Kernel error jupiter notebook что это

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Available kernels:
  ir         C:UserspmwashAppDataRoamingjupyterkernelsir
  python3    C:UserspmwashAppDataRoamingjupyterkernelspython3

How to Fix Kernel Error in Jupyter Notebook

Make your python codes run smoothly

Data science involves the use of technical programming tools. Most times these tools become complex to work with due to errors in analysis, system compatibility, technical difficulties, or errors from the software developers.

All developers must be familiar with Anaconda Navigator, right?

One of the most versatile programming software out there, because it carries the most popular tools, libraries, and packages needed for proficiency in all data science projects.

Among these libraries is the Jupyter Notebook. A tool used to create, develop and share files that contain live codes, visualizations, and narrative texts.

However, there are certain errors associated with using the notebook. On launch or while working on a project, there is a high chance you will come across a Kernel Error.

Before we get into the solutions, what is a kernel error?

A kernel error occurs basically when you try opening a python 3 file in the wrong directory. The truth is Jupyter and Python are two different software entirely. So, a kernel error occurs when Jupyter fails to connect with a specific version of Python.

When the system encounters difficulties locating the path which certain programs will run, this error is known as a kernel error in Jupyter Notebook.

Now that we are familiar with Jupyter Notebook and what the error is all about, let’s dive into the solution.

Enough theory, let’s get practical.

The Jupyter Notebook most of us work with runs on Anaconda. So to get to the back-end, open the Anaconda command prompt. Since you have Anaconda already installed in your system, click the search bar, type Anaconda prompt and run as administrator.

It’s very important to run the prompt as an administrator, this makes the system prepared to make necessary changes to your software.

2. Open Base Environment and select your directory.

The directory is where you want all your present and future projects to be stored. In less technical terms, it is choosing like a massive folder with easy access to keep your notes, text files, and libraries for recollection and transfer.

The black screen contains a preset directory which is c:/windows/system32 . However, it is recommended that you switch to your preferred directory before running codes on the notebook.

I do most of my development and analysis on my e:drive , under the folder linux . This is where I will work on further libraries and other analytical projects. Input the command and change the directory of your notebook.

3. Create another environment for your notebook.

Most kernel errors are often brought about by the failure of the Notebook to connect with other versions of Python.

By default, there are no other virtual environments in Jupyter Notebook apart from Python 3.

Press CTRL + C and create a new virtual environment using this code.

For this tutorial we will be using py you can use any environment you are comfortable with, it doesn’t necessarily have to be py just make sure to replace mine with yours while running the command.

4. Activate the environment you just created.

Use the code below to activate it

after activation, you can observe that the environment has changed from base to py this is a confirmation that the activation of the new environment worked perfectly.

5. Install ipykernel.

Install ipykernel using the code below.

I’m sure you’re wondering, what is ipykernel?

During its early stages, Jupyter notebooks used to be called Ipython Notebooks. So ipykernel is basically a way to connect and manage all of the different kernels.

6. Add kernel to the new environment.

Now we will run this next line of code to merge the kernel to our virtual environment and install it for this user.

That’s all about solving issues related to kernel errors. You can go ahead to restart Jupyter Notebook if you want to test out the changes immediately or you can install some of the valuable libraries you want in this new environment.

Источник

What to do when things go wrongпѓЃ

First, have a look at the common problems listed below. If you can figure it out from these notes, it will be quicker than asking for help.

Check that you have the latest version of any packages that look relevant. Unfortunately it’s not always easy to figure out what packages are relevant, but if there was a bug that’s already been fixed, it’s easy to upgrade and get on with what you wanted to do.

Jupyter fails to startпѓЃ

If you’re using a menu shortcut or Anaconda launcher to start it, try opening a terminal or command prompt and running the command jupyter notebook .

If it can’t find jupyter , you may need to configure your PATH environment variable. If you don’t know what that means, and don’t want to find out, just (re)install Anaconda with the default settings, and it should set up PATH correctly.

If Jupyter gives an error that it can’t find notebook , check with pip or conda that the notebook package is installed.

Try running jupyter-notebook (with a hyphen). This should normally be the same as jupyter notebook (with a space), but if there’s any difference, the version with the hyphen is the ‘real’ launcher, and the other one wraps that.

Jupyter doesn’t load or doesn’t work in the browser

Try in another browser (e.g. if you normally use Firefox, try with Chrome). This helps pin down where the problem is.

Try disabling any browser extensions and/or any Jupyter extensions you have installed.

Some internet security software can interfere with Jupyter. If you have security software, try turning it off temporarily, and look in the settings for a more long-term solution.

In the address bar, try changing between localhost and 127.0.0.1 . They should be the same, but in some cases it makes a difference.

Jupyter can’t start a kernel

Files called kernel specs tell Jupyter how to start different kinds of kernels. To see where these are on your system, run jupyter kernelspec list :

There’s a special fallback for the Python kernel: if it doesn’t find a real kernelspec, but it can import the ipykernel package, it provides a kernel which will run in the same Python environment as the notebook server. A path ending in ipykernel/resources , like in the example above, is this default kernel. The default often does what you want, so if the python3 kernelspec points somewhere else and you can’t start a Python kernel, try deleting or renaming that kernelspec folder to expose the default.

If your problem is with another kernel, not the Python one we maintain, you may need to look for support about that kernel.

Python EnvironmentsпѓЃ

Multiple python environments, whether based on Anaconda or Python Virtual environments, are often the source of reported issues. In many cases, these issues stem from the Notebook server running in one environment, while the kernel and/or its resources, derive from another environment. Indicators of this scenario include:

import statements within code cells producing ImportError or ModuleNotFound exceptions.

General kernel startup failures exhibited by nothing happening when attempting to execute a cell.

In these situations, take a close look at your environment structure and ensure all packages required by your notebook’s code are installed in the correct environment. If you need to run the kernel from different environments than your Notebook server, check out IPython’s documentation for using kernels from different environments as this is the recommended approach. Anaconda’s nb_conda_kernels package might also be an option for you in these scenarios.

Another thing to check is the kernel.json file that will be located in the aforementioned kernel specs directory identified by running jupyter kernelspec list . This file will contain an argv stanza that includes the actual command to run when launching the kernel. Oftentimes, when reinstalling python environments, a previous kernel.json will reference an python executable from an old or non-existent location. As a result, it’s always a good idea when encountering kernel startup issues to validate the argv stanza to ensure all file references exist and are appropriate.

Windows SystemsпѓЃ

Although Jupyter Notebook is primarily developed on the various flavors of the Unix operating system it also supports Microsoft Windows — which introduces its own set of commonly encountered issues, particularly in the areas of security, process management and lower-level libraries.

pywin32 IssuesпѓЃ

The primary package for interacting with Windows’ primitives is pywin32 .

Issues surrounding the creation of the kernel’s communication file utilize jupyter_core ’s secure_write() function. This function ensures a file is created in which only the owner of the file has access. If libraries like pywin32 are not properly installed, issues can arise when it’s necessary to use the native Windows libraries.

Here’s a portion of such a traceback:

As noted earlier, the installation of pywin32 can be problematic on Windows configurations. When such an issue occurs, you may need to revisit how the environment was setup. Pay careful attention to whether you’re running the 32 or 64 bit versions of Windows and be sure to install appropriate packages for that environment.

Here’s a portion of such a traceback:

Resolving pywin32 IssuesпѓЃ

In this case, your pywin32 module may not be installed correctly and the following should be attempted:

where Scripts is located in the active Python’s installation location.

Another common failure specific to Windows environments is the location of various python commands. On *nix systems, these typically reside in the bin directory of the active Python environment. However, on Windows, these tend to reside in the Scripts folder — which is a sibling to bin . As a result, when encountering kernel startup issues, again, check the argv stanza and verify it’s pointing to a valid file. You may find that it’s pointing in bin when Scripts is correct, or the referenced file does not include its .exe extension — typically resulting in FileNotFoundError exceptions.

This Worked An Hour AgoпѓЃ

The Jupyter stack is very complex and rightfully so, there’s a lot going on. On occassion you might find the system working perfectly well, then, suddenly, you can’t get past a certain cell due to import failures. In these situations, it’s best to ask yourself if any new python files were added to your notebook development area.

These issues are usually evident by carefully analyzing the traceback produced in the notebook error or the Notebook server’s command window. In these cases, you’ll typically find the Python kernel code (from IPython and ipykernel ) performing its imports and notice a file from your Notebook development error included in that traceback followed by an AttributeError :

What has happened is that you have named a file that conflicts with an installed package that is used by the kernel software and now introduces a conflict preventing the kernel’s startup.

Resolution: You’ll need to rename your file. A best practice would be to prefix or namespace your files so as not to conflict with any python package.

Asking for helpпѓЃ

As with any problem, try searching to see if someone has already found an answer. If you can’t find an existing answer, you can ask questions at:

Or in an issue on another repository, if it’s clear which component is responsible. Typical repositories include:

jupyter_core — secure_write() and file path issues

jupyter_client — kernel management issues found in Notebook server’s command window.

IPython and ipykernel — kernel runtime issues typically found in Notebook server’s command window and/or Notebook cell execution.

Gathering InformationпѓЃ

Should you find that your problem warrants that an issue be opened in notebook please don’t forget to provide details like the following:

What error messages do you see (within your notebook and, more importantly, in the Notebook server’s command window)?

What platform are you on?

How did you install Jupyter?

What have you tried already?

The jupyter troubleshoot command collects a lot of information about your installation, which can also be useful.

When providing textual information, it’s most helpful if you can scrape the contents into the issue rather than providing a screenshot. This enables others to select pieces of that content so they can search more efficiently and try to help.

Remember that it’s not anyone’s job to help you. We want Jupyter to work for you, but we can’t always help everyone individually.

Источник

What to do when things go wrongпѓЃ

First, have a look at the common problems listed below. If you can figure it out from these notes, it will be quicker than asking for help.

Check that you have the latest version of any packages that look relevant. Unfortunately it’s not always easy to figure out what packages are relevant, but if there was a bug that’s already been fixed, it’s easy to upgrade and get on with what you wanted to do.

Jupyter fails to startпѓЃ

If you’re using a menu shortcut or Anaconda launcher to start it, try opening a terminal or command prompt and running the command jupyter notebook .

If it can’t find jupyter , you may need to configure your PATH environment variable. If you don’t know what that means, and don’t want to find out, just (re)install Anaconda with the default settings, and it should set up PATH correctly.

If Jupyter gives an error that it can’t find notebook , check with pip or conda that the notebook package is installed.

Try running jupyter-notebook (with a hyphen). This should normally be the same as jupyter notebook (with a space), but if there’s any difference, the version with the hyphen is the ‘real’ launcher, and the other one wraps that.

Jupyter doesn’t load or doesn’t work in the browser

Try in another browser (e.g. if you normally use Firefox, try with Chrome). This helps pin down where the problem is.

Try disabling any browser extensions and/or any Jupyter extensions you have installed.

Some internet security software can interfere with Jupyter. If you have security software, try turning it off temporarily, and look in the settings for a more long-term solution.

In the address bar, try changing between localhost and 127.0.0.1 . They should be the same, but in some cases it makes a difference.

Jupyter can’t start a kernel

Files called kernel specs tell Jupyter how to start different kinds of kernels. To see where these are on your system, run jupyter kernelspec list :

There’s a special fallback for the Python kernel: if it doesn’t find a real kernelspec, but it can import the ipykernel package, it provides a kernel which will run in the same Python environment as the notebook server. A path ending in ipykernel/resources , like in the example above, is this default kernel. The default often does what you want, so if the python3 kernelspec points somewhere else and you can’t start a Python kernel, try deleting or renaming that kernelspec folder to expose the default.

If your problem is with another kernel, not the Python one we maintain, you may need to look for support about that kernel.

Python EnvironmentsпѓЃ

Multiple python environments, whether based on Anaconda or Python Virtual environments, are often the source of reported issues. In many cases, these issues stem from the Notebook server running in one environment, while the kernel and/or its resources, derive from another environment. Indicators of this scenario include:

import statements within code cells producing ImportError or ModuleNotFound exceptions.

General kernel startup failures exhibited by nothing happening when attempting to execute a cell.

In these situations, take a close look at your environment structure and ensure all packages required by your notebook’s code are installed in the correct environment. If you need to run the kernel from different environments than your Notebook server, check out IPython’s documentation for using kernels from different environments as this is the recommended approach. Anaconda’s nb_conda_kernels package might also be an option for you in these scenarios.

Another thing to check is the kernel.json file that will be located in the aforementioned kernel specs directory identified by running jupyter kernelspec list . This file will contain an argv stanza that includes the actual command to run when launching the kernel. Oftentimes, when reinstalling python environments, a previous kernel.json will reference an python executable from an old or non-existent location. As a result, it’s always a good idea when encountering kernel startup issues to validate the argv stanza to ensure all file references exist and are appropriate.

Windows SystemsпѓЃ

Although Jupyter Notebook is primarily developed on the various flavors of the Unix operating system it also supports Microsoft Windows — which introduces its own set of commonly encountered issues, particularly in the areas of security, process management and lower-level libraries.

pywin32 IssuesпѓЃ

The primary package for interacting with Windows’ primitives is pywin32 .

Issues surrounding the creation of the kernel’s communication file utilize jupyter_core ’s secure_write() function. This function ensures a file is created in which only the owner of the file has access. If libraries like pywin32 are not properly installed, issues can arise when it’s necessary to use the native Windows libraries.

Here’s a portion of such a traceback:

As noted earlier, the installation of pywin32 can be problematic on Windows configurations. When such an issue occurs, you may need to revisit how the environment was setup. Pay careful attention to whether you’re running the 32 or 64 bit versions of Windows and be sure to install appropriate packages for that environment.

Here’s a portion of such a traceback:

Resolving pywin32 IssuesпѓЃ

In this case, your pywin32 module may not be installed correctly and the following should be attempted:

where Scripts is located in the active Python’s installation location.

Another common failure specific to Windows environments is the location of various python commands. On *nix systems, these typically reside in the bin directory of the active Python environment. However, on Windows, these tend to reside in the Scripts folder — which is a sibling to bin . As a result, when encountering kernel startup issues, again, check the argv stanza and verify it’s pointing to a valid file. You may find that it’s pointing in bin when Scripts is correct, or the referenced file does not include its .exe extension — typically resulting in FileNotFoundError exceptions.

This Worked An Hour AgoпѓЃ

The Jupyter stack is very complex and rightfully so, there’s a lot going on. On occassion you might find the system working perfectly well, then, suddenly, you can’t get past a certain cell due to import failures. In these situations, it’s best to ask yourself if any new python files were added to your notebook development area.

These issues are usually evident by carefully analyzing the traceback produced in the notebook error or the Notebook server’s command window. In these cases, you’ll typically find the Python kernel code (from IPython and ipykernel ) performing its imports and notice a file from your Notebook development error included in that traceback followed by an AttributeError :

What has happened is that you have named a file that conflicts with an installed package that is used by the kernel software and now introduces a conflict preventing the kernel’s startup.

Resolution: You’ll need to rename your file. A best practice would be to prefix or namespace your files so as not to conflict with any python package.

Asking for helpпѓЃ

As with any problem, try searching to see if someone has already found an answer. If you can’t find an existing answer, you can ask questions at:

Or in an issue on another repository, if it’s clear which component is responsible. Typical repositories include:

jupyter_core — secure_write() and file path issues

jupyter_client — kernel management issues found in Notebook server’s command window.

IPython and ipykernel — kernel runtime issues typically found in Notebook server’s command window and/or Notebook cell execution.

Gathering InformationпѓЃ

Should you find that your problem warrants that an issue be opened in notebook please don’t forget to provide details like the following:

What error messages do you see (within your notebook and, more importantly, in the Notebook server’s command window)?

What platform are you on?

How did you install Jupyter?

What have you tried already?

The jupyter troubleshoot command collects a lot of information about your installation, which can also be useful.

When providing textual information, it’s most helpful if you can scrape the contents into the issue rather than providing a screenshot. This enables others to select pieces of that content so they can search more efficiently and try to help.

Remember that it’s not anyone’s job to help you. We want Jupyter to work for you, but we can’t always help everyone individually.

Источник

1

05.11.2018, 01:50. Показов 8911. Ответов 1

   Traceback (most recent call last):
  File "C:ProgramDataAnaconda3libsite-packagestornadoweb.py", line 1592, in _execute
    result = yield result
  File "C:ProgramDataAnaconda3libsite-packagestornadogen.py", line 1133, in run
    value = future.result()
  File "C:ProgramDataAnaconda3libsite-packagestornadogen.py", line 1141, in run
    yielded = self.gen.throw(*exc_info)
  File "C:ProgramDataAnaconda3libsite-packagesnotebookservicessessionshandlers.py", line 73, in post
    type=mtype))
File "C:ProgramDataAnaconda3libsite-packagestornadogen.py", line 1133, in run
    value = future.result()
  File "C:ProgramDataAnaconda3libsite-packagestornadogen.py", line 1141, in run
    yielded = self.gen.throw(*exc_info)
  File "C:ProgramDataAnaconda3libsite-packagesnotebookservicessessionssessionmanager.py", line 79, in create_session
    kernel_id = yield self.start_kernel_for_session(session_id, path, name, type, kernel_name)
  File "C:ProgramDataAnaconda3libsite-packagestornadogen.py", line 1133, in run
value = future.result()
  File "C:ProgramDataAnaconda3libsite-packagestornadogen.py", line 1141, in run
    yielded = self.gen.throw(*exc_info)
  File "C:ProgramDataAnaconda3libsite-packagesnotebookservicessessionssessionmanager.py", line 92, in start_kernel_for_session
    self.kernel_manager.start_kernel(path=kernel_path, kernel_name=kernel_name)
  File "C:ProgramDataAnaconda3libsite-packagestornadogen.py", line 1133, in run
    value = future.result()
  File "C:ProgramDataAnaconda3libsite-packagestornadogen.py", line 326, in wrapper
    yielded = next(result)
  File "C:ProgramDataAnaconda3libsite-packagesnotebookserviceskernelskernelmanager.py", line 160, in start_kernel
    super(MappingKernelManager, self).start_kernel(**kwargs)
  File "C:ProgramDataAnaconda3libsite-packagesjupyter_clientmultikernelmanager.py", line 110, in start_kernel
    km.start_kernel(**kwargs)
  File "C:ProgramDataAnaconda3libsite-packagesjupyter_clientmanager.py", line 259, in start_kernel
    **kw)
  File "C:ProgramDataAnaconda3libsite-packagesjupyter_clientmanager.py", line 204, in _launch_kernel
    return launch_kernel(kernel_cmd, **kw)
  File "C:ProgramDataAnaconda3libsite-packagesjupyter_clientlauncher.py", line 128, in launch_kernel
    proc = Popen(cmd, **kwargs)
  File "C:ProgramDataAnaconda3libsubprocess.py", line 756, in __init__
    restore_signals, start_new_session)
  File "C:ProgramDataAnaconda3libsubprocess.py", line 1155, in _execute_child
    startupinfo)
OSError: [WinError 87] Параметр задан неверно

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