### Dynamo Quickstart Guide Source: https://docs.nvidia.com/dynamo/llms.txt This quickstart guide helps users get started with NVIDIA Dynamo. It covers the essential steps to set up and run Dynamo for the first time. ```markdown ## Quickstart This guide offers a streamlined introduction to NVIDIA Dynamo, enabling users to quickly set up and run basic functionalities. [Link to Quickstart Guide](https://docs.dynamo.nvidia.com/dynamo/getting-started/quickstart.md) ``` -------------------------------- ### Dynamo Getting Started and Support Information Source: https://docs.nvidia.com/dynamo/llms.txt This snippet offers resources for getting started with Dynamo, including a quickstart guide, support matrix, feature matrix, and release artifacts. It also points to general examples. ```markdown - [Dynamo Docs Guide](https://docs.dynamo.nvidia.com/dynamo/dev/documentation/dynamo-docs-guide.md) - [Quickstart](https://docs.dynamo.nvidia.com/dynamo/getting-started/quickstart.md) - [Support Matrix](https://docs.dynamo.nvidia.com/dynamo/getting-started/support-matrix.md) - [Feature Matrix](https://docs.dynamo.nvidia.com/dynamo/getting-started/feature-matrix.md) - [Release Artifacts](https://docs.dynamo.nvidia.com/dynamo/getting-started/release-artifacts.md) - [Examples](https://docs.dynamo.nvidia.com/dynamo/getting-started/examples.md) ``` -------------------------------- ### Dynamo Installation Guide Source: https://docs.nvidia.com/dynamo/llms.txt This guide provides instructions for installing NVIDIA Dynamo. It covers prerequisites, installation steps, and initial configuration for various environments. ```markdown ## Installation This section provides comprehensive instructions for installing NVIDIA Dynamo, including prerequisites, different installation methods, and post-installation checks. [Link to Installation Guide](https://docs.dynamo.nvidia.com/dynamo/v-0-8-1/getting-started/installation.md) ``` -------------------------------- ### Dynamo Kubernetes Deployment Quickstart Source: https://docs.nvidia.com/dynamo/llms.txt This guide provides a quickstart for deploying NVIDIA Dynamo on a Kubernetes cluster. It covers the essential steps to get Dynamo up and running in a Kubernetes environment. ```markdown ## Deploying Dynamo on Kubernetes This quickstart guide assists users in deploying NVIDIA Dynamo onto a Kubernetes platform. It outlines the initial steps for a successful deployment. [Link to Kubernetes quickstart](https://docs.dynamo.nvidia.com/dynamo/v-0-8-1/kubernetes-deployment/deployment-guide/kubernetes-quickstart.md) ``` -------------------------------- ### Minikube Setup Source: https://docs.nvidia.com/dynamo/llms.txt Guide for setting up Dynamo on Minikube. ```APIDOC ## Minikube Setup ### Description Instructions for configuring and running Dynamo deployments on a Minikube environment. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/minikube-setup ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content for Minikube setup. #### Response Example ``` # Minikube Setup This guide details the steps to set up Dynamo on Minikube for local development... ``` ``` -------------------------------- ### Dynamo Minikube Setup Guide Source: https://docs.nvidia.com/dynamo/llms.txt This guide provides instructions for setting up NVIDIA Dynamo on Minikube, a tool for running Kubernetes locally. It's useful for development and testing purposes. ```markdown ## Minikube Setup Guide This guide explains how to set up and run NVIDIA Dynamo on Minikube, facilitating local development and testing of Dynamo deployments. [Link to Minikube Setup Guide](https://docs.dynamo.nvidia.com/dynamo/kubernetes-deployment/deployment-guide/minikube-setup.md) ``` -------------------------------- ### Detailed Installation Guide Source: https://docs.nvidia.com/dynamo/llms.txt Detailed instructions for installing NVIDIA Dynamo. ```APIDOC ## Detailed Installation Guide ### Description Offers in-depth instructions for the complete installation of NVIDIA Dynamo. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/detailed-installation-guide ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content of the detailed installation guide. #### Response Example ``` # Detailed Installation Guide Follow these steps for a thorough installation of Dynamo... ``` ``` -------------------------------- ### Dynamo Kubernetes Detailed Installation Guide Source: https://docs.nvidia.com/dynamo/llms.txt This guide provides a detailed installation procedure for NVIDIA Dynamo on Kubernetes. It covers advanced configurations and steps for a robust deployment. ```markdown ## Installation Guide for Dynamo Kubernetes Platform This document offers a detailed, step-by-step guide for installing NVIDIA Dynamo on Kubernetes, covering advanced configurations and best practices. [Link to Detailed Installation Guide](https://docs.dynamo.nvidia.com/dynamo/kubernetes-deployment/deployment-guide/detailed-installation-guide.md) ``` -------------------------------- ### Dynamo Examples Source: https://docs.nvidia.com/dynamo/llms.txt This section provides a collection of examples demonstrating the usage and capabilities of NVIDIA Dynamo. These examples serve as practical guides for users. ```markdown ## Dynamo Examples This section offers practical examples showcasing various use cases and functionalities of NVIDIA Dynamo, helping users learn and implement the system effectively. [Link to Examples](https://docs.dynamo.nvidia.com/dynamo/getting-started/examples.md) ``` -------------------------------- ### SLA-Driven Profiling and Planner Deployment Quick Start Source: https://docs.nvidia.com/dynamo/llms.txt This quick start guide explains how to use SLA-driven profiling and the Planner for deployment in NVIDIA Dynamo. It covers the initial steps for leveraging these features. ```markdown ## SLA-Driven Profiling and Planner Deployment Quick Start Guide This quick start guide introduces users to SLA-driven profiling and the Planner for efficient deployment within Dynamo, covering essential setup and usage. [Link to SLA Planner Quick Start](https://docs.dynamo.nvidia.com/dynamo/v-0-8-1/components/planner/sla-planner-quick-start.md) ``` -------------------------------- ### Observability Setup (Local) Source: https://docs.nvidia.com/dynamo/llms.txt Guides for monitoring Dynamo deployments locally using metrics, logging, and tracing. This includes setting up Prometheus and Grafana for visualization. ```yaml # Example Prometheus configuration snippet for scraping Dynamo metrics scrape_configs: - job_name: 'dynamo_metrics' static_configs: - targets: ['dynamo-service:8000'] # Assuming Dynamo service exposes metrics on port 8000 ``` -------------------------------- ### Run NeMo RL Examples Locally with Ray Source: https://docs.nvidia.com/nemo/rl/llms.txt This snippet demonstrates how to run NeMo RL examples locally using Ray. It shows how to automatically start a local cluster and how to control GPU visibility using the CUDA_VISIBLE_DEVICES environment variable. This is useful for local development and testing. ```shell CUDA_VISIBLE_DEVICES=0,3 uv run examples/run_grpo_math.py ``` -------------------------------- ### Prometheus + Grafana Setup Source: https://docs.nvidia.com/dynamo/llms.txt Guide for setting up Prometheus and Grafana for monitoring Dynamo deployments. ```APIDOC ## Prometheus + Grafana Setup ### Description Instructions for setting up Prometheus and Grafana to visualize and monitor metrics from Dynamo deployments. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/observability-local/prometheus-grafana-setup ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content on Prometheus + Grafana setup. #### Response Example ``` # Prometheus + Grafana Setup Integrate Prometheus and Grafana to gain deep insights into your Dynamo deployment's performance... ``` ``` -------------------------------- ### AlignScore Deployment and Setup Source: https://docs.nvidia.com/nemo/guardrails/llms.txt Guidance on deploying AlignScore as a fact-checking micro-service. This includes installing the `alignscore` package, selecting compatible Python and PyTorch versions, setting environment variables, and launching the server with desired models. ```bash # Ensure Python version is not 3.11 and PyTorch is 2.0.1 pip install alignscore export ALIGN_SCORE_PATH=/path/to/alignscore/models export ALIGN_SCORE_DEVICE=cuda nemoguardrails alignscore server --model --port 5000 ``` -------------------------------- ### Prometheus and Grafana Setup for Dynamo Metrics Source: https://docs.nvidia.com/dynamo/llms.txt This guide details the setup process for Prometheus and Grafana to visualize metrics from NVIDIA Dynamo. It covers the configuration required to integrate these tools for monitoring. ```markdown ## Metrics Visualization with Prometheus and Grafana This document provides instructions for setting up Prometheus and Grafana to visualize Dynamo metrics. It covers the necessary configuration for effective monitoring. [Link to Prometheus and Grafana setup](https://docs.dynamo.nvidia.com/dynamo/user-guides/observability-local/prometheus-grafana-setup.md) ``` -------------------------------- ### Deployment Guide Source: https://docs.nvidia.com/dynamo/llms.txt General guide for deploying LLMs using NVIDIA Dynamo. ```APIDOC ## Deployment Guide ### Description Provides an overview and steps for deploying LLMs with NVIDIA Dynamo. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/deployment-guide ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content of the deployment guide. #### Response Example ``` # Deployment Guide This document outlines the process of deploying LLMs using NVIDIA Dynamo... ``` ``` -------------------------------- ### Additional Examples and Demos Source: https://docs.nvidia.com/heavyai/llms.txt Access additional examples and demonstrations for HEAVY.AI, including specific use cases like forecasting. ```APIDOC ## Additional Examples and Demos ### Description Access additional examples and demonstrations for HEAVY.AI, including specific use cases like forecasting. ### Method N/A ### Endpoint N/A ### Parameters N/A ### Request Example N/A ### Response N/A ### Further Information - [Additional Examples](https://docs.nvidia.com/heavyai/python-data-science/additional-examples.mdx) - [Forecasting with HEAVY.AI and Prophet](https://docs.nvidia.com/heavyai/python-data-science/additional-examples/forecasting-with-omnisci-and-prophet.mdx) ``` -------------------------------- ### HEAVY.AI ODBC Connection Example Source: https://docs.nvidia.com/heavyai/llms.txt Provides an example of configuring and connecting to HEAVY.AI using an ODBC data source. This usually involves setting up an ODBC DSN (Data Source Name) and then using it in applications. ```text ; Example odbc.ini configuration for HEAVY.AI [HEAVYAI_DSN] Driver=/path/to/heavyai/odbc/driver.so Description=HEAVY.AI ODBC Driver Host=localhost Port=6278 Database=heavyai User=admin Password=password ``` -------------------------------- ### Dynamo Benchmarking Guide Source: https://docs.nvidia.com/dynamo/llms.txt Guide for benchmarking and comparing performance across Dynamo deployment configurations. ```APIDOC ## Dynamo Benchmarking Guide ### Description Provides instructions and methodologies for benchmarking Dynamo deployments and comparing the performance of different configurations. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/dynamo-benchmarking ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content on benchmarking. #### Response Example ``` # Dynamo Benchmarking Guide Benchmark your Dynamo deployments to evaluate performance and identify optimal settings... ``` ``` -------------------------------- ### Distributed Tracing with Tempo for Dynamo Source: https://docs.nvidia.com/dynamo/llms.txt This guide explains how to implement and utilize distributed tracing with Tempo for NVIDIA Dynamo. It covers the setup and configuration for tracing requests across distributed components. ```markdown ## Distributed Tracing with Tempo This document details how to integrate Tempo for distributed tracing within Dynamo, enabling detailed analysis of request flows across microservices. [Link to Tempo tracing documentation](https://docs.dynamo.nvidia.com/dynamo/user-guides/observability-local/tracing.md) ``` -------------------------------- ### Kubernetes Deployment with Dynamo Operator Source: https://docs.nvidia.com/dynamo/llms.txt Guides for deploying Dynamo on Kubernetes using the Dynamo Operator. This includes general deployment, detailed installation, and specific configurations like service discovery and webhooks. ```yaml # Example Kubernetes deployment manifest for Dynamo Operator apiVersion: apps/v1 kind: Deployment metadata: name: dynamo-operator namespace: dynamo-system spec: replicas: 1 selector: matchLabels: app: dynamo-operator template: metadata: labels: app: dynamo-operator spec: containers: - name: operator image: nvcr.io/nvidia/dynamo/dynamo-operator:latest ports: - containerPort: 8080 ``` -------------------------------- ### Multinode Deployments Source: https://docs.nvidia.com/dynamo/llms.txt Guide for setting up and managing multinode deployments of Dynamo. ```APIDOC ## Multinode Deployments ### Description Provides instructions and best practices for deploying Dynamo across multiple nodes for scalability and high availability. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/multinode-deployments ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content on multinode deployments. #### Response Example ``` # Multinode Deployments This guide covers the configuration and management of Dynamo deployments spanning multiple nodes... ``` ``` -------------------------------- ### Metrics Developer Guide Source: https://docs.nvidia.com/dynamo/llms.txt Developer guide for understanding and using metrics in Dynamo. ```APIDOC ## Metrics Developer Guide ### Description Provides developers with detailed information on Dynamo's metrics, how they are generated, and how to use them effectively. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/observability-local/metrics-developer-guide ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content for the metrics developer guide. #### Response Example ``` # Metrics Developer Guide This guide is for developers looking to leverage Dynamo's metrics for advanced monitoring and analysis... ``` ``` -------------------------------- ### Dynamo Metrics Developer Guide Source: https://docs.nvidia.com/dynamo/llms.txt This guide is intended for developers working with NVIDIA Dynamo's metrics system. It explains how to access, interpret, and potentially contribute to Dynamo's metrics collection. ```markdown ## Metrics Developer Guide This guide provides in-depth information for developers on understanding and utilizing Dynamo's metrics. It covers metric definitions, collection mechanisms, and best practices. [Link to Metrics Developer Guide](https://docs.dynamo.nvidia.com/dynamo/user-guides/observability-local/metrics-developer-guide.md) ``` -------------------------------- ### Service Discovery Source: https://docs.nvidia.com/dynamo/llms.txt Guide on configuring service discovery for Dynamo deployments. ```APIDOC ## Service Discovery ### Description Explains how to set up and utilize service discovery mechanisms within Dynamo deployments. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/service-discovery ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content on service discovery. #### Response Example ``` # Service Discovery This section covers configuring service discovery for your Dynamo deployments... ``` ``` -------------------------------- ### Observability (Local) Source: https://docs.nvidia.com/dynamo/llms.txt Guide on monitoring Dynamo deployments locally using metrics, logging, and tracing. ```APIDOC ## Observability (Local) ### Description Details how to monitor Dynamo deployments locally, covering metrics, logging, and tracing functionalities. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/observability-local ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content on local observability. #### Response Example ``` # Observability (Local) Monitor your local Dynamo deployments effectively using the provided observability tools... ``` ``` -------------------------------- ### Autoscaling Source: https://docs.nvidia.com/dynamo/llms.txt Guide on configuring autoscaling for Dynamo deployments. ```APIDOC ## Autoscaling ### Description Details how to configure autoscaling for Dynamo deployments to dynamically adjust resources based on load. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/autoscaling ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content on autoscaling. #### Response Example ``` # Autoscaling Dynamo supports autoscaling to ensure optimal performance and resource utilization... ``` ``` -------------------------------- ### Logging (Observability Local) Source: https://docs.nvidia.com/dynamo/llms.txt Guide on accessing and utilizing logs for local Dynamo deployments. ```APIDOC ## Logging (Observability Local) ### Description Details how to access and analyze logs generated by local Dynamo deployments for debugging and operational insights. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/observability-local/logging ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content on local logging. #### Response Example ``` # Logging (Observability Local) Accessing logs from your local Dynamo environment is essential for troubleshooting... ``` ``` -------------------------------- ### LoRA Adapters Source: https://docs.nvidia.com/dynamo/llms.txt Guide on serving fine-tuned LoRA adapters with dynamic loading and routing in Dynamo. ```APIDOC ## LoRA Adapters ### Description Explains how to serve fine-tuned LoRA adapters in Dynamo, including dynamic loading and routing capabilities. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/lo-ra-adapters ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content on LoRA adapters. #### Response Example ``` # LoRA Adapters Dynamo supports serving fine-tuned LoRA adapters efficiently with dynamic loading... ``` ``` -------------------------------- ### Tracing Source: https://docs.nvidia.com/dynamo/llms.txt Guide on enabling and using tracing for Dynamo deployments. ```APIDOC ## Tracing ### Description Explains how to enable and utilize distributed tracing within Dynamo deployments for end-to-end request monitoring. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/observability-local/tracing ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content on tracing. #### Response Example ``` # Tracing Implement distributed tracing in Dynamo to visualize request flows and identify performance bottlenecks... ``` ``` -------------------------------- ### Logging (Observability K8s) Source: https://docs.nvidia.com/dynamo/llms.txt Guide on accessing and utilizing logs for Dynamo deployments in Kubernetes. ```APIDOC ## Logging (Observability K8s) ### Description Explains how to access and analyze logs generated by Dynamo deployments in Kubernetes for debugging and operational insights. ### Method GET ### Endpoint /llmstxt/nvidia_llms_txt/logging-k8s ### Parameters None ### Request Example None ### Response #### Success Response (200) - **content** (string) - Markdown content on Kubernetes logging. #### Response Example ``` # Logging (Observability K8s) Accessing logs from your Dynamo pods in Kubernetes is crucial for troubleshooting... ``` ``` -------------------------------- ### JupyterLab Integration Source: https://docs.nvidia.com/heavyai/llms.txt Guides on installing, configuring, and using HEAVY.AI with JupyterLab for interactive data science tasks. ```APIDOC ## JupyterLab Integration ### Description Guides on installing, configuring, and using HEAVY.AI with JupyterLab for interactive data science tasks. ### Method N/A ### Endpoint N/A ### Parameters N/A ### Request Example N/A ### Response N/A ### Further Information - [JupyterLab Installation and Configuration](https://docs.nvidia.com/heavyai/python-data-science/get-started-jupyter.mdx) - [Using HEAVY.AI with JupyterLab](https://docs.nvidia.com/heavyai/python-data-science/using-omnisci-with-jupyterlab.mdx) ``` -------------------------------- ### HEAVY.AI JDBC Connection Example Source: https://docs.nvidia.com/heavyai/llms.txt Illustrates how to establish a JDBC connection to HEAVY.AI. This typically involves using a JDBC driver and providing connection details such as the URL, username, and password. ```java import java.sql.Connection; import java.sql.DriverManager; import java.sql.SQLException; public class HeavyAIJDBC { public static void main(String[] args) { String url = "jdbc:heavyai://localhost:6278/heavyai"; String user = "admin"; String password = "password"; try (Connection connection = DriverManager.getConnection(url, user, password)) { System.out.println("Connected to HEAVY.AI!"); // Perform database operations here } catch (SQLException e) { System.err.println("Connection failed: " + e.getMessage()); } } } ``` -------------------------------- ### Show Command in HeavyAI Source: https://docs.nvidia.com/heavyai/llms.txt Provides examples of the `SHOW` command in HeavyAI, used to display information about the database, tables, columns, or other database objects. ```sql SHOW TABLES; ``` -------------------------------- ### Dynamo Profiler Component Configuration and Usage Source: https://docs.nvidia.com/dynamo/llms.txt This snippet details the Dynamo Profiler component, offering guides and examples for performance analysis. It helps users understand and optimize the performance of their Dynamo deployments. ```markdown - [Profiler](https://docs.dynamo.nvidia.com/dynamo/components/profiler.md) - [Profiler Guide](https://docs.dynamo.nvidia.com/dynamo/components/profiler/profiler-guide.md) - [Profiler Examples](https://docs.dynamo.nvidia.com/dynamo/components/profiler/profiler-examples.md) ``` -------------------------------- ### Explain Query in HeavyAI Source: https://docs.nvidia.com/heavyai/llms.txt Shows how to use the `EXPLAIN` command in HeavyAI to analyze the execution plan of a SQL query. This is crucial for performance tuning. ```sql EXPLAIN SELECT * FROM my_table WHERE column_a > 100; ``` -------------------------------- ### Dynamo Planner Component Configuration and Usage Source: https://docs.nvidia.com/dynamo/llms.txt This snippet describes the Dynamo Planner component, providing guides and examples for its functionality. It aids in optimizing model serving strategies within Dynamo deployments. ```markdown - [Planner](https://docs.dynamo.nvidia.com/dynamo/components/planner.md) - [Planner Guide](https://docs.dynamo.nvidia.com/dynamo/components/planner/planner-guide.md) - [Planner Examples](https://docs.dynamo.nvidia.com/dynamo/components/planner/planner-examples.md) ``` -------------------------------- ### Dynamo Metrics Collection on Kubernetes Source: https://docs.nvidia.com/dynamo/llms.txt This guide details how to collect metrics from NVIDIA Dynamo when deployed on Kubernetes. It covers the setup and configuration for monitoring Dynamo's performance and health in a K8s environment. ```markdown ## Dynamo Metrics Collection on Kubernetes This document explains how to set up and configure metrics collection for Dynamo deployments on Kubernetes. It focuses on monitoring performance and health indicators. [Link to Kubernetes metrics documentation](https://docs.dynamo.nvidia.com/dynamo/kubernetes-deployment/observability-k-8-s/metrics.md) ``` -------------------------------- ### Dynamo Router Component Configuration and Usage Source: https://docs.nvidia.com/dynamo/llms.txt This snippet focuses on the Dynamo Router component, detailing how to enable KV-aware routing for Dynamo deployments. It includes guides and examples for its usage and a comparison with vLLM regarding KV event handling. ```markdown - [Router](https://docs.dynamo.nvidia.com/dynamo/components/router.md) - [Router Guide](https://docs.dynamo.nvidia.com/dynamo/dev/components/router/router-guide.md): Enable KV-aware routing using Router for Dynamo deployments - [Router Examples](https://docs.dynamo.nvidia.com/dynamo/components/router/router-examples.md) - [KV Event Replay — Dynamo vs vLLM](https://docs.dynamo.nvidia.com/dynamo/dev/components/router/kv-event-replay-dynamo-vs-v-llm.md): How the two systems handle gap detection, replay, and recovery for KV cache events ``` -------------------------------- ### Load Data with SQL in HeavyAI Source: https://docs.nvidia.com/heavyai/llms.txt Demonstrates how to load data into HeavyAI using SQL commands. This is typically done via the command line interface. It covers the basic syntax for the `LOAD DATA` command. ```sql LOAD DATA FROM '/path/to/your/data.csv' INTO my_table (col1 STRING, col2 INT); ``` -------------------------------- ### NeMo Guardrails on Vertex AI Setup and Evaluation Source: https://docs.nvidia.com/nemo/guardrails/llms.txt Guidance for setting up and evaluating an NVIDIA NeMo Guardrails-enabled LLM on Vertex AI. This includes prerequisite checks, configuration of guardrail parameters, and reviewing evaluation results for fine-tuning compliance and safety. ```python # Example configuration snippet for Vertex AI in Guardrails config config = { "llm": { "provider": "vertexai", "model": "text-bison", "project_id": "YOUR_PROJECT_ID" } } ``` -------------------------------- ### Vega Visualization Source: https://docs.nvidia.com/heavyai/llms.txt Documentation and tutorials for using Vega for declarative visualization within HEAVY.AI. ```APIDOC ## Vega Visualization ### Description Documentation and tutorials for using Vega for declarative visualization within HEAVY.AI. ### Method N/A ### Endpoint N/A ### Parameters N/A ### Request Example N/A ### Response N/A ### Further Information - [Vega](https://docs.nvidia.com/heavyai/apis-and-interfaces/vega.mdx) - [Vega Tutorials](https://docs.nvidia.com/heavyai/apis-and-interfaces/vega/vega-tutorials.mdx) - [Vega Reference Overview](https://docs.nvidia.com/heavyai/apis-and-interfaces/vega/vega-reference-overview.mdx) - [Vega Migration](https://docs.nvidia.com/heavyai/apis-and-interfaces/vega/vega-migration.mdx) - [Try Vega](https://docs.nvidia.com/heavyai/apis-and-interfaces/vega/try-vega.mdx) ``` -------------------------------- ### Vega Specification Example Source: https://docs.nvidia.com/heavyai/llms.txt A basic example of a Vega visualization specification. Vega allows for declarative creation of interactive visualizations. This JSON structure defines the data source, marks, and encodings for a chart. ```json { "$schema": "https://vega.github.io/schema/vega/v5.json", "description": "A simple bar chart example.", "width": 400, "height": 200, "padding": 5, "data": [ { "name": "table", "values": [ {"category": "A", "value": 10}, {"category": "B", "value": 20}, {"category": "C", "value": 15} ] } ], "marks": [ { "type": "rect", "from": {"data": "table"}, "encode": { "enter": { "x": {"scale": "xscale", "field": "category"}, "width": {"scale": "xscale", "band": 1}, "y": {"field": "value"}, "y2": {"value": 0} }, "update": { "fill": {"value": "steelblue"} } } } ], "scales": [ { "name": "xscale", "type": "band", "range": "width", "domain": {"data": "table", "field": "category"} }, { "name": "yscale", "range": "height", "nice": true, "domain": {"data": "table", "field": "value"} } ] } ``` -------------------------------- ### Create User and Database DDL in HeavyAI Source: https://docs.nvidia.com/heavyai/llms.txt Shows examples of DDL statements for managing users and databases within HeavyAI. This includes creating, altering, and dropping users and databases, along with privilege management. ```sql CREATE USER new_user IDENTIFIED BY 'password'; CREATE DATABASE analytics_db; GRANT ALL ON analytics_db TO new_user; ``` -------------------------------- ### Docker Deployment for NeMo Guardrails Source: https://docs.nvidia.com/nemo/guardrails/llms.txt Instructions for deploying NeMo Guardrails using Docker, suitable for spinning up local or cloud servers for testing or prototyping safety-enhanced LLM chats. It also explains how to run optional AlignScore fact-checking or jailbreak-detection services in separate containers. ```bash # Example Docker command (specific commands would be detailed in the documentation) docker run --name nemoguardrails -p 8080:8080 -v $(pwd)/config:/app/config nemoguardrails/cli:latest ```