pre-commit fixes

.pre-commit-config.yaml

@@ -37,3 +37,4 @@ repos:
     rev: v2.2.6
     hooks:
     - id: codespell
+      args: ["--skip=*.excalidraw"]

ai-ml/trainium-inferentia/main.tf

@@ -47,10 +47,6 @@ provider "kubectl" {
   }
 }
 
-data "aws_eks_cluster_auth" "this" {
-  name = module.eks.cluster_name
-}
-
 data "aws_ecrpublic_authorization_token" "token" {
   provider = aws.ecr
 }

analytics/terraform/superset-on-eks/addons.tf

@@ -109,7 +109,7 @@ module "eks_data_addons" {
   superset_helm_config = {
     values = [templatefile("${path.module}/helm-values/superset-values.yaml", {})]
   }
-    depends_on = [module.eks_blueprints_addons]
+  depends_on = [module.eks_blueprints_addons]
 
 }
 

analytics/terraform/superset-on-eks/helm-values/superset-values.yaml

@@ -50,10 +50,10 @@ supersetWorker:
     requests:
       cpu: 200m
       memory: 400Mi
-      
+
   persistence:
     enabled: true
-   
+
 
 postgresql:
   ## Set to false if bringing your own PostgreSQL.
@@ -92,4 +92,4 @@ redis:
       ## Access mode:
       accessModes:
         - ReadWriteOnce
-runAsUser: 1000
+runAsUser: 1000

gen-ai/inference/gradio-ui/gradio-app-mistral.py

@@ -62,4 +62,4 @@ def filter_harmful_content(text):
 )
 
 # Launch the ChatInterface
-chat_interface.launch()
+chat_interface.launch(server_name="0.0.0.0")

gen-ai/inference/llama3-8b-instruct-rayserve-inf2/ray_serve_llama3.py

@@ -70,7 +70,7 @@ def __init__(self):
                                 n_positions=4096
                             )
         self.neuron_model.to_neuron()
-        
+
         # Initialize tokenizer for the model
         self.tokenizer = AutoTokenizer.from_pretrained(model_id)
 

gen-ai/inference/mistral-7b-rayserve-inf2/ray-service-mistral.yaml

@@ -169,4 +169,4 @@ spec:
           service:
             name: mistral
             port:
-              number: 8000
+              number: 8000

gen-ai/inference/stable-diffusion-rayserve-gpu/ray-serve-stablediffusion.yaml

@@ -161,4 +161,4 @@ spec:
           service:
             name: stablediffusion
             port:
-              number: 8000
+              number: 8000

website/docs/blueprints/distributed-databases/clickhouse.md

@@ -2,7 +2,7 @@
 sidebar_position: 4
 sidebar_label: ClickHouse
 ---
-# ClickHouse on EKS 
+# ClickHouse on EKS
 [ClickHouse](https://clickhouse.com/) is a high-performance, column-oriented SQL database management system (DBMS) for online analytical processing (OLAP) that is open sourced under the Apache 2.0 license.
 
 
@@ -17,4 +17,4 @@ OLAP is software technology you can use to analyze business data from different
 * SQL Support: ClickHouse supports a subset of SQL, making it familiar and easy to use for developers and analysts who are already familiar with SQL-based databases.
 * Integrated Data Formats: ClickHouse supports various data formats, including CSV, JSON, Apache Avro, and Apache Parquet, making it flexible for ingesting and querying different types of data.
 
-**To deploy Clickhouse on EKS**, we recommend this [Clickhouse on EKS blueprint](https://github.com/Altinity/terraform-aws-eks-clickhouse) from Altinity, an AWS Partner who maintains the [Clickouse Kubernetes Operator](https://github.com/Altinity/clickhouse-operator). If you have any issues with the blueprint or operator, please create an issue on the corresponding Altinity GitHub repository. 
+**To deploy Clickhouse on EKS**, we recommend this [Clickhouse on EKS blueprint](https://github.com/Altinity/terraform-aws-eks-clickhouse) from Altinity, an AWS Partner who maintains the [Clickouse Kubernetes Operator](https://github.com/Altinity/clickhouse-operator). If you have any issues with the blueprint or operator, please create an issue on the corresponding Altinity GitHub repository.

website/docs/gen-ai/inference/Llama2-inf2.md

@@ -235,7 +235,7 @@ Discover how to create a user-friendly chat interface using [Gradio](https://www
 
 Let's move forward with setting up the Gradio app as a Docker container running on localhost. This setup will enable interaction with the Stable Diffusion XL model, which is deployed using RayServe.
 
-### Build the Gradio app docker container 
+### Build the Gradio app docker container
 
 First, lets build the docker container for the client app.
 
@@ -297,4 +297,4 @@ This script will cleanup the environment using `-target` option to ensure all th
 ```bash
 cd ../../../ai-ml/trainium-inferentia/
 ./cleanup.sh
-```
+```

website/docs/gen-ai/inference/StableDiffusion-inf2.md

@@ -208,7 +208,7 @@ Discover how to create a user-friendly chat interface using [Gradio](https://www
 
 Let's move forward with setting up the Gradio app as a Docker container running on localhost. This setup will enable interaction with the Stable Diffusion XL model, which is deployed using RayServe.
 
-### Build the Gradio app docker container 
+### Build the Gradio app docker container
 
 First, lets build the docker container for the client app.
 

website/docs/gen-ai/inference/llama3-inf2.md

@@ -26,7 +26,7 @@ In this tutorial, you will not only learn how to harness the power of Llama-3, b
 
 ### What is Llama-3-8B Instruct?
 
-Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. 
+Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
 
 More information on Llama3 sizes and model architecture can be found [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
 
@@ -36,7 +36,7 @@ One of the key challenges in deploying large language models (`LLMs`) like Llama
 
 In contrast, `Trn1/Inf2` instances, such as `trn1.32xlarge`, `trn1n.32xlarge`, `inf2.24xlarge` and `inf2.48xlarge`, are purpose built for high-performance deep learning (DL) training and inference of generative AI models, including LLMs. They offer both scalability and availability, ensuring that you can deploy and scale your `Llama-3` models as needed, without resource bottlenecks or delays.
 
-**Cost Optimization** 
+**Cost Optimization**
 
 Running LLMs on traditional GPU instances can be cost-prohibitive, especially given the scarcity of GPUs and their competitive pricing. **Trn1/Inf2** instances provide a cost-effective alternative. By offering dedicated hardware optimized for AI and machine learning tasks, Trn1/Inf2 instances allow you to achieve top-notch performance at a fraction of the cost. This cost optimization enables you to allocate your budget efficiently, making LLM deployment accessible and sustainable.
 
@@ -234,7 +234,7 @@ Discover how to create a user-friendly chat interface using [Gradio](https://www
 
 Let's move forward with setting up the Gradio app as a Docker container running on localhost. This setup will enable interaction with the Stable Diffusion XL model, which is deployed using RayServe.
 
-### Build the Gradio app docker container 
+### Build the Gradio app docker container
 
 First, lets build the docker container for the client app.
 

website/docs/gen-ai/inference/stablediffusion-gpus.md

@@ -152,7 +152,7 @@ Discover how to create a user-friendly chat interface using [Gradio](https://www
 
 Let's move forward with setting up the Gradio app as a Docker container running on localhost. This setup will enable interaction with the Stable Diffusion XL model, which is deployed using RayServe.
 
-### Build the Gradio app docker container 
+### Build the Gradio app docker container
 
 First, lets build the docker container for the client app.