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Prerequisites: python 3 (>3.3), and Jupyter 4 (with all dependencies).
The current alpha release version of the lambda notebook is 0.6.5, download the source here: https://github.com/rawlins/lambda-notebook/archive/v0.6.5.zip. The current master version can be downloaded at: https://github.com/rawlins/lambda-notebook/archive/master.zip.
Installation via anaconda+download (all platforms, recommended)
The best way to [http://jupyter.org/install](install Jupyter) is via Anaconda. Warning, this is a ~400MB download and the process can take a bit of time, so I don't recommend doing it in class (or the like).
- Install Anaconda. This provides all the prerequisites for the lambda notebook.
- Install the lambda notebook from github, either the most recent release, or the master.
- Run "lambda_notebook.py" in the install directory.
Installation via pip+download (all platforms)
Alternatively, you could install Jupyter from pip. See See http://jupyter.org/install for specifics.
- First, install current Python 3 via http://www.python.org/.
- From the command line, use pip to install IPython/Jupyter. Basically, run:
pip install jupyter(or
pip3 install jupyterdepending on your python setup; see the above link).
- Install the lambda notebook from github, e.g. by downloading from the link above or the master at https://github.com/rawlins/lambda-notebook/archive/master.zip, or by cloning the repository locally.
- At the command line in the lambda_notebook directory, run
python3 lambda_notebook.py, which will install the kernel and launch a notebook pointed at a directory with the pre-installed notebooks.
Other installation methods
- You can install the dependencies via your OS's package manager, such as macports. This is not recommended, as it may lead to out of date versions of Jupyter.
- If you are technically inclined and prefer virtualization, the lambda notebook can be installed with
docker pull rawlins/lambda-notebook. This also (at the moment) pulls the jupyter datascience-notebook, so it is a very large pull, ~5GB. See https://github.com/rawlins/ling-docker for more information.