Show HN: Attorch – PyTorch's nn module written in Python using OpenAI's Triton https://ift.tt/JzFRyf3

Show HN: Attorch – PyTorch's nn module written in Python using OpenAI's Triton attorch is a subset of PyTorch's nn module, written purely in Python using OpenAI's Triton. Its goal is to be an easily hackable, self-contained, and readable collection of neural network modules whilst maintaining or improving upon the efficiency of PyTorch. In other words, it intends to be a forkable project endowed with a simple, intuitive design that can serve as an accessible starting point for those who are seeking to develop custom deep learning operations but are not satisfied with the speed of a pure PyTorch implementation and do not have the technical expertise or resources to write CUDA kernels. There already exist a number of wonderful PyTorch-like frameworks powered by Triton, but most concentrate solely on Transformers and NLP applications, whereas attorch aims to be more inclusive by also presenting a variety of layers pertaining to areas besides NLP such as computer vision. Moreover, attorch is not an inference-only package and fully supports both forward and backward passes, meaning it can be used during training as well as inference, though its performance for the latter is generally not on par with dedicated inference engines. Questions and feedback are welcome in the comments sections. https://ift.tt/vsRWMr2 April 30, 2024 at 01:07AM

Show HN: Kaytu – Optimizing cloud costs using actual usage data https://ift.tt/GljxafX

Show HN: Kaytu – Optimizing cloud costs using actual usage data Reduce your cloud costs - SREs/DevOps/Cloud Engineers Hi community! We are Kaytu (“Kay-two,” named after the K2 mountain), and we've developed an open-source tool for engineering, DevOps, and SRE teams to reduce cloud costs. Cloud inflation (“cloud-flation”) is real—AWS EC2 costs are up 23% (4-5x global inflation average [1]), and 30% of the capacity that is paid for is simply wasted ([2]). The best way to improve cloud utilization is by simplifying the process so engineers can spot inefficiencies and suggest changes. We built a simple open-source CLI tool that recommends a cost-optimal workload based on actual usage data from observability tools. Check it out at https://ift.tt/iRhWqdV Currently, we support AWS EC2 On-Demand Servers & EBS Storage using observability data from CloudWatch to determine utilization. You can optimize EC2 Servers based on CPU, Network, Memory, and Storage. We're expanding support to include OS License, GPU metrics, RDS, and Prometheus integration, and we plan to add more AWS services like EKS and OpenSearch, as well as Azure. This is more than just a utility—we want to provide a no-nonsense platform that makes it ridiculously easy for engineers to build cost-effective apps on the cloud by optimizing workload configurations and customizing to scenarios. Open Core: Inspired by Sid Sijbrandij and GitLab, we've open-sourced our CLI and are actively working on the server side. Our tooling will always remain straightforward and support open-source tools for free. We made it as simple as possible to try out - it’s one command, no sign-up needed, no SaaS platform to share your credentials. We would love you to try it out and give us your feedback! If there are bugs, we would greatly appreciate it if you reported them on GitHub. Cheers, The Kaytu Team (Anil, Arta, Mahan, and Saleh) References: [1]Tangoe IT Trends Savings Recommendations and Liftr Insights data Cloud Pricing [2] Flexera State of Cloud Report - Multiple reports spanning 2017-2023 https://ift.tt/iRhWqdV April 29, 2024 at 09:27PM

Show HN: Bard PDF – Chat with Pdf in Google Bard or Gemini https://ift.tt/o8q7Fuh

Show HN: Bard PDF – Chat with Pdf in Google Bard or Gemini Chat with pdf in Google Bard or Gemini for free. Several ways to have conversations with pdfs in Google Bard or Gemini. https://bardpdf.dev April 29, 2024 at 08:31AM

Show HN: Dotenv, if it is a Unix utility https://ift.tt/39ISrxC

Show HN: Dotenv, if it is a Unix utility I like the idea of using dotenv files, but I dislike having to use different language-specific libraries to read them. To solve this, I created a small utility that lets you prefix any command with "dotenv" to load the ".env" file. This is how I imagine dotenv would work if it had started as a UNIX utility rather than a Node.js library. https://ift.tt/yN5Xu3U April 29, 2024 at 01:55AM

Show HN: OpenLIT – Open-Source LLM Observability with OpenTelemetry https://ift.tt/BHCuFhQ

Show HN: OpenLIT – Open-Source LLM Observability with OpenTelemetry Hey HN, we're super excited to share something we've been working on: OpenLIT. After an engaging preview that some of you might recall, we are now proudly announcing our first stable release! *What's OpenLIT?* Simply put, OpenLIT is an open-source tool designed to make monitoring your Large Language Model (LLM) applications straightforward. It’s built on OpenTelemetry, aiming to reduce the complexities that come with observing the behavior and usage of your LLM stack. *Beyond Basic Text Generation:* OpenLIT isn’t restricted to just text and chatbot outputs. It now includes automatic monitoring capabilities for GPT-4 Vision, DALL·E, and OpenAI Audio. Essentially, we're prepared to assist you with your multi-modal LLM projects all through a single platform and we're not stopping here; more updates and model support are on their way! *Key Features:* - *Instant Alerts:* Offers immediate insights on cost & token usage, in-depth usage analysis, and latency metrics. - *Comprehensive Coverage:* Supports a range of LLM Providers, Vector DBs, and Frameworks - everything from OpenAI and AnthropicAI to ChromaDB, Pinecone, and LangChain. - *Aligned with Standards:* OpenLIT follows the OpenTelemetry Semantic Conventions for GenAI, ensuring your monitoring efforts meet the community's best practices. *Wide Integration Compatibility:* For those already utilizing observability tools, OpenLIT integrates with various telemetry destinations, including OpenTelemetry Collector, Jaeger, Grafana Cloud, and more, expanding your data’s reach and utility. *Getting Started:* Check our quickstart guide and explore how OpenLIT can enhance your LLM project monitoring: https://ift.tt/YR2n6Tx We genuinely believe OpenLIT can change the game in how LLM projects are monitored and managed. Feedback from this community could be invaluable as we continue to improve and expand. So, if you have thoughts, suggestions, or questions, we’re all ears. Let’s push the boundaries of LLM observability together. Check out OpenLIT here: https://ift.tt/JRQ9olv Thanks for checking it out! https://ift.tt/JRQ9olv April 26, 2024 at 03:15PM

Show HN: Scenestamps – A website for sharing movie scenes with timestamps https://ift.tt/KIrhmA2

Show HN: Scenestamps – A website for sharing movie scenes with timestamps Hello hackers, I've launched a website specifically for sharing scenes, complete with descriptions and timestamps from various films and TV shows. I'm reaching out to gather your perspectives and recommendations in these domains to improve the site and extend my outreach. Link : https://scenestamps.com You are not required to register/login to browse the site. Scenestamps Features: 1. Search : You can directly search for a scene or a source. 2. Upload Posts : You can register with your google account, login and start posting right away. Unlike other sites in this specific domain, users are allowed to upload posts. There are two types of posts - scene - source Source is a movie,tv show, documentary, etc... One source can have multiple scenes While creating a scene post, source can be selected there. Scene post will have the timestamp fields. There are two types of it: - single - one input field of timestamp in which the scene happens. - from-to - two input fields, from and to within which the scene takes place. 3. Share posts : Share feature is available on both source and scene posts, with which you can share the post to your favorite social media platforms 4. Tagging system : You can add tag to the scene posts. You can also get all the scenes that has that tag name by clicking on it. I think people wanting to create scenes is quite a small audience, but I want to make this the best it can possibly be so please post any problems or suggestions in the replies or at reddit.com/scenestamps.com or message me x.com/gjpx_ if you prefer. April 26, 2024 at 12:10PM

Show HN: Htpy – generate HTML from Python without templates https://ift.tt/NH609G8

Show HN: Htpy – generate HTML from Python without templates I built a library that to generate HTML from Python. We have been using this library with Django the last couple of months instead of classic templates and find it to be productive. It is easy to debug, works great with static type checkers and it is easy to build reusable components/partials. Give it a try! https://htpy.dev April 28, 2024 at 01:04AM