Use the GPU on an NVIDIA Tesla P40 (24 GB VRAM, Pascal architecture) for data science work in Jupyter. GPU servers are not orderable yet; contact us to be notified when they are.
$ pip install cudf-cu12 cuml-cu12 jupyterlab && jupyter lab --ip=0.0.0.0
# Exemplum instructioe
GPU-accelerated data science moves heavy array and model computations onto the GPU. CuPy, XGBoost and PyTorch support the Pascal P40; NVIDIA RAPIDS (cuDF, cuML) requires a newer GPU.
Incolae Aestrienses et Aestrienses appellantur, qui incolae Aestrienses appellantur.
Incolae Iuvenenses et Iuvenenses-Cecilienses appellantur.
Multae res in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam in memoriam.
Incolae Pitienses vel Pitienses appellantur.
Data Science can use an NVIDIA GPU to speed up its work. VPS.org GPU servers are not available to order yet; contact us to hear when they are. Data Science is a general GPU-accelerated workload. Make sure your software has CUDA support and that your driver / runtime versions match the workload requirements for Data Science.
GPU servers are not available to order yet. Today you can run Data Science on a regular CPU plan, where it works far more slowly than on a GPU: SSH in and run pip install cudf-cu12 cuml-cu12 jupyterlab && jupyter lab --ip=0.0.0.0.
VRAM needs for Data Science depend on the model and batch size; check the project documentation for current figures.
In hoc tempore, usque ad annum 1999, nonnullae res in usu sunt, quae non sunt in usu hodierno.
Yes. Every VPS.org server comes with full root access, so you can run other tools alongside Data Science within the server's RAM and storage.
Every VPS.org server comes with full root SSH access, so you can install drivers, libraries and Data Science however you need.
No. No server comes with a CUDA runtime or NVIDIA driver preinstalled, and GPU servers are not available to order yet. You install the versions Data Science needs yourself.
Yes. VPS.org servers are persistent virtual machines, not ephemeral instances: Data Science models, configs and data stay on the server's SSD storage until you delete them.
Keep working data on the server's SSD for fast access during Data Science runs, and copy finished artifacts (weights, generations, embeddings) to storage outside VPS.org; snapshots and backups stay on the same storage cluster.
You can upgrade any VPS.org server to a larger plan from the control panel; the resize restarts it and your Data Science install carries over. GPU servers are not available yet.
Yes. Backups are a paid add-on (daily +30% of the plan price, kept 7 days; weekly +20%, kept 28 days) and snapshots are free, which is useful before long Data Science runs. Both stay on the same storage cluster as the server.
Contact support within 30 days of your first purchase to request a refund. GPU servers are not sold yet, so no GPU order is involved.
Capacitas GPU limitata est. Dite nos de tuae labores et respondemus cum disponibilitate et prezzo.