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EpistasisLab
TPU support for TPOT?
Is there any way to run TPOT on TPU? I didn't find any info regarding this in the docs, or in the ReadMe. Can anyone throw some light on this issue?
Alright. Would you also happen to know how much time does TPOT take on a regression problem (Just a rough estimate) on a normal GPU like V100 or a P100? @weixuanfu
I suppose that you are using the "TPOT cuML" for using GPU-accelerated estimators in RAPIDS cuML and DMLC XGBoost. Unfortunately, I do not know if RAPIDS cuML supports V100 or P100. I only have very limited experience on using it on regression problem. I have tested "TPOT cuML" on 2080Ti before with a regression benchmark with 50000 samples and 50 features and it took 1-2 days to finish 100 generations with 100 population size and cv=5.
Is it compulsory to use TPOT cuML for GPU acceleration, or would the vanilla "pip" install use GPU anyways?
Follow up question. Is it possible to use multiple gpu's while training tpot?
@carterrees yes, you can do this by starting a Dask CUDA cluster and setting use_dask=True. This brief recording shows an example. https://www.youtube.com/watch?v=7z4OJQdY_mw
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
cluster = LocalCUDACluster() # use every GPU on the machine by default
client = Client(cluster)
...
# TPOT as normal, passing use_dask=True and config_dict="TPOT cuML"
...
