May 2023
New on CoreWeave Cloud this month:
⚡ Get extremely fast model load times with CoreWeave Tensorizer
CoreWeave's Tensorizer is an S3 and local filesystem compatible module, model, and tensor serializer and deserializer that makes it possible to load models in less than five seconds, making it easier, more flexible, and more cost-efficient to serve models at scale. Reduce resource usage with flexible iterations.
🔓 Easy, secure log-ins with CoreWeave SSO
Single Sign-On, more commonly referred to as SSO, is an authentication scheme that allows the users in an organization to authenticate to CoreWeave Cloud from the same identity provider (IDP) used to log in to other organization-wide apps. Single Sign-On enhances security, and makes for a smoother log-in experience for your team.
CoreWeave currently supports Okta, JumpCloud, and general, generic IDP configurations.
💡 Even greater flexibility for Virtual Servers in a sleek new UI
Our sleek new Cloud UI overhaul for Virtual Servers makes creating high-performance virtual machines even easier than ever! And, for those who want even finer-grained control, the new YAML editor allows users to edit the Custom Resource Definition (CRD) directly, allowing for extreme flexibility.
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✨ New namespace access controls for users
With new per-namespace user access controls, your organization admin can now grant users in the organization access controls to one or more namespaces, allowing users the ability to easily spin up new Virtual Servers, allocate storage, and more!
🧠 Introducing Resource Pools for Determined AI
Resource Pools are groups of hardware selections plus memory requests and limits that make it simple to select resource groups for Determined AI deployments, helping users get their Determined AI experiments up and running faster.
⚡ Fine-tune Stable Diffusion with DreamBooth
Our Fine-tune Stable Diffusion Models demo now incorporates details for working with DreamBooth!
DreamBooth is a technique used to teach novel concepts to Stable Diffusion. The DreamBooth method allows you to fine-tune Stable Diffusion on a small number of examples to produce images containing a specific object or person. This method for fine-tuning diffusion models was introduced in a paper publish in 2022, DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. A lighter introductory text was also released along with the paper in this blog post.
The DreamBooth method is a way to teach a diffusion model about a specific object or style using approximately three to five example images. After the model is fine-tuned on a specific object using DreamBooth, it can produce images containing that object in new settings.
🤝 Meet our Partners: Zeet
Zeet is a software platform that runs on top of your Cloud account, making it simple for developers to deploy code on production-grade infrastructure. With CoreWeave's Kubernetes-native infrastructure and Zeet's team of Kubernetes engineers, we're helping our clients scale and realize value faster without having to build an entire infrastructure engineering team of their own.
Our partnership allows companies to tap into the industry's broadest selection of on-demand GPU compute resources and DevOps expertise.