Best AI tools for< Batch Data >

BuildShip is a batch processing tool for ChatGPT that allows users to process ChatGPT tasks in parallel on a spreadsheet UI with CSV/JSON import and export. It supports various OpenAI models, including GPT4, Claude 3, and Gemini. Users can start with readymade templates and customize them with their own logic and models. The data generated is stored securely on the user's own Google Cloud project, and team collaboration is supported with granular access control.

BulkGPT is a no-code AI workflow automation tool that combines web scraping and AI capabilities to help users create custom workflows for mass scraping web pages, generating SEO blogs, personalized messages, and more without the need for coding. It simplifies content creation and data extraction processes by enabling users to upload data, run it in Google Sheets, or integrate it with other tools via API. BulkGPT offers features like web scraping in Google Sheets, URL crawling, SEO content creation, e-commerce product description generation, ChatGPT automation, data scraping, and marketing email campaign creation.

Bulk Rename Utility is a free online file renaming tool that offers AI-powered and rule-based operations. Users can easily rename multiple files or folders without the need to upload them, ensuring privacy and security. The tool supports various file operations, diverse renaming rules, and provides a user-friendly interface for efficient batch file renaming. With features like AI-driven renaming, regex support, and custom JavaScript functions, Bulk Rename Utility simplifies the renaming process and enhances productivity.

MapsScraperAI is an AI-powered tool designed to extract leads and data from Maps. It offers businesses the ability to generate local B2B leads, conduct research, monitor competition, and obtain business contact details. With features like batch lookup, lightning-fast results, and the unique ability to extract email addresses, MapsScraperAI streamlines the process of data extraction without the need for coding. The tool mimics real user behavior to reduce the risk of being blocked by Maps and ensures timely updates to accommodate any changes on the Maps website.

Modal is a high-performance cloud platform designed for developers, AI data, and ML teams. It offers a serverless environment for running generative AI models, large-scale batch jobs, job queues, and more. With Modal, users can bring their own code and leverage the platform's optimized container file system for fast cold boots and seamless autoscaling. The platform is engineered for large-scale workloads, allowing users to scale to hundreds of GPUs, pay only for what they use, and deploy functions to the cloud in seconds without the need for YAML or Dockerfiles. Modal also provides features for job scheduling, web endpoints, observability, and security compliance.

Substratus.AI is a fully managed private LLMs platform that allows users to serve LLMs (Llama and Mistral) in their own cloud account. It enables users to keep control of their data while reducing OpenAI costs by up to 10x. With Substratus.AI, users can utilize LLMs in production in hours instead of weeks, making it a convenient and efficient solution for AI model deployment.

Hopsworks is an AI platform that offers a comprehensive solution for building, deploying, and monitoring machine learning systems. It provides features such as a Feature Store, real-time ML capabilities, and generative AI solutions. Hopsworks enables users to develop and deploy reliable AI systems, orchestrate and monitor models, and personalize machine learning models with private data. The platform supports batch and real-time ML tasks, with the flexibility to deploy on-premises or in the cloud.

Deepgram is a speech recognition and transcription service that uses artificial intelligence to convert audio into text. It is designed to be accurate, fast, and easy to use. Deepgram offers a variety of features, including: - Automatic speech recognition - Speaker diarization - Language identification - Custom acoustic models - Real-time transcription - Batch transcription - Webhooks - Integrations with popular platforms such as Zoom, Google Meet, and Microsoft Teams

Weavel is an AI tool designed to revolutionize prompt engineering for large language models (LLMs). It offers features such as tracing, dataset curation, batch testing, and evaluations to enhance the performance of LLM applications. Weavel enables users to continuously optimize prompts using real-world data, prevent performance regression with CI/CD integration, and engage in human-in-the-loop interactions for scoring and feedback. Ape, the AI prompt engineer, outperforms competitors on benchmark tests and ensures seamless integration and continuous improvement specific to each user's use case. With Weavel, users can effortlessly evaluate LLM applications without the need for pre-existing datasets, streamlining the assessment process and enhancing overall performance.

Monkt is a powerful document processing platform that transforms various document formats into AI-ready Markdown or structured JSON. It offers features like instant conversion of PDF, Word, PowerPoint, Excel, CSV, web pages, and raw HTML into clean markdown format optimized for AI/LLM systems. Monkt enables users to create intelligent applications, custom AI chatbots, knowledge bases, and training datasets. It supports batch processing, image understanding, LLM optimization, and API integration for seamless document processing. The platform is designed to handle document transformation at scale, with support for multiple file formats and custom JSON schemas.

Salad is a distributed GPU cloud platform that offers fully managed and massively scalable services for AI applications. It provides the lowest priced AI transcription in the market, with features like image generation, voice AI, computer vision, data collection, and batch processing. Salad democratizes cloud computing by leveraging consumer GPUs to deliver cost-effective AI/ML inference at scale. The platform is trusted by hundreds of machine learning and data science teams for its affordability, scalability, and ease of deployment.

Anyscale is a company that provides a scalable compute platform for AI and Python applications. Their platform includes a serverless API for serving and fine-tuning open LLMs, a private cloud solution for data privacy and governance, and an open source framework for training, batch, and real-time workloads. Anyscale's platform is used by companies such as OpenAI, Uber, and Spotify to power their AI workloads.

ImageTextify is a free, AI-powered OCR tool that enables users to extract text from images, PDFs, and handwritten notes with high accuracy and efficiency. The tool offers a wide range of features, including multi-format support, batch processing, and a mobile-friendly interface. ImageTextify is designed to cater to both personal and professional needs, providing a seamless solution for converting images to text. With a focus on privacy, speed, and support for multiple languages and formats, ImageTextify stands out as a reliable and user-friendly OCR tool.

Odyssey is a native Mac application designed for creating remarkable art, completing tasks efficiently, and automating repetitive tasks using AI and cutting-edge machine-learning models without the need for coding. It serves as an all-purpose tool for creators, students, educators, artists, marketers, photographers, AI hobbyists, developers, interior designers, and data analysts. Odyssey offers features like image generation and processing, stable diffusion models, controlNet support, super-resolution upscaling, background removal, image transitions, large language models, math equations, automation and batch workflows, private and secure processing, custom workflows, and more. It is a versatile tool that simplifies various tasks across different fields.

Hunch is an AI-first workspace tool designed for teams to leverage top AI models, tackle complex projects, and enhance productivity. It offers a visual canvas interface, templates for various tasks, and features like batch processing, web scraping, and code running. Users can easily create AI-based workflows, share tools, and benefit from the platform's productivity-focused design.

Designify is an AI-powered online tool that allows users to turn any photo into stunning designs automatically and for free. It offers advanced batch-editing capabilities, API integration, and customization options for creating professional product photos, engaging marketing visuals, and automotive images. Users can upload images, remove backgrounds, enhance colors, adjust shadows, and download or share their designs effortlessly. Designify Pro provides additional features such as high-resolution images, API access, and lifetime feature updates. The tool is GDPR compliant and ensures the security and privacy of user data.

WOXO is an AI-powered video generator that helps content creators boost their YouTube and TikTok views. It offers a range of features to streamline the video creation process, including idea generation, quick editing, and scheduling. With WOXO, content creators can save time, overcome creative blocks, and ensure consistency in their video output.

Pixlr is a free online photo editor, image generator, and design tool suite that offers a wide range of features for both beginners and experienced users. With its user-friendly interface and powerful AI-powered tools, Pixlr makes it easy to edit, enhance, and create stunning images. Whether you need to crop, resize, adjust colors, or add filters and effects, Pixlr has you covered. You can also use Pixlr to create collages, design social media graphics, and even generate AI-powered images from scratch. With its wide range of features and easy-to-use interface, Pixlr is the perfect tool for anyone who wants to edit and enhance their photos.

ThumbSnap is a free online AI art generator powered by Stable Diffusion. It allows users to create unique and realistic images from text prompts. With ThumbSnap, you can generate art in various styles, including realistic, abstract, fantasy, and more. The tool is easy to use and requires no prior artistic skills. Simply type in your desired art prompt and click "Create" to generate an image. You can also use the "Random" button to generate a random image.

Bulk Image Generation is an AI-powered tool that allows users to create up to 100 unique images in minutes. It features a convenient batch editor that is quick, intuitive, and saves significant time. Users can create characters, book illustrations, or any other design with endless creative possibilities.

Chronon is a platform that simplifies and improves ML workflows by providing a central place to define features, ensuring point-in-time correctness for backfills, simplifying orchestration for batch and streaming pipelines, offering easy endpoints for feature fetching, and guaranteeing and measuring consistency. It offers benefits over other approaches by enabling the use of a broad set of data for training, handling large aggregations and other computationally intensive transformations, and abstracting away the infrastructure complexity of data plumbing.

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It's the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data. Pathway comes with an **easy-to-use Python API** , allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**. You can install Pathway with pip: `pip install -U pathway` For any questions, you will find the community and team behind the project on Discord.

LakeSoul is a cloud-native Lakehouse framework that supports scalable metadata management, ACID transactions, efficient and flexible upsert operation, schema evolution, and unified streaming & batch processing. It supports multiple computing engines like Spark, Flink, Presto, and PyTorch, and computing modes such as batch, stream, MPP, and AI. LakeSoul scales metadata management and achieves ACID control by using PostgreSQL. It provides features like automatic compaction, table lifecycle maintenance, redundant data cleaning, and permission isolation for metadata.

BionicGPT is an on-premise replacement for ChatGPT, offering the advantages of Generative AI while maintaining strict data confidentiality. BionicGPT can run on your laptop or scale into the data center.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

Mobius is an AI infra platform including realtime computing and training. It is built on Ray, a distributed computing framework, and provides a number of features that make it well-suited for online machine learning tasks. These features include: * **Cross Language**: Mobius can run in multiple languages (only Python and Java are supported currently) with high efficiency. You can implement your operator in different languages and run them in one job. * **Single Node Failover**: Mobius has a special failover mechanism that only needs to rollback the failed node itself, in most cases, to recover the job. This is a huge benefit if your job is sensitive about failure recovery time. * **AutoScaling**: Mobius can generate a new graph with different configurations in runtime without stopping the job. * **Fusion Training**: Mobius can combine TensorFlow/Pytorch and streaming, then building an e2e online machine learning pipeline. Mobius is still under development, but it has already been used to power a number of real-world applications, including: * A real-time recommendation system for a major e-commerce company * A fraud detection system for a large financial institution * A personalized news feed for a major news organization If you are interested in using Mobius for your own online machine learning projects, you can find more information in the documentation.

Candle-vllm is an efficient and easy-to-use platform designed for inference and serving local LLMs, featuring an OpenAI compatible API server. It offers a highly extensible trait-based system for rapid implementation of new module pipelines, streaming support in generation, efficient management of key-value cache with PagedAttention, and continuous batching. The tool supports chat serving for various models and provides a seamless experience for users to interact with LLMs through different interfaces.

Aphrodite is the official backend engine for PygmalionAI, serving as the inference endpoint for the website. It allows serving Hugging Face-compatible models with fast speeds. Features include continuous batching, efficient K/V management, optimized CUDA kernels, quantization support, distributed inference, and 8-bit KV Cache. The engine requires Linux OS and Python 3.8 to 3.12, with CUDA >= 11 for build requirements. It supports various GPUs, CPUs, TPUs, and Inferentia. Users can limit GPU memory utilization and access full commands via CLI.

LightRAG is a repository hosting the code for LightRAG, a system that supports seamless integration of custom knowledge graphs, Oracle Database 23ai, Neo4J for storage, and multiple file types. It includes features like entity deletion, batch insert, incremental insert, and graph visualization. LightRAG provides an API server implementation for RESTful API access to RAG operations, allowing users to interact with it through HTTP requests. The repository also includes evaluation scripts, code for reproducing results, and a comprehensive code structure.

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

Hopsworks is a data platform for ML with a Python-centric Feature Store and MLOps capabilities. It provides collaboration for ML teams, offering a secure, governed platform for developing, managing, and sharing ML assets. Hopsworks supports project-based multi-tenancy, team collaboration, development tools for Data Science, and is available on any platform including managed cloud services and on-premise installations. The platform enables end-to-end responsibility from raw data to managed features and models, supports versioning, lineage, and provenance, and facilitates the complete MLOps life cycle.

xFinder is a model specifically designed for key answer extraction from large language models (LLMs). It addresses the challenges of unreliable evaluation methods by optimizing the key answer extraction module. The model achieves high accuracy and robustness compared to existing frameworks, enhancing the reliability of LLM evaluation. It includes a specialized dataset, the Key Answer Finder (KAF) dataset, for effective training and evaluation. xFinder is suitable for researchers and developers working with LLMs to improve answer extraction accuracy.

Weights & Biases (W&B) is a platform that helps users build better machine learning models faster by tracking and visualizing all components of the machine learning pipeline, from datasets to production models. It offers tools for tracking, debugging, evaluating, and monitoring machine learning applications. W&B provides integrations with popular frameworks like PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Lightning, XGBoost, and Sci-Kit Learn. Users can easily log metrics, visualize performance, and compare experiments using W&B. The platform also supports hosting options in the cloud or on private infrastructure, making it versatile for various deployment needs.

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

SimpleAICV_pytorch_training_examples is a repository that provides simple training and testing examples for various computer vision tasks such as image classification, object detection, semantic segmentation, instance segmentation, knowledge distillation, contrastive learning, masked image modeling, OCR text detection, OCR text recognition, human matting, salient object detection, interactive segmentation, image inpainting, and diffusion model tasks. The repository includes support for multiple datasets and networks, along with instructions on how to prepare datasets, train and test models, and use gradio demos. It also offers pretrained models and experiment records for download from huggingface or Baidu-Netdisk. The repository requires specific environments and package installations to run effectively.

The Qianfan SDK provides best practices for large model toolchains, allowing AI workflows and AI-native applications to access the Qianfan large model platform elegantly and conveniently. The core capabilities of the SDK include three parts: large model reasoning, large model training, and general and extension: * `Large model reasoning`: Implements interface encapsulation for reasoning of Yuyan (ERNIE-Bot) series, open source large models, etc., supporting dialogue, completion, Embedding, etc. * `Large model training`: Based on platform capabilities, it supports end-to-end large model training process, including training data, fine-tuning/pre-training, and model services. * `General and extension`: General capabilities include common AI development tools such as Prompt/Debug/Client. The extension capability is based on the characteristics of Qianfan to adapt to common middleware frameworks.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

LLM training in simple, pure C/CUDA. There is no need for 245MB of PyTorch or 107MB of cPython. For example, training GPT-2 (CPU, fp32) is ~1,000 lines of clean code in a single file. It compiles and runs instantly, and exactly matches the PyTorch reference implementation. I chose GPT-2 as the first working example because it is the grand-daddy of LLMs, the first time the modern stack was put together.

DOS Script expert aiding in batch file creation and optimization.

Creates standalone reusable PHP scripts, tools and batch processes.

Generates high-quality Buddhist images

Bath essentials and luxurious techniques.

A bathtub expert sharing styles, materials, installation tips, and bath advice.

Playful baby-like GPT offering creative ideas to pamper babies.