@@ -123,7 +123,7 @@ You can easily re-produce following competitive results with minor codes, which
PaddlePALM is a well-designed high-level NLP framework. You can efficiently achieve **supervised learning, unsupervised/self-supervised learning, multi-task learning and transfer learning** with minor codes based on PaddlePALM. There are three layers in PaddlePALM architecture, i.e., component layer, trainer layer and high-level trainer layer from bottom to top.
In component layer, PaddlePALM supplies 7**decoupled** components to achieve a NLP task. Each component contains rich `pre-defined` classes and a `Base` class. Pre-defined classes are aiming at typical NLP tasks, and the base class is to help users develop a new Class (based on pre-defined ones or from the base).
In component layer, PaddlePALM supplies 6**decoupled** components to achieve a NLP task. Each component contains rich `pre-defined` classes and a `Base` class. Pre-defined classes are aiming at typical NLP tasks, and the base class is to help users develop a new Class (based on pre-defined ones or from the base).
The trainer layer is to establish a computation graph with selected components and do training and predicting. The training strategy, model saving and loading, evaluation and predicting procedures are described in this layer. Noted a trainer can only process one task.
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@@ -235,7 +235,7 @@ To save models/checkpoints and logs during training, just call `trainer.set_save
To do predict/evaluation after a training stage, just create another three reader, backbone and head instance with `phase='predict'` (repeat step 1~4 above). Then do predicting with `predict` method in trainer (no need to create another trainer). More implementation details see [this](https://github.com/PaddlePaddle/PALM/tree/master/examples/predict).
#### Multiple GPUs
If there exists multiple GPUs in your environment, you can control the number and index of these GPUs through the environment variable [CUDA_VISIBLE_DEVICES](). For example, if 4 GPUs in your enviroment, indexed with 0,1,2,3, you can run with GPU2 only with following commands
If there exists multiple GPUs in your environment, you can control the number and index of these GPUs through the environment variable [CUDA_VISIBLE_DEVICES](https://devblogs.nvidia.com/cuda-pro-tip-control-gpu-visibility-cuda_visible_devices/). For example, if 4 GPUs in your enviroment, indexed with 0,1,2,3, you can run with GPU2 only with following commands
