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Set up your machine-learning environment

There are three ways to set up an environment in which to work on Machine Learning.

Docker command

  1. Install Docker
  2. Run:
$ docker run -it --name myembedpy kxsys/embedpy
KDB+ 3.5 2017.11.08 Copyright (C) 1993-2017 Kx Systems
l64/ 4(16)core 7905MB kx 0123456789ab EXPIRE 2018.12.04 KOD #0000000


You can drop straight into bash with:

$ docker run -it kxsys/embedpy bash
kx@b8279373a1d1:~$ conda info

     active environment : kx
    active env location : /home/kx/.conda/envs/kx

kx@b8279373a1d1:~$ q
KDB+ 3.5 2018.04.25 Copyright (C) 1993-2018 Kx Systems
l64/ 8()core 64304MB kx b8279373a1d1 EXPIRE 2019.05.21 KOD #0000000


Instructions for running headless or an existing q license are available

Build instructions for the image

Alternative setup with JupyterQ

Install Docker.


docker run --rm -it -p 8888:8888 kxsys/jupyterq

Now point your browser at http://localhost:8888/notebooks/kdb%2BNotebooks.ipynb.

Build instructions for the image

Download via Anaconda

The three Kx packages can be downloaded from

  • kdb
  • embedpy
  • jupyterq

Currently available for Linux and macOS; soon to be available for Windows too.

They are in a dependency tree. If you install embedpy it will automatically install kdb. If you install jupyterq it will install embedpy and kdb.

The commands are as follows:

conda install -c kx kdb
conda install -c kx embedpy
conda install -c kx jupyterq

At present, the packages work only from the base environment.

Before starting q, please run the following commands:

source deactivate base
source activate base

When you first run q it will ask the following questions:

Please provide your email (requires validation):
Please provide your name:
If applicable please provide your company name (press enter for none):

This will then reach out to the Kx license server and generate a kc.lic.

This in turn sends an email confirmation link to validate the license file.

Do it yourself

  1. Install kdb+
  2. Install embedPy
  3. Install JupyterQ