Add support for Modal so researchers can scale compute on demand for heavy computational tasks beyond running locally

docs/scientific-packages.md

@@ -16,6 +16,7 @@
 
 ## Data Management & Infrastructure
 - **LaminDB** - Open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable). Provides unified platform combining lakehouse architecture, lineage tracking, feature stores, biological ontologies (via Bionty plugin with 20+ ontologies: genes, proteins, cell types, tissues, diseases, pathways), LIMS, and ELN capabilities through a single Python API. Key features include: automatic data lineage tracking (code, inputs, outputs, environment), versioned artifacts (DataFrame, AnnData, SpatialData, Parquet, Zarr), schema validation and data curation with standardization/synonym mapping, queryable metadata with feature-based filtering, cross-registry traversal, and streaming for large datasets. Supports integrations with workflow managers (Nextflow, Snakemake, Redun), MLOps platforms (Weights & Biases, MLflow, HuggingFace, scVI-tools), cloud storage (S3, GCS, S3-compatible), array stores (TileDB-SOMA, DuckDB), and visualization (Vitessce). Deployment options: local SQLite, cloud storage with SQLite, or cloud storage with PostgreSQL for production. Use cases: scRNA-seq standardization and analysis, flow cytometry/spatial data management, multi-modal dataset integration, computational workflow tracking with reproducibility, biological ontology-based annotation, data lakehouse construction for unified queries, ML pipeline integration with experiment tracking, and FAIR-compliant dataset publishing
+- **Modal** - Serverless cloud platform for running Python code with minimal configuration, specialized for AI/ML workloads and scientific computing. Execute functions on powerful GPUs (T4, L4, A10, A100, L40S, H100, H200, B200), scale automatically from zero to thousands of containers, and pay only for compute used. Key features include: declarative container image building with uv/pip/apt package management, automatic autoscaling with configurable limits and buffer containers, GPU acceleration with multi-GPU support (up to 8 GPUs per container), persistent storage via Volumes for model weights and datasets, secret management for API keys and credentials, scheduled jobs with cron expressions, web endpoints for deploying serverless APIs, parallel execution with `.map()` for batch processing, input concurrency for I/O-bound workloads, and resource configuration (CPU cores, memory, disk). Supports custom Docker images, integration with Hugging Face/Weights & Biases, FastAPI for web endpoints, and distributed training. Free tier includes $30/month credits. Use cases: ML model deployment and inference (LLMs, image generation, embeddings), GPU-accelerated training, batch processing large datasets in parallel, scheduled compute-intensive jobs, serverless API deployment with autoscaling, scientific computing requiring distributed compute or specialized hardware, and data pipeline automation
 
 ## Cheminformatics & Drug Discovery
 - **Datamol** - Molecular manipulation and featurization with enhanced RDKit workflows