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.
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.
Install the python client with pip:
pip install ginkgo-ai-clientNote: 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, ...]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 |
This project is licensed under the MIT License. See the LICENSE file for details.
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.