hey @nreamaroon,
We already opened a PR for a DLC with transformers 4.6.0. I hope we can get as merged as soon as possible.
  

      github.com/aws/deep-learning-containers
  

  
    
  
    
  

  
    
      [huggingface_tensorflow, huggingface_pytorch] update for Transformers to 4.6.0  10
    

    
      aws:master ← philschmid:patch-1
    

    
      
        opened 
        
          
        
        May 13, 2021
      
      

      
        
          
          philschmid
        
      

      
        
          +8
          -8
        
      
    
  


  
    … 4.6.0

*Issue #, if available:*

## PR Checklist
- [x] I've prepended PR …tag with frameworks/job this applies to : [mxnet, tensorflow, pytorch] | [ei/neuron] | [build] | [test] | [benchmark] | [ec2, ecs, eks, sagemaker]
- [x] (If applicable) I've documented below the DLC image/dockerfile this relates to
- [x] (If applicable) I've documented below the tests I've run on the DLC image
- [x] (If applicable) I've reviewed the licenses of updated and new binaries and their dependencies to make sure all licenses are on the Apache Software Foundation Third Party License Policy Category A or Category B license list.  See [https://www.apache.org/legal/resolved.html](https://www.apache.org/legal/resolved.html).
- [x] (If applicable) I've scanned the updated and new binaries to make sure they do not have vulnerabilities associated with them.

## Pytest Marker Checklist
- [ ] (If applicable) I have added the marker `@pytest.mark.model("<model-type>")` to the new tests which I have added, to specify the Deep Learning model that is used in the test (use `"N/A"` if the test doesn't use a model)
- [ ] (If applicable) I have added the marker `@pytest.mark.integration("<feature-being-tested>")` to the new tests which I have added, to specify the feature that will be tested
- [ ] (If applicable) I have added the marker `@pytest.mark.multinode(<integer-num-nodes>)` to the new tests which I have added, to specify the number of nodes used on a multi-node test
- [ ] (If applicable) I have added the marker `@pytest.mark.processor(<"cpu"/"gpu"/"eia"/"neuron">)` to the new tests which I have added, if a test is specifically applicable to only one processor type

#### EIA/NEURON Checklist
* When creating a PR:
- [ ] I've modified `src/config/build_config.py` in my PR branch by setting `ENABLE_EI_MODE = True` or `ENABLE_NEURON_MODE = True`
* When PR is reviewed and ready to be merged:
- [ ] I've reverted the code change on the config file mentioned above
#### Benchmark Checklist
* When creating a PR:
- [ ] I've modified `src/config/test_config.py` in my PR branch by setting `ENABLE_BENCHMARK_DEV_MODE = True`
* When PR is reviewed and ready to be merged:
- [ ] I've reverted the code change on the config file mentioned above

## Reviewer Checklist
* For reviewer, before merging, please cross-check:
- [ ] I've verified the code change on the config file mentioned above has already been reverted

*Description:*

This PRs updates the `transformers` version inside the `huggingface` PyTorch and TensorFlow DLC.

*Tests run:*

https://github.com/huggingface/transformers/tree/master/tests/sagemaker


| ID                                  | Description                                                       | Platform                   | #GPUS | Collected & evaluated metrics            |
|-------------------------------------|-------------------------------------------------------------------|-----------------------------|-------|------------------------------------------|
| pytorch-transfromers-test-single    | test bert finetuning using BERT fromtransformerlib+PT             | SageMaker createTrainingJob | 1     | train_runtime, eval_accuracy & eval_loss |
| pytorch-transfromers-test-2-ddp     | test bert finetuning using BERT from transformer lib+ PT DPP      | SageMaker createTrainingJob | 16    | train_runtime, eval_accuracy & eval_loss |
| pytorch-transfromers-test-2-smd     | test bert finetuning using BERT from transformer lib+ PT SM DDP   | SageMaker createTrainingJob | 16    | train_runtime, eval_accuracy & eval_loss |
| pytorch-transfromers-test-1-smp     | test roberta finetuning using BERT from transformer lib+ PT SM MP | SageMaker createTrainingJob | 8     | train_runtime, eval_accuracy & eval_loss |
| tensorflow-transfromers-test-single | Test bert finetuning using BERT from transformer lib+TF           | SageMaker createTrainingJob | 1     | train_runtime, eval_accuracy & eval_loss |
| tensorflow-transfromers-test-2-smd  | test bert finetuning using BERT from transformer lib+ TF SM DDP   | SageMaker createTrainingJob | 16    | train_runtime, eval_accuracy & eval_loss |

*DLC image/dockerfile:*

The Hugging Face DLCs for PyTorch and TensorFlow


*Additional context:*
![Bildschirmfoto 2021-04-06 um 14 20 34](https://user-images.githubusercontent.com/32632186/114026575-afec6480-9876-11eb-892e-55144707367d.png)



By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license. I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.