notes for initial session

.gitignore

@@ -0,0 +1 @@
+.idea

README.md

@@ -1,2 +1,45 @@
-# docker-tutorial
-Supporting files for a series of docker tutorials for coworkers
+# Docker Tutorial
+
+Files & notes for a series of docker tutorials for coworkers.
+
+## Notes for the presenter
+
+1. Getting things working
+   1. Everybody run `docker run hello-world` to make sure Docker is installed and working.
+   2. `docker run -it <your-favorite-distro>` such as debian, rocky, alpine, etc.
+   3. Quiz: What does `-it` do? Could check with `docker run --help` or at https://docs.docker.com/reference/.
+
+2. Running something useful: Jupyter Notebooks
+   1. `docker run -p 8888:8888 quay.io/jupyter/datascience-notebook:latest`
+   2. Find the login URL in the output and open it in your browser. Should see the notebook interface. 
+   3. Do something.
+   4. Try something with a missing library such as `import scikit-learn`.
+
+3. Dockerfiles
+   1. Create a `Dockerfile` that installs the missing library.
+       ```Dockerfile
+      from quay.io/jupyter/datascience-notebook:latest
+      RUN conda install --quiet --yes scikit-learn tensorflow
+      ```
+        1. Note: Instead, you could `docker exec -it <container-id> bash` and install the library manually, but then it wouldn't be sharable or saved for future work.
+        2. Think of Docker images as snapshots that you can save for later or share. _"Infrastructure as code."_
+   2. Build a new local Docker image
+       ```shell
+       $ docker build . -t my-notebook
+       $ docker run -p 8888:8888 my-notebook
+       ```
+    3. [Try again with the missing library](https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_biclustering.html#generate-sample-data).
+      ```python
+      from matplotlib import pyplot as plt
+
+      from sklearn.datasets import make_checkerboard
+
+      n_clusters = (4, 3)
+      data, rows, columns = make_checkerboard(
+          shape=(300, 300), n_clusters=n_clusters, noise=10, shuffle=False, random_state=42
+      )
+
+      plt.matshow(data, cmap=plt.cm.Blues)
+      plt.title("Original dataset")
+      _ = plt.show()
+     ```