624cfc47f..4d675ac9f7 100644 --- a/docs/source_zh_cn/operator_list.md +++ b/docs/source_zh_cn/operator_list.md @@ -1,6 +1,6 @@ # 算子支持 -`Ascend` `GPU` `CPU` `模型开发` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `模型开发` `初级` `中级` `高级` diff --git a/docs/source_zh_cn/roadmap.md b/docs/source_zh_cn/roadmap.md index 1dfd7945b1..6b24fdc3e1 100644 --- a/docs/source_zh_cn/roadmap.md +++ b/docs/source_zh_cn/roadmap.md @@ -1,6 +1,6 @@ # RoadMap -`Ascend` `GPU` `CPU` `全流程` `框架开发` `中级` `高级` `贡献者` +`Linux` `Windows` `Ascend` `GPU` `CPU` `全流程` `框架开发` `中级` `高级` `贡献者` diff --git a/docs/source_zh_cn/technical_white_paper.md b/docs/source_zh_cn/technical_white_paper.md index b23cfaf698..29f1d3e4e2 100644 --- a/docs/source_zh_cn/technical_white_paper.md +++ b/docs/source_zh_cn/technical_white_paper.md @@ -1,6 +1,6 @@ # 技术白皮书 -`Ascend` `GPU` `CPU` `全流程` `框架开发` `中级` `高级` `贡献者` +`Linux` `Ascend` `GPU` `CPU` `全流程` `框架开发` `中级` `高级` `贡献者` diff --git a/tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md b/tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md index 29d10dbbbf..f9a96c3bdb 100644 --- a/tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md +++ b/tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md @@ -1,6 +1,6 @@ # Saving and Loading Model Parameters in the Hybrid Parallel Scenario -`Ascend` `GPU` `Model Training` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `Model Training` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/computer_vision_application.md b/tutorials/source_en/advanced_use/computer_vision_application.md index f340d98778..8ead2a76fd 100644 --- a/tutorials/source_en/advanced_use/computer_vision_application.md +++ b/tutorials/source_en/advanced_use/computer_vision_application.md @@ -1,6 +1,6 @@ # Computer Vision Applications -`Ascend` `GPU` `Whole Process` `Beginner` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `Whole Process` `Beginner` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/customized_debugging_information.md b/tutorials/source_en/advanced_use/customized_debugging_information.md index 8b2fc9ef70..4d2add7d7a 100644 --- a/tutorials/source_en/advanced_use/customized_debugging_information.md +++ b/tutorials/source_en/advanced_use/customized_debugging_information.md @@ -1,6 +1,6 @@ # Customized Debugging Information -`Ascend` `GPU` `CPU` `Model Optimization` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Model Optimization` `Intermediate` `Expert` @@ -311,12 +311,31 @@ When the training result deviates from the expectation on Ascend, the input and ## Log-related Environment Variables and Configurations MindSpore uses glog to output logs. The following environment variables are commonly used: -1. `GLOG_v` specifies the log level. The default value is 2, indicating the WARNING level. The values are as follows: 0: DEBUG; 1: INFO; 2: WARNING; 3: ERROR. -2. When `GLOG_logtostderr` is set to 1, logs are output to the screen. If the value is set to 0, logs are output to a file. Default value: 1 -3. GLOG_log_dir=*YourPath* specifies the log output path. If `GLOG_logtostderr` is set to 0, value of this variable must be specified. If `GLOG_log_dir is` specified and the value of `GLOG_logtostderr` is 1, logs are output to the screen but not to a file. Logs of C++ and Python will be output to different files. The file name of C++ log complies with the naming rule of GLOG log file. Here, the name is `mindspore.MachineName.UserName.log.LogLevel.Timestamp`. The file name of Python log is `mindspore.log`. -4. `MS_SUBMODULE_LOG_v="{SubModule1:LogLevel1,SubModule2:LogLevel2,...}"` specifies log levels of C++ sub modules of MindSpore. The specified sub module log level will overwrite the global log level. The meaning of submodule log level is same as `GLOG_v`, the sub modules of MindSpore grouped by source directory is as the bellow table. E.g. when set `GLOG_v=1 MS_SUBMODULE_LOG_v="{PARSER:2,ANALYZER:2}"` then log levels of `PARSER` and `ANALYZER` are WARNING, other modules' log levels are INFO. +- `GLOG_v` + + The environment variable specifies the log level. + The default value is 2, indicating the WARNING level. The values are as follows: 0: DEBUG; 1: INFO; 2: WARNING; 3: ERROR. -Sub moudles of MindSpore grouped by source directory: +- `GLOG_logtostderr` + + The environment variable specifies the log output mode. + When `GLOG_logtostderr` is set to 1, logs are output to the screen. If the value is set to 0, logs are output to a file. The default value is 1. + +- `GLOG_log_dir` + + The environment variable specifies the log output path. + If `GLOG_logtostderr` is set to 0, value of this variable must be specified. + If `GLOG_log_dir is` specified and the value of `GLOG_logtostderr` is 1, logs are output to the screen but not to a file. + Logs of C++ and Python will be output to different files. The file name of C++ log complies with the naming rule of `GLOG` log file. Here, the name is `mindspore.MachineName.UserName.log.LogLevel.Timestamp`. The file name of Python log is `mindspore.log`. + +- `MS_SUBMODULE_LOG_v` + + The environment variable specifies log levels of C++ sub modules of MindSpore. + The environment variable is assigned as: `MS_SUBMODULE_LOG_v="{SubModule1:LogLevel1,SubModule2:LogLevel2,...}"`. + The specified sub module log level will overwrite the global log level. The meaning of submodule log level is same as `GLOG_v`, the sub modules of MindSpore grouped by source directory is as the bellow table. + E.g. when set `GLOG_v=1 MS_SUBMODULE_LOG_v="{PARSER:2,ANALYZER:2}"` then log levels of `PARSER` and `ANALYZER` are WARNING, other modules' log levels are INFO. + +Sub modules of MindSpore grouped by source directory: | Source Files | Sub Module Name | | ------------ | --------------- | @@ -345,3 +364,4 @@ Sub moudles of MindSpore grouped by source directory: | mindspore/core/gvar | COMMON | | mindspore/core/ | CORE | +> The glog does not support log rotate. To control the disk space occupied by log files, use the log file management tool provided by the operating system, such as: logrotate of Linux. \ No newline at end of file diff --git a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md index 42c3fbe928..98c55656a9 100644 --- a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md +++ b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md @@ -1,6 +1,6 @@ # Debugging in PyNative Mode -`Ascend` `GPU` `Model Development` `Beginner` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `Model Development` `Beginner` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/differential_privacy.md b/tutorials/source_en/advanced_use/differential_privacy.md index 746f969dbb..17b28a7542 100644 --- a/tutorials/source_en/advanced_use/differential_privacy.md +++ b/tutorials/source_en/advanced_use/differential_privacy.md @@ -1,6 +1,6 @@ # Differential Privacy in Machine Learning -`Ascend` `Model Development` `Model Optimization` `Enterprise` `Expert` +`Linux` `Ascend` `Model Development` `Model Optimization` `Enterprise` `Expert` diff --git a/tutorials/source_en/advanced_use/distributed_training_ascend.md b/tutorials/source_en/advanced_use/distributed_training_ascend.md index 79e37f45fe..720106f612 100644 --- a/tutorials/source_en/advanced_use/distributed_training_ascend.md +++ b/tutorials/source_en/advanced_use/distributed_training_ascend.md @@ -1,6 +1,6 @@ # Parallel Distributed Training (Ascend) -`Ascend` `Model Training` `Intermediate` `Expert` +`Linux` `Ascend` `Model Training` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/graph_kernel_fusion.md b/tutorials/source_en/advanced_use/graph_kernel_fusion.md index 7475e3a051..25a44a0d46 100644 --- a/tutorials/source_en/advanced_use/graph_kernel_fusion.md +++ b/tutorials/source_en/advanced_use/graph_kernel_fusion.md @@ -1,6 +1,6 @@ # Graph Kernel Fusion -`Ascend` `Model Optimization` `Intermediate` `Expert` +`Linux` `Ascend` `Model Optimization` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/host_device_training.md b/tutorials/source_en/advanced_use/host_device_training.md index 135518dff1..559954774a 100644 --- a/tutorials/source_en/advanced_use/host_device_training.md +++ b/tutorials/source_en/advanced_use/host_device_training.md @@ -1,6 +1,6 @@ # Host+Device Hybrid Training -`Ascend` `CPU` `Model Training` `Intermediate` `Expert` +`Linux` `Ascend` `CPU` `Model Training` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/images/cifar10_c_transforms.png b/tutorials/source_en/advanced_use/images/cifar10_c_transforms.png new file mode 100644 index 0000000000000000000000000000000000000000..10dc267dc650764566f6d20b7f090e20c12f8e11 GIT binary patch literal 46693 zcmYJ41ymJZ)ad61aES|-M)CrRAl)fkkd~6}?k)uc;a*y$6{Jf_knT$bD@uk3bk}Qq>C>7xuhWtgT7T0`%zP6P9VyUIRjgj%s<<$O& 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`Intermediate` `Expert` +`Linux` `Ascend` `GPU` `Model Training` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/model_security.md b/tutorials/source_en/advanced_use/model_security.md index 1af2ab0416..5943973af3 100644 --- a/tutorials/source_en/advanced_use/model_security.md +++ b/tutorials/source_en/advanced_use/model_security.md @@ -1,6 +1,6 @@ # Model Security -`Ascend` `GPU` `CPU` `Data Preparation` `Model Development` `Model Training` `Model Optimization` `Enterprise` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Data Preparation` `Model Development` `Model Training` `Model Optimization` `Enterprise` `Expert` diff --git a/tutorials/source_en/advanced_use/network_migration.md b/tutorials/source_en/advanced_use/network_migration.md index 71511c4356..7f9ac4b69c 100644 --- a/tutorials/source_en/advanced_use/network_migration.md +++ b/tutorials/source_en/advanced_use/network_migration.md @@ -1,6 +1,6 @@ # Network Migration -`Ascend` `GPU` `CPU` `Whole Process` `Beginner` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Whole Process` `Beginner` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/nlp_application.md b/tutorials/source_en/advanced_use/nlp_application.md index f33da2ffbb..643d816288 100644 --- a/tutorials/source_en/advanced_use/nlp_application.md +++ b/tutorials/source_en/advanced_use/nlp_application.md @@ -1,6 +1,6 @@ # Natural Language Processing (NLP) Application -`GPU` `CPU` `Whole Process` `Beginner` `Intermediate` `Expert` +`Linux` `GPU` `CPU` `Whole Process` `Beginner` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/optimize_the_performance_of_data_preparation.md b/tutorials/source_en/advanced_use/optimize_the_performance_of_data_preparation.md new file mode 100644 index 0000000000..a37367e9b5 --- /dev/null +++ b/tutorials/source_en/advanced_use/optimize_the_performance_of_data_preparation.md @@ -0,0 +1,389 @@ +# Optimizing the Data Preparation Performance + +`Linux` `Ascend` `GPU` `CPU` `Data Preparation` `Beginner` `Intermediate` `Expert` + + + +- [Optimizing the Data Preparation Performance](#optimizing-the-data-preparation-performance) + - [Overview](#overview) + - [Overall Process](#overall-process) + - [Preparations](#preparations) + - [Importing Modules](#importing-modules) + - [Downloading the Required Dataset](#downloading-the-required-dataset) + - [Optimizing the Data Loading Performance](#optimizing-the-data-loading-performance) + - [Performance Optimization Solution](#performance-optimization-solution) + - [Code Example](#code-example) + - [Optimizing the Shuffle Performance](#optimizing-the-shuffle-performance) + - [Performance Optimization Solution](#performance-optimization-solution-1) + - [Code Example](#code-example-1) + - [Optimizing the Data Augmentation Performance](#optimizing-the-data-augmentation-performance) + - [Performance Optimization Solution](#performance-optimization-solution-2) + - [Code Example](#code-example-2) + - [Performance Optimization Solution Summary](#performance-optimization-solution-summary) + - [Multi-thread Optimization Solution](#multi-thread-optimization-solution) + - [Multi-process Optimization Solution](#multi-process-optimization-solution) + - [Compose Optimization Solution](#compose-optimization-solution) + - [Operator Fusion Optimization Solution](#operator-fusion-optimization-solution) + + + +   + + + +## Overview + +Data is the most important factor of deep learning. Data quality determines the upper limit of deep learning result, whereas model quality enables the result to approach the upper limit.Therefore, high-quality data input is beneficial to the entire deep neural network. During the entire data processing and data augmentation process, data continuously flows through a "pipeline" to the training system, as shown in the following figure: + +![title](./images/pipeline.png) + +MindSpore provides data processing and data augmentation functions for users. In the pipeline process, if each step can be properly used, the data performance will be greatly improved. This section describes how to optimize performance during data loading, data processing, and data augmentation based on the CIFAR-10 dataset. + +## Overall Process +- Prepare data. +- Optimize the data loading performance. +- Optimize the shuffle performance. +- Optimize the data augmentation performance. +- Summarize the performance optimization solution. + +## Preparations + +### Importing Modules + +The `dataset` module provides APIs for loading and processing datasets. + + +```python +import mindspore.dataset as ds +``` + +The `numpy` module is used to generate ndarrays. + + +```python +import numpy as np +``` + +### Downloading the Required Dataset + +1. Create the `./dataset/Cifar10Data` directory in the current working directory. The dataset used for this practice is stored in this directory. +2. Create the `./transform` directory in the current working directory. The dataset generated during the practice is stored in this directory. +3. Download [the CIFAR-10 dataset in binary format](https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz) and decompress the dataset file to the `./dataset/Cifar10Data/cifar-10-batches-bin` directory. The dataset will be used during data loading. +4. Download [the CIFAR-10 Python dataset in file-format](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz) and decompress the dataset file to the `./dataset/Cifar10Data/cifar-10-batches-py` directory. The dataset will be used for data conversion. + +The directory structure is as follows: + + + dataset/Cifar10Data + ├── cifar-10-batches-bin + │   ├── batches.meta.txt + │   ├── data_batch_1.bin + │   ├── data_batch_2.bin + │   ├── data_batch_3.bin + │   ├── data_batch_4.bin + │   ├── data_batch_5.bin + │   ├── readme.html + │   └── test_batch.bin + └── cifar-10-batches-py + ├── batches.meta + ├── data_batch_1 + ├── data_batch_2 + ├── data_batch_3 + ├── data_batch_4 + ├── data_batch_5 + ├── readme.html + └── test_batch + +In the preceding information: +- The `cifar-10-batches-bin` directory is the directory for storing the CIFAR-10 dataset in binary format. +- The `cifar-10-batches-py` directory is the directory for storing the CIFAR-10 dataset in Python file format. + +## Optimizing the Data Loading Performance + +MindSpore provides multiple data loading methods, including common dataset loading, user-defined dataset loading, and MindSpore data format loading. For details, see [Loading Datasets](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/loading_the_datasets.html). The dataset loading performance varies depending on the underlying implementation method. + +| | Common Dataset | User-defined Dataset | MindRecord Dataset | +| :----: | :----: | :----: | :----: | +| Underlying implementation | C++ | Python | C++ | +| Performance | High | Medium | High | + +### Performance Optimization Solution + +![title](./images/data_loading_performance_scheme.png) + +Suggestions on data loading performance optimization are as follows: +- Built-in loading operators are preferred for supported dataset formats. For details, see [Built-in Loading Operators](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.html). If the performance cannot meet the requirements, use the multi-thread concurrency solution. For details, see [Multi-thread Optimization Solution](#multi-thread-optimization-solution). +- For a dataset format that is not supported, convert the format to MindSpore data format and then use the `MindDataset` class to load the dataset. For details, see [Converting Datasets into MindSpore Data Format](https://www.mindspore.cn/tutorial/en/r0.7/use/data_preparation/converting_datasets.html). If the performance cannot meet the requirements, use the multi-thread concurrency solution, for details, see [Multi-thread Optimization Solution](#multi-thread-optimization-solution). +- For dataset formats that are not supported, the user-defined `GeneratorDataset` class is preferred for implementing fast algorithm verification. If the performance cannot meet the requirements, the multi-process concurrency solution can be used. For details, see [Multi-process Optimization Solution](#multi-process-optimization-solution). + +### Code Example + +Based on the preceding suggestions of data loading performance optimization, the `Cifar10Dataset` class of built-in loading operators, the `MindDataset` class after data conversion, and the `GeneratorDataset` class are used to load data. The sample code is displayed as follows: + +1. Use the `Cifar10Dataset` class of built-in operators to load the CIFAR-10 dataset in binary format. The multi-thread optimization solution is used for data loading. Four threads are enabled to concurrently complete the task. Finally, a dictionary iterator is created for the data and a data record is read through the iterator. + + + ```python + cifar10_path = "./dataset/Cifar10Data/cifar-10-batches-bin/" + + # create Cifar10Dataset for reading data + cifar10_dataset = ds.Cifar10Dataset(cifar10_path, num_parallel_workers=4) + # create a dictionary iterator and read a data record through the iterator + print(next(cifar10_dataset.create_dict_iterator())) + ``` + + The output is as follows: + ``` + {'image': array([[[235, 235, 235], + [230, 230, 230], + [234, 234, 234], + ..., + [248, 248, 248], + [248, 248, 248], + [249, 249, 249]], + ..., + [120, 120, 119], + [146, 146, 146], + [177, 174, 190]]], dtype=uint8), 'label': array(9, dtype=uint32)} + ``` + +2. Use the `Cifar10ToMR` class to convert the CIFAR-10 dataset into MindSpore data format. In this example, the CIFAR-10 dataset in Python file format is used. Then use the `MindDataset` class to load the dataset in MindSpore data format. The multi-thread optimization solution is used for data loading. Four threads are enabled to concurrently complete the task. Finally, a dictionary iterator is created for data and a data record is read through the iterator. + + + ```python + from mindspore.mindrecord import Cifar10ToMR + + cifar10_path = './dataset/Cifar10Data/cifar-10-batches-py/' + cifar10_mindrecord_path = './transform/cifar10.record' + + cifar10_transformer = Cifar10ToMR(cifar10_path, cifar10_mindrecord_path) + # executes transformation from Cifar10 to MindRecord + cifar10_transformer.transform(['label']) + + # create MindDataset for reading data + cifar10_mind_dataset = ds.MindDataset(dataset_file=cifar10_mindrecord_path, num_parallel_workers=4) + # create a dictionary iterator and read a data record through the iterator + print(next(cifar10_mind_dataset.create_dict_iterator())) + ``` + + The output is as follows: + ``` + {'data': array([255, 216, 255, ..., 63, 255, 217], dtype=uint8), 'id': array(30474, dtype=int64), 'label': array(2, dtype=int64)} + ``` + +3. The `GeneratorDataset` class is used to load the user-defined dataset, and the multi-process optimization solution is used. Four processes are enabled to concurrently complete the task. Finally, a dictionary iterator is created for the data, and a data record is read through the iterator. + + + ```python + def generator_func(num): + for i in range(num): + yield (np.array([i]),) + + # create GeneratorDataset for reading data + dataset = ds.GeneratorDataset(source=generator_func(5), column_names=["data"], num_parallel_workers=4) + # create a dictionary iterator and read a data record through the iterator + print(next(dataset.create_dict_iterator())) + ``` + + The output is as follows: + ``` + {'data': array([0], dtype=int64)} + ``` + +## Optimizing the Shuffle Performance + +The shuffle operation is used to shuffle ordered datasets or repeated datasets. MindSpore provides the `shuffle` function for users. A larger value of `buffer_size` indicates a higher shuffling degree, consuming more time and computing resources. This API allows users to shuffle the data at any time during the entire pipeline process. For details, see [Shuffle Processing](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/data_processing_and_augmentation.html#shuffle). However, because the underlying implementation methods are different, the performance of this method is not as good as that of setting the `shuffle` parameter to directly shuffle data by referring to the [Built-in Loading Operators](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.html). + +### Performance Optimization Solution + +![title](./images/shuffle_performance_scheme.png) + +Suggestions on shuffle performance optimization are as follows: +- Use the `shuffle` parameter of built-in loading operators to shuffle data. +- If the `shuffle` function is used and the performance still cannot meet the requirements, increase the value of the `buffer_size` parameter to improve the performance. + +### Code Example + +Based on the preceding shuffle performance optimization suggestions, the `shuffle` parameter of the `Cifar10Dataset` class of built-in loading operators and the `Shuffle` function are used to shuffle data. The sample code is displayed as follows: + +1. Use the built-in operator in `Cifar10Dataset` class to load the CIFAR-10 dataset. In this example, the CIFAR-10 dataset in binary format is used, and the `shuffle` parameter is set to True to perform data shuffle. Finally, a dictionary iterator is created for the data and a data record is read through the iterator. + + + ```python + cifar10_path = "./dataset/Cifar10Data/cifar-10-batches-bin/" + + # create Cifar10Dataset for reading data + cifar10_dataset = ds.Cifar10Dataset(cifar10_path, shuffle=True) + # create a dictionary iterator and read a data record through the iterator + print(next(cifar10_dataset.create_dict_iterator())) + ``` + + The output is as follows: + ``` + {'image': array([[[254, 254, 254], + [255, 255, 254], + [255, 255, 254], + ..., + [232, 234, 244], + [226, 230, 242], + [228, 232, 243]], + ..., + [ 64, 61, 63], + [ 63, 58, 60], + [ 61, 56, 58]]], dtype=uint8), 'label': array(9, dtype=uint32)} + ``` + +2. Use the `shuffle` function to shuffle data. Set `buffer_size` to 3 and use the `GeneratorDataset` class to generate data. + + + ```python + def generator_func(): + for i in range(5): + yield (np.array([i, i+1, i+2, i+3, i+4]),) + + ds1 = ds.GeneratorDataset(source=generator_func, column_names=["data"]) + print("before shuffle:") + for data in ds1.create_dict_iterator(): + print(data["data"]) + + ds2 = ds1.shuffle(buffer_size=3) + print("after shuffle:") + for data in ds2.create_dict_iterator(): + print(data["data"]) + ``` + ``` + The output is as follows: + + before shuffle: + [0 1 2 3 4] + [1 2 3 4 5] + [2 3 4 5 6] + [3 4 5 6 7] + [4 5 6 7 8] + after shuffle: + [2 3 4 5 6] + [0 1 2 3 4] + [4 5 6 7 8] + [1 2 3 4 5] + [3 4 5 6 7] + ``` + +## Optimizing the Data Augmentation Performance + +During image classification training, especially when the dataset is small, users can use data augmentation to preprocess images to enrich the dataset. MindSpore provides multiple data augmentation methods, including: +- Use the built-in C operator (`c_transforms` module) to perform data augmentation. +- Use the built-in Python operator (`py_transforms` module) to perform data augmentation. +- Users can define Python functions as needed to perform data augmentation. + +For details, see [Data Augmentation](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/data_processing_and_augmentation.html#id3). The performance varies according to the underlying implementation methods. + +| Module | Underlying API | Description | +| :----: | :----: | :----: | +| c_transforms | C++ (based on OpenCV) | High performance | +| py_transforms | Python (based on PIL) | This module provides multiple image augmentation functions and the method for converting PIL images into NumPy arrays. | + + +### Performance Optimization Solution + +![title](./images/data_enhancement_performance_scheme.png) + + +Suggestions on data augmentation performance optimization are as follows: +- The `c_transforms` module is preferentially used to perform data augmentation for its highest performance. If the performance cannot meet the requirements, refer to [Multi-thread Optimization Solution](#multi-thread-optimization-solution), [Compose Optimization Solution](#compose-optimization-solution), or [Operator Fusion Optimization Solution](#operator-fusion-optimization-solution). +- If the `py_transforms` module is used to perform data augmentation and the performance still cannot meet the requirements, refer to [Multi-thread Optimization Solution](#multi-thread-optimization-solution), [Multi-process Optimization Solution](#multi-process-optimization-solution), [Compose Optimization Solution](#compose-optimization-solution), or [Operator Fusion Optimization Solution](#operator-fusion-optimization-solution). +- The `c_transforms` module maintains buffer management in C++, and the `py_transforms` module maintains buffer management in Python. Because of the performance cost of switching between Python and C++, it is advised not to use different operator types together. +- If the user-defined Python functions are used to perform data augmentation and the performance still cannot meet the requirements, use the [Multi-thread Optimization Solution](#multi-thread-optimization-solution) or [Multi-process Optimization Solution](#multi-process-optimization-solution). If the performance still cannot be improved, in this case, optimize the user-defined Python code. + +### Code Example + +Based on the preceding suggestions of data augmentation performance optimization, the `c_transforms` module and user-defined Python function are used to perform data augmentation. The code is displayed as follows: + +1. The `c_transforms` module is used to perform data augmentation. During data augmentation, the multi-thread optimization solution is used. Four threads are enabled to concurrently complete the task. The operator fusion optimization solution is used and the `RandomResizedCrop` fusion class is used to replace the `RandomResize` and `RandomCrop` classes. + + + ```python + import mindspore.dataset.transforms.c_transforms as c_transforms + import mindspore.dataset.vision.c_transforms as C + import matplotlib.pyplot as plt + cifar10_path = "./dataset/Cifar10Data/cifar-10-batches-bin/" + + # create Cifar10Dataset for reading data + cifar10_dataset = ds.Cifar10Dataset(cifar10_path, num_parallel_workers=4) + transforms = C.RandomResizedCrop((800, 800)) + # apply the transform to the dataset through dataset.map() + cifar10_dataset = cifar10_dataset.map(operations=transforms, input_columns="image", num_parallel_workers=4) + + data = next(cifar10_dataset.create_dict_iterator()) + plt.imshow(data["image"]) + plt.show() + ``` + + The output is as follows: + + ![png](./images/cifar10_c_transforms.png) + + +2. A user-defined Python function is used to perform data augmentation. During data augmentation, the multi-process optimization solution is used, and four processes are enabled to concurrently complete the task. + + + ```python + def generator_func(): + for i in range(5): + yield (np.array([i, i+1, i+2, i+3, i+4]),) + + ds3 = ds.GeneratorDataset(source=generator_func, column_names=["data"]) + print("before map:") + for data in ds3.create_dict_iterator(): + print(data["data"]) + + func = lambda x:x**2 + ds4 = ds3.map(operations=func, input_columns="data", python_multiprocessing=True, num_parallel_workers=4) + print("after map:") + for data in ds4.create_dict_iterator(): + print(data["data"]) + ``` + + The output is as follows: + ``` + before map: + [0 1 2 3 4] + [1 2 3 4 5] + [2 3 4 5 6] + [3 4 5 6 7] + [4 5 6 7 8] + after map: + [ 0 1 4 9 16] + [ 1 4 9 16 25] + [ 4 9 16 25 36] + [ 9 16 25 36 49] + [16 25 36 49 64] + ``` + +## Performance Optimization Solution Summary + +### Multi-thread Optimization Solution + +During the data pipeline process, the number of threads for related operators can be set to improve the concurrency and performance. For example: +- During data loading, the `num_parallel_workers` parameter in the built-in data loading class is used to set the number of threads. +- During data augmentation, the `num_parallel_workers` parameter in the `map` function is used to set the number of threads. +- During batch processing, the `num_parallel_workers` parameter in the `batch` function is used to set the number of threads. + +For details, see [Built-in Loading Operators](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.html). + +### Multi-process Optimization Solution + +During data processing, operators implemented by Python support the multi-process mode. For example: +- By default, the `GeneratorDataset` class is in multi-process mode. The `num_parallel_workers` parameter indicates the number of enabled processes. The default value is 1. For details, see [Generator Dataset](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.html#mindspore.dataset.GeneratorDataset) +- If the user-defined Python function or the `py_transforms` module is used to perform data augmentation and the `python_multiprocessing` parameter of the `map` function is set to True, the `num_parallel_workers` parameter indicates the number of processes and the default value of the `python_multiprocessing` parameter is False. In this case, the `num_parallel_workers` parameter indicates the number of threads. For details, see [Built-in Loading Operators](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.html). + +### Compose Optimization Solution + +Map operators can receive the Tensor operator list and apply all these operators based on a specific sequence. Compared with the Map operator used by each Tensor operator, such Fat Map operators can achieve better performance, as shown in the following figure: + +![title](./images/compose.png) + +### Operator Fusion Optimization Solution + +Some fusion operators are provided to aggregate the functions of two or more operators into one operator. For details, see [Data Augmentation Operators](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.vision.html). Compared with the pipelines of their components, such fusion operators provide better performance. As shown in the figure: + +![title](./images/operator_fusion.png) diff --git a/tutorials/source_en/advanced_use/parameter_server_training.md b/tutorials/source_en/advanced_use/parameter_server_training.md index c000f7ab30..964659e386 100644 --- a/tutorials/source_en/advanced_use/parameter_server_training.md +++ b/tutorials/source_en/advanced_use/parameter_server_training.md @@ -1,6 +1,6 @@ # Parameter Server Training -`Ascend` `GPU` `Model Training` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `Model Training` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/quantization_aware.md b/tutorials/source_en/advanced_use/quantization_aware.md index 25a7027be5..4908573015 100644 --- a/tutorials/source_en/advanced_use/quantization_aware.md +++ b/tutorials/source_en/advanced_use/quantization_aware.md @@ -1,6 +1,6 @@ # Quantization -`Ascend` `GPU` `Model Optimization` `Expert` +`Linux` `Ascend` `GPU` `Model Optimization` `Expert` diff --git a/tutorials/source_en/advanced_use/serving.md b/tutorials/source_en/advanced_use/serving.md index a726e427cb..f892856eb5 100644 --- a/tutorials/source_en/advanced_use/serving.md +++ b/tutorials/source_en/advanced_use/serving.md @@ -1,6 +1,6 @@ # MindSpore-based Inference Service Deployment -`Ascend` `Environmental Setup` `Enterprise` `Expert` +`Linux` `Ascend` `Environmental Setup` `Enterprise` `Expert` diff --git a/tutorials/source_en/index.rst b/tutorials/source_en/index.rst index 402105ab2f..5d8268c3a5 100644 --- a/tutorials/source_en/index.rst +++ b/tutorials/source_en/index.rst @@ -31,6 +31,7 @@ MindSpore Tutorials advanced_use/computer_vision_application advanced_use/nlp_application + advanced_use/optimize_the_performance_of_data_preparation .. toctree:: :glob: diff --git a/tutorials/source_en/quick_start/quick_start.md b/tutorials/source_en/quick_start/quick_start.md index cec82c3d54..1519ace698 100644 --- a/tutorials/source_en/quick_start/quick_start.md +++ b/tutorials/source_en/quick_start/quick_start.md @@ -1,6 +1,6 @@ # Implementing an Image Classification Application -`Ascend` `GPU` `CPU` `Whole Process` `Beginner` `Intermediate` `Expert` +`Linux` `Windows` `Ascend` `GPU` `CPU` `Whole Process` `Beginner` `Intermediate` `Expert` diff --git a/tutorials/source_en/use/custom_operator.md b/tutorials/source_en/use/custom_operator.md index 2a09ea2f6d..eefd452bf8 100644 --- a/tutorials/source_en/use/custom_operator.md +++ b/tutorials/source_en/use/custom_operator.md @@ -1,6 +1,6 @@ # Custom Operators -`Ascend` `Model Development` `Expert` +`Linux` `Ascend` `Model Development` `Expert` diff --git a/tutorials/source_en/use/data_preparation/converting_datasets.md b/tutorials/source_en/use/data_preparation/converting_datasets.md index 1bb405c3d4..b1d8a21224 100644 --- a/tutorials/source_en/use/data_preparation/converting_datasets.md +++ b/tutorials/source_en/use/data_preparation/converting_datasets.md @@ -1,6 +1,6 @@ # Converting Datasets to the Mindspore Data Format -`Ascend` `GPU` `CPU` `Data Preparation` `Beginner` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Data Preparation` `Beginner` `Intermediate` `Expert` diff --git a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md index 67852d647c..f00f118ff9 100644 --- a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md +++ b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md @@ -1,6 +1,6 @@ # Data Processing and Augmentation -`Ascend` `GPU` `CPU` `Data Preparation` `Beginner` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Data Preparation` `Beginner` `Intermediate` `Expert` diff --git a/tutorials/source_en/use/data_preparation/loading_the_datasets.md b/tutorials/source_en/use/data_preparation/loading_the_datasets.md index 61a0732043..ad339502f6 100644 --- a/tutorials/source_en/use/data_preparation/loading_the_datasets.md +++ b/tutorials/source_en/use/data_preparation/loading_the_datasets.md @@ -1,6 +1,6 @@ # Loading the Dataset -`Ascend` `GPU` `CPU` `Data Preparation` `Beginner` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Data Preparation` `Beginner` `Intermediate` `Expert` diff --git a/tutorials/source_en/use/multi_platform_inference.md b/tutorials/source_en/use/multi_platform_inference.md index 9b4c526929..15b2ce276e 100644 --- a/tutorials/source_en/use/multi_platform_inference.md +++ b/tutorials/source_en/use/multi_platform_inference.md @@ -1,6 +1,6 @@ # Multi-Platform Inference -`Ascend` `GPU` `CPU` `Inference Application` `Beginner` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Inference Application` `Beginner` `Intermediate` `Expert` diff --git a/tutorials/source_en/use/saving_and_loading_model_parameters.md b/tutorials/source_en/use/saving_and_loading_model_parameters.md index 70518a0586..c361519ff9 100644 --- a/tutorials/source_en/use/saving_and_loading_model_parameters.md +++ b/tutorials/source_en/use/saving_and_loading_model_parameters.md @@ -1,6 +1,6 @@ # Saving and Loading Model Parameters -`Ascend` `GPU` `CPU` `Model Export` `Beginner` `Intermediate` `Expert` +`Linux` `Ascend` `GPU` `CPU` `Model Export` `Beginner` `Intermediate` `Expert` diff --git a/tutorials/source_zh_cn/advanced_use/bert_poetry.md b/tutorials/source_zh_cn/advanced_use/bert_poetry.md index 6cdb95fe9c..ffe45bb508 100644 --- a/tutorials/source_zh_cn/advanced_use/bert_poetry.md +++ b/tutorials/source_zh_cn/advanced_use/bert_poetry.md @@ -1,6 +1,6 @@ # 智能写诗 -`Ascend` `模型训练` `推理应用` `端侧` `高级` +`Linux` `Ascend` `模型训练` `推理应用` `端侧` `高级` @@ -18,7 +18,7 @@ - [训练](#训练) - [推理验证](#推理验证) - [服务部署](#服务部署) - - [参考资料](#参考资料) + - [参考文献](#参考文献) @@ -49,7 +49,7 @@ ### Pre-training -Pre-training是在无标签数据上进行的自编码训练，因此训练任务的设计尤为重要,BERT中的Pre-training包含两项任务MLM(Masked Language Model)和NSP(Next Sentence Prediction)。 +Pre-training是在无标签数据上进行的自编码训练，因此训练任务的设计尤为重要，BERT中的Pre-training包含两项任务MLM(Masked Language Model)和NSP(Next Sentence Prediction)。 - **MLM任务**是在输入时，随机将部分token置换为[MASK]标记，然后通过注意力机制，由其上下文预测出被遮挡位置的原始token。 @@ -208,97 +208,97 @@ python poetry.py --train=False --ckpt_path=/your/ckpt/path - 模型导出 -在使用Serving部署服务前，需要导出模型文件，在`poetry.py`中提供了`export_net`函数负责导出MINDIR模型，执行命令: + 在使用Serving部署服务前，需要导出模型文件，在`poetry.py`中提供了`export_net`函数负责导出MINDIR模型，执行命令: -``` -python poetry.py --export=True --ckpt_path=/your/ckpt/path -``` + ``` + python poetry.py --export=True --ckpt_path=/your/ckpt/path + ``` -会在当前路径下生成`poetry.pb`文件。 + 会在当前路径下生成`poetry.pb`文件。 - Serving服务 -在服务器侧启动Serving服务，并加载导出的MINDIR文件`poetry.pb`。 + 在服务器侧启动Serving服务，并加载导出的MINDIR文件`poetry.pb`。 -``` -cd serving -./ms_serving --model_path=/path/to/your/MINDIR_file --model_name=your_mindir.pb -``` + ``` + cd serving + ./ms_serving --model_path=/path/to/your/MINDIR_file --model_name=your_mindir.pb + ``` - 预处理及后处理的服务 -预处理及后处理通过Flask框架来快速实现，在服务器侧运行`bert_flask.py`文件，启动Flask服务。 + 预处理及后处理通过Flask框架来快速实现，在服务器侧运行`bert_flask.py`文件，启动Flask服务。 -``` -python bert_flask.py -``` + ``` + python bert_flask.py + ``` -通过以上步骤，服务端部署就已经完成。 + 通过以上步骤，服务端部署就已经完成。 - 客户端 -可用电脑作为客户端，修改`poetry_client.py`中的url请求地址为推理服务启动的服务器IP，并确保端口与服务端`bert_flask.py`中的端口一致，例如： - -``` -url = 'http://10.155.170.71:8080/' -``` - -运行`poetry_client.py`文件 - -``` -python poetry_client.py -``` - -此时在客户端输入指令，即可在远端服务器进行推理，返回生成的诗句。 - -``` -选择模式：0-随机生成，1：续写，2：藏头诗 -0 -``` -``` -一朵黄花叶， -千竿绿树枝。 -含香待夏晚， -澹浩长风时。 -``` - -``` -选择模式：0-随机生成，1：续写，2：藏头诗 -1 -输入首句诗 -明月 -``` -``` -明月照三峡， -长空一片云。 -秋风与雨过， -唯有客舟分。 -寒影出何处， -远林含不闻。 -不知前后事， -何道逐风君。 -``` - -``` -选择模式：0-随机生成，1：续写，2：藏头诗 -2 -输入藏头诗 -人工智能 -``` -``` -人生事太远， -工部与神期。 -智者岂无识， -能文争有疑。 -``` - -细读鉴赏一下，平仄、押韵、意味均有体现，AI诗人已然成形。 + 可用电脑作为客户端，修改`poetry_client.py`中的url请求地址为推理服务启动的服务器IP，并确保端口与服务端`bert_flask.py`中的端口一致，例如： + + ``` + url = 'http://10.155.170.71:8080/' + ``` + + 运行`poetry_client.py`文件 + + ``` + python poetry_client.py + ``` + + 此时在客户端输入指令，即可在远端服务器进行推理，返回生成的诗句。 + + ``` + 选择模式：0-随机生成，1：续写，2：藏头诗 + 0 + ``` + ``` + 一朵黄花叶， + 千竿绿树枝。 + 含香待夏晚， + 澹浩长风时。 + ``` + + ``` + 选择模式：0-随机生成，1：续写，2：藏头诗 + 1 + 输入首句诗 + 明月 + ``` + ``` + 明月照三峡， + 长空一片云。 + 秋风与雨过， + 唯有客舟分。 + 寒影出何处， + 远林含不闻。 + 不知前后事， + 何道逐风君。 + ``` + + ``` + 选择模式：0-随机生成，1：续写，2：藏头诗 + 2 + 输入藏头诗 + 人工智能 + ``` + ``` + 人生事太远， + 工部与神期。 + 智者岂无识， + 能文争有疑。 + ``` + + 细读鉴赏一下，平仄、押韵、意味均有体现，AI诗人已然成形。 > 友情提醒，修改其他类型数据集，也可以完成其他简单的生成类任务，如对春联，简单聊天机器人等，用户可尝试体验实现。 -## 参考资料 +## 参考文献 [1] [BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) diff --git a/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md b/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md index 2b51952eab..36f8ef9995 100644 --- a/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md +++ b/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md @@ -2,7 +2,7 @@ # 手动设置并行场景模型参数的保存和加载 -`Ascend` `GPU` `模型训练` `中级` `高级` +`Linux` `Ascend` `GPU` `模型训练` `中级` `高级` @@ -218,7 +218,7 @@ param_dict = load_checkpoint("./CKP-Integrated_1-4_32.ckpt") slice_list[0] --- [1, 2, 3, 4] 对应device0 slice_list[1] --- [5, 6, 7, 8] 对应device1 - 与slice_list类似，slice_moments_list 也被切分为两个shape为[1, 4]的Tensor。 + 与`slice_list`类似，`slice_moments_list` 也被切分为两个shape为[1, 4]的Tensor。 2. 在每个节点分别加载对应的数据切片。 @@ -253,7 +253,7 @@ load_param_into_net(opt, param_dict) ### 示例场景说明 -整体场景：训练分为两个阶段，两阶段的集群规模不一致， 模拟FC层MatMul算子并行。 +整体场景：训练分为两个阶段，两阶段的集群规模不一致，模拟FC层MatMul算子并行。 用户流程： diff --git a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md index 9d3f271063..10e9d5a0dc 100644 --- a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md +++ b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md @@ -1,6 +1,6 @@ # 计算机视觉应用 -`Ascend` `GPU` `全流程` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `全流程` `初级` `中级` `高级` @@ -9,11 +9,11 @@ - [图像分类](#图像分类) - [任务描述及准备](#任务描述及准备) - [下载CIFAR-10数据集](#下载cifar-10数据集) - - [数据预加载和预处理](#数据预加载和预处理) - - [定义卷积神经网络](#定义卷积神经网络) - - [定义损失函数和优化器](#定义损失函数和优化器) - - [调用`Model`高阶API进行训练和保存模型文件](#调用model高阶api进行训练和保存模型文件) - - [加载保存的模型，并进行验证](#加载保存的模型并进行验证) + - [数据预加载和预处理](#数据预加载和预处理) + - [定义卷积神经网络](#定义卷积神经网络) + - [定义损失函数和优化器](#定义损失函数和优化器) + - [调用`Model`高阶API进行训练和保存模型文件](#调用model高阶api进行训练和保存模型文件) + - [加载保存的模型，并进行验证](#加载保存的模型并进行验证) - [参考文献](#参考文献) @@ -82,7 +82,7 @@ tar -zvxf cifar-10-binary.tar.gz ``` -### 数据预加载和预处理 +## 数据预加载和预处理 1. 加载数据集 @@ -142,7 +142,7 @@ tar -zvxf cifar-10-binary.tar.gz ``` -### 定义卷积神经网络 +## 定义卷积神经网络 卷积神经网络已经是图像分类任务的标准算法了。卷积神经网络采用分层的结构对图片进行特征提取，由一系列的网络层堆叠而成，比如卷积层、池化层、激活层等等。 @@ -157,12 +157,11 @@ network = resnet50(class_num=10) 更多ResNet的介绍请参考：[ResNet论文](https://arxiv.org/abs/1512.03385) -### 定义损失函数和优化器 - +## 定义损失函数和优化器 接下来需要定义损失函数（Loss）和优化器（Optimizer）。损失函数是深度学习的训练目标，也叫目标函数，可以理解为神经网络的输出（Logits）和标签(Labels)之间的距离，是一个标量数据。 -常见的损失函数包括均方误差、L2损失、Hinge损失、交叉熵等等。图像分类应用通常采用交叉熵损失（CrossEntropy）。 +常见的损失函数包括均方误差、L2损失、Hinge损失、交叉熵等等。图像分类应用通常采用交叉熵损失（`CrossEntropy`）。 优化器用于神经网络求解（训练）。由于神经网络参数规模庞大，无法直接求解，因而深度学习中采用随机梯度下降算法（SGD）及其改进算法进行求解。MindSpore封装了常见的优化器，如`SGD`、`ADAM`、`Momemtum`等等。本例采用`Momentum`优化器，通常需要设定两个参数，动量（`moment`）和权重衰减项（`weight decay`）。 @@ -177,7 +176,7 @@ opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0. ``` -### 调用`Model`高阶API进行训练和保存模型文件 +## 调用`Model`高阶API进行训练和保存模型文件 完成数据预处理、网络定义、损失函数和优化器定义之后，就可以进行模型训练了。模型训练包含两层迭代，数据集的多轮迭代（`epoch`）和一轮数据集内按分组（`batch`）大小进行的单步迭代。其中，单步迭代指的是按分组从数据集中抽取数据，输入到网络中计算得到损失函数，然后通过优化器计算和更新训练参数的梯度。 @@ -201,7 +200,7 @@ loss_cb = LossMonitor() model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb]) ``` -### 加载保存的模型，并进行验证 +## 加载保存的模型，并进行验证 训练得到的模型文件（如`resnet.ckpt`）可以用来预测新图像的类别。首先通过`load_checkpoint`加载模型文件。然后调用`Model`的`eval`接口预测新图像类别。 diff --git a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md index 8d54f10ae6..2099dded63 100644 --- a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md +++ b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md @@ -1,6 +1,6 @@ # 自定义调试信息 -`Ascend` `GPU` `CPU` `模型调优` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `模型调优` `中级` `高级` @@ -26,17 +26,17 @@ ## Callback介绍 -Callback是回调函数的意思，但它其实不是一个函数而是一个类，用户可以使用回调函数来观察训练过程中网络内部的状态和相关信息，或在特定时期执行特定动作。 +`Callback`是回调函数的意思，但它其实不是一个函数而是一个类，用户可以使用回调函数来观察训练过程中网络内部的状态和相关信息，或在特定时期执行特定动作。 例如监控loss、保存模型参数、动态调整参数、提前终止训练任务等。 ### MindSpore的Callback能力 -MindSpore提供Callback能力，支持用户在训练/推理的特定阶段，插入自定义的操作。包括： +MindSpore提供`Callback`能力，支持用户在训练/推理的特定阶段，插入自定义的操作。包括： -- MindSpore框架提供的`ModelCheckpoint`、`LossMonitor`、`SummaryCollector`等Callback函数。 -- MindSpore支持用户自定义Callback。 +- MindSpore框架提供的`ModelCheckpoint`、`LossMonitor`、`SummaryCollector`等`Callback`函数。 +- MindSpore支持用户自定义`Callback`。 -使用方法：在`model.train`方法中传入Callback对象，它可以是一个Callback列表，例： +使用方法：在`model.train`方法中传入`Callback`对象，它可以是一个`Callback`列表，例： ```python ckpt_cb = ModelCheckpoint() @@ -45,16 +45,16 @@ summary_cb = SummaryCollector(summary_dir='./summary_dir') model.train(epoch, dataset, callbacks=[ckpt_cb, loss_cb, summary_cb]) ``` -`ModelCheckpoint`可以保存模型参数，以便进行再训练或推理。 -`LossMonitor`可以在日志中输出loss，方便用户查看，同时它还会监控训练过程中的loss值变化情况，当loss值为`Nan`或`Inf`时终止训练。 -`SummaryCollector` 可以把训练过程中的信息存储到文件中，以便后续进行查看或可视化展示。 -在训练过程中，Callback列表会按照定义的顺序执行Callback函数。因此在定义过程中，需考虑Callback之间的依赖关系。 +`ModelCheckpoint`可以保存模型参数，以便进行再训练或推理。 +`LossMonitor`可以在日志中输出loss，方便用户查看，同时它还会监控训练过程中的loss值变化情况，当loss值为`Nan`或`Inf`时终止训练。 +`SummaryCollector` 可以把训练过程中的信息存储到文件中，以便后续进行查看或可视化展示。 +在训练过程中，`Callback`列表会按照定义的顺序执行`Callback`函数。因此在定义过程中，需考虑`Callback`之间的依赖关系。 ### 自定义Callback 用户可以基于`Callback`基类，根据自身的需求，实现自定义`Callback`。 -Callback基类定义如下所示： +`Callback`基类定义如下所示： ```python class Callback(): @@ -84,22 +84,22 @@ class Callback(): pass ``` -Callback可以把训练过程中的重要信息记录下来，通过一个字典类型变量`cb_params`传递给Callback对象， -用户可以在各个自定义的Callback中获取到相关属性，执行自定义操作。也可以自定义其他变量传递给`cb_params`对象。 +`Callback`可以把训练过程中的重要信息记录下来，通过一个字典类型变量`cb_params`传递给`Callback`对象， +用户可以在各个自定义的`Callback`中获取到相关属性，执行自定义操作。也可以自定义其他变量传递给`cb_params`对象。 `cb_params`中的主要属性包括： -- loss_fn：损失函数 -- optimizer：优化器 -- train_dataset：训练的数据集 -- cur_epoch_num：当前的epoch数 -- cur_step_num：当前的step数 -- batch_num：一个epoch中step的数量 +- `loss_fn`：损失函数 +- `optimizer`：优化器 +- `train_dataset`：训练的数据集 +- `cur_epoch_num`：当前的epoch数 +- `cur_step_num`：当前的step数 +- `batch_num`：一个epoch中step的数量 - ... -用户可以继承Callback基类自定义Callback对象。 +用户可以继承`Callback`基类自定义`Callback`对象。 -下面通过两个例子，进一步了解自定义Callback的用法。 +下面通过两个例子，进一步了解自定义`Callback`的用法。 - 在规定时间内终止训练。 @@ -168,7 +168,7 @@ Callback可以把训练过程中的重要信息记录下来，通过一个字典 model.train(epoch_size, train_dataset=ds_train, callback=SaveCallback(model, ds_eval)) ``` - 具体实现逻辑为：定义一个callback对象，初始化对象接收model对象和ds_eval(验证数据集)。在step_end阶段验证模型的精度，当精度为当前最高时，手动触发保存checkpoint方法，保存当前的参数。 + 具体实现逻辑为：定义一个`Callback`对象，初始化对象接收`model`对象和`ds_eval`(验证数据集)。在`step_end`阶段验证模型的精度，当精度为当前最高时，手动触发保存checkpoint方法，保存当前的参数。 ## MindSpore metrics功能介绍 @@ -203,11 +203,11 @@ output = model.eval(ds_eval) 以`accuracy`算子举例说明其内部实现原理： -`accuracy`继承了`EvaluationBase`基类，重写了上述三个方法。 -`clear`方法会把类中相关计算参数初始化。 -`update`方法接受预测值和标签值，更新accuracy内部变量。 -`eval`方法会计算相关指标，返回计算结果。 -调用`accuracy`的`eval`方法，即可得到计算结果。 +`accuracy`继承了`EvaluationBase`基类，重写了上述三个方法。 +`clear`方法会把类中相关计算参数初始化。 +`update`方法接受预测值和标签值，更新accuracy内部变量。 +`eval`方法会计算相关指标，返回计算结果。 +调用`accuracy`的`eval`方法，即可得到计算结果。 通过如下代码可以更清楚了解到`accuracy`是如何运行的： @@ -315,12 +315,29 @@ val:[[1 1] ## 日志相关的环境变量和配置 MindSpore采用glog来输出日志，常用的几个环境变量如下： -1. `GLOG_v` 控制日志的级别，默认值为2，即WARNING级别，对应关系如下：0-DEBUG、1-INFO、2-WARNING、3-ERROR。 -2. `GLOG_logtostderr` 值设置为1时，日志输出到屏幕；值设置为0时，日志输出到文件。默认值为1。 -3. GLOG_log_dir=*YourPath* 指定日志输出的路径。若`GLOG_logtostderr`的值为0，则必须设置此变量。若指定了`GLOG_log_dir`且`GLOG_logtostderr`的值为1时，则日志输出到屏幕，不输出到文件。C++和Python的日志会被输出到不同的文件中，C++日志的文件名遵从GLOG日志文件的命名规则，这里是`mindspore.机器名.用户名.log.日志级别.时间戳`，Python日志的文件名为`mindspore.log`。 -4. `MS_SUBMODULE_LOG_v="{SubModule1:LogLevel1,SubModule2:LogLevel2,...}"` 指定MindSpore C++各子模块的日志级别，被指定的子模块的日志级别将覆盖GLOG_v在此模块内的设置，此处子模块的日志级别LogLevel与`GLOG_v`的日志级别含义相同，MindSpore子模块的划分如下表。如可以通过`GLOG_v=1 MS_SUBMODULE_LOG_v="{PARSER:2,ANALYZER:2}"`把`PARSER`和`ANALYZER`模块的日志级别设为WARNING，其他模块的日志级别设为INFO。 +- `GLOG_v` -> glog不支持日志文件的绕接，如果需要控制日志文件对磁盘空间的占用，可选用操作系统提供的日志文件管理工具，例如：Linux的logrotate。 + 该环境变量控制日志的级别。 + 该环境变量默认值为2，即WARNING级别，对应关系如下：0-DEBUG、1-INFO、2-WARNING、3-ERROR。 + +- `GLOG_logtostderr` + + 该坏境变量控制日志的输出方式。 + 该环境变量的值设置为1时，日志输出到屏幕；值设置为0时，日志输出到文件。默认值为1。 + +- `GLOG_log_dir` + + 该环境变量指定日志输出的路径。 + 若`GLOG_logtostderr`的值为0，则必须设置此变量。 + 若指定了`GLOG_log_dir`且`GLOG_logtostderr`的值为1时，则日志输出到屏幕，不输出到文件。 + C++和Python的日志会被输出到不同的文件中，C++日志的文件名遵从`GLOG`日志文件的命名规则，这里是`mindspore.机器名.用户名.log.日志级别.时间戳`，Python日志的文件名为`mindspore.log`。 + +- `MS_SUBMODULE_LOG_v` + + 该环境变量指定MindSpore C++各子模块的日志级别。 + 该环境变量赋值方式为：`MS_SUBMODULE_LOG_v="{SubModule1:LogLevel1,SubModule2:LogLevel2,...}"`。 + 其中被指定子模块的日志级别将覆盖`GLOG_v`在此模块内的设置，此处子模块的日志级别`LogLevel`与`GLOG_v`的日志级别含义相同，MindSpore子模块的划分如下表。 + 例如可以通过`GLOG_v=1 MS_SUBMODULE_LOG_v="{PARSER:2,ANALYZER:2}"`把`PARSER`和`ANALYZER`模块的日志级别设为WARNING，其他模块的日志级别设为INFO。 MindSpore子模块按照目录划分如下： @@ -351,3 +368,7 @@ MindSpore子模块按照目录划分如下： | mindspore/core/gvar | COMMON | | mindspore/core/ | CORE | +> glog不支持日志文件的绕接，如果需要控制日志文件对磁盘空间的占用，可选用操作系统提供的日志文件管理工具，例如：Linux的logrotate。 + + + diff --git a/tutorials/source_zh_cn/advanced_use/dashboard.md b/tutorials/source_zh_cn/advanced_use/dashboard.md index 0d46d1a545..898695dfe2 100644 --- a/tutorials/source_zh_cn/advanced_use/dashboard.md +++ b/tutorials/source_zh_cn/advanced_use/dashboard.md @@ -185,7 +185,8 @@ - Tensor加载后，在张量可视的表格视图下，最大支持查看10万个数值，如果所选择的维度查询得到的数值超过这一限制，则无法显示。 5. 由于张量可视（`TensorSummary`）会记录原始张量数据，需要的存储空间较大。使用`TensorSummary`前和训练过程中请注意检查系统存储空间充足。 - 通过以下方法可以降低张量可视功能的存储空间占用： + + 通过以下方法可以降低张量可视功能的存储空间占用： 1）避免使用`TensorSummary`记录较大的Tensor。 @@ -195,5 +196,5 @@ 备注：估算`TensorSummary`空间使用量的方法如下： - 一个`TensorSummary`数据的大小 ＝ Tensor中的数值个数 * 4 bytes。假设使用`TensorSummary`记录的Tensor大小为32 * 1 * 256 * 256，则一个`TensorSummary`数据大约需要32 * 1 * 256 * 256 * 4 bytes = 8,388,608 bytes = 8MiB。`TensorSummary`默认会记录20个步骤的数据，则记录这20组数据需要的空间约为20 * 8 MiB ＝ 160MiB。需要注意的是，由于数据结构等因素的开销，实际使用的存储空间会略大于160MiB。 + 一个`TensorSummary数据的大小 ＝ Tensor中的数值个数 * 4 bytes`。假设使用`TensorSummary`记录的Tensor大小为`32 * 1 * 256 * 256`，则一个`TensorSummary`数据大约需要`32 * 1 * 256 * 256 * 4 bytes = 8,388,608 bytes = 8MiB`。`TensorSummary`默认会记录20个步骤的数据，则记录这20组数据需要的空间约为`20 * 8 MiB ＝ 160MiB`。需要注意的是，由于数据结构等因素的开销，实际使用的存储空间会略大于160MiB。 6. 当使用`TensorSummary`时，由于记录完整Tensor数据，训练日志文件较大，MindInsight需要更多时间解析训练日志文件，请耐心等待。 \ No newline at end of file diff --git a/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md b/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md index fd4a8fed7e..ef1deef9f8 100644 --- a/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md +++ b/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md @@ -1,6 +1,6 @@ # 使用PyNative模式调试 -`Ascend` `GPU` `模型开发` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `模型开发` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/differential_privacy.md b/tutorials/source_zh_cn/advanced_use/differential_privacy.md index 7e9dac091d..3aabb9761d 100644 --- a/tutorials/source_zh_cn/advanced_use/differential_privacy.md +++ b/tutorials/source_zh_cn/advanced_use/differential_privacy.md @@ -1,6 +1,6 @@ # 机器学习中的差分隐私 -`Ascend` `模型开发` `模型调优` `企业` `高级` +`Linux` `Ascend` `模型开发` `模型调优` `企业` `高级` @@ -12,7 +12,7 @@ - [预处理数据集](#预处理数据集) - [建立模型](#建立模型) - [引入差分隐私](#引入差分隐私) - - [引用](#引用) + - [参考文献](#参考文献) @@ -346,7 +346,7 @@ ds_train = generate_mnist_dataset(os.path.join(cfg.data_path, "train"), ============== Accuracy: 0.9698 ============== ``` -### 引用 +## 参考文献 [1] C. Dwork and J. Lei. Differential privacy and robust statistics. In STOC, pages 371–380. ACM, 2009. diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md b/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md index a0e4e8864f..b162e4bd8b 100644 --- a/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md +++ b/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md @@ -1,6 +1,6 @@ # 分布式并行训练 (Ascend) -`Ascend` `模型训练` `中级` `高级` +`Linux` `Ascend` `模型训练` `中级` `高级` @@ -33,9 +33,9 @@ ### 下载数据集 -本样例采用`CIFAR-10`数据集，由10类32*32的彩色图片组成，每类包含6000张图片。其中训练集共50000张图片，测试集共10000张图片。 +本样例采用CIFAR-10数据集，由10类32*32的彩色图片组成，每类包含6000张图片。其中训练集共50000张图片，测试集共10000张图片。 -> `CIFAR-10`数据集下载链接：。 +> CIFAR-10数据集下载链接：。 将数据集下载并解压到本地路径下，解压后的文件夹为`cifar-10-batches-bin`。 @@ -321,6 +321,7 @@ cd ../ - `RANK_TABLE_FILE`：组网信息文件的路径。 - `DEVICE_ID`：当前卡在机器上的实际序号。 - `RANK_ID`: 当前卡的逻辑序号。 + 其余环境变量请参考安装教程中的配置项。 运行时间大约在5分钟内，主要时间是用于算子的编译，实际训练时间在20秒内。用户可以通过`ps -ef | grep pytest`来监控任务进程。 diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training_gpu.md b/tutorials/source_zh_cn/advanced_use/distributed_training_gpu.md index 93318259a8..918e0b5030 100644 --- a/tutorials/source_zh_cn/advanced_use/distributed_training_gpu.md +++ b/tutorials/source_zh_cn/advanced_use/distributed_training_gpu.md @@ -1,6 +1,6 @@ # 分布式并行训练 (GPU) -`GPU` `模型训练` `中级` `高级` +`Linux` `GPU` `模型训练` `中级` `高级` @@ -27,7 +27,7 @@ ### 下载数据集 -本样例采用`CIFAR-10`数据集，数据集的下载以及加载方式和Ascend 910 AI处理器一致。 +本样例采`CIFAR-10数据集，数据集的下载以及加载方式和Ascend 910 AI处理器一致。 > 数据集的下载和加载方式参考： > @@ -109,7 +109,7 @@ echo "start training" mpirun -n 8 pytest -s -v ./resnet50_distributed_training.py > train.log 2>&1 & ``` -脚本需要传入变量`DATA_PATH`，表示数据集的路径。此外，我们需要修改下`resnet50_distributed_training.py`文件，由于在GPU上，我们无需设置`DEVICE_ID`环境变量，因此，在脚本中不需要调用`int(os.getenv('DEVICE_ID'))`来获取卡的物理序号，同时`context`中也无需传入`device_id`。我们需要将`device_target`设置为`GPU`，并调用`init("nccl")`来使能NCCL。日志文件保存到device目录下，关于Loss部分结果保存在train.log中。将loss值grep出来后，示例如下： +脚本需要传入变量`DATA_PATH`，表示数据集的路径。此外，我们需要修改下`resnet50_distributed_training.py`文件，由于在GPU上，我们无需设置`DEVICE_ID`环境变量，因此，在脚本中不需要调用`int(os.getenv('DEVICE_ID'))`来获取卡的物理序号，同时`context`中也无需传入`device_id`。我们需要将`device_target`设置为`GPU`，并调用`init("nccl")`来使能NCCL。日志文件保存到`device`目录下，关于Loss部分结果保存在`train.log`中。将loss值grep出来后，示例如下： ``` epoch: 1 step: 1, loss is 2.3025854 @@ -124,7 +124,7 @@ epoch: 1 step: 1, loss is 2.3025854 ## 运行多机脚本 -若训练涉及多机，则需要额外在`mpirun`命令中设置多机配置。你可以直接在`mpirun`命令中用`-H`选项进行设置，比如`mpirun -n 16 -H DEVICE1_IP:8,DEVICE2_IP:8 python hello.py`，表示在ip为DEVICE1_IP和DEVICE2_IP的机器上分别起8个进程运行程序；或者也可以构造一个如下这样的hostfile文件，并将其路径传给`mpirun`的`--hostfile`的选项。hostfile文件每一行格式为`[hostname] slots=[slotnum]`，hostname可以是ip或者主机名。 +若训练涉及多机，则需要额外在`mpirun`命令中设置多机配置。你可以直接在`mpirun`命令中用`-H`选项进行设置，比如`mpirun -n 16 -H DEVICE1_IP:8,DEVICE2_IP:8 python hello.py`，表示在ip为DEVICE1_IP和DEVICE2_IP的机器上分别起8个进程运行程序；或者也可以构造一个如下这样的`hostfile`文件，并将其路径传给`mpirun`的`--hostfile`的选项。`hostfile`文件每一行格式为`[hostname] slots=[slotnum]`，`hostname`可以是ip或者主机名。 ```bash DEVICE1 slots=8 DEVICE2 slots=8 diff --git a/tutorials/source_zh_cn/advanced_use/fuzzer.md b/tutorials/source_zh_cn/advanced_use/fuzzer.md index 9ab97020ac..9bf85defe5 100644 --- a/tutorials/source_zh_cn/advanced_use/fuzzer.md +++ b/tutorials/source_zh_cn/advanced_use/fuzzer.md @@ -1,6 +1,6 @@ # AI模型安全测试 -`Ascend` `GPU` `CPU` `数据准备` `模型开发` `模型训练` `模型调优` `企业` `高级` +`Linux` `Ascend` `GPU` `CPU` `数据准备` `模型开发` `模型训练` `模型调优` `企业` `高级` @@ -60,7 +60,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target) ### 运用Fuzzer -1. 建立LeNet模型，加载MNIST数据集，操作同[模型安全]() +1. 建立LeNet模型，加载MNIST数据集，操作同[模型安全]()。 ```python ... @@ -72,10 +72,10 @@ context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target) ds = generate_mnist_dataset(data_list, batch_size, sparse=False) train_images = [] for data in ds.create_tuple_iterator(): - images = data[0].astype(np.float32) - train_images.append(images) - train_images = np.concatenate(train_images, axis=0) - + images = data[0].astype(np.float32) + train_images.append(images) + train_images = np.concatenate(train_images, axis=0) + # get test data data_list = "./MNIST_unzip/test" batch_size = 32 @@ -83,10 +83,10 @@ context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target) test_images = [] test_labels = [] for data in ds.create_tuple_iterator(): - images = data[0].astype(np.float32) - labels = data[1] - test_images.append(images) - test_labels.append(labels) + images = data[0].astype(np.float32) + labels = data[1] + test_images.append(images) + test_labels.append(labels) test_images = np.concatenate(test_images, axis=0) test_labels = np.concatenate(test_labels, axis=0) ``` @@ -95,9 +95,9 @@ context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target) 设置数据变异方法及参数。目前支持的数据变异方法包含三类： - - 图像仿射变换方法：Translate、Scale、Shear、Rotate。 - - 基于图像像素值变化的方法： Contrast、Brightness、Blur、Noise。 - - 基于对抗攻击的白盒、黑盒对抗样本生成方法：FGSM、PGD、MDIIM。 + - 图像仿射变换方法：`Translate`、`Scale`、`Shear`、`Rotate`。 + - 基于图像像素值变化的方法： `Contrast`、`Brightness`、`Blur`、`Noise`。 + - 基于对抗攻击的白盒、黑盒对抗样本生成方法：`FGSM`、`PGD`、`MDIIM`。 数据变异方法一定要包含基于图像像素值变化的方法。 @@ -172,8 +172,8 @@ context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target) ```python if metrics: - for key in metrics: - LOGGER.info(TAG, key + ': %s', metrics[key]) + for key in metrics: + LOGGER.info(TAG, key + ': %s', metrics[key]) ``` Fuzz测试后结果如下： @@ -190,6 +190,6 @@ context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target) ![fuzz_seed](./images/fuzz_seed.png) -​ Fuzz生成的变异图片： + Fuzz生成的变异图片： ![fuzz_res](./images/fuzz_res.png) \ No newline at end of file diff --git a/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md b/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md index f982312405..73924f9669 100644 --- a/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md +++ b/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md @@ -1,12 +1,12 @@ # 梯度累积 -`Ascend` `GPU` `模型调优` `中级` `高级` +`Linux` `Ascend` `GPU` `模型调优` `中级` `高级` - [梯度累积](#梯度累积) - [概述](#概述) - - [建立梯度累积模型](#建立梯度累积模型) + - [创建梯度累积模型](#创建梯度累积模型) - [导入需要的库文件](#导入需要的库文件) - [加载数据集](#加载数据集) - [定义网络](#定义网络) @@ -14,7 +14,6 @@ - [定义训练过程](#定义训练过程) - [训练并保存模型](#训练并保存模型) - [实验结果](#实验结果) - @@ -59,17 +58,17 @@ from model_zoo.official.cv.lenet.src.lenet import LeNet5 ### 加载数据集 -利用MindSpore的dataset提供的`MnistDataset`接口加载MNIST数据集，此部分代码由model_zoo中lenet目录下的[dataset.py]()导入。 +利用MindSpore的`dataset`提供的`MnistDataset`接口加载MNIST数据集，此部分代码由`model_zoo`中`lenet`目录下的[dataset.py]()导入。 ### 定义网络 -这里以LeNet网络为例进行介绍，当然也可以使用其它的网络，如ResNet-50、BERT等, 此部分代码由model_zoo中lenet目录下的[lenet.py]()导入。 +这里以LeNet网络为例进行介绍，当然也可以使用其它的网络，如ResNet-50、BERT等, 此部分代码由`model_zoo`中`lenet`目录下的[lenet.py]()导入。 ### 定义训练模型 将训练流程拆分为正向反向训练、参数更新和累积梯度清理三个部分： -- `TrainForwardBackward`计算loss和梯度，利用grad_sum实现梯度累加。 +- `TrainForwardBackward`计算loss和梯度，利用`grad_sum`实现梯度累加。 - `TrainOptim`实现参数更新。 -- `TrainClear`实现对梯度累加变量grad_sum清零。 +- `TrainClear`实现对梯度累加变量`grad_sum`清零。 ```python _sum_op = C.MultitypeFuncGraph("grad_sum_op") @@ -135,8 +134,7 @@ class TrainClear(Cell): ``` ### 定义训练过程 -每个Mini-batch通过正反向训练计算loss和梯度，通过mini_steps控制每次更新参数前的累加次数。达到累加次数后进行参数更新和 -累加梯度变量清零。 +每个Mini-batch通过正反向训练计算loss和梯度，通过`mini_steps`控制每次更新参数前的累加次数。达到累加次数后进行参数更新和累加梯度变量清零。 ```python class GradientAccumulation: @@ -253,7 +251,7 @@ if __name__ == "__main__": **验证模型** -通过model_zoo中lenet目录下的[eval.py]()，使用保存的CheckPoint文件，加载验证数据集，进行验证。 +通过`model_zoo`中`lenet`目录下的[eval.py]()，使用保存的CheckPoint文件，加载验证数据集，进行验证。 ```shell $ python eval.py --data_path=./MNIST_Data --ckpt_path=./gradient_accumulation.ckpt diff --git a/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md b/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md index 279e7ea4f6..b6a81aefae 100644 --- a/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md +++ b/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md @@ -1,6 +1,6 @@ # 图算融合 -`Ascend` `模型调优` `中级` `高级` +`Linux` `Ascend` `模型调优` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/host_device_training.md b/tutorials/source_zh_cn/advanced_use/host_device_training.md index 6ef3c35f91..801e1eb4ba 100644 --- a/tutorials/source_zh_cn/advanced_use/host_device_training.md +++ b/tutorials/source_zh_cn/advanced_use/host_device_training.md @@ -1,6 +1,6 @@ # Host+Device混合训练 -`Ascend` `CPU` `模型训练` `中级` `高级` +`Linux` `Ascend` `CPU` `模型训练` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/mixed_precision.md b/tutorials/source_zh_cn/advanced_use/mixed_precision.md index c59cdca7c3..82d5a35d2b 100644 --- a/tutorials/source_zh_cn/advanced_use/mixed_precision.md +++ b/tutorials/source_zh_cn/advanced_use/mixed_precision.md @@ -1,6 +1,6 @@ # 混合精度 -`Ascend` `GPU` `模型训练` `中级` `高级` +`Linux` `Ascend` `GPU` `模型训练` `中级` `高级` @@ -102,7 +102,7 @@ MindSpore还支持手动混合精度。假定在网络中只有一个Dense Layer 2. 配置混合精度: 通过`net.to_float(mstype.float16)`，把该Cell及其子Cell中所有的算子都配置成FP16；然后，将模型中的dense算子手动配置成FP32； -3. 使用TrainOneStepCell封装网络模型和优化器。 +3. 使用`TrainOneStepCell`封装网络模型和优化器。 代码样例如下： diff --git a/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md b/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md index db88e9d539..3822551830 100644 --- a/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md +++ b/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md @@ -1,5 +1,5 @@ # MobileNetV2 增量学习 -`CPU` `Ascend` `GPU` `模型开发` `中级` `高级` +`Linux` `CPU` `Ascend` `GPU` `模型开发` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/model_security.md b/tutorials/source_zh_cn/advanced_use/model_security.md index 1d445b489b..c7e159b233 100644 --- a/tutorials/source_zh_cn/advanced_use/model_security.md +++ b/tutorials/source_zh_cn/advanced_use/model_security.md @@ -1,6 +1,6 @@ # 模型安全 -`Ascend` `GPU` `CPU` `数据准备` `模型开发` `模型训练` `模型调优` `企业` `高级` +`Linux` `Ascend` `GPU` `CPU` `数据准备` `模型开发` `模型训练` `模型调优` `企业` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/network_migration.md b/tutorials/source_zh_cn/advanced_use/network_migration.md index 0ffe505585..663c24616e 100644 --- a/tutorials/source_zh_cn/advanced_use/network_migration.md +++ b/tutorials/source_zh_cn/advanced_use/network_migration.md @@ -1,6 +1,6 @@ # 网络迁移 -`Ascend` `GPU` `CPU` `全流程` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `全流程` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/nlp_application.md b/tutorials/source_zh_cn/advanced_use/nlp_application.md index dd6df6a34b..5a336c1843 100644 --- a/tutorials/source_zh_cn/advanced_use/nlp_application.md +++ b/tutorials/source_zh_cn/advanced_use/nlp_application.md @@ -1,6 +1,6 @@ # 自然语言处理应用 -`GPU` `CPU` `全流程` `初级` `中级` `高级` +`Linux` `GPU` `CPU` `全流程` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md b/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md index 05ea6013c2..ffb7b073c8 100644 --- a/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md +++ b/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md @@ -1,6 +1,6 @@ # 优化数据准备的性能 -`Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/parameter_server_training.md b/tutorials/source_zh_cn/advanced_use/parameter_server_training.md index 86a8ce0ae3..4d60d053c4 100644 --- a/tutorials/source_zh_cn/advanced_use/parameter_server_training.md +++ b/tutorials/source_zh_cn/advanced_use/parameter_server_training.md @@ -1,6 +1,6 @@ # Parameter Server训练 -`Ascend` `GPU` `模型训练` `中级` `高级` +`Linux` `Ascend` `GPU` `模型训练` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/quantization_aware.md b/tutorials/source_zh_cn/advanced_use/quantization_aware.md index 72782ed785..4d72f7f6a7 100644 --- a/tutorials/source_zh_cn/advanced_use/quantization_aware.md +++ b/tutorials/source_zh_cn/advanced_use/quantization_aware.md @@ -1,6 +1,6 @@ # 量化 -`Ascend` `GPU` `模型调优` `高级` +`Linux` `Ascend` `GPU` `模型调优` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/second_order_optimizer_for_resnet50_application.md b/tutorials/source_zh_cn/advanced_use/second_order_optimizer_for_resnet50_application.md index 5aff6381f4..f43267dae6 100644 --- a/tutorials/source_zh_cn/advanced_use/second_order_optimizer_for_resnet50_application.md +++ b/tutorials/source_zh_cn/advanced_use/second_order_optimizer_for_resnet50_application.md @@ -1,6 +1,6 @@ # ResNet-50二阶优化实践 -`Ascend` `GPU` `模型开发` `模型调优` `高级` +`Linux` `Ascend` `GPU` `模型开发` `模型调优` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/serving.md b/tutorials/source_zh_cn/advanced_use/serving.md index 43dc1c5589..b7a7b58ef2 100644 --- a/tutorials/source_zh_cn/advanced_use/serving.md +++ b/tutorials/source_zh_cn/advanced_use/serving.md @@ -1,6 +1,6 @@ # 基于MindSpore部署推理服务 -`Ascend` `环境准备` `企业` `高级` +`Linux` `Ascend` `环境准备` `企业` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md b/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md index 773b4c4535..df6dad7351 100644 --- a/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md +++ b/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md @@ -1,6 +1,6 @@ # 同步训练和验证模型 -`Ascend` `GPU` `CPU` `初级` `中级` `高级` `模型导出` `模型训练` +`Linux` `Ascend` `GPU` `CPU` `初级` `中级` `高级` `模型导出` `模型训练` diff --git a/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md b/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md index 4f88abb2f4..c1830d7e16 100644 --- a/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md +++ b/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md @@ -1,6 +1,6 @@ # 在云上使用MindSpore -`Ascend` `全流程` `初级` `中级` `高级` +`Linux` `Ascend` `全流程` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/quick_start/linear_regression.md b/tutorials/source_zh_cn/quick_start/linear_regression.md index 9a042225ba..4d6a041baa 100644 --- a/tutorials/source_zh_cn/quick_start/linear_regression.md +++ b/tutorials/source_zh_cn/quick_start/linear_regression.md @@ -1,6 +1,6 @@ # 实现简单线性函数拟合 -`GPU` `全流程` `初级` `中级` `高级` +`Linux` `GPU` `全流程` `初级` `中级` `高级` 作者：[杨奕](https://github.com/helloyesterday)    编辑：[吕明赋](https://gitee.com/lvmingfu) diff --git a/tutorials/source_zh_cn/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md index ac26e962ae..34de261fed 100644 --- a/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/tutorials/source_zh_cn/quick_start/quick_start.md @@ -1,6 +1,6 @@ # 实现一个图片分类应用 -`Ascend` `GPU` `CPU` `全流程` `初级` `中级` `高级` +`Linux` `Windows` `Ascend` `GPU` `CPU` `全流程` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/use/custom_operator.md b/tutorials/source_zh_cn/use/custom_operator.md index 4916ebc1a6..1d90e904aa 100644 --- a/tutorials/source_zh_cn/use/custom_operator.md +++ b/tutorials/source_zh_cn/use/custom_operator.md @@ -1,6 +1,6 @@ # 自定义算子 -`Ascend` `模型开发` `高级` +`Linux` `Ascend` `模型开发` `高级` diff --git a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md index 83f9652da3..f152de92b1 100644 --- a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md +++ b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md @@ -1,6 +1,6 @@ # 将数据集转换为MindSpore数据格式 -`Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md index 88cf85f0a8..5df486f35b 100644 --- a/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md +++ b/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md @@ -1,6 +1,6 @@ # 数据处理与数据增强 -`Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md index 581feb4833..68f38bc46a 100644 --- a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md +++ b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md @@ -1,6 +1,6 @@ # 加载数据集 -`Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/use/multi_platform_inference.md b/tutorials/source_zh_cn/use/multi_platform_inference.md index 83b588e41a..5628bdc5c1 100644 --- a/tutorials/source_zh_cn/use/multi_platform_inference.md +++ b/tutorials/source_zh_cn/use/multi_platform_inference.md @@ -1,6 +1,6 @@ # 多平台推理 -`Ascend` `GPU` `CPU` `推理应用` `初级` `中级` `高级` + `Linux` `Ascend` `GPU` `CPU` `推理应用` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md b/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md index e1d5f8b246..f624b8712c 100644 --- a/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md +++ b/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md @@ -1,6 +1,6 @@ # 模型参数的保存和加载 -`Ascend` `GPU` `CPU` `模型导出` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `模型导出` `初级` `中级` `高级` -- Gitee