Hi @Mostapha am trying to run an adaptive comfort model in pollination. When using the model locally on cpu the recipe and simulation works fine. The same model is throwing an error in pollination with adaptive comfort recipe as set modifier construction. I can share the gh file here if needed.
The error states: issue with radiance material but the model runs fine for annual daylight analysis in pollination.
Are you using the latest version of the recipe? The issue is that your HBJSON file is generated by a newer version of the LBT than the one that this version of the recipe is using.
Plastic used to be plastic.
Cc @chris!
Yes @Mostapha I am using the latest version of LBT Plugins(dev version).
My question was about the latest version of the recipe not LBT. I just checked and you are using version 0.2.12 which is the latest version.
@chris, if I’m not mistaken this looks like a case that we need to update lbt-honeybee dependencies version for honeybee-radiance.
Hey @asisnath . Thanks for reporting. The issue was that I forgot to update one of the 6 Pollination plugins that the comfort mapping recipes use (in this case, lbt-honeybee). I just merged a fix for all 3 comfort mapping recipes and it is running correctly now. So, once you use version 0.2.13 of the adaptive comfort maps, you should not experience this issue.
Thanks for the update @Chris @Mostapha . The error is gone. When I run the Adaptive model for 2500 points its taking too long (more than 1hr and still running). Just wanted to know is it common or is there any process to speed things up in pollination. I have set sensor count as default 200 for 2500 points.
Hi @asisnath 
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I had a quick look at the job you are running and it’s looking like the speed issue is a cloud problem on our end. We use an autoscalable pool of machines on Google Cloud to run your workflows at scale and it looks like our account is being limited to 20 machines where we should be able to add 100s.
We will be taking this up with Google Cloud next week to increase the number of machines we can run workflows on and therefore increase the speed of your cloud jobs 
Hi @antoine . Thank you for the clearification. You guys have done awsome job with cloud computing. Will give it a try again after coulple of days.
Regards.
@m.k.dang I’m roping you into this performance conversation as I noticed you are also running a very large job with 80 parallel energy simulations of an urban scale model. This has pushed our system to reach it’s maximum compute allowance on Google Cloud which is why the Job has been taking so long to finish 
Sorry for the delay with this, the Jobs will complete eventually but you might want to go ahead and cancel them if you’re not too interested in the results and would like other on the platform to be able to test it out
