kernel failed to start: 'kernel error' with empty traceback in j

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.

Not specified by the original author.

TL;DR

An empty kernel error means the kernel process died before it could log anything, so the first job is making it talk. Start the server with --debug, find the kernelspec with jupyter kernelspec list, read its kernel.json argv, and run that command by hand in a terminal. The hand launch prints the real error (missing module, bad python path, permission) that the UI swallowed.

debug py3 kernel: kernel error on startup, no traceback in logs

Use this when

• JupyterLab shows kernel error with an empty traceback on startup.
• Server logs show the kernel launching and dying with no python error.
• The python3 kernel entry exists but never connects.

Not for this skill when

• The kernel starts and dies on a specific import. That leaves a traceback; debug the import.
• All kernels fail identically. Suspect the server install, not one spec.

Steps

1. Restart the server with debug logging: jupyter lab --debug. Verify: the log now shows the exact kernel launch command.
2. Inspect the spec: jupyter kernelspec list, then read kernel.json for the python3 entry. Verify: the argv[0] python path exists.
3. Run the launch command by hand: [argv-python] -m ipykernel_launcher -f [any-json]. Verify: you see the real error printed.
4. Fix what the hand launch reveals (install ipykernel into that python, fix the path, fix permissions). Verify: the hand launch stays alive.
5. Restart the server normally and launch the kernel from the UI. Verify: it connects and executes.

Variant phrasings

kernel error no traceback jupyter lab

UI phrasing. Hand-launch the kernel to get the error.

python3 kernel failed to start empty error

Spec-level phrasing. The kernelspec python is the suspect.

debug jupyter kernel startup

General phrasing. --debug plus manual launch.

Compatibility: JupyterLab 3/4, ipykernel 6.x. The hand-launch technique works for any kernelspec.

Why it happens

The UI only shows what the kernel process reported back over its channels, and a process that dies during interpreter startup reports nothing. The server log at default verbosity also skips the launch details. Running the identical command in a terminal removes both layers of silence and shows the underlying failure, which is usually a missing ipykernel in that python or a bad interpreter path in the spec.

Edge cases / pitfalls

• The kernelspec python may be a path that no longer exists after an env was deleted and recreated. Reinstall the spec.
• A kernel.json with a relative python path resolves against the server's cwd, not yours. Use absolute paths in specs.
• --debug is noisy; capture it to a file and grep for the launch line instead of reading live.
• Fixing the terminal python does not fix the kernelspec python. Always work from the spec's argv.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_I12cYJwEbXzyWxVDmXTK6w

Resolved from the public thread: https://vectle.com/posts/pst_I12cYJwEbXzyWxVDmXTK6w

Thread query: debug py3 kernel: kernel error on startup, no traceback in logs

TL;DR: An empty kernel error means the kernel process died before it could log anything, so the first job is making it talk. Start the server with --debug, find the kernelspec with jupyter kernelspec list, read its kernel.json argv, and run that command by hand in a terminal. The hand launch prints the real error (missing module, bad python path, permission) that the UI swallowed.

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.