  We present a search for galaxy-scale strong gravitational lenses in the
initial 2 500 square degrees of the Canada-France Imaging Survey (CFIS). We
designed a convolutional neural network (CNN) committee that we applied to a
selection of 2 344 002 exquisite-seeing $r$-band images of color-selected
luminous red galaxies (LRGs). Our classification uses a realistic training set
where the lensing galaxies and the lensed sources are both taken from real
data, namely the CFIS $r$-band images themselves and the Hubble Space Telescope
(HST). A total of 9 460 candidates obtain a score above 0.5 with the CNN
committee. After a visual inspection of the candidates, we find a total of 133
lens candidates, of which 104 are completely new. The set of false positives
mainly contains ring, spiral, and merger galaxies, and to a lesser extent
galaxies with nearby companions. We classify 32 of the lens candidates as
secure lenses and 101 as maybe lenses. For the 32 highest quality lenses, we
also fit a singular isothermal ellipsoid mass profile with external shear along
with an elliptical Sersic profile for the lens and source light. This automated
modeling step provides distributions of properties for both sources and lenses
that have Einstein radii in the range $0.5\arcsec<\theta_E<2.5\arcsec$.
Finally, we introduce a new lens and/or source single-band deblending algorithm
based on auto-encoder representation of our candidates. This is the first time
an end-to-end lens-finding and modeling pipeline is assembled together, in view
of future lens searches in a single band, as will be possible with Euclid.
