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System information

• TensorFlow version (you are using): 2.1
• Are you willing to contribute it (Yes/No): No

Hi, I've moved to Cuda 11.0 and I am getting warnings below while compiling. I just can't remember exactly, but I don't think I saw these with 10.2. I don't know if it related, but run_test.py is also failing when it comes to Distribution related tests. I can post test's log when the build is complete, if it is helpful. Thank you.

[3950/5005] Building NVCC (Device) object caffe2/CMakeF

Reference from TensorFlow: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/matrix-band-part

This op is used by the Music Transformer model.

We plan to gradually migrate brain.js to TypeScript, code base is pretty large, so we would love your help! 💪

How to contribute?

• Convert a file from .js to .ts
• Add types, fix all type errors.
• Submit a PR! 🎉

Here you can find a guide on how to contribute.

Want to convert something, let us know in the comment an

Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.

Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)

Bug Report

These tests were run on s390x. s390x is big-endian architecture.

Failure log for helper_test.py

________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________

self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>

    def test_make_tensor(self):  # type: () -> None
    

What would you like to be added: As title

Why is this needed: All pruning schedule except AGPPruner only support level, L1, L2. While there are FPGM, APoZ, MeanActivation and Taylor, it would be much better if we can choose any pruner with any pruning schedule.

**Without this feature, how does current nni

Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))