jupyter_tensorboard | Start Tensorboard in Jupyter Notebook | Machine Learning library

by lspvic Python Version: 0.2.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | jupyter_tensorboard Summary

jupyter_tensorboard is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Jupyter applications. jupyter_tensorboard has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install jupyter_tensorboard' or download it from GitHub, PyPI.

Start Tensorboard in Jupyter Notebook

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Quality

Security

License

Reuse

Support

jupyter_tensorboard has a low active ecosystem.

It has 463 star(s) with 79 fork(s). There are 16 watchers for this library.

It had no major release in the last 6 months.

There are 40 open issues and 29 have been closed. On average issues are closed in 31 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of jupyter_tensorboard is 0.2.0

Quality

jupyter_tensorboard has 0 bugs and 7 code smells.

Security

jupyter_tensorboard has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

jupyter_tensorboard code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

jupyter_tensorboard is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

jupyter_tensorboard releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

jupyter_tensorboard saves you 233 person hours of effort in developing the same functionality from scratch.

It has 569 lines of code, 39 functions and 9 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed jupyter_tensorboard and discovered the below as its top functions. This is intended to give you an instant insight into jupyter_tensorboard implemented functionality, and help decide if they suit your requirements.

Start TensorBoard WSGI application
Start the reloader
Adds a TensorBoard instance
Return the next available name
Create a TensorBoard WSGI application
Create a new instance
Create a new TbApp instance
Trim the notebook directory
Get a TensorBoard instance
Get all registered tasks
Delete a TensorBoard instance
Terminate a tensorboard instance
Find the version string
Read file contents
Read the content of a file
Run install
Launch extension after install
Run the develop

Get all kandi verified functions for this library.

jupyter_tensorboard Key Features

No Key Features are available at this moment for jupyter_tensorboard.

jupyter_tensorboard Examples and Code Snippets

No Code Snippets are available at this moment for jupyter_tensorboard.

Community Discussions

Trending Discussions on jupyter_tensorboard

How to log back in using SSH after logging out?

QUESTION

How to log back in using SSH after logging out?

Asked 2019-Jul-13 at 18:57

I logged out of my jupyter notebook which I had set up on my google cloud using SSH. Now, it needs password to log me in again. To log back in (and get a password), I use the following code in the SSH window:

...

ANSWER

Answered 2019-Jul-13 at 18:57

You have opened only the port 8888 in your GCP network settings so you will get connection refused on any other port. Try to kill your existing jupyter processes and free up port 8888 and try again

Source https://stackoverflow.com/questions/57021726

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install jupyter_tensorboard

You can install using 'pip install jupyter_tensorboard' or download it from GitHub, PyPI.
You can use jupyter_tensorboard like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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