Our holistic approach to MLOps is inspired by the idea of creating a work environment that will enable data scientists and ML experts to get value from their data faster and easier than ever before, with zero carbon footprint. csquare is a framework agnostic, high-performance, and secure platform enabling AI Data Scientists to intuitively and efficiently train deep learning models in a collaborative environment. You can use csquare for any kind of machine learning and deep learning projects, including supervised and unsupervised learning. We support Python and R, and incorporate with the most popular open-source tools like PyTorch, TensorFlow, and many others.

csquare helps to streamline workflows, visualize datasets, perform data wrangling, design the neural network architecture and organize and document experiments from start to finish, seamlessly parallelizing jobs across multiple powerful hardware nodes.

Collaborative workflow

Reproducible workflow

Hyperparameter optimisation

Sharing and retrieving models

Reviewing experiments

Data set visualisation

Job parallelisation

Framework agnostic

TensorFlow
Keras
PyTorch
Jupyter Notebook
Julia

Compatible with your favorites toolsets

Your favourite toolsets are immediately available to you on the platform: TensorFlow, Keras, PyTorch, jupyter notebooks and much more. You focus on designing your neural networks, training your models, csquare looks after your training assets, the software and hardware.

HPC clusters

csquare is a hybrid multi-cloud platform, which adaptively deploys workloads to the most appropriate resources, either to one or more of our dedicated HPC clusters (built with parallelization of deep learning training in mind) or cloud based for availability and inference.

Unlike multi-purpose clouds, we don’t have any virtualization layer. Instead, we use linux namespaces and cgroups for isolation and jobs resources limits enforcement. Clusters are highly efficient, powered by renewable energy, and are cooled employing Liquid Immersion Cooling, reducing the environmental footprint.