Update 05.container_gpu.md

Removed maverick specific reference and replaced maverick to maverick2 for GPU

Author: Jawon Song

docs/05.container_gpu.md

@@ -4,22 +4,20 @@ title: Run Containers on GPUs
 tagline:
 ---
 
-Utilizing GPGPUs on the Maverick supercomputer through containerized environments.
+Utilizing GPUs on the TACC supercomputer through containerized environments.
 
 <br>
 #### Choosing the Right System
 
-You can register your app to ANY system at TACC, but Maverick may not always be the best choice if you don't always need GPUs.
+You can register your app to ANY system at TACC, depending on your needs.
 
 | System     | Cores/Node | Pros                                      | Limitations                                |
 |:-----------|:-----------|:------------------------------------------|:-------------------------------------------|
-| Stampede   | 16         | Thousands of nodes, Xeon Phi accelerators | Retiring ~ Dec2017                         |
 | Stampede 2 Phase1 | 68         | Thousands of nodes, KNL processors | Slow for serial code                       |
 | Stampede 2 Phase2 | 48         | Thousands of nodes, Skylake processors | Coming Soon, High Demand               |
 | Lonestar 5 | 24         | Compute, GPUs, Large-mem | UT only, slow external network                              |
 | Wrangler   | 24         | SSD Filesystem for fast I/O, Hosted Databases, Hadoop, HDFS | Low node-count           |
 | Jetstream  | 24         | Long running instances, root access       | Limited storage                            |
-| [Maverick](https://portal.tacc.utexas.edu/user-guides/maverick)   | 20         | GPUs, high memory nodes                   | Deprecated software stack                 |
 | Chameleon  | Variable   | GPUs, bare metal VM, software defined networking | Difficult to configure              |
 | Catapult   | 16         | FPGAs                                     | Windows-only                               |
 
@@ -73,7 +71,6 @@ Utilize your knowledge of Docker to create Singularity images.
 
 To help with this area of development, TACC has a set of base containers for each system
 
-- Maverick - https://hub.docker.com/r/gzynda/tacc-maverick-cuda8/
 - Stampede2 - https://hub.docker.com/r/gzynda/tacc-stampede2-mpi/
 
 with libraries for the specialize hardware, and a simple base image that will work with Singularity on all TACC system.
@@ -94,7 +91,7 @@ These containers are run without root, so you simply
 <br>
 #### GPU containers
 
-Since Singularity supported docker containers, it has been fairly simple to utilize GPUs for machine learning code like [TensorFlow](https://www.tensorflow.org/). From Maverick, which is TACC's GPU system:
+Since Singularity supported docker containers, it has been fairly simple to utilize GPUs for machine learning code like [TensorFlow](https://www.tensorflow.org/). From TACC's GPU system:
 
 ```
 # Work from a compute node
@@ -141,7 +138,7 @@ singularity exec --nv tacc-maverick-ml-latest.img python $HOME/models/tutorials/
 
 You probably noticed that we check out the models repository into your `$HOME` directory. This is because your `$HOME` and `$WORK` directories are only available inside the container if the root folders `/home` and `/work` exist inside the container. In the case of `tensorflow-latest-gpu.img`, the `/work` directory does _not_ exist, so any files there are inaccessible to the container.
 
-You may be thinking "what about overlayfs??". The Linux kernel on Maverick does not support overlayfs, so it had to be disabled in our singularity install.
+You may be thinking "what about overlayfs??". The Linux kernel on Maverick2 does not support overlayfs, so it had to be disabled in our singularity install.
 
 <br>
 #### Prototyping