Python library for topic modelling and document similarity providing Word2Vec, Doc2Vec, LDA and streaming corpora that exceed available memory. Built and maintained by WIEWAVE for Azure Marketplace, AWS Marketplace and Google Cloud 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, AWS Marketplace and Google Cloud Marketplace |
| Offer types | Public listing, with private offers on request |
| Marketplace | Status |
|---|---|
| Azure Marketplace | Published |
| AWS Marketplace | Published |
| Google Cloud Marketplace | Published |
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.
Extends pandas with geometry columns so spatial joins, reprojection and shapefile or PostGIS input work through a familiar dataframe API.
Apache MXNet with the Gluon imperative API for defining and training neural networks, including pretrained vision and language model zoos.
Development image preloaded with the Google Cloud AI Platform (Vertex AI) Python SDK and gcloud CLI for training, tuning and deploying models.
Base compute image with NVIDIA drivers, CUDA toolkit and container runtime configured so GPU workloads such as training or inference run without further driver setup.
Data science environment combining the H2O machine learning library with NumPy and pandas plus Jupyter, ready for tabular modelling workflows.
Open source H2O platform for distributed machine learning, exposing AutoML, GLM, GBM and deep learning through Flow, Python and R interfaces.
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.
