Sold by: NVIDIA
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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/digital-biology-examples/blob/main/examples/nims/msa-search/nim-msa-search-v2-1-0_aws_marketplace.ipynb for deploying the endpoint.
Generates a multiple sequence alignment from a query sequence and a protein sequence database search.
Overview
The MSA search NIM is powered by GPU MMSeqs2. GPU MMSeqs2 is a GPU-accelerated toolkit for protein database search and Multiple Sequence Alignment (MSA). While not a deep learning model, MMSeqs2 does require large protein databases for sequence similarity search.
The MSA Search NIM enables researchers and commercial entities in the Drug Discovery, Life Sciences, and Digital Biology fields to rapidly generate multiple sequence alignments (MSA). The output MSA can be used in downstream protein structure prediction and evolutionary analysis applications.
Highlights
- The MSA Search NIM is based on GPU MMSeqs2, a highly-sensitive, highly-performant, GPU-accelerated package for a variety of sequence-alignment tasks. The NIM accuracy should match that of the open-source version of MMSeqs2 in most settings.
Details
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Pricing
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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.12xlarge Inference (Real-Time) Recommended | 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 based on the AWS instance type you run for protein sequence alignment. One option, ml.g5.12xlarge, runs in batch mode for processing groups of sequences together. The other eight options run in real-time mode for on-demand requests. These span the g6e, p4d, p4de, p5, p5e, and p5en instance families. Larger instance sizes carry more GPU and memory capacity, so cost scales with the instance you choose. You are billed only for the hours each instance runs.
Top-of-mind questions for buyers
One host hour is one hour that a single instance of the chosen type runs the inference workload. You are billed per running instance-hour, not per sequence searched or per request. Cost accrues while the instance is active regardless of how many alignments you process during that hour.
The ml.g5.12xlarge batch option processes groups of sequences together in scheduled runs. The eight real-time options handle requests on demand as they arrive. Both bill by host hour on their instance type. Choose batch for bulk processing and real-time for interactive, request-driven alignment.
Software charges apply per host hour while the instance runs. A stopped instance does not accrue host-hour charges. Underlying AWS infrastructure fees, such as storage, may still apply separately depending on your setup. The software meter counts running time only.
docs.nvidia.com
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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
payload = { "sequence": ( "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN" ), "e_value": 0.0001, "iterations": 2, "databases": ["Uniref30_2302", "colabfold_envdb_202108", "PDB70_220313"], }
Custom attributes
The following table describes custom attributes for real-time inference endpoints.
Field name | Description | Constraints | Required |
|---|---|---|---|
paired | Run the paired MSA calculations | runtime = boto3.client('sagemaker-runtime', region_name='us-east-1')
# Test single
r1 = runtime.invoke_endpoint(
EndpointName=ENDPOINT_NAME,
ContentType='application/json',
Body=json.dumps({"sequence": "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"})
)
print(f"Single MSA: {len(r1['Body'].read())} bytes ✅")
# Test paired
r2 = runtime.invoke_endpoint(
EndpointName='MSA-Search-NIM-v2-1-0-fix4',
ContentType='application/json',
Body=json.dumps({"sequences": ["MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG", "MHHHHHHSSGVDLGTENLYFQSNAM"]}),
CustomAttributes='route=paired'
)
print(f"Paired MSA: {len(r2['Body'].read())} bytes ✅")
| No |
Resources
Support
Vendor support
Free support via NVIDIA NIM Developer Forum:
AWS infrastructure support
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