Ray with the head and worker roles scripted, for distributing training, tuning and batch inference across a cluster. Built and maintained by WIEWAVE for Azure Marketplace and AWS Marketplace, on Ubuntu, Debian, Red Hat Enterprise Linux, AlmaLinux and Rocky Linux.
| Built & maintained by | WIEWAVE |
|---|---|
| Category | AI & Machine Learning |
| Operating systems | Ubuntu, Debian, Red Hat Enterprise Linux, AlmaLinux, Rocky Linux |
| Marketplaces | Azure Marketplace and AWS Marketplace |
| Offer types | Public listing, with private offers on request |
| Marketplace | Status |
|---|---|
| Azure Marketplace | Published |
| AWS Marketplace | Published |
| Google Cloud Marketplace | Available on request |
Need it on another marketplace, or as a private offer for your organisation? cloud@wiewave.com
Each image follows its distribution's own provisioning model, package manager and security tooling — not one build relabelled several times.
LTS and interim releases, Minimal and Pro variants, built to Canonical's cloud-image conventions.
Stable and oldstable, with backports where a workload needs a newer runtime than the release ships.
RHEL 8 and 9 images, including BYOS and pay-as-you-go licensing models on each marketplace.
The 1:1 RHEL-compatible rebuild, and the default landing spot for teams migrating off CentOS Linux.
Enterprise-grade RHEL compatibility with a community governance model, on 8 and 9 streams.
The same four steps behind every offer we've published, including the hardening and CIS Benchmark checks every build goes through.
The distribution, licensing model and target marketplaces are agreed before anything is built.
Packer templates, Ansible provisioning and a pinned package set — then hardened, scanned and checked against the CIS Benchmark for its distribution.
Taken through each cloud's own certification pipeline before it goes live on the marketplace.
Rebuilt on the upstream security cadence and re-published, with old versions retired without breaking deployments.
If yours isn't here, ask our marketplace team directly.
GPU-ready training and inference images with drivers, CUDA and frameworks already matched to each other.
Meta's applied reinforcement learning platform, formerly Horizon, built on PyTorch for training and evaluating decision policies from logged production data.
Stack for observing deployed machine learning models in production, collecting prediction data to track drift, data quality and accuracy over time.
Development image with AWS SDK and CLI tooling configured for building image and video analysis workflows against the Amazon Rekognition service.
Reinforcement learning library built on Ray that provides distributed implementations of PPO, DQN and other algorithms behind a common training API.
Transformer language model from the BERT family, packaged with Hugging Face tooling for fine-tuning on classification, extraction and other NLP tasks.
OpenAI's Bullet-based robot simulation environments for reinforcement learning research, providing locomotion and control tasks behind a Gym-compatible interface.
Tell us the distribution, the marketplace and the commercial model — we'll build, certify and publish it as a public listing or a private offer.
