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Added a --reduce_memory option to the training script to keep training

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data on disc as a memmap rather than in memory
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Matthew Carrigan 2019-03-21 17:04:12 +00:00
parent 7d1ae644ef
commit 06a30cfdf3
1 changed files with 3 additions and 1 deletions
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examples/lm_finetuning/README.md
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@ -58,7 +58,9 @@ recent GPUs. `--max_seq_len` defaults to 128 but can be set as high as 512.
Higher values may yield stronger language models at the cost of slower and more memory-intensive training
In addition, if memory usage is an issue, especially when training on a single GPU, reducing `--train_batch_size` from
the default 32 to a lower number (4-16) can be helpful.
the default 32 to a lower number (4-16) can be helpful. There is also a `--reduce_memory` option for both the
`pregenerate_training_data.py` and `finetune_on_pregenerated.py` scripts that spills data to disc in shelf objects
or numpy memmaps rather than retaining it in memory, which hugely reduces memory usage with little performance impact.
###Examples
#####Simple fine-tuning