The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
# [Environment Requirements](#contents)
...
...
@@ -119,9 +120,9 @@ Major parameters in train.py and config.py are:
You can start training using python or shell scripts. The usage of shell scripts as follows:
sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH)
```
sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH)
```
> Notes:
RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
...
...
@@ -140,7 +141,7 @@ sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH)
### Result
Training result will be stored in the example path. Checkpoints will be stored at `. /LOG0/chec_deeplabv3-*` by default, and training log will be redirected to `./log.txt` like followings.
Training result(8p) will be stored in the example path. Checkpoints will be stored at `. /train_parallel0/` by default, and training log will be redirected to `./train_parallel0/log.txt` like followings.
@@ -46,6 +46,7 @@ Dataset used can refer to paper.
## [Mixed Precision(Ascend)](#contents)
The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
# [Environment Requirements](#contents)
...
...
@@ -131,7 +132,7 @@ sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
sh run_standalone_train.sh DEVICE_ID DATA_PATH
```
> Notes:
RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as [Link]https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
