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GOVERNING DOWNLOAD TERMS: Use of this container is governed by the Apache 2.0 license.

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MambaSV Docker Usage

MambaSV is a long-read structural variant (SV) caller for PacBio HiFi and Oxford Nanopore R10 sequencing data. The Docker interface supports two workflows:

germline calling from one sample BAM
somatic calling from matched normal/tumor BAMs.

Each workflow runs Mamba-based deep learning breakpoint prediction with bundled checkpoints, converts predictions to VCF records, then applies read-evidence post-filtering. Somatic mode adds an additional matched-sample filtering step.

MambaSV v0.1.2 is a GA release intended for research and development use.

Release image:

nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2\n

The v0.1.2 image supports Linux amd64 hosts and NVIDIA Ampere, Ada, Hopper, and Blackwell GPUs. Use a host NVIDIA driver that supports CUDA 12.8.

The image includes CUDA 12.8.1, CPython 3.14 ABI, PyTorch 2.11.0+cu128, Triton 3.6.0, mamba-ssm 2.2.6.post3, and causal-conv1d 1.6.2.post1.

Pull the public image without signing in to NGC:

docker pull nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2\n

Requirements

NVIDIA GPU with a CUDA 12.8-compatible driver.
Linux amd64 host. ARM64 is not included in v0.1.2.
Docker with NVIDIA Container Toolkit.
Coordinate-sorted, haplotype-tagged long-read BAM inputs with .bai indexes.
Reference FASTA with .fai index.
Matching contig names between the BAM header and reference FASTA, such as chr1 versus 1.
A writable output directory mounted into the container.
GPU sorting is not supported in this image; do not pass --gpusort.

Supported read types:

--read_type pb: PacBio HiFi.
--read_type ont: Oxford Nanopore R10.

Smoke Test

Run the included small PacBio HiFi example first to confirm that Docker, GPU access, MambaSV, and somatic filtering work on your system. The example contains a normal BAM with reference-like reads and a tumor BAM with deletion-supporting reads, so it produces at least one somatic call. It is a system check, not a biological validation.

mkdir -p mambasv_smoke_output\n\ndocker run --rm \\\n  --gpus device=0 \\\n  --ipc=host \\\n  --user \"$(id -u):$(id -g)\" \\\n  -v \"$PWD/mambasv_smoke_output:/output\" \\\n  nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 \\\n  mambasv somatic \\\n    --read_type pb \\\n    --ref /opt/mambasv/examples/smoke_test/small_ref.fa \\\n    --bam_normal /opt/mambasv/examples/smoke_test/small_normal.bam \\\n    --bam_tumor /opt/mambasv/examples/smoke_test/small_tumor.bam \\\n    --output_dir /output/mambasv \\\n    --chroms chr1 \\\n    --num_processes 1 \\\n    --num_dataloader_workers 0 \\\n    --batch_size 1\n

Expected final outputs:

mambasv_smoke_output/mambasv/mambasv.germline.vcf\nmambasv_smoke_output/mambasv/mambasv.somatic.vcf\n

This smoke test is only intended to validate the container and runtime setup. Use your own sorted, indexed, haplotype-tagged normal and tumor BAMs for biological analysis.

Input BAM Assumptions

MambaSV expects long-read BAMs that are ready for indexed regional queries:

BAMs must be coordinate-sorted and indexed. The index may be either sample.bam.bai or sample.bai.
BAMs must be haplotype-tagged with HP tags.
PacBio runs should use HiFi reads with --read_type pb.
ONT runs should use R10 reads with --read_type ont.
The reference FASTA must be indexed with samtools faidx, producing reference.fa.fai.
Reference contig names must match BAM contig names exactly.

Default BAM Realignment

mambasv germline runs consensus-guided BAM realignment before inference by default. mambasv somatic uses the input BAMs directly by default.

Refined BAMs are written under _refined_bam/ using the input BAM basename:

/output/mambasv/_refined_bam/a.b.c.d.mambasv_refined.bam\n\">

/data/a.b.c.d.bam -> /output/mambasv/_refined_bam/a.b.c.d.mambasv_refined.bam\n

When refinement is enabled, MambaSV automatically uses the refined BAMs for inference and filtering. To use the input BAM directly for germline, pass --skip_bam_refinement or set bam_refinement.skip_bam_refinement = true in the config file. To enable paired refinement for somatic calling, set bam_refinement.skip_bam_refinement = false in a config file.

Germline Calling

docker run --rm \\\n  --gpus device=0 \\\n  --ipc=host \\\n  --user \"$(id -u):$(id -g)\" \\\n  -v /path/to/data:/data:ro \\\n  -v /path/to/output:/output \\\n  nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 \\\n  mambasv germline \\\n    --read_type pb \\\n    --ref /data/reference.fa \\\n    --bam /data/sample.bam \\\n    --output_dir /output/mambasv\n

Use --read_type ont for ONT R10 data.

Final germline outputs:

/output/mambasv/_refined_bam/sample.mambasv_refined.bam\n/output/mambasv/_refined_bam/sample.mambasv_refined.bam.bai\n/output/mambasv/mambasv.germline.vcf\n/output/mambasv/log.txt\n/output/mambasv/inference_args.json\n

The raw germline VCF is retained at /output/mambasv/_intermediate/germline.vcf.

Somatic Calling

docker run --rm \\\n  --gpus device=0 \\\n  --ipc=host \\\n  --user \"$(id -u):$(id -g)\" \\\n  -v /path/to/data:/data:ro \\\n  -v /path/to/output:/output \\\n  nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 \\\n  mambasv somatic \\\n    --read_type pb \\\n    --ref /data/reference.fa \\\n    --bam_normal /data/normal.bam \\\n    --bam_tumor /data/tumor.bam \\\n    --output_dir /output/mambasv\n

Use --read_type ont for ONT R10 data.

Final somatic outputs:

/output/mambasv/mambasv.germline.vcf\n/output/mambasv/mambasv.somatic.vcf\n/output/mambasv/log.txt\n/output/mambasv/inference_args.json\n

Raw and first-pass VCFs are retained under /output/mambasv/_intermediate/.

Final Outputs

Use the mambasv.*.vcf files for downstream analysis.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Mode	Final VCFs
Germline	`mambasv.germline.vcf`
Somatic	`mambasv.germline.vcf`, `mambasv.somatic.vcf`

Every successful MambaSV run also writes:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Path	Contents
`log.txt`	Single top-level terminal transcript from optional BAM realignment, inference, and filtering.
`inference_args.json`	Effective run configuration after bundled defaults, optional config file values, and CLI overrides.
`_refined_bam/`	Final refined BAMs and `.bai` indexes when refinement is enabled.
`_logs/`	Detailed inference diagnostics and progress/status files.

Intermediate VCFs are moved under _intermediate/ after filtering. Germline runs retain _intermediate/germline.vcf. Somatic runs retain _intermediate/germline.vcf, _intermediate/somatic.vcf, _intermediate/all.vcf, and _intermediate/somatic_filtered.vcf when those files are produced.

Validated BND adjacencies are emitted as reciprocal records linked by MATEID and EVENT. Intrachromosomal BND records use SVLEN=abs(posA-posB); interchromosomal BND records omit SVLEN.

Workflow Status Lines

MambaSV writes lifecycle lines to both stdout and log.txt. Each line identifies a workflow stage and one of STARTED, COMPLETED, SKIPPED, or FAILED.

[MambaSV][stage=workflow][status=STARTED] mode=somatic read_type=pb output_dir=/output/mambasv\n[MambaSV][stage=bam_refinement][status=SKIPPED] reason='using input BAMs directly'\n[MambaSV][stage=inference][status=STARTED]\n[MambaSV][stage=inference][status=COMPLETED] elapsed_seconds=29.400\n[MambaSV][stage=finalize][status=COMPLETED] elapsed_seconds=0.002\n[MambaSV][stage=workflow][status=COMPLETED] elapsed_seconds=35.100\n

Somatic workflows also report postfilter and somatic_postfilter; germline workflows report postfilter. A failed stage writes status=FAILED with available diagnostic information. Detailed progress information is available under _logs/.

Input Validation

Before starting BAM refinement or inference, MambaSV validates:

command-line enums and numeric values,
TOML syntax, section/key names, value types, ranges, and supported enum values,
conflicting options and inconsistent GPU rank/device settings,
readable FASTA/BAM inputs and their required indexes,
requested contigs against the reference .fai, and
optional BED and model override paths.

Invalid input exits nonzero with a concise mambasv: error: diagnostic. Configuration typos include a suggestion when a close supported name exists.

mambasv: error: invalid TOML in /input/malformed.toml: Invalid value (at end of document)\nmambasv: error: config /input/run.toml: unknown config key inference.batch_szie; did you mean batch_size?\nmambasv: error: Missing required input or index: --ref /missing/ref.fa, /missing/ref.fa.fai\n

--dry_run validates the effective configuration and command relationships while allowing the main FASTA/BAM paths to remain unavailable. A normal run additionally validates those input files, indexes, and requested contigs before creating the output directory or starting compute.

Common Options

--skip_bam_refinement: use the input BAMs directly instead of running BAM realignment. This is already the default for somatic calls.
--gpusort: not supported in v0.1.2. Use the default sorting behavior.
--chroms chr1,chr2,chr3: limit the run to selected chromosomes.
--nproc_per_node 2: run inference with two GPU ranks inside the container.
--cuda_visible_devices 0,1: set CUDA-visible device ordinals inside the container.
--num_processes 32: override CPU workers for conversion/filtering steps.
--batch_size 8: override inference batch size when GPU memory is constrained.
--bnd_validation_min_support 3: set the minimum inference-time BND/INV support required to emit a validated call.

Example multi-GPU run:

docker run --rm \\\n  --gpus '\"device=0,1\"' \\\n  --ipc=host \\\n  --user \"$(id -u):$(id -g)\" \\\n  -v /path/to/data:/data:ro \\\n  -v /path/to/output:/output \\\n  nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 \\\n  mambasv germline \\\n    --read_type pb \\\n    --ref /data/reference.fa \\\n    --bam /data/sample.bam \\\n    --output_dir /output/mambasv \\\n    --nproc_per_node 2 \\\n    --cuda_visible_devices 0,1\n

Config File

MambaSV provides its supported user-configurable settings in a TOML template.

Use a config file when you need to tune resources, restrict regions, override bundled checkpoints, control BAM realignment, or have fine-grained control of model behavior. Common examples include GPU/CPU worker counts, BAM realignment threads, batch size, chromosome lists, BED target regions, max reads per window, read-evidence thresholds, and somatic filtering thresholds.

Set postfilter.debug = true only when you need per-SV post-filter diagnostics such as polished tandem-duplication span messages; normal runs keep those lines off to keep log.txt readable.

To generate the editable template:

mambasv_defaults.toml\n\">

docker run --rm nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 mambasv config-template > mambasv_defaults.toml\n

Edit mambasv_defaults.toml, then mount and pass it:

docker run --rm \\\n  --gpus device=0 \\\n  --ipc=host \\\n  --user \"$(id -u):$(id -g)\" \\\n  -v /path/to/data:/data:ro \\\n  -v /path/to/output:/output \\\n  -v \"$PWD/mambasv_defaults.toml:/config/mambasv_defaults.toml:ro\" \\\n  nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 \\\n  mambasv germline \\\n    --config /config/mambasv_defaults.toml \\\n    --read_type pb \\\n    --ref /data/reference.fa \\\n    --bam /data/sample.bam \\\n    --output_dir /output/mambasv\n

Explicit CLI options override values in the config file. The generated template includes comments for each visible setting; leave fields unchanged unless you have a reason to tune that part of the workflow. The final effective settings for each run are saved to inference_args.json in the output directory.

Troubleshooting

Missing reference index: create /data/reference.fa.fai with samtools faidx reference.fa.
Missing BAM index: create /data/sample.bam.bai with samtools index sample.bam.
Permission errors: make sure the mounted output directory is writable by the container user, or run Docker with an appropriate --user value.
Port conflicts: pass a different --master_port.
Failed or incomplete runs: start with top-level log.txt, then inspect _logs/inference_status.txt and _logs/inference_progress.tsv for inference-stage details. If a run fails before final output organization, partial VCFs may still be in the output root.
GPU selection: Docker maps selected host GPUs to container ordinals. With --gpus device=1, the visible GPU inside the container is usually ordinal 0.

Get Help

For questions, bug reports, or other MambaSV support requests, please use the Parabricks developer forum.

License/Terms of Use

By pulling and using the Parabricks MambaSV container, you accept the governing terms: The software and materials are governed by the NVIDIA Software License Agreement (found at https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the Product-Specific Terms for NVIDIA AI Products (found at https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); except for the model which is governed by the NVIDIA Models Community License Agreement(found at NVIDIA Community Model License). ADDITIONAL INFORMATION: Apache 2.0.

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The Docker interface supports two workflows:\n\n- germline calling from one sample BAM\n- somatic calling from matched normal/tumor BAMs.\n\nEach workflow runs Mamba-based deep learning breakpoint prediction with bundled checkpoints, converts predictions to VCF records, then applies read-evidence post-filtering. Somatic mode adds an additional matched-sample filtering step.\n\nMambaSV v0.1.2 is a GA release intended for research and development use.\n\nRelease image:\n\n```text\nnvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2\n```\n\nThe v0.1.2 image supports Linux amd64 hosts and NVIDIA Ampere, Ada, Hopper, and Blackwell GPUs. Use a host NVIDIA driver that supports CUDA 12.8.\n\nThe image includes CUDA 12.8.1, CPython 3.14 ABI, PyTorch 2.11.0+cu128, Triton 3.6.0, mamba-ssm 2.2.6.post3, and causal-conv1d 1.6.2.post1.\n\nPull the public image without signing in to NGC:\n\n```bash\ndocker pull nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2\n```\n\n## Requirements\n\n- NVIDIA GPU with a CUDA 12.8-compatible driver.\n- Linux amd64 host. ARM64 is not included in v0.1.2.\n- Docker with NVIDIA Container Toolkit.\n- Coordinate-sorted, haplotype-tagged long-read BAM inputs with `.bai` indexes.\n- Reference FASTA with `.fai` index.\n- Matching contig names between the BAM header and reference FASTA, such as `chr1` versus `1`.\n- A writable output directory mounted into the container.\n- GPU sorting is not supported in this image; do not pass `--gpusort`.\n\nSupported read types:\n\n- `--read_type pb`: PacBio HiFi.\n- `--read_type ont`: Oxford Nanopore R10.\n\n## Smoke Test\n\nRun the included small PacBio HiFi example first to confirm that Docker, GPU access, MambaSV, and somatic filtering work on your system. The example contains a normal BAM with reference-like reads and a tumor BAM with deletion-supporting reads, so it produces at least one somatic call. It is a system check, not a biological validation.\n\n```bash\nmkdir -p mambasv_smoke_output\n\ndocker run --rm \\\n --gpus device=0 \\\n --ipc=host \\\n --user \"$(id -u):$(id -g)\" \\\n -v \"$PWD/mambasv_smoke_output:/output\" \\\n nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 \\\n mambasv somatic \\\n --read_type pb \\\n --ref /opt/mambasv/examples/smoke_test/small_ref.fa \\\n --bam_normal /opt/mambasv/examples/smoke_test/small_normal.bam \\\n --bam_tumor /opt/mambasv/examples/smoke_test/small_tumor.bam \\\n --output_dir /output/mambasv \\\n --chroms chr1 \\\n --num_processes 1 \\\n --num_dataloader_workers 0 \\\n --batch_size 1\n```\n\nExpected final outputs:\n\n```text\nmambasv_smoke_output/mambasv/mambasv.germline.vcf\nmambasv_smoke_output/mambasv/mambasv.somatic.vcf\n```\n\nThis smoke test is only intended to validate the container and runtime setup. Use your own sorted, indexed, haplotype-tagged normal and tumor BAMs for biological analysis.\n\n## Input BAM Assumptions\n\nMambaSV expects long-read BAMs that are ready for indexed regional queries:\n\n- BAMs must be coordinate-sorted and indexed. The index may be either `sample.bam.bai` or `sample.bai`.\n- BAMs must be haplotype-tagged with `HP` tags.\n- PacBio runs should use HiFi reads with `--read_type pb`.\n- ONT runs should use R10 reads with `--read_type ont`.\n- The reference FASTA must be indexed with `samtools faidx`, producing `reference.fa.fai`.\n- Reference contig names must match BAM contig names exactly.\n\n## Default BAM Realignment\n\n`mambasv germline` runs consensus-guided BAM realignment before inference by default. `mambasv somatic` uses the input BAMs directly by default.\n\nRefined BAMs are written under `_refined_bam/` using the input BAM basename:\n\n```text\n/data/a.b.c.d.bam -> /output/mambasv/_refined_bam/a.b.c.d.mambasv_refined.bam\n```\n\nWhen refinement is enabled, MambaSV automatically uses the refined BAMs for inference and filtering. To use the input BAM directly for germline, pass `--skip_bam_refinement` or set `bam_refinement.skip_bam_refinement = true` in the config file. To enable paired refinement for somatic calling, set `bam_refinement.skip_bam_refinement = false` in a config file.\n\n## Germline Calling\n\n```bash\ndocker run --rm \\\n --gpus device=0 \\\n --ipc=host \\\n --user \"$(id -u):$(id -g)\" \\\n -v /path/to/data:/data:ro \\\n -v /path/to/output:/output \\\n nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 \\\n mambasv germline \\\n --read_type pb \\\n --ref /data/reference.fa \\\n --bam /data/sample.bam \\\n --output_dir /output/mambasv\n```\n\nUse `--read_type ont` for ONT R10 data.\n\nFinal germline outputs:\n\n```text\n/output/mambasv/_refined_bam/sample.mambasv_refined.bam\n/output/mambasv/_refined_bam/sample.mambasv_refined.bam.bai\n/output/mambasv/mambasv.germline.vcf\n/output/mambasv/log.txt\n/output/mambasv/inference_args.json\n```\n\nThe raw germline VCF is retained at `/output/mambasv/_intermediate/germline.vcf`.\n\n## Somatic Calling\n\n```bash\ndocker run --rm \\\n --gpus device=0 \\\n --ipc=host \\\n --user \"$(id -u):$(id -g)\" \\\n -v /path/to/data:/data:ro \\\n -v /path/to/output:/output \\\n nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 \\\n mambasv somatic \\\n --read_type pb \\\n --ref /data/reference.fa \\\n --bam_normal /data/normal.bam \\\n --bam_tumor /data/tumor.bam \\\n --output_dir /output/mambasv\n```\n\nUse `--read_type ont` for ONT R10 data.\n\nFinal somatic outputs:\n\n```text\n/output/mambasv/mambasv.germline.vcf\n/output/mambasv/mambasv.somatic.vcf\n/output/mambasv/log.txt\n/output/mambasv/inference_args.json\n```\n\nRaw and first-pass VCFs are retained under `/output/mambasv/_intermediate/`.\n\n## Final Outputs\n\nUse the `mambasv.*.vcf` files for downstream analysis.\n\n\n| Mode | Final VCFs |\n| -------- | --------------------------------------------- |\n| Germline | `mambasv.germline.vcf` |\n| Somatic | `mambasv.germline.vcf`, `mambasv.somatic.vcf` |\n\n\nEvery successful MambaSV run also writes:\n\n\n| Path | Contents |\n| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |\n| `log.txt` | Single top-level terminal transcript from optional BAM realignment, inference, and filtering. |\n| `inference_args.json` | Effective run configuration after bundled defaults, optional config file values, and CLI overrides. |\n| `_refined_bam/` | Final refined BAMs and `.bai` indexes when refinement is enabled. |\n| `_logs/` | Detailed inference diagnostics and progress/status files. |\n\n\nIntermediate VCFs are moved under `_intermediate/` after filtering. Germline runs retain `_intermediate/germline.vcf`. Somatic runs retain `_intermediate/germline.vcf`, `_intermediate/somatic.vcf`, `_intermediate/all.vcf`, and `_intermediate/somatic_filtered.vcf` when those files are produced.\n\nValidated BND adjacencies are emitted as reciprocal records linked by `MATEID` and `EVENT`. Intrachromosomal BND records use `SVLEN=abs(posA-posB)`; interchromosomal BND records omit `SVLEN`.\n\n## Workflow Status Lines\n\nMambaSV writes lifecycle lines to both stdout and `log.txt`. Each line identifies a workflow stage and one of `STARTED`, `COMPLETED`, `SKIPPED`, or `FAILED`.\n\n```text\n[MambaSV][stage=workflow][status=STARTED] mode=somatic read_type=pb output_dir=/output/mambasv\n[MambaSV][stage=bam_refinement][status=SKIPPED] reason='using input BAMs directly'\n[MambaSV][stage=inference][status=STARTED]\n[MambaSV][stage=inference][status=COMPLETED] elapsed_seconds=29.400\n[MambaSV][stage=finalize][status=COMPLETED] elapsed_seconds=0.002\n[MambaSV][stage=workflow][status=COMPLETED] elapsed_seconds=35.100\n```\n\nSomatic workflows also report `postfilter` and `somatic_postfilter`; germline workflows report `postfilter`. A failed stage writes `status=FAILED` with available diagnostic information. Detailed progress information is available under `_logs/`.\n\n## Input Validation\n\nBefore starting BAM refinement or inference, MambaSV validates:\n\n- command-line enums and numeric values,\n- TOML syntax, section/key names, value types, ranges, and supported enum values,\n- conflicting options and inconsistent GPU rank/device settings,\n- readable FASTA/BAM inputs and their required indexes,\n- requested contigs against the reference `.fai`, and\n- optional BED and model override paths.\n\nInvalid input exits nonzero with a concise `mambasv: error:` diagnostic. Configuration typos include a suggestion when a close supported name exists.\n\n```text\nmambasv: error: invalid TOML in /input/malformed.toml: Invalid value (at end of document)\nmambasv: error: config /input/run.toml: unknown config key inference.batch_szie; did you mean batch_size?\nmambasv: error: Missing required input or index: --ref /missing/ref.fa, /missing/ref.fa.fai\n```\n\n`--dry_run` validates the effective configuration and command relationships while allowing the main FASTA/BAM paths to remain unavailable. A normal run additionally validates those input files, indexes, and requested contigs before creating the output directory or starting compute.\n\n## Common Options\n\n- `--skip_bam_refinement`: use the input BAMs directly instead of running BAM realignment. This is already the default for somatic calls.\n- `--gpusort`: not supported in v0.1.2. Use the default sorting behavior.\n- `--chroms chr1,chr2,chr3`: limit the run to selected chromosomes.\n- `--nproc_per_node 2`: run inference with two GPU ranks inside the container.\n- `--cuda_visible_devices 0,1`: set CUDA-visible device ordinals inside the container.\n- `--num_processes 32`: override CPU workers for conversion/filtering steps.\n- `--batch_size 8`: override inference batch size when GPU memory is constrained.\n- `--bnd_validation_min_support 3`: set the minimum inference-time BND/INV support required to emit a validated call.\n\nExample multi-GPU run:\n\n```bash\ndocker run --rm \\\n --gpus '\"device=0,1\"' \\\n --ipc=host \\\n --user \"$(id -u):$(id -g)\" \\\n -v /path/to/data:/data:ro \\\n -v /path/to/output:/output \\\n nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 \\\n mambasv germline \\\n --read_type pb \\\n --ref /data/reference.fa \\\n --bam /data/sample.bam \\\n --output_dir /output/mambasv \\\n --nproc_per_node 2 \\\n --cuda_visible_devices 0,1\n```\n\n## Config File\n\nMambaSV provides its supported user-configurable settings in a TOML template.\n\nUse a config file when you need to tune resources, restrict regions, override bundled checkpoints, control BAM realignment, or have fine-grained control of model behavior. Common examples include GPU/CPU worker counts, BAM realignment threads, batch size, chromosome lists, BED target regions, max reads per window, read-evidence thresholds, and somatic filtering thresholds.\n\nSet `postfilter.debug = true` only when you need per-SV post-filter diagnostics such as polished tandem-duplication span messages; normal runs keep those lines off to keep `log.txt` readable.\n\nTo generate the editable template:\n\n```bash\ndocker run --rm nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 mambasv config-template > mambasv_defaults.toml\n```\n\nEdit `mambasv_defaults.toml`, then mount and pass it:\n\n```bash\ndocker run --rm \\\n --gpus device=0 \\\n --ipc=host \\\n --user \"$(id -u):$(id -g)\" \\\n -v /path/to/data:/data:ro \\\n -v /path/to/output:/output \\\n -v \"$PWD/mambasv_defaults.toml:/config/mambasv_defaults.toml:ro\" \\\n nvcr.io/nvidia/clara/clara-parabricks-mambasv:v0.1.2 \\\n mambasv germline \\\n --config /config/mambasv_defaults.toml \\\n --read_type pb \\\n --ref /data/reference.fa \\\n --bam /data/sample.bam \\\n --output_dir /output/mambasv\n```\n\nExplicit CLI options override values in the config file. The generated template includes comments for each visible setting; leave fields unchanged unless you have a reason to tune that part of the workflow. The final effective settings for each run are saved to `inference_args.json` in the output directory.\n\n## Troubleshooting\n\n- Missing reference index: create `/data/reference.fa.fai` with `samtools faidx reference.fa`.\n- Missing BAM index: create `/data/sample.bam.bai` with `samtools index sample.bam`.\n- Permission errors: make sure the mounted output directory is writable by the container user, or run Docker with an appropriate `--user` value.\n- Port conflicts: pass a different `--master_port`.\n- Failed or incomplete runs: start with top-level `log.txt`, then inspect `_logs/inference_status.txt` and `_logs/inference_progress.tsv` for inference-stage details. If a run fails before final output organization, partial VCFs may still be in the output root.\n- GPU selection: Docker maps selected host GPUs to container ordinals. With `--gpus device=1`, the visible GPU inside the container is usually ordinal `0`.\n\n## Get Help\n\nFor questions, bug reports, or other MambaSV support requests, please use the [Parabricks developer forum](https://forums.developer.nvidia.com/c/healthcare/parabricks/).\n\n## License/Terms of Use\nBy pulling and using the Parabricks MambaSV container, you accept the governing terms: The software and materials are governed by the NVIDIA Software License Agreement (found at https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the Product-Specific Terms for NVIDIA AI Products (found at https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); except for the model which is governed by the NVIDIA Models Community License Agreement(found at NVIDIA Community Model License). 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GOVERNING DOWNLOAD TERMS: Use of this container is governed by the Apache 2.0 license.

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