Open m946107011 opened 4 weeks ago
Thanks for the issue @m946107011. I have a quick question, first one being what is the data size in each of the models.
From the code I think each prediction is indeed running in the GPU, but I don't know if this is a great fit currently. Iterating throigh so many models will have a significant overhead when compared to singular large model predictions. That said, @hcho3 might be a good person to give some feedback for parallel tree inference like this.
Thank you for your quick reply, @dantegd. The largest model is 100 MB, and the smallest is 852 KB. For the dataset, I use the HFS file format; the largest file is 61 MB, and the smallest is 35 KB.
RH
What is your question? Hi,
I have pretrained several Random Forest (RF) models using cuRFC. I need to iterate through these models to make predictions and add the results to a DataFrame. However, the process is currently very slow. (iterate 2233 models need more than 3 hrs) I am wondering if there is an API available to ensure that I am using the GPU to accelerate the prediction process? (A100 40G x2).
` def pre_read_model(layer_count):
` def Add_NF(data, task_list, problem_mode, layer_count,c,workers)