update readme for original examples

Author: conglongli

examples/README.md

@@ -1,30 +1,3 @@
-## Recipes and Scripts
+# Original examples by NVIDIA/Megatron-LM
 
-Please note that some of the script examples (e.g., pretrain_*.sh directly under ```Megatron-DeepSpeed/examples/``` folder) are from the original NVIDIA's Megatron-LM and does not have DeepSpeed integration (scripts with DeepSpeed integration should include the ```deepspeed``` keyword). Below we list various examples that do have DeepSpeed integration.
-
-### Azure
-
-We strongly recommend to start with AzureML recipe in the ```azureml``` folder.
-
-If you have a custom infrastructure (e.g. HPC clusters) or Azure VM and VMSS based environments, please refer to the bash scripts in the ```azure``` folder.
-
-### MoE
-
-Please see the ```MoE``` folder for different training recipes and scripts for Mixture-of-expert based models and dense models. These recipes are for GPT-style NLG models.
-
-### Data Efficiency 
-
-The ```data_efficiency``` folder includes GPT-3 and BERT pretraining examples for DeepSpeed Data Efficiency Library. Please refer to the detailed tutorials in data_efficiency/README.MD.
-
-### Curriculum Learning
-
-Curriculum learning recipes are in the ```curriculum_learning``` folder. Please refer to the detailed tutorials linked inside. These recipes are for GPT-style NLG models.
-Note that the DeepSpeed Data Efficiency Library above includes a more general curriculum learning support. This legacy curriculum learning feature is still compatible, but we recommend using the DeepSpeed Data Efficiency Library above.
-
-### Model Compression
-
-The ```compression``` folder includes examples about layer reduction for task-agnostic compression. Please refer to [this tutorial](https://www.deepspeed.ai/tutorials/model-compression/#11-layer-reduction) about the DeepSpeed Model Compression Library. These recipes are for GPT-style NLG models.
-
-### BERT example
-
-The ```bert_with_pile``` folder includes examples about BERT-style model pre-training (using the public Pile data or user's own data) with DeepSpeed integration. Please refer to the readme in the folder for tutorial.
+This folder includes examples from the original NVIDIA/Megatron-LM repo. All of them do NOT have DeepSpeed technologies integrations, and some of them may not work due to changes in this Megatron-DeepSpeed repo. Thus we recommend you to go to ```../examples_deepspeed/``` folder which includes examples that have DeepSpeed technologies integrated and are tested by DeepSpeed team.