OpenScience can run analysis on your machine or use resources you connect. Model access and compute are separate: an Ace Wallet does not include unlimited experiments on external machines.
Local analysis
Open Customize → Compute and check the Python and R starter setup. If either needs setup or repair, use the offered action and wait for it to finish.
The Python starter includes common analysis libraries such as NumPy, pandas, SciPy, and Matplotlib. The R starter includes tidyverse, ggplot2, and jsonlite. Additional tools may need a separate installation or account.
Shell and local compute receipts identify the selected default Python separately
from any measured version. A command can explicitly choose another interpreter,
so this default is not proof of which Python an arbitrary shell command used.
On Windows, python selects this default; an independently installed python3
or an absolute executable path can select another interpreter.
Local compute using the default Python measures that binary in the admitted execution environment.
Remote submissions label local source metadata as belonging to the submitter;
an unmeasured remote interpreter is left unknown.
Local compute on Windows requires Git Bash. If discovery fails, install Git for
Windows or set OPENSCIENCE_GIT_BASH_PATH to its installed bash.exe. OpenScience
reports a missing or incompatible shell before dispatching a POSIX job script.
Try a small task first:
Check that Python can read data/samples.csv and save a histogram to
results/sample-counts.png. Explain any missing dependency before changing
the environment.
Scientific tools
Open Customize → Tools to see which scientific tools are available on the device or through a connected account. Follow a tool's setup action when offered.
A skill can describe how to use a package without that package being installed. See Scientific tools for availability and limitations.
Connect your own remote resources
The Compute panel supports configured services and SSH hosts. Use your own account or machine credentials, then test the connection.
For an SSH host, supply the host details or import an existing SSH configuration, verify the connection, and record any scheduler or working-directory requirements. Supported scheduler choices are shown in the panel.
For a connected compute service, review its resource defaults and timeout before asking for a job. Saving a credential alone does not guarantee that every service feature is supported.
Approve a concrete run
Before launching a paid or substantial experiment, ask OpenScience to state the inputs, resources, duration, output files, and estimated cost if available.
Prepare a pilot using the configured remote host. Show the command,
resource request, input files, and expected output location. Ask before
launching, and stop after the pilot so we can review the result.
Use the external service's spending controls as well as a task budget. Its charges are governed by that service's terms.
Follow progress and collect outputs
Use the project's Compute view to inspect jobs. Ask for current status, failures, and saved outputs when a task is long-running.
If a response is interrupted, check whether the job is still running before starting another. Stopping a chat response is not proof that every external job has stopped.
After completion, ask OpenScience to retrieve the output files, verify that they are readable, and report the exact project paths. A remote job marked complete is only useful if its results are available for review.
Detailed compute guides
Python and R covers interactive analysis and persistent variables. Remote compute documents Modal, SSH, Slurm, PBS, and other account connections. Compute jobs walks through planning, launch, status, cancellation, and output recovery.
When setup fails
Check the error shown by Compute, available disk space, network access, credentials, and the requested resource type. Keep the failed job identifier when asking for help.
For a tool marked unavailable, choose a supported alternative or resolve the stated prerequisite. Do not assume that a different model can remove a missing dependency or account requirement.