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Error in sjSDM_cv #131
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Hi @YJ781, If you set n_gpu = 1L , sjSDM_cv is expecting a CPU parallelization which you didn't specify, so either you turn on CPU parallelization by using n_cores = X or you set n_gpu = NULL (n_gpu = NULL and n_gpu = 1 is actually the same, in both cases only one GPU is used). Thanks for reporting, I will implement a check for that. |
Hi @MaximilianPi , n_gpu = NULL n_gpu=2 |
Hi @YJ781, This is a bug, I forgot to move a tensor to the right device. I pushed a fix to the development branch (master branch of the github repository), you can install the development version by running the following: |
Hi @MaximilianPi , Thank you so much. It worked. But for n_gpu=2, I'm having the same error, Error in cbind(nodes, (n_gpu - 1):0) : object 'nodes' not found How can I deal with it? |
Hi @YJ781, Do you still have this error? |
Yes. Same error.
…________________________________
From: MaximilianPi ***@***.***>
Sent: Tuesday, November 14, 2023 5:21:27 AM
To: TheoreticalEcology/s-jSDM ***@***.***>
Cc: YJ781 ***@***.***>; Mention ***@***.***>
Subject: Re: [TheoreticalEcology/s-jSDM] Error in sjSDM_cv (Issue #131)
Hi @YJ781<https://github.com/YJ781>,
Do you still have this error?
—
Reply to this email directly, view it on GitHub<#131 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AQ4SGZIXH32WTN6K5SQS4B3YENH3PAVCNFSM6AAAAAA5GX3EKWVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQMJQGAYTSMJWGM>.
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|
Hi @MaximilianPi , I feel sorry I forgot to @ you in the last comment. I'm still having the same error. Thanks! |
Hi @YJ781, |
Hi all,
I am running the model with GPU on the supercomputer and have the following error:
Error in cbind(nodes, (n_gpu - 1):0) : object 'nodes' not found
Calls: sjSDM_cv -> tune_func -> cbind
This is what I set for sjSDM_cv
(env = as.matrix(train_X), Y = as.matrix(train_Y),
learning_rate = 0.01, iter = 100L, CV = 10,
tune_steps = 40,
lambda_cov = seq(0, 0.1, 0.001),
lambda_coef = seq(0, 0.1, 0.001),
alpha_cov = seq(0, 1, 0.05),
alpha_coef = seq(0, 1, 0.05),
device = "gpu",
n_gpu = 1,
sampling = 100L,
biotic = bioticStruct(df=dim(train_Y)[2]),
blocks = 6L,
step_size = 4L,
family=gaussian("identity")
)
Do you know how I can figure this out?
Thanks!
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