Requirements - Bioptimus Documentation

Prerequisites depend on how you deploy:

AWS & SageMaker
On-premise
Hugging Face (academic)

AWS account & IAM role

An IAM role with AmazonSageMakerFullAccess, plus permission to subscribe to AWS Marketplace products.

Marketplace subscription

An active subscription to the relevant model package (H-Optimus / M-Optimus) to obtain the Model Package ARN.

Compute & quota

A SageMaker execution environment and sufficient quota for ml.g5.xlarge instances in your region. Outside SageMaker, set the execution role ARN explicitly (get_execution_role() is unavailable).

Requirement	Detail
Docker	Version 20.10 or later
NVIDIA Container Toolkit	Required for GPU inference (install guide)
GPU	NVIDIA GPU with CUDA Compute Capability 8.6 (e.g. A10G-class). A 24 GB GPU — equivalent to SageMaker `ml.g5.xlarge` — is a good baseline. A CUDA GPU is required (container exits without one).
CPU architecture	x86-64 (CC 8.6 + x86-64 runs out of the box; other architectures require recompilation)
GPU access	Direct passthrough via the NVIDIA Container Toolkit (`--gpus all`)
OS	Linux with an NVIDIA GPU
Disk	Enough to hold the image archive (weights are baked in)

See the on-premise guide for the full install flow.

Requirement	Detail
License	CC-BY-NC-ND 4.0 — non-commercial academic research only
Access	Gated; accept the terms on Hugging Face using an institutional email
Python	`timm`, `torch`, `torchvision`, `huggingface_hub`; a CUDA GPU for inference

See the Hugging Face guide.

Python SDK & dependencies

The Bioptimus SDK handles WSI reading, tiling, tissue masking, and concurrent dispatch for both the on-premise and SageMaker backends. The SageMaker reference notebooks pin:

pip install sagemaker==2.254.1 boto3==1.42.2

Deployment overview Validation & acceptance testing

​Python SDK & dependencies

Python SDK & dependencies