BLAS : Program is Terminated. Because you tried to allocate too many memory regions.

When training K-nearest-neighbors (KNN) models, sometimes a rare error can occur that crashes the entire process:

BLAS : Program is Terminated. Because you tried to allocate too many memory regions.
Segmentation fault: 11

It has so far only occurred on machines with >300 GB of memory (2 confirmed instances). In both cases, the system was not low on memory and had plenty to complete the task, yet the error still occurred.

[awethaileslassie]

This worked for me. “BLAS stands for Basic Linear Algebra Subprograms. BLAS provides standard interfaces for linear algebra, including BLAS1 (vector-vector operations), BLAS2 (matrix-vector operations), and BLAS3 (matrix-matrix operations). As per the documentation, if your application is already multi-threaded, it will conflict with OpenBLAS multi-threading. Therefore, you must set OpenBLAS to use a single thread. So, it seems that your application is conflicting with OpenBLAS multi-threading. You need to run the followings on the command line and it should fix the error:”

export OPENBLAS_NUM_THREADS=1
export GOTO_NUM_THREADS=1
export OMP_NUM_THREADS=1

https://www.discoverbits.in/2509/program-terminated-because-tried-allocate-memory-regions

[Innixma]

Thanks for the info!

To understand how to specify custom hyperparameters, refer to the hyperparameters argument documentation: https://auto.gluon.ai/stable/_modules/autogluon/tabular/predictor/predictor.html#TabularPredictor.fit

The default hyperparameters are:

hyperparameters = {
    'NN': {},
    'GBM': [
        {},
        {'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}},
        'GBMLarge',
    ],
    'CAT': {},
    'XGB': {},
    'FASTAI': {},
    'RF': [
        {'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}},
        {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}},
        {'criterion': 'mse', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression']}},
    ],
    'XT': [
        {'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}},
        {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}},
        {'criterion': 'mse', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression']}},
    ],
    'KNN': [
        {'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}},
        {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}},
    ],
}

You would want to edit KNN (example to enforce using only 16 cores):

'KNN': [
    {'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}, 'n_jobs': 16},
    {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}, 'n_jobs': 16},
],

Another option is to disable KNN entirely:

excluded_model_types = ['KNN']