Closed denvercal1234GitHub closed 1 year ago
Hi @denvercal1234GitHub
I am not familiar enough with tensorflow / keras to see where this error is coming from. Have you tested these packages independently on infinityFlow
to see if they work on your machine ? Also have you made sure to disable parallelization by using cores = 1L
in infinity_flow()
?
Best, Etienne
Thank you @ebecht for all your help and patience with all my crazy questions.
I was finally able to fix the error and run infinity_flow()
with neural net after I activate tensorflow conda environment before running R.
My laptop mac m1 has Total Number of Cores: 10 (8 performance and 2 efficiency)
. So I tried cores = 2L
and obtained the following outputs.
Q1. Would you mind informing me what does the "L" in 2L
indicate? Do you recommend using all 8 cores for infinity_flow
?
Q2. Although the output still said "I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support", the pipeline appeared to run fine and GPU was in fact used (viewed in Activity Monitor). Is the output below what you also observed when you ran neural net on your data?
Thanks again!
Hello again,
Q1. The L indicates that the argument is of type integer
rather than numeric
. More core means it is going to run faster, you can use as many as your hardware supports. It should not affect the quality of the results, only the speed of computation
Q2. It's nice that you managed to use the GPU, I actually could not. Training and using the models should have been much faster then. Good for you !
Thanks Etienne!
Hi there,
When I followed the "training_non_default_regression_models" tutorial and sample data as below, I encountered errors as shown.
Would you mind helping me to fix the issue in R environment, because it does not seem that my TensorFlow is actually using GPU?
Thank you for your help.
Output
Output