deprecate acceleration/multi_gpu_test.ipynb (#1532)

fixes #1528
multigpu workflows should typically follow
-
https://github.com/Project-MONAI/tutorials/blob/main/acceleration/distributed_training/unet_training_workflows.py
-
https://github.com/Project-MONAI/model-zoo/blob/dev/models/spleen_ct_segmentation/configs/multi_gpu_train.json

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [ ] Avoid including large-size files in the PR.
- [ ] Clean up long text outputs from code cells in the notebook.
- [ ] For security purposes, please check the contents and remove any
sensitive info such as user names and private key.
- [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use
relative paths for tutorial repo files (3) put figure and graphs in the
`./figure` folder
- [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

---------

Signed-off-by: Wenqi Li <831580+wyli@users.noreply.github.com>
Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Author: wyli

README.md

@@ -197,8 +197,6 @@ This notebook compares the performance of `Dataset`, `CacheDataset` and `Persist
 ##### [fast_training_tutorial](./acceleration/fast_training_tutorial.ipynb)
 This tutorial compares the training performance of pure PyTorch program and optimized program in MONAI based on NVIDIA GPU device and latest CUDA library.
 The optimization methods mainly include: `AMP`, `CacheDataset`, `GPU transforms`, `ThreadDataLoader`, `DiceCELoss` and `SGD`.
-##### [multi_gpu_test](./acceleration/multi_gpu_test.ipynb)
-This notebook is a quick demo for devices, run the Ignite trainer engine on CPU, GPU and multiple GPUs.
 ##### [threadbuffer_performance](./acceleration/threadbuffer_performance.ipynb)
 Demonstrates the use of the `ThreadBuffer` class used to generate data batches during training in a separate thread.
 ##### [transform_speed](./acceleration/transform_speed.ipynb)

acceleration/README.md

@@ -18,8 +18,6 @@ This notebook compares the performance of `Dataset`, `CacheDataset` and `Persist
 #### [fast_training_tutorial](./fast_training_tutorial.ipynb)
 This tutorial compares the training performance of pure PyTorch program and optimized program in MONAI based on NVIDIA GPU device and latest CUDA library.
 The optimization methods mainly include: `AMP`, `CacheDataset` and `Novograd`.
-#### [multi_gpu_test](./multi_gpu_test.ipynb)
-This notebook is a quick demo for devices, run the Ignite trainer engine on CPU, GPU and multiple GPUs.
 #### [threadbuffer_performance](./threadbuffer_performance.ipynb)
 Demonstrates the use of the `ThreadBuffer` class used to generate data batches during training in a separate thread.
 #### [transform_speed](./transform_speed.ipynb)