chore(deps): Bump axios, @docusaurus/core and @docusaurus/preset-clas…

…sic in /website (#383)

ai-ml/mlflow/README.md

@@ -1,6 +1,6 @@
 # MLflow on EKS
 
-Docs comming soon ...
+Docs coming soon ...
 
 ## Requirements
 

ai-ml/mlflow/helm-values/aws-for-fluentbit-values.yaml

@@ -49,7 +49,7 @@ filter:
     Kube_CA_File        /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
     Kube_Token_File     /var/run/secrets/kubernetes.io/serviceaccount/token
 
-# CATION: Donot use `cloudwatch` plugin. This Golang Plugin is not recommnded by AWS anymore instead use C plugin(`cloudWatchLogs`) for better performance.
+# CATION: Do not use `cloudwatch` plugin. This Golang Plugin is not recommended by AWS anymore instead use C plugin(`cloudWatchLogs`) for better performance.
 # cloudWatch:
 #   enabled: false
 

ai-ml/mlflow/karpenter-provisioners/00-karpenter-provisioner-cpu.yaml

@@ -4,7 +4,7 @@ kind: Provisioner
 metadata:
   name: default
 spec:
-  # Wich AWS Node Template to pick
+  # Which AWS Node Template to pick
   providerRef:
     name: default
 

ai-ml/mlflow/mlflow-core.tf

@@ -199,9 +199,9 @@ module "mlflow_irsa" {
   tags = local.tags
 }
 
-#---------------------------------------------------------------
-# IAM policy for MLflow for accesing S3 artifacts and RDS Postgres backend
-#---------------------------------------------------------------
+#--------------------------------------------------------------------------
+# IAM policy for MLflow for accessing S3 artifacts and RDS Postgres backend
+#--------------------------------------------------------------------------
 resource "aws_iam_policy" "mlflow" {
   count = var.enable_mlflow_tracking ? 1 : 0
 

website/docs/blueprints/ai-ml/emr-spark-rapids.md

@@ -174,7 +174,7 @@ This dataset is sourced from [Fannie Mae’s Single-Family Loan Performance Data
 4. Click on  `Download Data` and choose `Single-Family Loan Performance Data`
 5. You will find a tabular list of Acquisition and Performance` files sorted based on year and quarter. Click on the file to download. You can download three years(2020, 2021 and 2022 - 4 files for each year and one for each quarter) worth of data that will be used in our example job.  e.g.,: 2017Q1.zip
 6. Unzip the download file to extract the csv file to your local machine. e.g.,: 2017Q1.csv
-7. Copy only the CSV files to an S3 bucket under ${S3_BUCKET}/${EMR_VIRTUAL_CLUSTER_ID}/spark-rapids-emr/input/fannie-mae-single-family-loan-performance/. The example below uses three years of data (one file for each quarter, 12 files in total). Note: `${S3_BUCKET}` and `${EMR_VIRTUAL_CLUSTER_ID}` values can be extracted from Terraform outputs.
+7. Copy only the CSV files to an S3 bucket under `${S3_BUCKET}/${EMR_VIRTUAL_CLUSTER_ID}/spark-rapids-emr/input/fannie-mae-single-family-loan-performance/`. The example below uses three years of data (one file for each quarter, 12 files in total). Note: `${S3_BUCKET}` and `${EMR_VIRTUAL_CLUSTER_ID}` values can be extracted from Terraform outputs.
 
 ```
  aws s3 ls s3://emr-spark-rapids-<aws-account-id>-us-west-2/949wt7zuphox1beiv0i30v65i/spark-rapids-emr/input/fannie-mae-single-family-loan-performance/

website/docs/blueprints/amazon-emr-on-eks/emr-eks-studio.md

@@ -113,13 +113,13 @@ To submit a job we will use Below you use `start-job-run` command with AWS CLI.
 
 Before you run the command below, make sure to change update the following parameters with the on created by your own deployment.
 
-    - <CLUSTER-ID> – The EMR virtual cluster ID, which you get from the AWS CDK output
-    - <SPARK-JOB-NAME> – The name of your Spark job
-    - <ROLE-ARN> – The execution role you created, which you get from the AWS CDK output
-    - <S3URI-CRITICAL-DRIVER> – The Amazon S3 URI of the driver pod template, which you get from the AWS CDK output
-    - <S3URI-CRITICAL-EXECUTOR> – The Amazon S3 URI of the executor pod template, which you get from the AWS CDK output
-    - <Log_Group_Name> – Your CloudWatch log group name
-    - <Log_Stream_Prefix> – Your CloudWatch log stream prefix
+    - \<CLUSTER-ID\> – The EMR virtual cluster ID, which you get from the AWS CDK output
+    - \<SPARK-JOB-NAME\> – The name of your Spark job
+    - \<ROLE-ARN\> – The execution role you created, which you get from the AWS CDK output
+    - \<S3URI-CRITICAL-DRIVER\> – The Amazon S3 URI of the driver pod template, which you get from the AWS CDK output
+    - \<S3URI-CRITICAL-EXECUTOR\> – The Amazon S3 URI of the executor pod template, which you get from the AWS CDK output
+    - \<Log_Group_Name\> – Your CloudWatch log group name
+    - \<Log_Stream_Prefix\> – Your CloudWatch log stream prefix
 
 <details>
     <summary>AWS CLI for start-job-run command</summary>

website/docs/blueprints/data-analytics/observability-spark-on-eks.md

@@ -16,7 +16,7 @@ let's navigate to one example folder under spark-k8s-operator and run the shell
 ```bash
 cd data-on-eks/analytics/terraform/spark-k8s-operator/examples/cluster-autoscaler/nvme-ephemeral-storage
 
-# replace <S3_BUCKET> with your S3 bucket and <REGION> with your region, then run
+# replace \<S3_BUCKET\> with your S3 bucket and \<REGION\> with your region, then run
 ./taxi-trip-execute.sh
 ```
 
@@ -30,7 +30,7 @@ When you submit a Spark application, Spark context is created which ideally give
 
 When your application is done with the processing, Spark context will be terminated so your Web UI as well. and if you wanted to see the monitoring for already finished application, we cannot do it.
 
-To try Spark web UI, let's update <S3_BUCKET> with your bucket name and <JOB_NAME> with "nvme-taxi-trip" in nvme-ephemeral-storage.yaml
+To try Spark web UI, let's update \<S3_BUCKET\> with your bucket name and \<JOB_NAME\> with "nvme-taxi-trip" in nvme-ephemeral-storage.yaml
 
 ```bash
   kubectl apply -f nvme-ephemeral-storage.yaml
@@ -51,11 +51,13 @@ As mentioned above, spark web UI will be terminated once the spark job is done.
 
 In this example, we installed Spark history Server to read logs from S3 bucket. In your spark application yaml file, make sure you have the following setting:
 
+```yaml
     sparkConf:
         "spark.hadoop.fs.s3a.aws.credentials.provider": "com.amazonaws.auth.InstanceProfileCredentialsProvider"
         "spark.hadoop.fs.s3a.impl": "org.apache.hadoop.fs.s3a.S3AFileSystem"
         "spark.eventLog.enabled": "true"
         "spark.eventLog.dir": "s3a://<your bucket>/logs/"
+```
 
 Run port forward command to expose spark-history-server service.
 ```bash

website/docs/blueprints/streaming-platforms/flink.md

@@ -219,6 +219,7 @@ chmod +x install.sh
 
 Verify the cluster status
 
+```bash
     ➜ kubectl get nodes -A
     NAME                                         STATUS   ROLES    AGE   VERSION
     ip-10-1-160-150.us-west-2.compute.internal   Ready    <none>   24h   v1.24.11-eks-a59e1f0
@@ -234,9 +235,11 @@ Verify the cluster status
     cert-manager-77fc7548dc-dzdms             1/1     Running   0          24h
     cert-manager-cainjector-8869b7ff7-4w754   1/1     Running   0          24h
     cert-manager-webhook-586ddf8589-g6s87     1/1     Running   0          24h
+```
 
 To list all the resources created for Flink team to run Flink jobs using this namespace
 
+```bash
     ➜  ~ kubectl get all,role,rolebinding,serviceaccount --namespace flink-team-a-ns
     NAME                                               CREATED AT
     role.rbac.authorization.k8s.io/flink-team-a-role   2023-04-06T13:17:05Z
@@ -247,7 +250,7 @@ To list all the resources created for Flink team to run Flink jobs using this na
     NAME                             SECRETS   AGE
     serviceaccount/default           0         22h
     serviceaccount/flink-team-a-sa   0         22h
-
+```
 
 </CollapsibleContent>
 

website/docs/gen-ai/inference/Llama2.md

@@ -185,7 +185,7 @@ llama2-ingress   nginx   *       k8s-ingressn-ingressn-randomid-randomid.elb.us-
 
 Now, you can access the Ray Dashboard from the Load balancer URL below.
 
-    http://<NLB_DNS_NAME>/dashboard/#/serve
+    http://\<NLB_DNS_NAME\>/dashboard/#/serve
 
 If you don't have access to a public Load Balancer, you can use port-forwarding and browse the Ray Dashboard using localhost with the following command:
 
@@ -206,7 +206,7 @@ Once you see the status of the model deployment is in `running` state then you c
 
 You can use the following URL with a query added at the end of the URL.
 
-    http://<NLB_DNS_NAME>/serve/infer?sentence=what is data parallelism and tensor parallelisma and the differences
+    http://\<NLB_DNS_NAME\>/serve/infer?sentence=what is data parallelism and tensor parallelisma and the differences
 
 You will see an output like this in your browser:
 

website/docusaurus.config.js

@@ -1,8 +1,8 @@
 // @ts-check
 // Note: type annotations allow type checking and IDEs autocompletion
 
-const lightCodeTheme = require('prism-react-renderer/themes/github');
-const darkCodeTheme = require('prism-react-renderer/themes/dracula');
+const lightCodeTheme = require('prism-react-renderer').themes.github;
+const darkCodeTheme = require('prism-react-renderer').themes.dracula;
 
 /** @type {{onBrokenLinks: string, organizationName: string, plugins: string[], title: string, url: string, onBrokenMarkdownLinks: string, i18n: {defaultLocale: string, locales: string[]}, trailingSlash: boolean, baseUrl: string, presets: [string,Options][], githubHost: string, tagline: string, themeConfig: ThemeConfig & UserThemeConfig & AlgoliaThemeConfig, projectName: string}} */
 const config = {