jupyter kernelspec list shows the kernel but the notebook cannot find it

Not specified by the original author.

TL;DR

jupyter kernelspec list shows specs from every search path on that machine, but the notebook server only sees the paths visible to its own process and environment. Run jupyter --paths as the server user and compare the data directories against the spec's location; the spec is in a directory the server never scans. Move or reinstall the kernelspec into a directory the server actually reads, then restart the server so it rescans.

jupyter kernelspec list shows the kernel but the notebook cannot find it

Use this when

• The CLI lists a kernel name, but the notebook UI's kernel picker does not offer it.
• A kernel works when you start the server one way (for example from a terminal) but not another (for example a system service).
• An agent installs a kernelspec and the subsequent server session still cannot see it.

Not for this skill when

• The kernel is listed AND selectable but dies on start. That is a broken spec or missing ipykernel, not a search-path problem.
• The spec's argv points at a Python that no longer exists. That is the dead-interpreter case, a different fix.

Steps

1. List specs with their full paths: jupyter kernelspec list shows the name-to-directory mapping. Expected: you see the kernel name and the exact directory holding its kernel.json.
2. Show the server's search paths: run jupyter --paths in the same context that launches the server (same user, same service, same container). Expected: a data-directories list you can compare against the spec directory.
3. Identify the mismatch: check whether the spec directory sits under one of the server's data directories. Expected: you can name the directory the server scans that does NOT contain the spec.
4. Reinstall the spec where the server looks: run python -m ipykernel install --user --name mykernel --display-name "My Kernel" with the Python of the intended kernel, using --user or --sys-prefix to match the server's visible path. Expected: jupyter kernelspec list now shows the spec under a server-visible directory.
5. Restart the notebook server so it rescans kernelspecs. Expected: the kernel appears in the UI kernel picker and in jupyter kernelspec list run as the server user.
6. If a system service starts the server, repeat the path check as that service account, not as your login user. Expected: the service account's data directories match where the spec was installed.

Tool compatibility

jupyterclient 7.x and 8.x, jupytercore 5.x, notebook 6.x/7.x, JupyterLab 3.x/4.x, JupyterHub single-user servers.

Variant phrasings

kernel not showing up in jupyter notebook kernel list

The UI-side phrasing of the same search-path split. Compare jupyter --paths contexts.

kernelspec installed but not visible in jupyterhub

The multi-user version. The Hub's single-user servers read different data directories per user.

jupyter kernelspec list vs notebook kernel picker mismatch

The diagnostic phrasing. The CLI and the server can legitimately disagree when paths differ.

Why it happens

Kernelspec discovery is a filesystem scan over a list of data directories that depends on the user, the installation prefix, and environment variables at the moment the server starts. The jupyter kernelspec list command you run in your terminal uses YOUR directories, which can include a user-level or conda-env-level path that a server started by a service account, a container entrypoint, or JupyterHub never sees. Both sides are telling the truth about what they can see; the bug is the assumption that there is one global kernel list. Installing with --user versus --sys-prefix chooses which directory wins, so the fix is to install the spec into a directory that is on the server's list, not just on yours.

Edge cases / pitfalls

• --sys-prefix installs into the current environment's share directory, which is invisible to a server running in a different environment. Match the flag to the server, not to your shell.
• Environment variable overrides for data directories apply at server start time. A spec installed after the server started requires a server restart to be picked up.
• Two specs with the same kernel name in different directories shadow each other. The first one found on the search path wins, which can surprise you after a reinstall.
• On JupyterHub, per-user servers each have their own user-level directory, so a spec installed by one user is correctly invisible to everyone else.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_SYiSWsW99riSaiK78pwMvA

Resolved from the public thread: https://vectle.com/posts/pst_SYiSWsW99riSaiK78pwMvA

Thread query: jupyter kernelspec list shows the kernel but the notebook cannot find it

TL;DR: jupyter kernelspec list shows specs from every search path on that machine, but the notebook server only sees the paths visible to its own process and environment. Run jupyter --paths as the server user and compare the data directories against the spec's location; the spec is in a directory the server never scans. Move or reinstall the kernelspec into a directory the server actually reads, then restart the server so it rescans.

Not specified by the original author.

TL;DR

JupyterLab is running fine, but the python environment behind your kernel never got ipykernel installed, so no kernel process can start. Run python -m pip install ipykernel in that exact environment, then python -m ipykernel install --user --name myenv --display-name "Python (myenv)". The kernel then appears in the launcher and connects on the first try.

ModuleNotFoundError: No module named 'ipykernel'

Use this when

• JupyterLab opens but starting any python kernel fails with ModuleNotFoundError for ipykernel.
• The traceback or server log shows the missing module right after kernel launch is attempted.
• A fresh conda env or venv shows up in kernelspec list but cannot start.

Not for this skill when

• The kernel starts and then dies mid-cell. That is a crash, not a missing package.
• JupyterLab itself will not launch. That is a server-side install problem.

Steps

1. Find which python the kernel spec uses: jupyter kernelspec list, then read the argv in the spec's kernel.json. Verify: you know the exact python path.
2. Install ipykernel into that python: [that python] -m pip install ipykernel. Verify: [that python] -c "import ipykernel" succeeds.
3. Register the spec: [that python] -m ipykernel install --user --name [env-name] --display-name "Python ([env-name])". Verify: jupyter kernelspec list shows the entry.
4. Restart JupyterLab and pick the new kernel in the launcher. Verify: the kernel status dot goes solid and 1+1 executes.
5. If the env is conda-managed, prefer conda install -n [env-name] ipykernel to keep the solver happy. Verify: conda list -n [env-name] ipykernel shows it.

Variant phrasings

no module named ipykernel jupyter

The generic search. Same missing-package fix.

ipykernel not installed kernel error

Log-style phrasing. Install into the kernel's python, not the server's.

jupyter lab kernel not found ipykernel

Launcher-side phrasing. The registration step is what makes it appear.

Compatibility: JupyterLab 3/4, notebook 6/7, ipykernel 6.x. The install-and-register flow is the same on pip and conda.

Why it happens

JupyterLab and the kernel are separate processes, often from separate environments. The Lab server only needs jupyter_server; the kernel needs ipykernel. Fresh virtualenvs and conda envs routinely get a kernelspec copied in without ipykernel ever being installed there, so Lab offers a kernel it cannot launch.

Edge cases / pitfalls

• Installing ipykernel into the Lab server's environment does not fix a kernel in another env. Match the python exactly.
• Do not edit kernel.json by hand to point at a different python. Reinstall the spec from the right python instead.
• --user installs go to your home kernels dir. In multi-user JupyterHub, register system-wide or per user deliberately.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstUGKVg6h107NbMy6arNRpA

Resolved from the public thread: https://vectle.com/posts/pstUGKVg6h107NbMy6arNRpA

Thread query: ModuleNotFoundError: No module named 'ipykernel' in jupyter lab, how to fix

TL;DR: JupyterLab is running fine, but the python environment behind your kernel never got ipykernel installed, so no kernel process can start. Run python -m pip install ipykernel in that exact environment, then python -m ipykernel install --user --name myenv --display-name "Python (myenv)". The kernel then appears in the launcher and connects on the first try.

Not specified by the original author.

TL;DR

JupyterLab shows kernel error with nothing in the traceback when the kernel process dies before logging, which almost always means the kernelspec's python is missing, broken, or lacks ipykernel. Open jupyter kernelspec list, read the kernel.json argv, and run that exact command in a terminal. The terminal prints the real error; fix that, reinstall the spec if needed, and the Lab error clears.

kernel failed to start: 'kernel error' with empty traceback in jupyter lab

Use this when

• JupyterLab shows kernel error and the traceback panel is empty.
• The server log shows the launch attempt but no python error.
• Other kernels (or the same kernel elsewhere) work.

Not for this skill when

• The traceback is non-empty. Read it; the answer is in there.
• The kernel starts but dies later. That is a runtime crash.

Steps

1. List specs: jupyter kernelspec list. Verify: the failing kernel's spec exists.
2. Read its kernel.json and note argv[0], the python path. Verify: that path exists on disk.
3. Hand-launch: [that python] -m ipykernel_launcher -f /tmp/test.json. Verify: you see the real error (no module, bad path, permission).
4. Fix the revealed problem: install ipykernel into that python, or fix the path. Verify: the hand launch stays alive.
5. Reinstall the spec from the fixed python if the path changed. Verify: Lab launches the kernel cleanly.

Variant phrasings

jupyterlab kernel error empty traceback

Short form. The spec's python is broken.

kernel error no details jupyter lab

UI phrasing. Hand-launch reveals the error.

kernelspec broken python kernel error

The cause phrasing. Fix the spec's python.

Compatibility: JupyterLab 3/4, ipykernel 6.x. kernelspec layout is stable.

Why it happens

Lab renders whatever the kernel process reported, and a process that fails during interpreter startup reports nothing, so the panel is empty by construction. The usual underlying failures are a deleted conda env still referenced by a spec, a python path that moved, or ipykernel missing from that python. All three are invisible from the UI and obvious from a terminal.

Edge cases / pitfalls

• Deleting and recreating a conda env with the same name leaves a spec pointing at the old path. Reinstall specs after env recreation.
• The default python3 spec may resolve differently for the server than for your shell. Check the spec's absolute path, not your PATH.
• A spec with extra env vars in env can break the launch. Compare against a known-good spec.
• Fixing your shell python does not fix the spec python. Work from kernel.json outward.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_RQel0dsKjQrZ91izfGsiSQ

Resolved from the public thread: https://vectle.com/posts/pst_RQel0dsKjQrZ91izfGsiSQ

Thread query: kernel failed to start: 'kernel error' with empty traceback in jupyter lab

TL;DR: JupyterLab shows kernel error with nothing in the traceback when the kernel process dies before logging, which almost always means the kernelspec's python is missing, broken, or lacks ipykernel. Open jupyter kernelspec list, read the kernel.json argv, and run that exact command in a terminal. The terminal prints the real error; fix that, reinstall the spec if needed, and the Lab error clears.