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SearchSearch thousands of GPU-optimized Containers, pretrained Models, SDKs, and Helm charts—ready to accelerate AI, digital twins, and HPC from cloud to edge.
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Displaying 24 results
NVIDIA Developer Program
Google
Google
AlphaFold2
A widely used model for predicting the 3D structures of proteins from their amino acid sequences.
Container
NVIDIA
NVIDIA
NVIDIA Parabricks
NVIDIA Parabricks is an accelerated compute framework that supports applications across the genomics industry, primarily supporting analytical workflows for DNA, RNA, and somatic mutation detection applications.
Container
NVIDIA
NVIDIA
PyG
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
Container
NVIDIA Developer Program
arc
Evo 2 40B
Evo 2 is a biological foundation model that is able to integrate information over long genomic sequences.
Container
NVIDIA Developer Program
Google
Google
AlphaFold2-Multimer
A widely used model for predicting the 3D structures of proteins from their amino acid sequences. This version of the container supports multimers, i.e. proteins made up of 2 or more polypeptide chains.
Container
NVIDIA Developer Program
arc
Evo 2
Evo 2 is a biological foundation model that is able to integrate information over long genomic sequences.
Container
NVIDIA
NVIDIA
DeepSAP
DeepSAP is a transformer-based workflow designed to enhance splice junction detection in RNA-seq data.
Container
NVIDIA Clara Parabricks
NVIDIA Clara Parabricks
Container for Training Parabricks DeepVariant
This container is used for running the data preprocessing part of DeepVariant Training pipeline including make_examples and shuffle
Container
NVIDIA
NVIDIA
NVIDIA Parabricks Amazon Linux
The Parabricks container based on Amazon Linux 2
Container
NVIDIA
NVIDIA
Parabricks Umi_Fgbio
Parabricks Umi_Fgbio is a pipeline that processes sequencing reads with molecular barcodes, and provides impressive error correction and increased accuracy using a sequencing consensus read level.
Container
Clara Parabricks
Nvidia Clara Parabricks Bare Metal Debian Package
Nvidia Clara Parabricks Bare Metal Debian Package for Ubuntu 18.04
Resource
NVIDIA
NVIDIA
Parabricks DeepVariant Retraining Notebook
This notebook shows how to retrain DeepVariant models using Parabricks.
Resource
NVIDIA
NVIDIA
Dsci Atac-seq Low Cell Count 1M Reads
Model trained on single cell atac-seq data using dsci-ATAC protocol using AtacWorks tool
Model
NVIDIA
NVIDIA
Dsci Atac-seq Low Cell Count 0.2M Reads
Model trained on single cell atac-seq data using dsci-ATAC protocol
Model
NVIDIA
NVIDIA
Parabricks Deepvariant Models
Deepvariant Models converted to TensorRT for GPU types not directly supported by the Parabricks container.
Resource
NVIDIA
NVIDIA
Dsc Atac-seq Low Cell Count 1M Reads
Model trained on single cell atac-seq data using dsc-ATAC protocol
Model
NVIDIA
NVIDIA
Dsc Atac-seq Low Cell Count 0.2M Reads
Model trained on single cell atac-seq data using dsc-ATAC protocol
Model
NVIDIA
NVIDIA
Bulk Atac-seq Low Coverage 1M Reads
Model trained on bulk atac-seq data.
Model
NVIDIA
NVIDIA
Bulk Atac-seq Low Coverage 20M Reads
De-noise 20 million read depth Atac-seq signal to resemble 50 million read depth Atac-seq signal.
Model
NVIDIA
NVIDIA
Bulk Atac-seq Low Coverage 10M Reads
Model trained on bulk atac-seq data.
Model
NVIDIA
NVIDIA
Bulk Atac-seq Low Coverage 5M Reads
Model trained on bulk atac-seq data.
Model
NVIDIA
NVIDIA
Bulk Atac-seq Low Quality 20M Reads
Model trained on low quality bulk atac-seq data.
Model
NVIDIA
NVIDIA
Bulk Atac-seq Low Coverage 0.2M Reads
Model trained on bulk atac-seq data.
Model
NVIDIA
NVIDIA
CodonFM Encodon
CodonFM predicts masked codons in mRNA sequences from codon-level context to enable variant effect interpretation and codon optimization
Model