Best AI tools for< Troubleshoot Deployment Errors >

The website displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. It provides a code 'DEPLOYMENT_NOT_FOUND' and an ID 'sin1::hvszl-1727628856344-bdd94893e618'. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::n894q-1726678978147-1c9e4ad82a70) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::lpcgw-1726939089118-f134fdcd683c) for reference. Users are directed to consult the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::k52hk-1728147113444-17b9d56f17fd) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a '404: NOT_FOUND' error message along with a code and ID indicating a deployment not found issue. Users encountering this error are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::gwh5l-1728060486264-1caee7008fee) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::zgchc-1727888586770-95afe6303495). The message suggests checking the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. Users encountering this error are advised to refer to the documentation for more information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::tszrz-1723627812794-26f3e29ebbda). Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. Users encountering this error are directed to refer to the documentation for more information and troubleshooting.

The website page displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::4wq5g-1718736845999-777f28b346ca) for reference. Users are advised to consult the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code and an ID for reference, along with a suggestion to check the documentation for more information and troubleshooting.

The website displays a 404 error message indicating that the requested deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::ltfvp-1727369324219-e2d8330c3f8d) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::7rd4m-1725901316906-8c71a7a2cbd7) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::vphzj-1726938947015-5b9ee22b5622) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code 'DEPLOYMENT_NOT_FOUND' and an ID 'sin1::zdhct-1723140771934-b5e5ad909fad'. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::l44g5-1727283130745-f99c9f7f28f4) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::qhrjt-1726765433586-bc18f7adaa0c) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::ggptb-1727542270172-dbd5ec692f5f) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website page displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. It provides a code 'DEPLOYMENT_NOT_FOUND' and an ID 'sin1::fd55k-1727629228031-7a0d0ffffbbb'. Users are directed to refer to the documentation for further information and troubleshooting.

Cluster Toolkit is an open-source software by Google Cloud for deploying AI/ML and HPC environments on Google Cloud. It allows easy deployment following best practices, with high customization and extensibility. The toolkit includes tutorials, examples, and documentation for various modules designed for AI/ML and HPC use cases.

The Looker GenAI Extension provides code examples and resources for building a Looker Extension that integrates with Vertex AI Large Language Models (LLMs). Users can leverage the power of LLMs to enhance data exploration and analysis within Looker. The extension offers generative explore functionality to ask natural language questions about data and generative insights on dashboards to analyze data by asking questions. It leverages components like BQML Remote Models, BQML Remote UDF with Vertex AI, and Custom Fine Tune Model for different integration options. Deployment involves setting up infrastructure with Terraform and deploying the Looker Extension by creating a Looker project, copying extension files, configuring BigQuery connection, connecting to Git, and testing the extension. Users can save example prompts and configure user settings for the extension. Development of the Looker Extension environment includes installing dependencies, starting the development server, and building for production.

Auto_Jobs_Applier_AIHawk is an AI-powered job search assistant that revolutionizes the job search and application process. It automates application submissions, provides personalized recommendations, and enhances the chances of landing a dream job. The tool offers features like intelligent job search automation, rapid application submission, AI-powered personalization, volume management with quality, intelligent filtering, dynamic resume generation, and secure data handling. It aims to address the challenges of modern job hunting by saving time, increasing efficiency, and improving application quality.

Open Data QnA is a Python library that allows users to interact with their PostgreSQL or BigQuery databases in a conversational manner, without needing to write SQL queries. The library leverages Large Language Models (LLMs) to bridge the gap between human language and database queries, enabling users to ask questions in natural language and receive informative responses. It offers features such as conversational querying with multiturn support, table grouping, multi schema/dataset support, SQL generation, query refinement, natural language responses, visualizations, and extensibility. The library is built on a modular design and supports various components like Database Connectors, Vector Stores, and Agents for SQL generation, validation, debugging, descriptions, embeddings, responses, and visualizations.

HolmesGPT is an open-source DevOps assistant powered by OpenAI or any tool-calling LLM of your choice. It helps in troubleshooting Kubernetes, incident response, ticket management, automated investigation, and runbook automation in plain English. The tool connects to existing observability data, is compliance-friendly, provides transparent results, supports extensible data sources, runbook automation, and integrates with existing workflows. Users can install HolmesGPT using Brew, prebuilt Docker container, Python Poetry, or Docker. The tool requires an API key for functioning and supports OpenAI, Azure AI, and self-hosted LLMs.

doc-comments-ai is a tool designed to automatically generate code documentation using language models. It allows users to easily create documentation comment blocks for methods in various programming languages such as Python, Typescript, Javascript, Java, Rust, and more. The tool supports both OpenAI and local LLMs, ensuring data privacy and security. Users can generate documentation comments for methods in files, inline comments in method bodies, and choose from different models like GPT-3.5-Turbo, GPT-4, and Azure OpenAI. Additionally, the tool provides support for Treesitter integration and offers guidance on selecting the appropriate model for comprehensive documentation needs.

Humanoid-Gym is a reinforcement learning framework designed for training locomotion skills for humanoid robots, focusing on zero-shot transfer from simulation to real-world environments. It integrates a sim-to-sim framework from Isaac Gym to Mujoco for verifying trained policies in different physical simulations. The codebase is verified with RobotEra's XBot-S and XBot-L humanoid robots. It offers comprehensive training guidelines, step-by-step configuration instructions, and execution scripts for easy deployment. The sim2sim support allows transferring trained policies to accurate simulated environments. The upcoming features include Denoising World Model Learning and Dexterous Hand Manipulation. Installation and usage guides are provided along with examples for training PPO policies and sim-to-sim transformations. The code structure includes environment and configuration files, with instructions on adding new environments. Troubleshooting tips are provided for common issues, along with a citation and acknowledgment section.

DB-GPT is a personal database administrator that can solve database problems by reading documents, using various tools, and writing analysis reports. It is currently undergoing an upgrade. **Features:** * **Online Demo:** * Import documents into the knowledge base * Utilize the knowledge base for well-founded Q&A and diagnosis analysis of abnormal alarms * Send feedbacks to refine the intermediate diagnosis results * Edit the diagnosis result * Browse all historical diagnosis results, used metrics, and detailed diagnosis processes * **Language Support:** * English (default) * Chinese (add "language: zh" in config.yaml) * **New Frontend:** * Knowledgebase + Chat Q&A + Diagnosis + Report Replay * **Extreme Speed Version for localized llms:** * 4-bit quantized LLM (reducing inference time by 1/3) * vllm for fast inference (qwen) * Tiny LLM * **Multi-path extraction of document knowledge:** * Vector database (ChromaDB) * RESTful Search Engine (Elasticsearch) * **Expert prompt generation using document knowledge** * **Upgrade the LLM-based diagnosis mechanism:** * Task Dispatching -> Concurrent Diagnosis -> Cross Review -> Report Generation * Synchronous Concurrency Mechanism during LLM inference * **Support monitoring and optimization tools in multiple levels:** * Monitoring metrics (Prometheus) * Flame graph in code level * Diagnosis knowledge retrieval (dbmind) * Logical query transformations (Calcite) * Index optimization algorithms (for PostgreSQL) * Physical operator hints (for PostgreSQL) * Backup and Point-in-time Recovery (Pigsty) * **Continuously updated papers and experimental reports** This project is constantly evolving with new features. Don't forget to star ⭐ and watch 👀 to stay up to date.

Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

Phoenix is a tool that provides MLOps and LLMOps insights at lightning speed with zero-config observability. It offers a notebook-first experience for monitoring models and LLM Applications by providing LLM Traces, LLM Evals, Embedding Analysis, RAG Analysis, and Structured Data Analysis. Users can trace through the execution of LLM Applications, evaluate generative models, explore embedding point-clouds, visualize generative application's search and retrieval process, and statistically analyze structured data. Phoenix is designed to help users troubleshoot problems related to retrieval, tool execution, relevance, toxicity, drift, and performance degradation.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

APIPark is an open-source AI Gateway and Developer Portal that enables users to easily manage, integrate, and deploy AI and API services. It provides robust API management features, including creation, monitoring, and access control, to help developers efficiently and securely develop and manage their APIs. The platform aims to solve challenges such as connecting to powerful AI models, managing complex AI & API call relationships, overseeing API creation and security, simplifying fault detection and troubleshooting, and enhancing the visibility and valuation of data assets.

The AI-Gateway repository explores the AI Gateway pattern through a series of experimental labs, focusing on Azure API Management for handling AI services APIs. The labs provide step-by-step instructions using Jupyter notebooks with Python scripts, Bicep files, and APIM policies. The goal is to accelerate experimentation of advanced use cases and pave the way for further innovation in the rapidly evolving field of AI. The repository also includes a Mock Server to mimic the behavior of the OpenAI API for testing and development purposes.

This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

The `expo-stable-diffusion` repository provides a tool for generating images using Stable Diffusion natively on iOS devices within Expo and React Native apps. Users can install and configure the module to create images based on prompts. The repository includes information on updating iOS deployment targets, enabling increased memory limits, and building iOS apps. Additionally, users can obtain Stable Diffusion models from various sources. The repository also addresses troubleshooting tips related to model load times and image generation durations. The developer seeks sponsorship to further enhance the project, including adding Android support.

OpenSSA is an open-source framework for creating efficient, domain-specific AI agents. It enables the development of Small Specialist Agents (SSAs) that solve complex problems in specific domains. SSAs tackle multi-step problems that require planning and reasoning beyond traditional language models. They apply OODA for deliberative reasoning (OODAR) and iterative, hierarchical task planning (HTP). This "System-2 Intelligence" breaks down complex tasks into manageable steps. SSAs make informed decisions based on domain-specific knowledge. With OpenSSA, users can create agents that process, generate, and reason about information, making them more effective and efficient in solving real-world challenges.

The AI Horde is an enterprise-level ML-Ops crowdsourced distributed inference cluster for AI Models. This middleware can support both Image and Text generation. It is infinitely scalable and supports seamless drop-in/drop-out of compute resources. The Public version allows people without a powerful GPU to use Stable Diffusion or Large Language Models like Pygmalion/Llama by relying on spare/idle resources provided by the community and also allows non-python clients, such as games and apps, to use AI-provided generations.

A Kubernetes error debugger offering diagnostic and resolution guidance.

Cloud engineer expert in Terraform for AWS/GCP infrastructures.

Learn Kubernetes by Hands-on Labs and AI

Kubernetes Helm Chart expert with in-depth knowledge from official docs.

Expert DevOps advisor, formal and considerate.

French Linux devops expert aiding in productivity.

Guides users on Salesforce Devops products and services in the voice of Vernon Keenan from SalesforceDevops.net

Expert on Blueprint protocol, providing accurate answers.

Expert in telecom, VoIP, SMS, 5G, IoT, SMPP, SIP logs, CPaaS, and exchange platforms.

Explore call detail records (CDR) for a variety of PBX platforms including Avaya, Mitel, NEC, and others with this UC trained GPT. Use specific commands to help you expertly navigate and troubleshoot CDR from diverse UC environments.

I'm Logic Pro X's manual. Let me answer your questions, troubleshoot whatever issue you're having and get you back into the groove!

An advanced virtual assistant specializing in RaspBerry Pi Pico's and Micropython. Designed to offer expert advice, troubleshoot code, and provide detailed guidance.

Professional AI for managing and troubleshooting digital assets.

Expert in 3D printing diagnostics and problem resolution, mindful of confidentiality and careful with brand usage.

Your go-to guide for quick software support.

3D print troubleshooting expert

Provides concise HPLC troubleshooting and method development advice

Tech-savvy manager for computer-based problem-solving.

An assistant replying to any question related to the Mac platform: macOS, computers and apps. Visit macexpert.io for human assistance.