OmniBioAI Dev Environment

Full AI/Bioinformatics development environment for GPU/DGX machines. Built on NVIDIA PyTorch 25.10 with CUDA support.

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What This Repository Is

This is an optional developer convenience environment — think of it as:

"A researcher's laptop in a container."

It is intended for:

• OmniBioAI contributors and researchers
• Exploratory AI/LLM prototyping
• Single-cell and multi-omics experimentation
• Notebook-driven development (Python + R)
• GPU-accelerated model training and inference

What This Repository Is NOT

This is not part of the OmniBioAI production stack. It is not:

• A Tool Execution Service (TES) runtime
• A production or cloud deployment image
• Used by OmniBioAI pipelines at runtime

Component	Role
OmniBioAI Workbench	Production platform
Tool Execution Service (TES)	Stateless execution
Tool Runtime Images	Minimal, contract-driven
This Repository	Interactive development only

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What's Inside

Category	Tools
Deep Learning	PyTorch 2.9 (GPU), TorchVision, TensorRT, Flash Attention, Transformer Engine
ML/Data Science	Scikit-learn, XGBoost, LightGBM, Polars, Pandas, NumPy, SciPy
Bioinformatics	GATK 4.5, Samtools, BCFTools, FastQC, SnpEff, Nextflow, BEDTools
Genomics/R	R 4.x, Bioconductor, DESeq2, limma, edgeR, ComplexHeatmap, scran, scater
LLM/AI	Transformers, HuggingFace Hub, Accelerate, Safetensors, Ollama
Visualization	Matplotlib, Seaborn, Plotly, Bokeh, TensorBoard
Notebook	JupyterLab 4.x (pre-configured, GPU-enabled)

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Requirements

• NVIDIA GPU (A100, H100, or DGX system recommended)
• NVIDIA Container Toolkit installed on host
• Docker with GPU support
• 50GB+ free disk space

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Quick Start

Option A — Pull from GHCR (recommended)

docker pull ghcr.io/man4ish/omnibioai-dev-env:latest
bash run_ai_dev.sh

Option B — Build locally

git clone https://github.com/man4ish/omnibioai-dev-docker
cd omnibioai-dev-docker
docker build -t ghcr.io/man4ish/omnibioai-dev-env:latest .
bash run_ai_dev.sh

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Running the Container

bash run_ai_dev.sh [OPTIONS]

Options:
  --jupyter    Start JupyterLab automatically
  --ollama     Start Ollama server automatically
  --build      Force rebuild image from Dockerfile
  --help       Show help

This launches an interactive container with:

• Full GPU access (--gpus all)
• Shared memory for PyTorch DataLoader (--ipc=host)
• HuggingFace cache mounted (~/.cache/huggingface)
• Ollama models mounted (~/.ollama)
• Current directory mounted as /workspace
• JupyterLab on port 8888
• Ollama server on port 11434

Start JupyterLab:

# Via flag (recommended)
bash run_ai_dev.sh --jupyter

# Or manually inside container
jupyter lab --ip=0.0.0.0 --port=8888 --allow-root --no-browser

Then open: http://localhost:8888

Start Ollama:

# Via flag
bash run_ai_dev.sh --ollama

# Or manually inside container
ollama serve &
ollama pull llama3
ollama run llama3 "Summarize the role of TP53 in cancer"

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GPU Validation

Inside the container, verify CUDA and all tools:

# PyTorch GPU
python -c "import torch; print('CUDA:', torch.cuda.is_available()); print('GPU:', torch.cuda.get_device_name(0))"

# Bioinformatics tools
nextflow -version
gatk --version
samtools --version | head -1
fastqc --version
snpeff -version

# R + Bioconductor
R -e "library(DESeq2); packageVersion('DESeq2')"

If CUDA returns False, verify:

• NVIDIA drivers are installed
• Docker is configured with GPU support
• nvidia-container-toolkit is available

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HuggingFace Authentication

# Inside container
huggingface-cli login

# Or via environment variable
export HUGGINGFACE_TOKEN=hf_xxxxxxxxxxxxxxxxxxxxx

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Example — GPU-accelerated single-cell analysis

import torch
import scanpy as sc

print(f"Using GPU: {torch.cuda.get_device_name(0)}")

adata = sc.read_h5ad("/workspace/data/sample.h5ad")
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata)
sc.tl.pca(adata)
sc.pl.pca_variance_ratio(adata)

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Typical Use Cases

• Prototyping new OmniBioAI plugins
• Exploratory single-cell analysis (Scanpy/Seurat workflows)
• LLM prompt and RAG experimentation
• Model benchmarking and training
• Notebook-based research
• Rapid testing before productionization

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Repository Structure

File	Description
Dockerfile	Main image definition
requirements.txt	Portable Python dependencies
requirements.dgx.txt	DGX-specific packages (pre-installed in base, docs only)
run_ai_dev.sh	Container launch script
tests/	Validation tests
pyproject.toml	Project metadata
.gitignore	Prevents secrets/caches from being committed
.dockerignore	Prevents data/models from being baked into image

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Part of the OmniBioAI Ecosystem

This dev environment is designed to work alongside the OmniBioAI platform — a unified AI-powered bioinformatics workbench supporting:

• 97 bioinformatics plugins
• RNA-seq, single-cell, spatial omics, variant calling
• TES workflow execution (Slurm, K8s, AWS Batch, Azure)
• RAG-powered literature search (PubMed + FAISS)
• ML model registry
• Multi-cloud support (AWS, Azure, GCP)

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License

Apache License 2.0 — see LICENSE

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Citation

If you use this environment in your research, please cite:

OmniBioAI Dev Environment (2025)
Manish Kumar
https://github.com/man4ish/omnibioai-dev-docker