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| | | * `<unk>`: indicates the out-of-vocabulary token |
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| | | |
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| | | ## Stage 3: Training |
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| | | This stage achieves the training of the specified model. To start training, you should manually set `exp_dir`, `CUDA_VISIBLE_DEVICES` and `gpu_num`, which have already been explained above. By default, the best `$keep_nbest_models` checkpoints on validation dataset will be averaged to generate a better model and adopted for decoding. |
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| | | This stage achieves the training of the specified model. To start training, users should manually set `exp_dir`, `CUDA_VISIBLE_DEVICES` and `gpu_num`, which have already been explained above. By default, the best `$keep_nbest_models` checkpoints on validation dataset will be averaged to generate a better model and adopted for decoding. |
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| | | |
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| | | * DDP Training |
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| | | |
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| | |
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| | | |
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| | | * DataLoader |
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| | | |
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| | | [comment]: <> (We support two types of DataLoaders for small and large datasets, respectively. By default, the small DataLoader is used and you can set `dataset_type=large` to enable large DataLoader. For small DataLoader, ) |
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| | | We support an optional iterable-style DataLoader based on [Pytorch Iterable-style DataPipes](https://pytorch.org/data/beta/torchdata.datapipes.iter.html) for large dataset and you can set `dataset_type=large` to enable it. |
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| | | We support an optional iterable-style DataLoader based on [Pytorch Iterable-style DataPipes](https://pytorch.org/data/beta/torchdata.datapipes.iter.html) for large dataset and users can set `dataset_type=large` to enable it. |
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| | | |
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| | | * Configuration |
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| | | |
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| | | The parameters of the training, including model, optimization, dataset, etc., are specified by a YAML file in `conf` directory. Also, you can directly specify the parameters in `run.sh` recipe. Please avoid to specify the same parameters in both the YAML file and the recipe. |
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| | | The parameters of the training, including model, optimization, dataset, etc., can be set by a YAML file in `conf` directory. Also, users can directly set the parameters in `run.sh` recipe. Please avoid to set the same parameters in both the YAML file and the recipe. |
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| | | |
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| | | * Training Steps |
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| | | |
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| | | We support two parameters to specify the training steps, namely `max_epoch` and `max_update`. `max_epoch` indicates the total training epochs while `max_update` indicates the total training steps. If these two parameters are specified at the same time, once the training reaches any one of the two parameters, the training will be stopped. |
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| | | We support two parameters to specify the training steps, namely `max_epoch` and `max_update`. `max_epoch` indicates the total training epochs while `max_update` indicates the total training steps. If these two parameters are specified at the same time, once the training reaches any one of these two parameters, the training will be stopped. |
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| | | |
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| | | * Tensorboard |
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| | | |
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| | | You can use tensorboard to observe the loss, learning rate, etc. Please run the following command: |
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| | | Users can use tensorboard to observe the loss, learning rate, etc. Please run the following command: |
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| | | ``` |
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| | | tensorboard --logdir ${exp_dir}/exp/${model_dir}/tensorboard/train |
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| | | ``` |
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