This container is used for running the data preprocessing part of DeepVariant Training pipeline including make_examples and shuffle.\nIt will generate output files as tfrecord.gz which can be fed to DeepVariants model_train step.

This is a template command for running GPU accelerated make_examples:

: nvcr.io/nvidia/clara/deepvariant_train:4.2.0-1 \\\n pbrun make_examples --ref --reads --truth-variants --confident_regions \\\n --examples --disable-use-window-selector-model --channel-insert-size \\\n --num-gpus --num-cpu-threads-per-stream --num-zipper-threads \n\">

docker run --gpus all --rm -v <DATA_DIR>:<DATA_DIR> nvcr.io/nvidia/clara/deepvariant_train:4.2.0-1 \\\n  pbrun make_examples --ref <REF_FILE> --reads <BAM_FILE> --truth-variants <TRUTH_VCF> --confident_regions <TRUTH_BED> \\\n  --examples <TFRECORD_FILE> --disable-use-window-selector-model --channel-insert-size \\\n  --num-gpus <GPU_NUM> --num-cpu-threads-per-stream <WORKER_THREAD_NUM> --num-zipper-threads <ZIPPER_THREAD_NUM>\n

This is a template command for running accelerated shuffle:

: nvcr.io/nvidia/clara/deepvariant_train:4.2.0-1 \\\n pbrun shuffle --input_pattern_list --output_pattern_prefix \\\n --output_dataset_config --output_dataset_name --direct-num-workers \n\">

docker run --gpus all --rm -v <DATA_DIR>:<DATA_DIR> nvcr.io/nvidia/clara/deepvariant_train:4.2.0-1 \\\n  pbrun shuffle --input_pattern_list <INPUT_PATTERN_LIST> --output_pattern_prefix <OUTPUT_PATTERN_PREFIX> \\\n  --output_dataset_config <OUTPUT_PBTXT_FILE> --output_dataset_name <DATASET_NAME> --direct-num-workers <WORKER_THREAD_NUM>\n

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nvcr.io/nvidia/clara/deepvariant_train:4.2.0-1 \\\n pbrun shuffle --input_pattern_list --output_pattern_prefix \\\n --output_dataset_config --output_dataset_name --direct-num-workers \n```","Container for Training Parabricks DeepVariant",["D",1726528055295],"NVIDIA Clara Parabricks",[186,242,239],"/api/image/nvidia/clara/containers/deepvariant_train",1405953639,"hasSignedTag",[284],[268,269,270,271],[],[264],[],{"_91":265,"_123":265,"_124":266,"_126":267},"signed images",{"_128":267,"_123":265},"system",{"_91":280,"_123":281,"_124":282,"_126":127},{"_91":276,"_123":277,"_124":278,"_126":127},{"_91":274,"_123":274,"_124":275,"_126":127},{"_91":272,"_123":272,"_124":273,"_126":127},"Parabricks",{"_128":127,"_123":272},"NSPECT-NCUX-AJJM",{"_128":127,"_123":274},"Healthcare","indus_healthcare",{"_128":279,"_123":277},"industry","Genomics","soln_genomics",{"_128":283,"_123":281},"technology",{"_91":285,"_123":285,"_124":286,"_126":287},"NVIDIA AI Enterprise 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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655],[600,601,602,603,604],{"_401":494,"_91":494,"_90":403,"_375":626},{"_401":460,"_91":460,"_90":403,"_375":621},{"_401":614,"_91":614,"_90":403,"_375":615},{"_401":610,"_91":610,"_90":403,"_375":611},{"_401":605,"_91":605,"_90":403,"_375":606},"nvcrm.my.site.com",[607],{"_240":608,"_408":605,"_409":26,"_90":609},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[612],{"_240":613,"_408":610,"_409":26,"_90":443},"edge","github.com",[616,617],{"_240":620,"_408":614,"_409":26,"_90":619},{"_240":618,"_408":614,"_409":26,"_90":619},"logged_in","This domain is owned by Github. 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