docs: docker tags (#96)

* docs: docker tags
  - fixes #86
* fix broken link

content/docs/self-hosted-runners.md

@@ -19,7 +19,7 @@ instances on your AWS or Azure account (GCP support is forthcoming!).
 For example, the following workflow deploys a `t2.micro` instance on AWS EC2 and
 trains a model on the instance. After the job runs, the instance automatically
 shuts down. You might notice that this workflow is quite similar to the
-[basic use case](#usage) highlighted in the beginning of the docs- that's
+[basic use case](/doc/usage) highlighted in the beginning of the docs- that's
 because it is! What's new is that we've added `cml-runner`, plus a few
 environmental variables for passing your cloud service credentials to the
 workflow.
@@ -73,12 +73,27 @@ CML and its dependencies setup to use CML functions like `cml-send-comment` from
 your instance, you can create your favorite training environment in the cloud by
 pulling the Docker container of your choice.
 
-We like the
-[CML container](https://github.com/iterative/cml/blob/master/Dockerfile)
+We like the [CML Docker images](#docker-images)
 (`docker://iterativeai/cml:0-dvc2-base1`) because it comes loaded with Python,
 Git, Node JS and other essentials for full-stack data science. But we don't mind
 if you do it your way :)
 
+## Docker Images
+
+The CML Docker images (`docker://iterativeai/cml` or
+`docker://ghcr.io/iterative/cml`) come loaded with Python, CUDA, `git`, `node`
+and other essentials for full-stack data science. Different versions of these
+essentials are available from different `iterativeai/cml` image tags. The tag
+convention is `{CML_VER}-dvc{DVC_VER}-base{BASE_VER}{-gpu}`:
+
+| `{BASE_VER}` | Software included (`-gpu`)                      |
+| ------------ | ----------------------------------------------- |
+| 0            | Ubuntu 18.04, Python 2.7 (CUDA 10.1, CuDNN 7)   |
+| 1            | Ubuntu 20.04, Python 3.8 (CUDA 11.0.3, CuDNN 8) |
+
+For example, `docker://iterativeai/cml:0-dvc2-base1-gpu`, or
+`docker://ghcr.io/iterative/cml:0-dvc2-base1`.
+
 ## Options
 
 The function `cml-runner` accepts the following arguments:

content/docs/usage.md

@@ -42,10 +42,10 @@ jobs:
 CML provides a number of helper functions to help package outputs from ML
 workflows, such as numeric data and data vizualizations about model performance,
 into a CML report. The library comes pre-installed on our
-[custom Docker images](https://github.com/iterative/cml/blob/master/Dockerfile).
-In the above example, note the field
-`container: docker://iterativeai/cml:0-dvc2-base1` specifies the CML Docker
-image with Python 3 will be pulled by the GitHub Actions runner.
+[custom Docker images](/doc/self-hosted-runners#docker-images). In the above
+example, note the field `container: docker://iterativeai/cml:0-dvc2-base1`
+specifies the CML Docker image with Python 3 will be pulled by the GitHub
+Actions runner.
 
 Below is a list of CML functions for writing markdown reports and delivering
 those reports to your CI system (GitHub Actions or GitLab CI).