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markjiang1/
NeuroTaskFM

any-to-any

apache-2.0

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NeuroTaskFM

NeuroTaskFM is a CUDA-native research system for learning relationships among brain structure, resting-state activity, task function, behavior, and longitudinal clinical state.

Research use only. This project is not a medical device and must not be used for autonomous diagnosis or treatment decisions.

Main components

NeuroTaskFM-T: large multimodal teacher model.
NeuroCompiler: deterministic C++/CUDA MRI preprocessing and feature-pack compiler.
NeuroTaskFM-C: compiled-space model designed for two-B200 inference.
NeuroSignature: participant-specific state updated across observations and visits.
Go platform: command, API, job, and bounded compiler-agent services.

The model and GPU tools use C++20, LibTorch, CUDA, cuFFT, cuDNN, NCCL, HDF5, NIfTI, DCMTK, and OpenCV. Platform services use Go. No Python runtime is required by the project.

Hardware

NeuroTaskFM is CUDA-only and targets NVIDIA B200:

training: 32 B200 GPUs across four NVL8 nodes;
inference and personalization: two B200 GPUs in one NVLink domain.

There is no CPU or alternate-GPU compute path.

Build

The recommended build is the DGX B200 container:

docker build -f deploy/Dockerfile.b200 -t neurotaskfm:0.1.0 .

On a configured DGX host with LibTorch under /opt/libtorch:

make build

Important binaries:

text

build/bin/neurocompile
build/bin/ntfm-train
build/bin/ntfm-infer
build/bin/ntfm-personalize
build/bin/ntfm-tool
build/bin/neurotask-web
bin/ntfm
bin/neurotaskd

Prepare data

Each observation is one JSONL manifest row. Keep every task, visit, and derivative from the same participant or family in one split.

bash

build/bin/ntfm-tool validate-manifest \
  --manifest /data/manifests/train.jsonl

build/bin/ntfm-tool prepare-packs \
  --manifest /data/manifests/train.jsonl \
  --gpus 0,1,2,3,4,5,6,7

The primary numerical inputs are NIfTI volumes and compiled HDF5 feature packs. Native tools also support DICOM conversion, image/video decoding, HDF5 conversion, volume normalization, and Cartesian CUDA k-space reconstruction:

build/bin/ntfm-tool --help

K-space reconstruction is limited to dense Cartesian 2D/3D inverse FFT with root-sum-of-squares coil combination. Use a protocol-qualified reconstruction pipeline for non-Cartesian, parallel-imaging, or compressed-sensing data.

Compile one observation

bash

build/bin/neurocompile \
  --t1 /data/sub-0001/t1w.nii.gz \
  --fmri /data/sub-0001/rest_bold.nii.gz \
  --atlas /data/atlas.nii.gz \
  --config configs/compiler/neurocompiler.yaml \
  --subject-key sub-0001 --task rest --tr 0.8 \
  --out /data/compiled/sub-0001_rest.h5

Train

bash

sbatch workflows/slurm/train_teacher.sbatch
sbatch workflows/slurm/train_compiled.sbatch
sbatch workflows/slurm/train_signature.sbatch
sbatch workflows/slurm/train_clinical_ad.sbatch
sbatch workflows/slurm/train_clinical_pd.sbatch

Infer on two B200 GPUs

bash

mpirun -np 2 build/bin/ntfm-infer \
  --config configs/deployment/product_b200x2.yaml \
  --request examples/inference_request.json

Go command and service

bash

bin/ntfm compile --request examples/compile_request.json
bin/ntfm infer --request examples/inference_request.json
NTFM_API_TOKEN='replace-with-a-secret' bin/neurotaskd --listen :8080

Repository guide

text

src/neurotaskfm_cpp/  LibTorch model, training, inference, and data tools
src/neurocompiler/    C++/CUDA MRI compiler
src/platform/         Go CLI and services
configs/              model, training, deployment, and cluster settings
contracts/            JSON schemas
workflows/            local and Slurm launchers
docs/                 detailed design and operating documentation

Read data preparation, training, deployment, and architecture for details.

⚠️ DISCLAIMER: This model is for research purposes only. It is strictly NOT intended for clinical, diagnostic, or therapeutic use.

markjiang1/NeuroTaskFM

NeuroTaskFM

Main components

Hardware

Build

Prepare data

Compile one observation

Train

Infer on two B200 GPUs

Go command and service

Repository guide

markjiang1/
NeuroTaskFM