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NOTE: This deployment requires a specific SageMaker Inference AMI selection (al2-ami-sagemaker-inference-gpu-3-1). Please use the example notebook provided at https://github.com/NVIDIA/nim-deploy/blob/main/cloud-service-providers/aws/sagemaker/aws_marketplace_notebooks/nim-evo2-40b-v2-1-0_aws_marketplace.ipynb for deploying the endpoint.
Evo 2 is a biological foundation model that can interpret and generate DNA sequences across various biological scales: from individual molecules to entire genomes while retaining sensitivity to single-nucleotide changes, enabling zero-shot predictions and complex biological system designs.
Overview
Evo2 potential applications spans from accelerating drug discovery to advancing synthetic biology.
Evo 2 model was trained by Arc Institute. The model training involved a vast dataset of genomes, which enabled Evo 2 to perform a wide range of tasks, from predicting the impact of mutations on protein performance to generating complex molecular systems like CRISPR-Cas complexes. For example, the model demonstrated that it was able to design new versions of the CRISPR genome editor showcased its potential for creating novel biological tools.
Highlights
- Evo2 is a biological foundation model that can integrate information across long genomic sequences while retaining sensitivity to single-nucleotide changes.
- Evo2 can perform zero-shot function prediction for genes. Evo also can perform multi-element generation tasks, such as generating synthetic CRISPR-Cas molecular complexes.
- Evo 2 can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to at least 1M kb in length. Advances in multi-modal and multi-scale learning with Evo provide a promising path toward improving our understanding and control of biology across multiple levels of complexity.
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Try this product free for 31 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.12xlarge Inference (Batch) Recommended | Model inference on the ml.g5.12xlarge instance type, batch mode | $1.00 |
ml.g6e.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6e.xlarge instance type, real-time mode | $1.00 |
ml.g6e.12xlarge Inference (Real-Time) | Model inference on the ml.g6e.12xlarge instance type, real-time mode | $1.00 |
ml.g6e.24xlarge Inference (Real-Time) | Model inference on the ml.g6e.24xlarge instance type, real-time mode | $1.00 |
ml.g6e.48xlarge Inference (Real-Time) | Model inference on the ml.g6e.48xlarge instance type, real-time mode | $1.00 |
ml.p4d.24xlarge Inference (Real-Time) | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $1.00 |
ml.p4de.24xlarge Inference (Real-Time) | Model inference on the ml.p4de.24xlarge instance type, real-time mode | $1.00 |
ml.p5.48xlarge Inference (Real-Time) | Model inference on the ml.p5.48xlarge instance type, real-time mode | $1.00 |
ml.p5e.48xlarge Inference (Real-Time) | Model inference on the ml.p5e.48xlarge instance type, real-time mode | $1.00 |
ml.p5en.48xlarge Inference (Real-Time) | Model inference on the ml.p5en.48xlarge instance type, real-time mode | $1.00 |
Dimensions summary
You pay by the host hour for running this DNA foundation model on SageMaker. Pricing depends on the AWS instance type you choose and the inference mode. One option runs in batch mode on the ml.g5.12xlarge instance. The rest run in real-time mode across a range of instance sizes, from ml.g6e.xlarge up through larger multi-GPU instances like ml.g6e.48xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.p5e.48xlarge, and ml.p5en.48xlarge. Larger or more capable instances carry a higher hourly rate. You are billed only for the hours each instance runs.
Top-of-mind questions for buyers
Batch mode runs on the ml.g5.12xlarge instance and processes queued sequence requests together. Real-time mode runs across the other instance types and returns predictions as each request arrives. Both bill by the host hour, but the mode you pick reflects whether you need immediate responses or scheduled bulk processing.
One host hour is one hour that a chosen SageMaker instance runs the model. Billing tracks the instance itself, not the number of DNA sequences you process. If you run multiple instances at once, each accrues host hours separately and adds to your total.
Charges accrue for each hour a real-time instance stays running, whether or not it handles requests. Real-time endpoints stay active to respond immediately, so they meter running time. To stop software charges, shut the instance down. Underlying AWS infrastructure fees may still apply separately.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Additional details
Inputs
- Summary
The model accepts JSON requests with parameters on /invocations and /ping APIs that can be used to control the generated text. See examples and field descriptions below.
- Input MIME type
- application/json
sm_runtime = boto3.client("sagemaker-runtime", region_name=region) generate_payload = { "sequence": "ACGTACGTACGT", "num_tokens": 100, "temperature": 0.7, "top_k": 3, } response = sm_runtime.invoke_endpoint( EndpointName='Evo2-40b-2-1-0', ContentType="application/json", Body=json.dumps(generate_payload), ) result = json.loads(response["Body"].read()) print("Generated DNA:", result["sequence"]) print("Elapsed (ms):", result.get("elapsed_ms"))
Custom attributes
The following table describes custom attributes for real-time inference endpoints.
Field name | Description | Constraints | Required |
|---|---|---|---|
Forward | Run the model forward pass and save the layers outputs Endpoint path: /biology/arc/evo2/forward
| forward_payload = { "sequence": "ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGT", "output_layers": [ "decoder.layers.0.mlp", "decoder.layers.10.mlp.linear_fc2", ], } response = sm_runtime.invoke_endpoint( EndpointName='Evo2-40b-2-1-0', ContentType="application/json", Body=json.dumps(forward_payload), CustomAttributes="route=forward", # routes through Caddy to /forward ) forward_result = json.loads(response["Body"].read()) print("Elapsed (ms):", forward_result["elapsed_ms"]) print("NPZ size (bytes):", len(forward_result["data"]))
| No |
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