Installation

Kyle Rawlins edited this page Nov 28, 2018 · 32 revisions

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).

  1. Install Anaconda. This provides all the prerequisites for the lambda notebook.
  2. Install the lambda notebook from github, either the most recent release, or the master.
  3. 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.

  1. First, install current Python 3 via http://www.python.org/.
  2. From the command line, use pip to install IPython/Jupyter. Basically, run: pip install jupyter (or pip3 install jupyter depending on your python setup; see the above link).
  3. 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.
  4. 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 This is not recommended, as it may lead to out of date versions of Jupyter.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.
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