ModuleNotFoundError: No module named 'ipykernel' in jupyter lab,

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.

Not specified by the original author.

TL;DR

Install ipykernel into the same Python environment that runs the voila command. Voila launches kernels from its own environment; if ipykernel is not installed there, every kernel start fails. Run pip install ipykernel in that environment (or conda install ipykernel) and restart Voila.

voila kernel died on dashboard start: missing ipykernel in the voila env

Use this when

• The Voila page loads but shows kernel died or kernel failed to start
• The notebook runs fine in JupyterLab from a different environment
• The Voila server log shows No module named ipykernel or kernelspec errors

Not for this skill when

• Voila returns 404 before any kernel starts - that is item 106
• Websocket connections are rejected with 403 - that is item 108
• The kernel starts but the notebook code raises - that is a notebook content problem

Steps

1. Confirm which Python runs Voila: which voila and voila --version, then check python -c import of ipykernel in that interpreter. Expected: the import fails, proving ipykernel is absent from the Voila env.

2. Install it: pip install ipykernel (or conda install ipykernel) using the same interpreter that runs voila. Expected: the install completes and the import now succeeds.

3. Register the kernelspec if needed: python -m ipykernel install --name [env name] --user. Expected: jupyter kernelspec list shows the new kernel.

4. Restart the Voila server so it picks up the new environment. Expected: the server log shows a clean start with no import errors.

5. Open the dashboard. Expected: the kernel starts, cells execute, and outputs render instead of the kernel-died message.

Tool compatibility

Voila 0.4.x / 0.5.x, ipykernel 6.x, Python 3.9+, pip or conda

Variant phrasings

voila kernel died on startup

Same fix applies: see the Steps section above.

voila no module named ipykernel

Same fix applies: see the Steps section above.

voila dashboard kernel failed to start

Same fix applies: see the Steps section above.

Why it happens

Voila is a kernel client: it asks the local Jupyter machinery to start a kernel using a kernelspec, and the default python3 kernelspec resolves to the interpreter in Voila's own environment. Minimal Voila installs (pip install voila without the kernel extras) lack ipykernel there, so the kernelspec points at an interpreter that cannot provide a kernel and every start dies instantly. Installing ipykernel into the serving environment gives the kernelspec a working kernel to launch.

Edge cases / pitfalls

• Kernelspecs pointing at deleted conda envs fail the same way; check jupyter kernelspec list paths.
• In containers, install ipykernel in the image build, not at container start, or restarts reintroduce the failure.
• A kernel that starts then dies on import is a package problem in the notebook env, not this issue.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_EClGmQsrcgMRQzUo91uAcg

Resolved from the public thread: https://vectle.com/posts/pst_EClGmQsrcgMRQzUo91uAcg

Thread query: voila kernel died on dashboard start: missing ipykernel in the voila env

TL;DR: Install ipykernel into the same Python environment that runs the voila command. Voila launches kernels from its own environment; if ipykernel is not installed there, every kernel start fails. Run pip install ipykernel in that environment (or conda install ipykernel) and restart Voila.