From 8809eb6c93c55c86ab69df7adbfdeb4d2e753f0e Mon Sep 17 00:00:00 2001 From: Thomas Wolf Date: Fri, 14 Dec 2018 16:59:39 +0100 Subject: [PATCH] update readme with information on NVIDIA's apex --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 87afa88b52..5962dfca66 100644 --- a/README.md +++ b/README.md @@ -360,7 +360,9 @@ Here is how to use these techniques in our scripts: - **Gradient Accumulation**: Gradient accumulation can be used by supplying a integer greater than 1 to the `--gradient_accumulation_steps` argument. The batch at each step will be divided by this integer and gradient will be accumulated over `gradient_accumulation_steps` steps. - **Multi-GPU**: Multi-GPU is automatically activated when several GPUs are detected and the batches are splitted over the GPUs. - **Distributed training**: Distributed training can be activated by supplying an integer greater or equal to 0 to the `--local_rank` argument (see below). -- **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scaling` flag (see the previously linked documentation for details on loss scaling). If the loss scaling is too high (`Nan` in the gradients) it will be automatically scaled down until the value is acceptable. The default loss scaling is 128 which behaved nicely in our tests. +- **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scale` flag (see the previously linked documentation for details on loss scaling). The loss scale can be zero in which case the scale is dynamically adjuted or a positive power of two in which case the scaling is static. + +To use 16-bits training and distributed training, you need to install NVIDIA's apex extension [as detailed here](https://github.com/nvidia/apex). You will find more information reguarding the internals of `apex` and how to use `apex` in [the doc and the associated repository](https://github.com/nvidia/apex). The results of the tests perfomed on pytorch-BERT by the NVIDIA team (and my trials at reproducing them) can be consulted in [the relevant PR of the present repository](https://github.com/huggingface/pytorch-pretrained-BERT/pull/116). Note: To use *Distributed Training*, you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see [the above mentioned blog post]((https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255)) for more details): ```bash