Comments (13)
This is already in, no?
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@navdeep-G for C/C++ yes but we still need something in Python (and possibly R?).
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Yep. We should have it for those APIs.
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I'm not sure what's the best practice for R but in Python we probably can just use the standard import logging
. We just need to map the verbosity
number to a String level representation (INFO, DEBUG etc.). Ideally we'd add a Python/R call to our C/C++ backend which would map int -> String
according to what is hardcoded in the C code (there are definitions for those in the logger files) so we don't have to hardcode that for example 500 is DEBUG several times in C, Python and R code.
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We just need to expose the verbosity in Python API and then we can modify it as an option in R. We can also use futile.logger package if needed.
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Yes, only needs to be in python and we can use reticulate to invoke it in R
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python already has logging, used in DAI alot: https://docs.python.org/3/library/logging.html
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@pseudotensor yes, we just need to replace the print statements in our code for those logger calls and set the log level appropriately based on the verbosity
level.
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I can start working on this soon if that’s okay with you guys.
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Copy/paste the logger setup arno did for DAI. It's non-trivial to set it up and now it's very nice for DAI.
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This isn't really very high priority, we can do this after we have tSVD in DAI and are done with preparations for GTC I think.
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Sounds good. Not sure what the process of tsvd in DAI is but can check.
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Agreed on priority, just laying out what would be done to someone doesn't do crazy amounts of independent work.
We should try cusolver in DAI, easy to try, just busy getting release out.
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Related Issues (20)
- R random_forest_classifier problem with high dimensions HOT 2
- Support setuptools > 39.1.0
- KMeans on 4 gpus HOT 1
- If i run the below command i am getting attached error. HOT 2
- dependencies incompatibilities between h2o4gpu and cudf HOT 7
- Lightgbm hangs sometimes HOT 4
- take of dask or cupy arrays as input for logistic regression, elastic_net, ridge and linear_regression
- Low GPU utilization during Random Forest predict
- Posted to wrong project
- LogisticRegression default solver is outdated
- LogististicRegression default multi_class parameter is outdated
- get_gpu_info_c not detecting any GPU (compatibility issue with CUDA 11?) HOT 2
- AttributeError: 'NoneType' object has no attribute 'make_ptr_double' HOT 4
- Logistic Regressions predict_proba returns predictions, not probabilities
- install_github failing
- Elastic Net Regression in R HOT 1
- AMD gpu HOT 1
- Installation of h2o4gpu on Kaggle using reticulate package HOT 1
- NameError: name 'output_margin' is not defined
- H2o4GPU build Failed
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