Training of naivebyes classifier on Qiime databse

I am trying to train the fit-classifier-naive-bayes on the Unite database (sh_qiime_release_25.07.2023)
but every time the killed result is coming out.

The specification of my computer is
Processor- 13th Gen Intel(R) Core(TM) i9-13900 2.00 GHz
Ram- 64.0 GB
storage 2TB
The command i am using is as below.
I would be so thankful if anyone can help me.

(qiime2-metagenome-2024.5) salman@Salman:~ cp /mnt/d/sh_qiime_release_25.07.2023/sh_taxonomy_qiime_ver9_97_25.07.2023.txt ~/ nt/d/sh_qiime_release_25.07.2023/sh_refs_qiime_ver9_97_25.07.2023.fasta ~/ (qiime2-metagenome-2024.5) salman@Salman:~ cp /mnt/d/sh_qiime_release_25.07.2023/sh_refs_qiime_ver9_97_25.07.2023.fasta ~/
(qiime2-metagenome-2024.5) salman@Salman:~$ qiime tools import \

--type 'FeatureData[Sequence]'
--input-path ~/sh_refs_qiime_ver9_97_25.07.2023.fasta
--output-path ref-seqs.qza
Imported /home/salman/sh_refs_qiime_ver9_97_25.07.2023.fasta as DNASequencesDirectoryFormat to ref-seqs.qza
(qiime2-metagenome-2024.5) salman@Salman:~ qiime tools import \ --type 'FeatureData[Taxonomy]' \ --input-format HeaderlessTSVTaxonomyFormat \ --input-path ~/sh_taxonomy_qiime_ver9_97_25.07.2023.txt \ tput-path ref-taxonomy.qza --output-path ref-taxonomy.qza Imported /home/salman/sh_taxonomy_qiime_ver9_97_25.07.2023.txt as HeaderlessTSVTaxonomyFormat to ref-taxonomy.qza (qiime2-metagenome-2024.5) salman@Salman:~ qiime feature-classifier fit-classifier-naive-bayes
--i-reference-reads ref-seqs.qza
--i-reference-taxonomy ref-taxonomy.qza
--o-classifier classifier.qza

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Hello Muhammad,

Welcome to the forums! :qiime2:

This means the program can canceled/stopped by the operating system:

This can happen when a command uses too much memory. 64 GB is a lot, but step may need even more!

I've made ready to use pre-trained classifiers so you can simply run qiime feature-classifier classify-sklearn. This second step does not need as much memory.

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Thankyou soo much for your help. I'll try it and then reply you, if i faced any further problem.
Best Regrds.

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