Modelling and tting the spectral energy distribution (SED) of galaxies or regions of galaxies is one of the most useful methods available to the astronomer nowadays. By modelling the SEDs and comparing the models to the observations, we can collect important information on the physical processes at play in the formation and evolution of galaxies. The models allow to follow the evolution of the galaxies from their formation on. The versatility of code is crucial because of the diversity of galaxies. The analysis is only relevant and useful if the models can correctly reproduce this diversity now and across (as best as possible) all redshifts. On the other hand, the code needs to run fast to compare several million or tens of millions of models and to select the best (on a probabilistic basis) one that best resembles the observations. With this important point in mind, it seems logical that we should efficiently make use of the computer power available to the average astronomer. For instance, it seems difficult, today, to model and fit SEDs without a parallelized code. We present the new Python version of CIGALE SED fitting code and its characteristics. CIGALE comes in two main avours: CIGALE Classic to fit SEDs and CIGALE Model to create spectra and SEDs of galaxies at all redshifts. The latest can potentially be used in conjunction with galaxy evolution models of galaxy formation and evolution such as semi-analytic ones.

ABSTRACT
1. INTRODUCTION
2. THE BAYESIAN METHOD
3. THE NEW CIGALE
4. CONCLUSION
REFERENCES

• Burgarella Denis(Aix-Marseille Universite, CNRS)
• Boquien Mederic(Institute of Astronomy, University of Cambridge, Madingley Road)
• Buat Veronique(Aix-Marseille Universite, CNRS)
• Ciesla Laure(University of Crete)
• Rhoelly Yannick(Aix-Marseille Universite, CNRS)