> ## 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. # Glossary > Key terms used across Bioptimus documentation. A quick reference for technical and non-technical readers. ## Products & interfaces | Term | Meaning | | -------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Bioptimus SDK** | The Python client package. Handles WSI reading, tiling, tissue masking, bulk RNA alignment, and concurrent dispatch, against either the on-premise server or a SageMaker endpoint. See the [Bioptimus SDK](/guides/get-started/sdk). | | **`Inference` (pipeline)** | The high-level, one-object pipeline (config + `tissue()`/`embed()`/`predict()`/`run()`/`save_config()`), with cached masks, a structured workspace, and reproducible config. Its `api_url` argument is the on-premise server URL — the same value as `Backbone`'s `base_url`. See the [Inference pipeline guide](/guides/get-started/inference-pipeline). | | **`SlideInference`** | The lower-level, per-slide inference class (`Backbone` + mask provider + writer) for explicit control. | | **`Cohort`** | A batch manifest of slides (and optional bulk RNA) that is the single source of truth for an experiment. Supports late-binding bulk RNA. | | **`AWSClient`** | The low-level per-tile client for a SageMaker endpoint; injects `model_name`/`mode` into each request. | | **`OutputFormat`** | The output writer format — Zarr (default), HDF5, or NPZ. | | **Model Server / API** | The FastAPI inference server (shipped in the on-premise container) that exposes the REST endpoints (`/api/embed/h1`, `/api/predict/m-optimus`, …) and a Swagger UI at `/docs`. See the [API reference](/api-reference/introduction). | | **Backbone** | The Bioptimus SDK factory class used to obtain a model client, e.g. `Backbone(Models.H1, backend="remote", base_url=...)` or `backend="aws"`. Takes a `Models` enum member (`Models.H1`, `Models.M_OPTIMUS`); the companion tissue model uses the string `"tissue-seg"`. | | **Inference endpoint** | A running model service that accepts tile requests and returns outputs — an on-premise container or a SageMaker endpoint. | | **SageMaker** | AWS's managed ML hosting service used for Bioptimus cloud deployment; the Bioptimus SDK reaches it via the `/invocations` dispatch endpoint. | | **Model package** | The on-premise container variant — **H1** (H-Optimus + tissue-seg) or **M** (M-Optimus + tissue-seg). | ## Model & backend identifiers The same model is referred to by a product name, a version, an SDK enum, and a server/endpoint id depending on context. This table is the canonical mapping. | Model | Current version | SDK identifier | Server id | Endpoints | Hugging Face | | ----------------------- | --------------- | ---------------------------------- | ------------ | ------------------------------------------------ | ----------------------- | | **H-Optimus** | H-Optimus-1 | `Models.H1` (`"h1"`) | `h1` | `/api/embed/h1` | `bioptimus/H-optimus-1` | | **M-Optimus** | M-Optimus-1 | `Models.M_OPTIMUS` (`"m-optimus"`) | `m-optimus` | `/api/embed/m-optimus`, `/api/predict/m-optimus` | — | | **Tissue segmentation** | — | `"tissue-seg"` (string) | `tissue-seg` | `/api/predict/tissue-seg` | — | The Bioptimus SDK reaches a deployment through a `backend` argument. The product term and the code value differ: | Platform (product term) | SDK `backend` | Key connection args | | --------------------------------- | ------------- | -------------------------------------------------- | | On-premise container | `"remote"` | `base_url` (`api_url` on the `Inference` pipeline) | | AWS SageMaker | `"aws"` | `endpoint_name`, `region_name` | | In-process (local GPU, no server) | `"local"` | `model_dir` / `checkpoint` | ## Models & outputs | Term | Meaning | | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Foundation model (FM)** | A large model pre-trained on vast data that produces general-purpose representations reused across many downstream tasks. | | **H-Optimus** | Bioptimus's histology foundation model; outputs tile embeddings. Current version: H-Optimus-1. | | **M-Optimus** | Bioptimus's multimodal model; predicts spatial gene expression from histology (and optional bulk RNA), and also outputs embeddings. | | **Embedding** | A numeric feature vector summarizing a tile. H-Optimus returns the **CLS token** of size **1536**. | | **1536-d** | The dimensionality of H-Optimus and M-Optimus tile embeddings — a 1536-number feature vector per tile. | | **CLS token** | The transformer's summary output used as the tile embedding. H-Optimus returns the 1536-d CLS token; M-Optimus returns its MLP output. The embedding type is fixed (not user-selectable). | | **Spatial gene expression** | Gene expression mapped to locations across a slide. M-Optimus predicts this from H\&E tiles. | | **Tissue segmentation (`tissue-seg`)** | A companion model (bundled in both packages) that produces a binary tissue/background mask. | ## Imaging & data | Term | Meaning | | --------------------------- | ---------------------------------------------------------------------------------------------------- | | **WSI (whole slide image)** | A digitized microscope slide — often gigapixel-scale — split into tiles for processing. | | **H\&E** | Hematoxylin and eosin, the standard tissue stain in routine pathology. | | **Tile (patch)** | A fixed-size crop of a WSI. Embeddings use 224×224; tissue segmentation uses 512×512. | | **MPP (microns per pixel)** | Physical image resolution. Embeddings use 0.5 µm/px; tissue segmentation uses 8.0 µm/px. | | **Bulk RNA-seq** | Aggregate (non-spatial) gene expression for a sample. Optional input to M-Optimus prediction. | | **Ensembl gene ID** | Standardized gene identifier (e.g. `ENSG00000000003`) used in bulk RNA inputs and gene-set metadata. | | **Zarr / HDF5 / NPZ** | Output file formats the Bioptimus SDK writes per-slide results to. | ## Infrastructure | Term | Meaning | | ---------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | **CUDA Compute Capability** | An NVIDIA GPU architecture version. Compute Capability **8.6** on **x86-64** runs the container out of the box; other architectures require recompilation. | | **NVIDIA Container Toolkit** | Enables GPU access inside Docker containers (`--gpus all`). | | **`ml.g5.xlarge`** | The recommended SageMaker instance (a single 24 GB A10G-class GPU). | ## Benchmarks | Term | Meaning | | ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------- | | **PathBench** | A multi-task, multi-organ pathology FM benchmark (HKUST). H-Optimus-1 ranks first overall. | | **HEST** | A benchmark for predicting gene expression from histology (Harvard; Jaume et al. 2025), scored by Pearson correlation. | | **MIL (multiple instance learning)** | A method that aggregates many tile-level features into a slide-level prediction. |