getwilds/ollama repository overview

⁠Ollama

This directory contains Docker images for Ollama⁠, an LLM inference server, bundled with the Sprocket⁠ WDL validator, the Python ollama SDK⁠, OpenCode⁠, an open-source AI coding agent, and ChromaDB⁠, an open-source vector database tool. Also includes lexical/semantic similarity evaluation support via RapidFuzz⁠, sentence-transformers⁠, and a CPU-only build of PyTorch⁠. Designed for benchmarking LLM-generated WDL scripts.

⁠Available Versions

• latest ( Dockerfile⁠ | Vulnerability Report⁠ )
• 0.21.0 ( Dockerfile⁠ | Vulnerability Report⁠ )

⁠Image Details

These Docker images are built from ollama/ollama:0.21.0 and include:

• Ollama v0.21.0: LLM inference server for running models locally
• Sprocket v0.23.0: WDL script validator
• OpenCode v1.14.39: open-source AI coding agent
• Python ollama SDK v0.6.1: Python client library for interacting with Ollama
• chromadb v1.5.9: open-source vector database for embeddings and RAG workflows
• RapidFuzz v3.14.5: fast string similarity scoring for lexical evaluation
• sentence-transformers v5.5.1: sentence/text embedding models for semantic similarity
• PyTorch v2.12.0 (CPU build): tensor library underlying sentence-transformers — installed from the PyTorch CPU wheel index so CUDA wheels are not pulled in (Ollama owns the GPU; the embedding model runs on CPU)
• sentence-transformers/all-MiniLM-L6-v2 model (~91MB) pre-cached under /opt/hf_cache (world-readable, so it works under Apptainer's non-root execution model) — HF_HOME is set to that path in the image, so the embedding model can be loaded offline (HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1); point HF_HOME=/opt/hf_cache explicitly if your runtime overrides it
• Python 3 (system version from base image)
• git, openssh-client, and ripgrep (system versions from base image) — supporting tools for repository workflows and fast code search used by OpenCode

Sprocket is installed from prebuilt binaries published on the Sprocket GitHub releases page⁠. OpenCode is installed from prebuilt binaries published on the OpenCode GitHub releases page⁠.

Note on Sprocket usage: When this image is run via Apptainer (e.g., on an HPC cluster), Sprocket is intended for static analysis only — sprocket lint, sprocket check, and sprocket format all work inside the container and are useful for validating LLM-generated WDL on the fly. Executing workflows with sprocket run is not supported from within the container, since WDL tasks typically declare their own runtime containers that cannot be launched from inside an Apptainer image. Run workflows on the host (or via Cromwell/miniwdl on the cluster) instead.

⁠Platform Availability

Available for: linux/amd64, linux/arm64

A GPU is not required to run this image, but is highly encouraged — CPU-only execution of LLMs is significantly slower.

⁠Citation

This image bundles several independent tools. If you use them in your research, please cite the original authors:

• Ollama (LLM inference server): https://ollama.com/⁠
• Sprocket (WDL execution engine): https://github.com/stjude-rust-labs/sprocket⁠
• OpenCode (AI coding agent): https://github.com/sst/opencode⁠
• Chroma (vector database): https://www.trychroma.com/⁠
• sentence-transformers (semantic text embeddings): Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. https://www.sbert.net/⁠
• PyTorch (tensor/ML framework): Paszke, A., et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. https://pytorch.org/⁠

⁠Usage

⁠Docker

# Pull the latest version
docker pull getwilds/ollama:latest

# Or pull a specific version
docker pull getwilds/ollama:0.21.0

# Alternatively, pull from GitHub Container Registry
docker pull ghcr.io/getwilds/ollama:latest

⁠Singularity/Apptainer

# Pull the latest version
apptainer pull docker://getwilds/ollama:latest

# Or pull a specific version
apptainer pull docker://getwilds/ollama:0.21.0

# Alternatively, pull from GitHub Container Registry
apptainer pull docker://ghcr.io/getwilds/ollama:latest

⁠Example Commands

# Check installed versions
docker run --rm getwilds/ollama:latest ollama --version
docker run --rm getwilds/ollama:latest sprocket --version
docker run --rm getwilds/ollama:latest opencode --version

# Start the container with GPU access
docker run --rm --gpus all -it getwilds/ollama:latest

# Inside the container, start the Ollama server
ollama serve &

# Pull a model
ollama pull llama3

# Generate a WDL script and validate it with Sprocket
python3 -c "
import ollama
response = ollama.chat(model='llama3', messages=[
    {'role': 'user', 'content': 'Write a WDL task that runs fastqc on a FASTQ file'}
])
with open('/tmp/output.wdl', 'w') as f:
    f.write(response['message']['content'])
"
sprocket lint /tmp/output.wdl

⁠Dockerfile Structure

The Dockerfile follows these main steps:

1. Uses ollama/ollama:0.21.0 as the base image
2. Adds metadata labels for documentation and attribution
3. Installs system dependencies with pinned versions (Python, curl, git, openssh-client, ripgrep)
4. Installs CPU-only PyTorch from the PyTorch CPU wheel index, then the Python ollama SDK, chromadb, RapidFuzz, and sentence-transformers via pip
5. Downloads the prebuilt Sprocket binary for the target architecture
6. Downloads the prebuilt OpenCode binary for the target architecture
7. Runs smoke tests to verify all tools are installed correctly

⁠Security Scanning and CVEs

These images are regularly scanned for vulnerabilities using Docker Scout. However, due to the nature of bioinformatics software and their dependencies, some Docker images may contain components with known vulnerabilities (CVEs).

Use at your own risk: While we strive to minimize security issues, these images are primarily designed for research and analytical workflows in controlled environments.

For the latest security information about this image, please check the CVEs_*.md files in this directory⁠, which are automatically updated through our GitHub Actions workflow. If a particular vulnerability is of concern, please file an issue⁠ in the GitHub repo citing which CVE you would like to be addressed.

⁠Source Repository

These Dockerfiles are maintained in the WILDS Docker Library⁠ repository.