Set up your machine-learning environment¶
There are three ways to set up an environment in which to work on Machine Learning.
Docker command¶
- Install Docker
- 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 172.17.0.2 EXPIRE 2018.12.04 bob@example.com KOD #0000000
q)
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 172.17.0.3 EXPIRE 2019.05.21 bob@example.com KOD #0000000
q)
Instructions for running headless or an existing q license are available
Build instructions for the image
Alternative setup with JupyterQ¶
Install Docker.
Run
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 anaconda.org/kx:
kdbembedpyjupyterq
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.