Finishing quick start example

README.md

@@ -9,9 +9,11 @@ datasets with a focus on those generated from multiplexed staining protocols.  T
 For more information, see: [Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging](https://www.biorxiv.org/content/early/2018/11/04/460980)
 
 
-### Installation and Requirements
+### Quick Start
 
-Installing and configuring Cytokit currently involves little more than installing [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) and building or downloading the Cytokit container image (not on Docker Hub yet), but this inherently limits support to Linux operating systems for GPU-acceleration.  Additional limitations include:
+Installing and configuring Cytokit currently involves little more than installing [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) 
+and building or downloading the Cytokit container image, but this inherently limits support to Linux operating 
+systems for GPU-acceleration.  Additional limitations include:
 
 - There is currently no CPU-only docker image 
 - Generating and running pipelines requires working knowledge of JupyterLab and a little tolerance for yaml/json files as well as command lines
@@ -20,6 +22,22 @@ Installing and configuring Cytokit currently involves little more than installin
 - 3 dimensional images are supported but cell segmentation and related outputs are currently 2 dimensional 
 - General system requirements include at least 24G RAM and 8G of GPU memory (per GPU)
 
+Once nvidia-docker is installed, the container can be launched and used as follows:
+
+```bash
+nvidia-docker pull hammerlab/cytokit:latest
+
+# Run the container with an attached volume to contain raw images and results  
+nvidia-docker run --rm -ti -p 8888:8888 -p 8787:8787 -p 8050:8050 \
+-v $LOCAL_IMAGE_DATA_DIR:/lab/data \
+hammerlab/cytokit
+```
+
+This will launch JupyterLab on port 8888.  After navigating to localhost:8888 and entering the access token 
+printed on the command line following ```nvidia-docker run```, you can then run an example notebook 
+like [cellular_marker_profiling_example](python/notebooks/examples/marker_profiling_example.ipynb), which can be found 
+at ```/lab/repos/cytokit/python/notebooks/examples``` in the JupyterLab file navigator. 
+
 ### Example
 
 One of the goals of Cytokit is to make it as easy as possible to reproduce complicated workflows on big image datasets and to that end, the majority of the logic that drives how Cytokit functions is determined by json/yaml configurations.  

docker/README.md

@@ -1,18 +1,20 @@
 # Docker Instructions
 
 
-Running the production container:
+Build and run the production container:
 
 ```bash
 # Building a development container
 nvidia-docker pull hammerlab/cytokit:latest
 
+# Run and open ports for jupyter, tensorboard, dask, and dash
 nvidia-docker run --rm -ti -p 8888:8888 -p 6006:6006 -p 8787:8787 -p 8050:8050 \
 -v $CYTOKIT_DATA_DIR:/lab/data \
 eczech/cytokit
 ```
 
-Running the development container:
+
+Build and run the development container:
 
 ```bash
 
@@ -23,7 +25,6 @@ nvidia-docker build -t cytokit-dev -f Dockerfile.dev .
 export CYTOKIT_DATA_DIR=/data/disk1/
 export CYTOKIT_REPO_DIR=/home/eczech/repos/cytokit
 
-# Run and open ports for jupyter, tensorboard, dask, and dash
 nvidia-docker run --rm -ti -p 8888:8888 -p 6006:6006 -p 8787:8787 -p 8050:8050 \
 -v $CYTOKIT_DATA_DIR:/lab/data \
 -v $CYTOKIT_REPO_DIR:/lab/repos/cytokit \