scgpt

scGPT — Single-Cell Foundation Model

Prerequisites

Requirement	Minimum	Recommended
Python	3.10+	3.11
CUDA	12.1+	12.4+
GPU VRAM	16 GB	24 GB+

How to run

Loading the vocabulary and checkpoint

scGPT checkpoints are raw directories (args.json, best_model.pt, vocab.json) — not Hugging Face hub repos. Point at the directory, not an HF repo id.

from scgpt.tokenizer.gene_tokenizer import GeneVocab
gv = GeneVocab.from_file("/path/to/scgpt-human/vocab.json")
print(len(gv))   # 60697 for the released human checkpoint

Embedding an AnnData

import anndata as ad
from scgpt.tasks import embed_data

adata = ad.read_h5ad("dataset.h5ad")        # var must contain a gene-name column
emb = embed_data(
    adata,
    model_dir="/path/to/scgpt-human",
    gene_col="feature_name",
    use_fast_transformer=False,             # see Gotchas
)
# emb is an AnnData with .obsm["X_scGPT"]

Output format

embed_data returns an AnnData whose .obsm["X_scGPT"] is the per-cell embedding (n_cells × emb_dim, 512 by default). Downstream: feed to scanpy.pp.neighbors / scanpy.tl.umap.

Remote compute

Needs ≥24 GB VRAM and the released human checkpoint (~200 MB: args.json, best_model.pt, vocab.json). Read compute_details({provider, mode:'read'}) for an environment with scgpt and a pre-cached checkpoint directory, then:

c = host.compute.create(provider)
job = c.submit_job(
    intent="scGPT embed 50k cells — 1×GPU, ~5 min",
    inputs=[
        {"src": "dataset.h5ad", "dst_filename": "dataset.h5ad"},
        {"src": "embed.py", "dst_filename": "embed.py"},
    ],
    command="python3 embed.py",
    environment=...,   # env name from compute_details
    outputs=["embedded.h5ad"],
    timeout_seconds=1800,
)
print(job.job_id)   # cell ends here — kernel never blocks on compute

Then call the wait_for_notification brain-tool. When the compute_done notification arrives, act on its payload: