chore: Rename 'docker' directory to 'containers'

containers/README.md

@@ -0,0 +1,80 @@
+# Using GPUs on CHTC via Linux containers
+
+## Docker
+
+Docker is software that helps bundle software programs, libraries and
+dependencies in a package called a **container**.
+Once built into container images, these containers can be run on different
+machines that have the Docker Engine.
+Programs with complex dependencies are often packaged with Docker and made
+available for download on [DockerHub](https://hub.docker.com).
+
+The Docker Engine needs special configuration to give the software inside a
+container access to a GPU. CHTC does this behind the scenes with
+`nvidia-docker`. Any Docker container that wants to use `nvidia-docker` must
+contain the Nvidia CUDA toolkit inside it. Here we have working examples and
+also some pointers on how to find Docker container images or build your own
+Docker container images that can access the GPU.
+
+### Examples
+
+1. **Hello\_GPU**
+ This is a simple example to see if we can access the GPU from inside a Docker
+container on CHTC. It uses the
+[nvidia/cuda](https://hub.docker.com/r/nvidia/cuda) Docker container image which
+is a tiny container that only contains the Nvidia CUDA toolkit.
+ [Click here to access this example](./hello_gpu/).
+
+2. **Matrix Multiplication with TensorFlow (Python)**
+ This example uses a [TensorFlow](https://www.tensorflow.org) [Docker
+container](https://hub.docker.com/r/tensorflow/tensorflow/) to benchmark matrix
+multiplication on a GPU vs the same matrix multiplication on a CPU.
+ [Click here to access this example](./tensorflow_python/).
+
+3. **Convolutional Neural Network with PyTorch (Python)**
+ This example shows how to send training and test data to the compute node
+along with the script.  After processing the trained network is returned to the
+submit node.
+ [Click here to access this example](./pytorch_python/).
+
+### Finding container images
+1. Pick a Docker container image that is built on a more modern version of
+CUDA Toolkit.
+Although the toolkits are backwards compatible, the more modern the toolkit,
+the less likely you are to run into problems.
+2. [Nvidia Catalog](https://ngc.nvidia.com/catalog/landing) has a good
+   selection of container images that use the GPU for machine learning, inference,
+visualization etc. They need to be uploaded to your own account on Dockerhub
+before being used. This can be done with the Docker application or with the
+Docker Automated Builder (see below).
+3. [Rocker](https://hub.docker.com/u/rocker) is a great place to find GPU
+   enabled machine learning software for the [R Project for Statistical
+Computing](https://www.r-project.org)
+
+
+### Building container images
+Building your own container images to access a GPU requires a bit of work and
+will not be described fully here.
+It is best to start with a basic Docker container image that can access the GPU
+and then build upon that image.
+The PyTorch Docker container image is built on top of Nvidia Cuda and is a
+[good example to follow](https://github.com/pytorch/pytorch/blob/main/.devcontainer/Dockerfile).
+
+```Dockerfile
+FROM nvidia/cuda:10.1-base-ubuntu18.04
+#....
+```
+or
+```Dockerfile
+# Pull from Nvidia's catalog
+FROM nvcr.io/nvidia/pytorch:19.07-py3
+
+# conda is already installed so just install packages
+RUN conda install package_1 package_2 package_etc
+```
+
+Once you have a working `Dockerfile`, you need to build a Docker container image
+with the Docker app and then upload it to Dockerhub so that CHTC can access your
+container image.
+Alternatively, you can have the DockerHub Cloud service directly build it for
+you on DockerHub.

shared/README.md

@@ -3,5 +3,5 @@ Nothing here is intended to be run on its own.
 To find runnable examples, navigate to one of the following subdirectories:
 
 [`conda`](../conda),
-[`docker`](../docker),
+[`containers`](../containers),
 or [`test`](../test)