An example of custom gpu kernels for deep learning on Sherlock #10 (#34)

* Adding gpu recipe contribution from @zqfang

examples/recipe.sherlock-gpu.md

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+A recipe for interactive computing using custom Jupyter kernels on Stanford's Sherlock.
+
+# Setting up custom conda environment on Sherlock's login node
+## 1. Download and install Miniconda
+```bash
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+# install
+bash Miniconda3-latest-Linux-x86_64.sh 
+conda config --set always_yes yes 
+```
+
+
+## 2. Install jupyter notebook/lab and secure your notebooks with a password  
+
+```bash
+# install the default py3 kernel for jupyter notebook
+conda install ipython jupyter notebook jupyterlab
+# add password
+jupyter notebook password
+```
+
+## 3. (Optional) Add custom conda environment. i.e. fastai
+```bash
+conda create -n fastai ipython ipykernel 
+# add the custom to Jupyter notebook
+conda activate fastai
+python -m ipykernel install --user --name fastai --display-name FastAI
+
+```
+you could also add R, Julia etc kernel.
+
+## 4. Install pytorch/tensorflow
+
+You should select the existed cuda version which installed in Sherlock
+```bash
+conda install -c pytorch pytorch torchvision cudatoolkit=10.1 
+```
+tensorflow
+```bash
+conda install tensorflow-gpu cudatoolkit=10.1
+```
+
+## 5. Load gpu modules. Select the corresponding cuda version you've just installed 
+```bash
+# this is my version
+module load cuda/10.1.168
+module load cudnn/7.6.4
+module load nccl
+```
+
+## 6. now, open ipython, run
+```python
+import torch
+print(torch.cuda.is_avilable())
+```
+if print out is `True`, then you'er OK to use GPUs.
+
+# Follow these steps on your local machine
+see details [here](https://vsoch.github.io/lessons/sherlock-jupyter/).
+
+## 7. Download the `forward` repo
+```bash
+git clone https://github.com/vsoch/forward
+cd forward
+```
+## 8. Generate your parameters
+```
+bash setup.sh
+```
+Select Sherlock partition: <span style="color: red">gpu</span>
+
+## 9. SSH Credentials
+
+```bash
+bash hosts/sherlock_ssh.sh >> ~/.ssh/config
+```
+
+## 10. create a sbatch script in forward/sbatches/sherlock and save as `jupyter-gpu.sbatch`
+
+```bash
+#!/bin/bash
+
+PORT=$1
+NOTEBOOK_DIR=$2
+if [ -z "$NOTEBOOK_DIR" ]; then
+    cd $SCRATCH
+else
+    cd $NOTEBOOK_DIR
+fi
+
+## to compile libtorch C++ code, load these modules
+# module load gcc/7.3.0
+# module load gdb
+# module load cmake
+# export CC=$(which gcc)
+# export CXX=$(which g++)
+
+# select cuda version you need
+module load cuda/10.1.168
+module load cudnn/7.6.4
+module load nccl
+
+# activate fastai env 
+source activate fastai 
+jupyter lab --no-browser --port=$PORT
+```
+
+## 11. Start a session
+The default working directory is `$SCRATCH`
+```bash
+bash start.sh jupyter-gpu
+```
+change the working directory 
+```
+bash start.sh jupyter /path/to/dir
+```
+
+## 12. open your browser in local machine and type  
+
+if your port is 51888, then
+```
+http://localhost:51888/
+```
+here is my jupyter lab computing environment. Have fun!
+
+
+## 13. Resume a session
+```bash
+bash resume.sh jupyter-gpu
+# or
+bash resume.sh jupyter-gpu /path/to/dir
+```
+## 14. Stop a session
+
+```bash
+bash end.sh jupyter-gpu
+# or
+bash end.sh jupyter-gpu /path/to/dir
+```
+
+
+
+

sbatches/sherlock/jupyter-gpu.sbatch

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+#!/bin/bash
+
+PORT=$1
+NOTEBOOK_DIR=$2
+if [ -z "$NOTEBOOK_DIR" ]; then
+    cd $SCRATCH
+else
+    cd $NOTEBOOK_DIR
+fi
+
+## to compile libtorch C++ code, load these modules
+# module load gcc/7.3.0
+# module load gdb
+# module load cmake
+# export CC=$(which gcc)
+# export CXX=$(which g++)
+
+# please select correct cuda version match to the pytorch/tensorflow
+module load cuda/10.1.168
+module load cudnn/7.6.4
+module load nccl
+
+source activate base 
+jupyter lab --no-browser --port=$PORT