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There is a rough equation to estimate amount of ram: runners * chunks_per_runner * chunk_size < 100000 * įor example, a GPU with 8 GB of memory would require: runners * chunks_per_runner * chunk_size < 800000 Increasing this number may improve performance on GPUs with a large number of compute cores, but will increase GPU memory use.

Number of GPU runners per device ( -gpu_runners_per_device): The number of neural network runners to create per CUDA device.

Increasing this figure will increase GPU basecalling performance when it is enabled.

Max chunks per runner ( -chunks_per_runner): The maximum number of chunks which can be submitted to a single neural network runner before it starts computation.When performing GPU basecalling there is always one CPU support thread per GPU caller, so the number of callers ( -num_callers) dictates the maximum number of CPU threads used.

For reference the fast calling mode was ~8 minutes. So from the above we see in high accuracy mode it take the Xavier ~41 minutes to complete the base calling using the default configuration files. Model file: /opt/ont/guppy/data/template_r9.4.1_450bps_modbases_dam-dcm-cpg_hac.jsn ONT Guppy basecalling software version 3.4.1+213a60d0Ĭonfig file: /opt/ont/guppy/data/dna_r9.4.1_450bps_modbases_dam-dcm-cpg_hac.cfg Guppy_basecaller -disable_pings -compress_fastq -c dna_r9.4.1_450bps_modbases_dam-dcm-cpg_hac.cfg -fast5_out -i flongle_fast5_pass/ -s flongle_hac_fastq -x 'auto ' -recursive $ guppy_basecaller -compress_fastq -c dna_r9.4.1_450bps_modbases_dam-dcm-cpg_hac.cfg -i flongle_fast5_pass/ -s flongle_hac_fastq -x 'auto' -recursive

For android download USB Drive Letter Manager 5.5.11