**Note:** (1) When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other. (2) Training needs to be launched separately on multiple machines. The command to view the ip address of the machine is `ifconfig`. (3) For more details about the distributed training speedup ratio, please refer to [Distributed Training Tutorial](./distributed_training_en.md).
**Note:** (1) When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other. (2) Training needs to be launched separately on multiple machines. The command to view the ip address of the machine is `ifconfig`. (3) For more details about the distributed training speedup ratio, please refer to [Distributed Training Tutorial](./distributed_training_en.md).
## 2.6. Training with Knowledge Distillation
## 2.6. Training on other platform(Windows/macOS/Linux DCU)
coming soon!
## 2.7. Training on other platform(Windows/macOS/Linux DCU)
- Windows GPU/CPU
- Windows GPU/CPU
The Windows platform is slightly different from the Linux platform:
The Windows platform is slightly different from the Linux platform:
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@@ -229,7 +224,7 @@ GPU mode is not supported, you need to set `use_gpu` to False in the configurati
...
@@ -229,7 +224,7 @@ GPU mode is not supported, you need to set `use_gpu` to False in the configurati
Running on a DCU device requires setting the environment variable `export HIP_VISIBLE_DEVICES=0,1,2,3`, and the rest of the training and evaluation prediction commands are exactly the same as the Linux GPU.
Running on a DCU device requires setting the environment variable `export HIP_VISIBLE_DEVICES=0,1,2,3`, and the rest of the training and evaluation prediction commands are exactly the same as the Linux GPU.
## 2.8 Fine-tuning
## 2.7. Fine-tuning
In the actual use process, it is recommended to load the officially provided pre-training model and fine-tune it in your own data set. For the fine-tuning method of the table recognition model, please refer to: [Model fine-tuning tutorial](./finetune.md).
In the actual use process, it is recommended to load the officially provided pre-training model and fine-tune it in your own data set. For the fine-tuning method of the table recognition model, please refer to: [Model fine-tuning tutorial](./finetune.md).
