Final spell check

docs/.wordlist.txt

@@ -1,6 +1,7 @@
 addons
 API
 APIs
+balancer
 CharmHub
 CLI
 DEX
@@ -11,12 +12,12 @@ EKS
 favicon
 filesystem
 Grafana
+hostname
 https
 IAM
 installable
 JSON
 Juju
-Juju’s
 Kubernetes
 Kubeflow
 lifecycle

docs/how-to/deploy-eks.rst

@@ -45,7 +45,7 @@ Create a controller:
 
 .. note:: You can use whatever controller name you like here, we chose ``kubeflow-controller``.
 
-Add a juju model:
+Add a Juju model:
 
 .. code-block:: bash
 

docs/how-to/deploy-mlflow-kubeflow-eks.rst

@@ -25,7 +25,7 @@ See our `EKS creation guide <https://charmed-kubeflow.io/docs/create-eks-cluster
 Setup Juju
 ----------
 
-Set up your local ``juju`` to talk to the remote Kubernetes cloud. First, install juju with:
+Set up your local ``juju`` to talk to the remote Kubernetes cloud. First, install Juju with:
 
 .. code-block:: bash
 
@@ -45,7 +45,7 @@ Create the controller:
 
 .. note:: we chose the name ``kubeflow-controller``, but you can choose any other name.
 
-Add a juju model:
+Add a Juju model:
 
 .. code-block:: bash
 

docs/tutorial/mlflow.rst

@@ -11,7 +11,7 @@ Welcome to the tutorial on Charmed MLflow! `MLflow <https://mlflow.org/>`_ is an
 
 So wait, what does "Charmed MLflow" mean? Is it the same thing as MLflow? Yes and no. MLflow is a complex application, consisting of many components running together and communicating with each other. Charmed MLflow is a `charm bundle <https://juju.is/docs/sdk/charm-bundles>`_ that allows us to deploy MLflow quickly and easily. Don't worry too much about what a "charm bundle" is right now. The key thing is that it's going to make deploying MLflow very convenient for us: we'll get MLflow up and running with just a few command line commands!
 
-In this tutorial, we're going to explore Charmed MLflow in a practical way. Using the CLI tool `juju <https://juju.is/>`_, we'll deploy MLflow to a local `MicroK8s <https://microk8s.io/>`_ cloud.
+In this tutorial, we're going to explore Charmed MLflow in a practical way. Using the `Juju <https://juju.is/>`_ CLI tool, we'll deploy MLflow to a local `MicroK8s <https://microk8s.io/>`_ cloud.
 
 Prerequisites
 -------------
@@ -42,16 +42,16 @@ It is also useful to make sure the user has the proper access and ownership of a
 
    sudo chown -f -R $USER ~/.kube
 
-Enable the following Microk8s addons to configure your Kubernetes cluster with extra services needed to run Charmed Kubeflow.
+Enable the following MicroK8s addons to configure your Kubernetes cluster with extra services needed to run Charmed Kubeflow.
 
 .. code-block:: bash
 
    microk8s enable dns hostpath-storage ingress metallb:10.64.140.43-10.64.140.49
 
 Here, we added a ``dns`` service, so the applications can find each other, storage, an ingress controller so we can access Kubeflow components and the ``MetalLB`` load-balancer application.
-You can see that we added some detail when enabling MetalLB, in this case the address pool to use.
+You can see that we added some detail when enabling ``MetalLB``, in this case the address pool to use.
 
-> See More : `Microk8s | How to use addons <https://microk8s.io/docs/addons>`_
+> See More : `MicroK8s | How to use addons <https://microk8s.io/docs/addons>`_
 
 We've now installed and configured MicroK8s. It will start running automatically, but can take 5 minutes or so before it's ready for action. Run the following command to tell MicroK8s to report its status to us when it's ready:
 
@@ -87,7 +87,7 @@ Now, run the following command to deploy a Juju controller to the Kubernetes we
 
 Sit tight while the command completes! The controller may take a minute or two to deploy.
 
-The controller is Juju’s agent, running on Kubernetes, which can be used to deploy and control the components of Kubeflow.
+The controller is the agent of Juju, running on Kubernetes, which can be used to deploy and control the components of Kubeflow.
 
 Next, we'll need to add a model for Kubeflow to the controller. Run the following command to add a model called ``kubeflow``:
 
@@ -110,7 +110,7 @@ Before deploying, run these commands:
    sudo sysctl fs.inotify.max_user_instances=1280
    sudo sysctl fs.inotify.max_user_watches=655360
 
-We need to run the above because under the hood, MicroK8s uses inotify to interact with the filesystem, and in large MicroK8s deployments sometimes the default ``inotify`` limits are exceeded.
+We need to run the above because under the hood, MicroK8s uses ``inotify`` to interact with the filesystem, and in large MicroK8s deployments sometimes the default ``inotify`` limits are exceeded.
 
 Let's now use Juju to deploy Charmed MLflow. Run the following command:
 
@@ -130,13 +130,13 @@ To access MLflow, visit the following URL in your web browser:
 
 This will take you to the MLflow UI.
 
-.. note:: by default Charmed MLflow creates a `nodeport <https://kubernetes.io/docs/concepts/services-networking/service/#type-nodeport>`_ on port 31380 where you can access the MLflow UI.
+.. note:: by default Charmed MLflow creates a `NodePort <https://kubernetes.io/docs/concepts/services-networking/service/#type-nodeport>`_ on port 31380 where you can access the MLflow UI.
 
 That's it! Charmed MLflow has been deployed locally with MicroK8s and Juju. You can now start using MLflow.
 
 Reference: Object storage credentials
 -------------------------------------
-To use mlflow you need to have credentials to the object storage. The aforementioned bundle comes with MinIO. To get the minio credentials run the following command:
+To use mlflow you need to have credentials to the object storage. The aforementioned bundle comes with MinIO. To get the ``MinIO`` credentials run the following command:
 
 .. code-block:: bash