Comments (4)
Currently we do not have good support for training the model on large datasets.
For now, please just call the fit()
function multiple times on different shards of your data. Do not try to train on all data at once.
We will be working on some improvement over the fit()
function later to better support this behavior.
Also, do not try to save all data into one single npz file... Just save different shards into different files, and load one each time.
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Currently we do not have good support for training the model on large datasets.
For now, please just call the
fit()
function multiple times on different shards of your data. Do not try to train on all data at once.We will be working on some improvement over the
fit()
function later to better support this behavior.Also, do not try to save all data into one single npz file... Just save different shards into different files, and load one each time.
Thanks!
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We just committed some fixes to make sure the training works better when calling fit()
multiple times.
We also added some suggestions to README.md
:
https://github.com/google/uis-rnn#training-on-large-datasets
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@wq2012 Why didn't you use torch.nn.dataparallel to support large dataset and multi gpu cards training?
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Related Issues (20)
- Embedding Extraction Procedure HOT 1
- about model HOT 1
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- what is train data format? HOT 1
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- [Question] The dimension of toy test data [test_sequence] is (25, 95, 256) what does the first 2 dimension represent? Toy train data [train_sequence] has dimension (4627, 256) which is understandable. HOT 1
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