Description

This is a documentation bug. The parameter of API mxnet.test_utils.check_numeric_gradient is not consistent between signature and Parameter section. There is a parameter check_eps in the Parameter section, but it is not in the signature.

Link to document: https://mxnet.apache.org/versions/1.6/api/python/docs/api/mxnet/test_utils/index.html#mxnet.test_utils.check_numeric_gra

Although the results look nice and ideal in all TensorFlow plots and are consistent across all frameworks, there is a small difference (more of a consistency issue). The result training loss/accuracy plots look like they are sampling on a lesser number of points. It looks more straight and smooth and less wiggly as compared to PyTorch or MXNet.

It can be clearly seen in chapter 6([CNN Lenet](ht

New Operator

Describe the operator

Why is this operator necessary? What does it accomplish?

This is a frequently used operator in tensorflow/keras

Can this operator be constructed using existing onnx operators?

If so, why not add it as a function?

I don't know.

Is this operator used by any model currently? Which one?

Are you willing to contribute it?

Hi,
I need to download the something-to-something and jester datasets. But the 20bn website "https://20bn.com" are not available for weeks, the error message is "503 Service Temporarily Unavailable".

I have already downloaded the video data of something-to-something v2, and I need the label dataset. For the Jester, I need both video and label data. Can someone share me the

Related: awslabs/autogluon#1479

Add a scikit-learn compatible API wrapper of TabularPredictor:

• TabularClassifier
• TabularRegressor

Required functionality (may need more than listed):

• init API
• fit API
• predict API
• works in sklearn pipelines

Hi, I was wondering, is DALI currently compatible with tensorcom? And if so, are there any examples showing how to use DALI with a tensorcom data server, for example, like this?

I have the same hardware envs, same network, but I could not get the result as you, almost half as you. Any best practices and experience? thanks very much! for bytePS with 1 instance and 8 GPU, I have similar testing result.

Use case

Polyaxon tracking can be used to automatically log all information generated by Ludwig.

We should probably think about adding support for Polyaxon's tracking module in Ludwig contrib.

Description

(A clear and concise description of what the feature is.)

• util.cumsum implementation https://github.com/awslabs/gluon-ts/blob/master/src/gluonts/mx/util.py#L326 does not scale under mx.ndarray
• cumsum is 2-5 times slower than nd.cumsum under both mx.sym and mx.ndarray, and even fails for large 4-dim input

Sample test

Code

# import ...
def test_

Description

Implement a YOLO model and add it to the DJL model zoo

References

• https://arxiv.org/abs/1506.02640

Description

While using tokenizers.create with the model and vocab file for a custom corpus, the code throws an error and is not able to generate the BERT vocab file

Error Message

ValueError: Mismatch vocabulary! All special tokens specified must be control tokens in the sentencepiece vocabulary.

To Reproduce

from gluonnlp.data import tokenizers
tokenizers.create('spm', model_p