In this article
A prepared image simplifies initial setup, while the project still owns authentication, data paths, dependencies and a tested restoration procedure.
Selectel documents a Data Science environment for Jupyter on a GPU cloud server. Create the resource through that image’s instructions, configure its intended access and test a small notebook. A ready interface does not establish file durability or compatibility with every dependency in the project.
Record the prepared environment
The image guide lists included tools, requirements and initial steps. Save the versions actually deployed rather than relying on the broad Data Science label. Reproducing an experiment needs the environment identity as well as the notebook file.
Check the software’s relevant terms and your code’s dependencies. If you install or change a package manually, put that change into the repeatable recipe. A successful session is difficult to reproduce when it depends on an unrecorded package edit.
Follow the documented network workflow
The Selectel instructions for this environment describe a public floating IP and the sequence for server creation and JupyterLab access. Use the current image-specific process rather than copying another cloud’s notebook command.
Verify that the intended person authenticates and an unauthorised visitor cannot read project files. Do not share a URL containing a live token. HTTPS reachability and correct application access control are two separate acceptance results.
Identify where the notebook writes
Cloud-server storage options have different resource arrangements. Create a control file, find its absolute path and associate it with the actual disk. A notebook can save successfully to a resource that is later deleted deliberately.
Separate inputs, edited annotations and outputs. Kernel memory is not an independent saved artifact. Preserve needed objects explicitly and ensure a new kernel can reconstruct the working state without an old hidden variable.
Test a small labelling project
Before processing a collection, verify GPU visibility, reading a few inputs and preserving edited labels. Open the exported copy outside the original session. A small non-sensitive control input helps another person test the environment without accessing the full working dataset.
| Step | Acceptance evidence |
|---|---|
| Login | Intended user reaches Jupyter |
| Environment | Versions recorded and GPU visible |
| Data | Control inputs read correctly |
| Preservation | Notebook and labels open elsewhere |
| Repeat | Clean kernel follows the recorded order |
Close with code, results and a secret-free recipe. Give disks, addresses and other retained resources an owner and expiry decision. Closing the notebook interface is not proof that every associated billable service has ended.
Turn the check into a practical next step
A prepared image simplifies initial setup, while the project still owns authentication, data paths, dependencies and a tested restoration procedure.
For the documented details, consult the primary documentation; configuration reference. Continue with selectel gpu server access options and cloud gpu jupyter ssh tunnel.