  The Asteroid Terrestrial impact Last Alert System (ATLAS) system consists of
two 0.5m Schmidt telescopes with cameras covering 29 square degrees at plate
scale of 1.86 arcsec per pixel. Working in tandem, the telescopes routinely
survey the whole sky visible from Hawaii (above $\delta > -50^{\circ}$) every
two nights, exposing four times per night, typically reaching $o < 19$
magnitude per exposure when the moon is illuminated and $c < 19.5$ per exposure
in dark skies. Construction is underway of two further units to be sited in
Chile and South Africa which will result in an all-sky daily cadence from 2021.
Initially designed for detecting potentially hazardous near earth objects, the
ATLAS data enable a range of astrophysical time domain science. To extract
transients from the data stream requires a computing system to process the
data, assimilate detections in time and space and associate them with known
astrophysical sources. Here we describe the hardware and software
infrastructure to produce a stream of clean, real, astrophysical transients in
real time. This involves machine learning and boosted decision tree algorithms
to identify extragalactic and Galactic transients. Typically we detect 10-15
supernova candidates per night which we immediately announce publicly. The
ATLAS discoveries not only enable rapid follow-up of interesting sources but
will provide complete statistical samples within the local volume of 100 Mpc. A
simple comparison of the detected supernova rate within 100 Mpc, with no
corrections for completeness, is already significantly higher (factor 1.5 to 2)
than the current accepted rates.
