Set up your machine-learning environment¶
There are three ways to set up an environment in which to work on Machine Learning.
- Install Docker
- Create a directory
qand save your
l64.zipfiles in it
$ docker run --rm -it -v `pwd`/q:/tmp/q kxsys/embedpy kx@1ba5d6c29709:~$ q 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 email@example.com KOD #0000000 q)
You can drop straight into q with:
$ docker run --rm -it -v `pwd`/q:/tmp/q kxsys/embedpy q 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 firstname.lastname@example.org KOD #0000000 q)
Lastly you can also pipe your program in:
$ echo 'p)print(1+2)' | docker run --rm -i -v `pwd`/q:/tmp/q kxsys/embedpy q -q 3
Alternative setup with JupyterQ¶
Install Docker. Create a directory
q and place your
l64.zip files in it.
docker run --rm -it -v `pwd`/q:/tmp/q -p 8888:8888 kxsys/jupyterq
Now point your browser at http://localhost:8888/notebooks/kdb%2BNotebooks.ipynb.
Download via Anaconda¶
The three Kx packages can be downloaded from anaconda.org/kx:
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
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
This in turn sends an email confirmation link to validate the license file.