> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bioptimus.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Requirements

> Prerequisites for each deployment option.

Prerequisites depend on how you deploy:

<Tabs>
  <Tab title="AWS & SageMaker">
    <Steps>
      <Step title="AWS account & IAM role">
        An IAM role with `AmazonSageMakerFullAccess`, plus permission to subscribe to AWS Marketplace products.
      </Step>

      <Step title="Marketplace subscription">
        An active subscription to the relevant model package ([H-Optimus](https://aws.amazon.com/marketplace/pp/prodview-cuad7l27fobx4) / M-Optimus) to obtain the Model Package ARN.
      </Step>

      <Step title="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).
      </Step>
    </Steps>
  </Tab>

  <Tab title="On-premise">
    | Requirement              | Detail                                                                                                                                                                                                  |
    | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
    | Docker                   | Version 20.10 or later                                                                                                                                                                                  |
    | NVIDIA Container Toolkit | Required for GPU inference ([install guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html))                                                                      |
    | 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](/deployment/platforms/on-premise) for the full install flow.
  </Tab>

  <Tab title="Hugging Face (academic)">
    | 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](/deployment/platforms/hugging-face).
  </Tab>
</Tabs>

## Python SDK & dependencies

The [Bioptimus SDK](/guides/get-started/sdk) handles WSI reading, tiling, tissue masking, and concurrent dispatch for both the on-premise and SageMaker backends. The SageMaker reference notebooks pin:

```bash theme={null}
pip install sagemaker==2.254.1 boto3==1.42.2
```