ginkgo-ai-client 0.4.0

Client for the public Ginkgo AI API

Ginkgo's AI model API client

Work in progress: this repo was just made public and we are still working on integration

A python client for Ginkgo's AI model API, to run inference on public and Ginkgo-proprietary models. Learn more in the Model API announcement.

Prerequisites

Register at https://models.ginkgobioworks.ai/ to get credits and an API KEY (of the form xxxxxxx-xxxx-xxxx-xxxx-xxxxxxxx). Store the API KEY in the GINKGOAI_API_KEY environment variable.

Installation

Install the python client with pip:

pip install ginkgo-ai-client

Usage:

Note: This is an alpha version of the client and its interface may vary in the future.

Example : masked inference with Ginkgo's AA0 model

The client requires an API key (and defaults to os.environ.get("GINKGOAI_API_KEY") if none is explicitly provided)

from ginkgo_ai_client import GinkgoAIClient, MaskedInferenceQuery

client = GinkgoAIClient()
model = "ginkgo-aa0-650M"

# SINGLE QUERY

query = MaskedInferenceQuery(sequence="MPK<mask><mask>RRL", model=model)
prediction = client.send_request(query)
# prediction.sequence == "MPKRRRRL"

# BATCH QUERY

sequences = ["MPK<mask><mask>RRL", "M<mask>RL", "MLLM<mask><mask>R"]
queries = [MaskedInferenceQuery(sequence=seq, model=model) for seq in sequences]
predictions = client.send_batch_request(queries)
# predictions[0].sequence == "MPKRRRRL"

Changing the model parameter to esm2-650M or esm2-3b in this example will perform masked inference with the ESM2 model.

Example : embedding computation with Ginkgo's 3'UTR language model

from ginkgo_ai_client import GinkgoAIClient, MeanEmbeddingQuery

client = GinkgoAIClient()
model = "ginkgo-maskedlm-3utr-v1"

# SINGLE QUERY

query = MeanEmbeddingQuery(sequence="ATTGCG", model=model)
prediction = client.send_request(query)
# prediction.embedding == [1.05, -2.34, ...]

# BATCH QUERY

sequences = ["ATTGCG", "CAATGC", "GCGCACATGT"]
queries = [MeanEmbeddingQuery(sequence=seq, model=model) for seq in sequences]
predictions = client.send_batch_request(queries)
# predictions[0].embedding == [1.05, -2.34, ...]

Available models

See the example folder and reference docs for more details on usage and parameters.

Model	Description	Reference	Supported queries	Versions
ESM2	Large Protein language model from Meta	Github	Embeddings, masked inference	3B, 650M
AA0	Ginkgo's proprietary protein language model	Announcement	Embeddings, masked inference	650M
3UTR	Ginkgo's proprietary 3'UTR language model	Preprint	Embeddings, masked inference	v1

License

This project is licensed under the MIT License. See the LICENSE file for details.

Releases

Make sure the changelog is up to date and the top section reads Unreleased. Increment the version with the bumpversion workflow in Actions - it will update the version everywhere in the repo, create a tag, and trigger a Github release. If all looks good, publish to PyPI via the publish workflow in Actions.