Fix inline code in README files in kubernetes directory

kubernetes/README.md

@@ -1,6 +1,6 @@
 # Distributed Optimization on Kubernetes
 
-This folder contains two kinds of examples with Kubernetes: one is based on [sklearn_simple.py](../sklearn/sklearn_simple.py) and the other is based on [pytorch_lightning_simple.py](../pytorch/pytorch_lightning_simple.py) with MLflow.
+This folder contains two kinds of examples with Kubernetes: one is based on [`sklearn_simple.py`](../sklearn/sklearn_simple.py) and the other is based on [`pytorch_lightning_simple.py`](../pytorch/pytorch_lightning_simple.py) with MLflow.
 
-Currently, both [simple/sklearn_distributed.py](./simple/sklearn_distributed.py) and [mlflow/pytorch_lightning_distributed.py](./mlflow/pytorch_lightning_distributed.py) use POSTGRESQL for their backend of `optuna.Study.optimize` to be parallelized.  
+Currently, both [`simple/sklearn_distributed.py`](./simple/sklearn_distributed.py) and [`mlflow/pytorch_lightning_distributed.py`](./mlflow/pytorch_lightning_distributed.py) use POSTGRESQL for their backend of `optuna.Study.optimize` to be parallelized.
 Though we do not use it for MLflow records.  Of course, you can use POSTGRESQL as backend store of MLflow (https://mlflow.org/docs/latest/tracking.html#where-runs-are-recorded), current example uses HTTP server.

kubernetes/mlflow/README.md

@@ -2,7 +2,7 @@
 
 This example is only verified on minikube.
 
-This example's code is based on ../../pytorch/pytorch_lightning_simple.py example with the following changes:
+This example's code is based on [`pytorch_lightning_simple.py`](../../pytorch/pytorch_lightning_simple.py) example with the following changes:
 
 1. It gives a name to the study and sets `load_if_exists` to `True` in order to avoid errors when the code is run from multiple workers.
 2. It sets the storage address to the postgres pod deployed with the workers.
@@ -18,7 +18,7 @@ First run `run.sh` which takes two arguments `$IsMinikube` and `$IMAGE_NAME`
 $ bash run.sh True optuna-kubernetes-mlflow:example
  ```
 
-- If you want to run in cloud, please change the `IMAGE_NAME` accordingly in k8s-manifest.yaml and run as follows. Also please make sure that your kubernetes context is set correctly.
+- If you want to run in cloud, please change the `IMAGE_NAME` accordingly in `k8s-manifest.yaml` and run as follows. Also please make sure that your kubernetes context is set correctly.
 
  ```bash
 $ bash run.sh False $IMAGE_NAME

kubernetes/simple/README.md

@@ -1,9 +1,9 @@
 # Distributed Optimization on Kubernetes
 
-This example's code is mostly the same as the sklearn_simple.py example, 
+This example's code is mostly the same as the [`sklearn_simple.py`](../../sklearn/sklearn_simple.py) example,
 except for two things:
 
-1 - It gives a name to the study and sets load_if_exists to True
+1 - It gives a name to the study and sets `load_if_exists` to `True`
 in order to avoid errors when the code is run from multiple workers.
 
 2 - It sets the storage address to the postgres pod deployed with the workers.
@@ -18,7 +18,7 @@ Run `run.sh` which takes two arguments `$IsMinikube` and `$IMAGE_NAME`
 $ bash run.sh True optuna-kubernetes:example
  ```
 
-- If you want to run in cloud, please change the IMAGE_NAME accordingly in k8s-manifest.yaml and run as follows. Also please make sure that you kubernetes context is set correctly.
+- If you want to run in cloud, please change the `IMAGE_NAME` accordingly in `k8s-manifest.yaml` and run as follows. Also please make sure that you kubernetes context is set correctly.
 
  ```bash
 $ bash run.sh False $IMAGE_NAME