Managed GPU Notebooks
Launch a GPU-enabled Jupyter Notebook from a browser using a pre-built template or an existing project. The environments are managed and containerized, reducing the need to configure servers and dependencies manually.
Paperspace
Freemium
Paperspace is a cloud platform for developing, training, and deploying machine learning applications with managed notebooks, GPU machines, and deployment workflows. It serves ML developers, data scientists, researchers, and teams that need on-demand accelerated computing.
O que é Paperspace?
Paperspace is a cloud platform for building, training, and deploying machine learning applications. It brings together browser-based managed Jupyter Notebooks for exploration and prototyping, GPU-powered Machines for training and compute, and deployment tools for serving models through scalable API endpoints.
The platform abstracts much of the infrastructure work involved in accelerated computing. Users can start from templates or their own projects, choose GPU or IPU-backed instances, track experiments and datasets, and move models from development toward production. It supports individual work as well as team collaboration through shared projects, invitations, permissions, versioning, and utilization controls.
Paperspace is now part of DigitalOcean. Available usage and workspace options include free access for exploration, paid individual and team plans, and separate private, hybrid, on-premises, or self-hosted arrangements described on the pricing pages.
O que Paperspace pode fazer?
GPU and IPU Compute
Machines provide on-demand access to multiple GPU instance types, while the platform also describes IPU-backed instances for accelerated workloads. Instance options support model training, fine-tuning, simulations, rendering, and other GPU compute tasks.
Model Development Workflow
Use Notebooks, Machines, and Deployments together or independently to explore data, develop models, train them, and move applications toward production.
Reproducible Project Tools
Notebook capabilities include persistent storage, versioning, tagging, dataset tracking, terminals, system metrics, log streaming, and lifecycle controls for starting, cloning, and stopping environments.
Team and Programmatic Access
Teams can invite collaborators, set permissions, fork public projects, and monitor utilization. The platform also provides an API, Python CLI, and SDK for programmatic workflows.
Framework and Source-Control Support
Paperspace states that it supports major machine learning frameworks and libraries, and its AI platform page identifies GitHub connectivity for managing work and compute resources with Git.
Casos de uso
“Prototype in a Managed Notebook”
A developer or researcher can choose a template, launch a browser-based Jupyter environment, explore a dataset, and build a proof of concept without setting up a local GPU server.
“Train or Fine-Tune Models”
Teams can move notebook work to GPU-backed Machines to train models or fine-tune foundation models, selecting an instance type suited to the workload and scaling compute as needed.
“Develop Generative AI Applications”
Builders can use the platform to develop LLM-based or generative-media applications, then continue into training and deployment workflows as the project matures.
“Serve Models Through APIs”
After development and training, users can use Deployments to bring an application to life by converting models into scalable API endpoints for inference.
“Collaborate on Shared ML Projects”
Small teams, research groups, and organizations can use shared workspaces, project visibility controls, invitations, permissions, versioning, and utilization information to coordinate experiments.
Perguntas frequentes
What products are included in Paperspace’s machine learning workflow?
The platform describes three main components: Notebooks for development and exploration, Machines for GPU or IPU-backed compute and training, and Deployments for serving models through scalable API endpoints. They can be used together or independently.
How do I start a notebook?
You can launch a GPU-enabled Jupyter Notebook from a browser, select a pre-built template or bring your own project, and begin working without manually configuring servers or dependencies. Notebooks can be started, cloned, and stopped from the workspace.
Can Paperspace support team projects?
Yes. The source describes team creation, collaborator invitations, permissions, shared and private projects, public links or projects where applicable, and visibility into utilization. Paid team plans add different workspace, storage, notebook, and collaboration allowances.
How is usage billed?
Paperspace pricing includes free and paid workspace plans, with compute usage charged separately for paid instance types. The pricing pages describe per-second pricing for the latest GPUs and IPUs, as well as storage overage charges on applicable plans. Exact instance and plan costs vary by option.
Can Paperspace run outside its hosted cloud?
The pricing information describes private cloud, on-premises, hybrid, and self-hosted options for certain managed services. Availability and configuration depend on the selected arrangement and should be confirmed with the provider.
Informações rápidas
- Category
- Cloud AI and machine learning platform
- Core products
- Notebooks, Machines, and Deployments
- Primary users
- ML developers, data scientists, researchers, and teams
- Development environment
- Browser-based, GPU-enabled Jupyter Notebooks
- Compute model
- On-demand GPU and IPU instances with usage-based pricing
- Company context
- Paperspace is now part of DigitalOcean
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