Public | Automated Build

Last pushed: 2 years ago
Short Description
Automated traceback capturing for IPython and reporting to Slack
Full Description


A client and server to post tracebacks to the NSLS2 DAMA slack chat

Conda Recipes

Install the most recent tagged build: conda install exceptional -c lightsource2-tag

Install the most recent tagged build: conda install exceptional -c lightsource2-dev

Find the tagged recipe here and the dev recipe here

Installation of client

conda install exceptional

Then in the ipython profile configuration, add these three lines

import exceptional
exceptional.HOST = 'bcart01'

Installation of server

There are a number of things that need to be specified in order for this
app to work

  • docker image to run (nsls2/exceptional)
  • data storage folder (/exceptional/data)
  • database name (/exceptional/data/db.json)
  • host (bcart01)
  • port (5000)
  • slack token

Build the docker containers

New images are built on docker hub when new code is pushed to this git repo
and are available from nsls2/exceptional

Run the exceptioanl server

docker run -p 5000:5000 \
-e DB_PATH="/exceptional/data/db.json" \
-e SLACK_TOKEN=cat /exceptional/slack.token \
-d \


The DB_PATH environmental variable that gets passed to the docker image will
be the location of the .json based database. This holds all of the
traceback information.

Docker Pull Command
Source Repository