Python utilities for lightweight pipelining: transparent disk caching of function results, parallel loops and efficient persistence of large NumPy arrays. Built and maintained by WIEWAVE for Azure Marketplace and AWS Marketplace, on Ubuntu and Debian.
| Built & maintained by | WIEWAVE |
|---|---|
| Category | AI & Machine Learning |
| Operating systems | Ubuntu, Debian |
| 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.
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.
Minimal Jupyter base environment from the official docker-stacks lineage, giving a conda-managed Python kernel to build custom notebook images on.
Interactive notebook server for Python, letting you mix code, plots and prose in the browser; includes the IPython kernel and scientific packages.
JupyterHub with per-user notebook servers, an authenticator configured and the scientific Python stack pre-installed.
Jupyter's tabbed workspace interface with file browser, terminals, notebooks and extension support, served over HTTP with token authentication.
Python framework for structuring reproducible data science pipelines, providing a data catalog, node abstractions and pipeline visualisation tooling.
Deep learning stack pairing the Keras high-level API with a TensorFlow backend, preinstalled with Python bindings for training and inference.
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.
