fix main readme for medmnist branch

README.md

@@ -1,62 +1,26 @@
-# New to Onnx
-- Official ORT documentation: https://www.onnxruntime.ai/  
-- Official ORT GitHub Repo: https://github.com/microsoft/onnxruntime
-- Official ORT Samples Repo: https://github.com/microsoft/onnxruntime-training-examples
+# On-Device Training Example with BloodMNIST
 
-# What is ONNX Runtime for PyTorch
+## Step 1: Set up environment
 
-ONNX Runtime for PyTorch gives you the ability to accelerate training of large transformer PyTorch models. The training time and cost are reduced with just a one line code change.
+Starting from the `decai-fe` directory:
 
-- One line code change: ORT provides a one-line addition for existing PyTorch training scripts allowing easier experimentation and greater agility.
-```python
-    from torch_ort import ORTModule
-    model = ORTModule(model)
+```
+python -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
 ```
 
-- Flexible and extensible hardware support: The same model and API works with NVIDIA and AMD GPUs; the extensible "execution provider" architecture allow you to plug-in custom operators, optimizer and hardware accelerators.
-
-- Faster Training: Optimized kernels provide up to 1.4X speed up in training time.
-
-- Larger Models: Memory optimizations allow fitting a larger model such as GPT-2 on 16GB GPU, which runs out of memory with stock PyTorch.
-
-- Composable with other acceleration libraries such as Deepspeed, Fairscale, Megatron for even faster and more efficient training
-
-- Part of the PyTorch Ecosystem. It is available via the torch-ort python package.
-
-- Built on top of highly successful and proven technologies of ONNX Runtime and ONNX format.
-
-# ONNX Runtime Training Examples
-
-This repo has examples for using [ONNX Runtime](https://github.com/microsoft/onnxruntime) (ORT) for accelerating training of [Transformer](https://arxiv.org/abs/1706.03762) models. These examples focus on large scale model training and achieving the best performance in [Azure Machine Learning service](https://azure.microsoft.com/en-us/services/machine-learning/). ONNX Runtime has the capability to train existing PyTorch models (implemented using `torch.nn.Module`) through its optimized backend. The examples in this repo demonstrate how `ORTModule` can be used to switch the training backend. 
-
-## Examples
-
-Outline the examples in the repository.
-
-| Example                | Performance Comparison                      | Model Change                                |
-|------------------------|---------------------------------------------|---------------------------------------------|
-| HuggingFace BART       | See [BART](huggingface/BART.md)             | No model change required |
-| HuggingFace BERT       | See [BERT](huggingface/BERT.md)             | No model change required |
-| HuggingFace DeBERTa    | See [DeBERTa](huggingface/DeBERTa.md)       | See [this commit](https://github.com/microsoft/huggingface-transformers/commit/0b2532a4f1df90858472d1eb2ca3ac4eaea42af1)|
-| HuggingFace DistilBERT | See [DistilBERT](huggingface/DistilBERT.md) | No model change required |
-| HuggingFace GPT2       | See [GPT2](huggingface/GPT2.md)             | No model change required|
-| HuggingFace RoBERTa    | See [RoBERTa](huggingface/RoBERTa.md)       | See [this commit](https://github.com/microsoft/huggingface-transformers/commit/b25c43e533c5cadbc4734cc3615563a2304c18a2)|
-| t5-large               | See [T5](huggingface/T5.md)                 | See [this PR](https://github.com/microsoft/huggingface-transformers/pull/4/files) |
-<!-- 
-| `CONTRIBUTING.md` | Guidelines for contributing to the sample. |
--->
-
+## Step 2: Download dataset
 
-## Contributing
+```
+python ./on_device_training/conversion.py --data_flag 'bloodmnist' --output_dir './on_device_training/web/web-bundler/public/data'
+```
 
-This project welcomes contributions and suggestions.  Most contributions require you to agree to a
-Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
-the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
+## Step 3: Launch Demo
 
-When you submit a pull request, a CLA bot will automatically determine whether you need to provide
-a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
-provided by the bot. You will only need to do this once across all repos using our CLA.
+Navigate to ``on_device_training/web/web-bundler``, then:
 
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
-contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
+```
+npm ci
+npm start
+```

requirements.txt

@@ -0,0 +1,3 @@
+numpy
+medmnist
+tqdm