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

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
    

Hi, thanks for the great code!

I wonder do you have plans to support resuming from checkpoints for classification? As we all know, in terms of training ImageNet, the training process is really long and it can be interrupted somehow, but I haven't notice any code related to "resume" in scripts/classification/train_imagenet.py.

Maybe @hetong007 ? Thanks in advance.

How do i resume training for text classification?

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.

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.

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

Description

We currently only check for the underlying prediction net to have the same parameters. This can create issues when constructing a Predictor using the correct prediction net but other parameters, like freq or transformation.

Maybe we can add proper checks the other inputs as well.

References

[this note](https://github.com/awslabs/gluon-ts/blob/726f52e720f6afc72c86e

Description

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

References

• https://arxiv.org/abs/1506.02640