Notebook Workspaces
GPU-backed Jupyter and VS Code workspaces with curated ML environments (PyTorch, TensorFlow, RAPIDS) and persistent storage. Sessions spin up in seconds with on-demand GPU attachment and release when idle — no more setting up libraries for every experiment.
Overview
A data scientist should spend time on the science, not on fighting a bare server into shape. Notebook Workspaces are pre-configured, GPU-backed Jupyter environments with curated data science and ML frameworks — PyTorch, TensorFlow, JAX, Scikit-learn, Hugging Face, R, and Python data stack — all ready at session start. Every workspace attaches GPU compute (NVIDIA A100, H100, or H200) on demand, mounts persistent storage for datasets and notebooks, and releases the accelerator when the session closes. No driver installation, no framework compilation, no environment debugging. The first line of code runs within minutes of the browser opening.
Clevertek scopes every engagement to your environment — capacity, sites, compliance and support model — so you get a tailored plan rather than a fixed SKU. Pricing is quote-only, and our solutions architects will work through your requirements before any proposal.
Our approach
We deploy and manage GPU-accelerated Jupyter notebook environments for data science, ML experimentation, and collaborative analysis. Each workspace is a pre-configured container or VM image with the full data science stack — PyTorch 2.x, TensorFlow 2.x, JAX, CUDA 12.x, cuDNN, NCCL, MLflow, Dask, Pandas, NumPy, Scikit-learn, and Hugging Face Transformers — all tested and versioned as a release. GPU attachment is per-session: request a workspace with or without an accelerator, attach NVIDIA A100, H100, or H200 GPUs when the workload requires it, and release when done. Persistent home directories and dataset mounts survive session restarts. Workspaces can be shared with colleagues as a reproducible environment link — no setup instructions, no dependency lists. We manage the infrastructure, storage lifecycle, GPU scheduling, and user authentication so your data scientists start working from the moment they log in.
Why work with us
GPU-backed on demand, zero idle cost
Attach NVIDIA A100, H100, or H200 GPU to a workspace session when the workload requires it. The accelerator releases when the session closes — no GPU cost for exploration, prototyping, or documentation work.
Pre-configured, tested environments
PyTorch 2.x, TensorFlow 2.x, JAX, CUDA 12.x, cuDNN, NCCL, MLflow, Dask, Pandas, and Hugging Face — all pre-installed and version-tested. No framework compilation, no dependency conflicts, no environment debugging at session start.
Persistent storage that survives sessions
Home directories, dataset mounts, and installed packages persist across workspace restarts. A notebook saved at session close is there when the workspace reopens, including GPU kernel state for long-running experiments.
Shareable as a reproducible link
Send a workspace link to a colleague and they open the exact same environment — same framework versions, same libraries, same dataset mounts. No setup instructions, no dependency lists, no environment drift between team members.
Key benefits
What this solution delivers for your business.
Zero setup time per experiment
Frameworks and libraries are pre-installed and tested. The first experiment begins in minutes, not hours — no driver installation, no CUDA compilation, no environment debugging before starting real work.
GPU attached only when needed
Per-session GPU attachment means you pay for the accelerator only when the workload uses it. Exploration, code review, and documentation happen on CPU, and GPU cost is tied to actual computation.
Reproducible environments across the team
Environment images are versioned and aligned with the AI Studio platform. Every team member works from the same foundation — no more notebook that runs on one machine and fails on another.
Collaboration without setup friction
Share a workspace link for pair programming or code review. The colleague opens the exact same environment without any setup steps — dependencies, datasets, and GPU configuration are all identical.
What's included
Part of this managed service.
GPU-backed Jupyter workspaces
JupyterLab and Jupyter Classic environments with per-session NVIDIA GPU attachment. Request A100, H100, or H200 GPUs when the workload needs acceleration; release them when done.
- JupyterLab with GPU-backed kernels
- NVIDIA A100 and H100 per-session attach
- CPU-only mode for lightweight work
- Automated GPU release on session close
Pre-installed ML framework stack
Curated environment images with PyTorch, TensorFlow, JAX, CUDA, cuDNN, NCCL, Scikit-learn, Pandas, Dask, MLflow, and Hugging Face. Every image version-tested as a release.
- PyTorch 2.x and TensorFlow 2.x
- CUDA 12.x with cuDNN and NCCL
- JAX, Hugging Face, and MLflow
- Pandas, NumPy, Dask, Scikit-learn
Persistent storage and data access
Persistent home directory with notebook auto-save, configurable dataset mounts from object storage or NAS, and environment package persistence across workspace restarts.
- Persistent home directory per user
- Dataset mounts from S3-compatible storage
- Package persistence across sessions
- Automated notebook version history
Collaborative workspace sharing
Share a workspace link with team members for pair programming or code review. The shared environment loads the exact same framework versions, dataset mounts, and GPU configuration.
- Shareable workspace URLs
- Reproducible environment per link
- Real-time collaboration with JupyterLab
- No environment setup for recipients
Where it helps
Real-world scenarios where this solution delivers measurable outcomes.
Data exploration and feature engineering
A data scientist loads a 50 GB dataset from object storage into a workspace with 8 CPU cores and 64 GB RAM. After exploring the data distribution and engineering features, they attach an H100 GPU for model training on the prepared dataset — all from the same workspace session.
Collaborative ML experiment review
A senior data scientist shares a workspace link with a junior team member for code review. The junior opens the exact environment — same library versions, same dataset mounts, same GPU configuration — and runs the experiment to verify results without any setup steps.
Questions buyers actually ask
Is this just a hosted Jupyter notebook?
More than that — it is the complete environment (frameworks, GPU, storage, collaboration) wired together as a managed service. A hosted Jupyter notebook leaves you to set up drivers, install libraries, and provision storage. Workspaces deliver all of it pre-configured and tested.
Do I pay for GPU when the workspace is open but idle?
No. GPU attachment is per-session and you only pay for the accelerator when it is attached to your workspace. A workspace open on CPU cores alone incurs no GPU cost. GPU billing stops when you detach the accelerator, even if the workspace remains open.
How do Workspaces relate to AI Studio?
Notebook Workspaces are the exploration and experimentation surface inside the broader AI Studio and Data Platform. They connect to the shared feature store, experiment tracker, and model registry — but can also be used standalone for ad-hoc analysis.
Can I install additional packages?
Yes. Workspaces support pip, conda, and apt package installation. Installed packages persist across session restarts within the same workspace. For team-wide additions, we can update the base environment image.
Ready to scope a solution?
Talk to a Clevertek solutions architect about your requirements — no obligation.