If you want to specify a specific gpu or use multiple gpus for training, please use **`CUDA_VISIBLE_DEVICES`**, for example:
```shell
CUDA_VISIBLE_DEVICES=0,1,2 python run.py
CUDA_VISIBLE_DEVICES=0,1 python run.py
```
Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
If you want to specify a specific gpu or use multiple gpus for training, please use **`CUDA_VISIBLE_DEVICES`**, for example:
```shell
CUDA_VISIBLE_DEVICES=0,1,2 python run.py
CUDA_VISIBLE_DEVICES=0,1 python run.py
```
Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
If you want to specify a specific gpu or use multiple gpus for training, please use **`CUDA_VISIBLE_DEVICES`**, for example:
```shell
CUDA_VISIBLE_DEVICES=0,1,2 python run.py
CUDA_VISIBLE_DEVICES=0,1 python run.py
```
Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
Some logs will be shown below:
```
...
...
@@ -84,7 +86,16 @@ After the run, you can view the saved models in the `outputs/` folder and the pr
### Step 3: Evaluate
Once you have the prediction, you can run the evaluation script to evaluate the model:
#### Library Dependencies
Before the evaluation, you need to install `nltk` and download the `punkt` tokenizer for nltk:
```shell
pip insall nltk
python -m nltk.downloader punkt
```
#### Evaluate
You can run the evaluation script to evaluate the model:
If you want to specify a specific gpu or use multiple gpus for training, please use **`CUDA_VISIBLE_DEVICES`**, for example:
```shell
CUDA_VISIBLE_DEVICES=0,1,2 python run.py
CUDA_VISIBLE_DEVICES=0,1 python run.py
```
Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
Some logs will be shown below:
```
...
...
@@ -83,10 +85,12 @@ python predict-intent.py
If you want to specify a specific gpu or use multiple gpus for predict, please use **`CUDA_VISIBLE_DEVICES`**, for example:
Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
After the run, you can view the predictions in the `outputs/predict-slot` folder and `outputs/predict-intent` folder. Here are some examples of predictions:
If you want to specify a specific gpu or use multiple gpus for predict, please use **`CUDA_VISIBLE_DEVICES`**, for example:
```shell
CUDA_VISIBLE_DEVICES=0,1,2 python run.py
CUDA_VISIBLE_DEVICES=0,1 python run.py
```
Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
If you want to specify a specific gpu or use multiple gpus for training, please use **`CUDA_VISIBLE_DEVICES`**, for example:
```shell
CUDA_VISIBLE_DEVICES=0,1,2 python run.py
CUDA_VISIBLE_DEVICES=0,1 python run.py
```
Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
