Added notes on using CML with local runner

cml.md

@@ -0,0 +1,38 @@
+# CML
+
+## Self-hosted runner
+To setup a self-hosted runner to perform github actions with CML on a local machine with a GPU access. You will need to make sure that `docker` and `node` are installed, then run:
+```shell
+$ sudo npm install --location=global @dvcorg/cml
+```
+### Starting the runner
+To start the runner you will need to create gh access token with `repo` and `workflow` permissions. Then run:
+```shell
+$ cml runner launch \
+  --repo=$REPO_URL \
+  --token=$ACCESS_TOKEN \
+  --labels="cml,gpu" \
+  --idle-timeout=3000
+```
+replacing `REPO_URL` with your github repository url and `ACCESS_TOKEN` with you gh access token.
+
+The runner should provide a confirmation message when it is started, but you can check that it is available to your repository by going to github `<REPOSITORY> > Settings > Actions > Runners` and you should see the runner listed.
+
+### GH Action
+With the runner available you should now be able to create a workflow to utilise it. Below is an example of a basic action to use the local runner and print the available GPU spec and details:
+```yaml
+name: test_gpu
+on: [push]
+jobs:
+  run:
+    runs-on: [self-hosted,cml,gpu]
+    steps:
+      - uses: actions/checkout@v3
+      - uses: iterative/setup-cml@v1
+      - name: Chek GPU spec
+        env:
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          nvidia-smi
+```
+Save this into `.github/workflows/test_gpu.yaml` and open a pull request. The action should execute and the output should provide you details about the available GPU.