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Bedrock Batches

Use Amazon Bedrock Batch Inference API through LiteLLM.

PropertyDetails
DescriptionAmazon Bedrock Batch Inference allows you to run inference on large datasets asynchronously
Provider DocAWS Bedrock Batch Inference ↗
Cost Tracking✅ Supported

Overview​

Use this to:

  • Run batch inference on large datasets with Bedrock models
  • Control batch model access by key/user/team (same as chat completion models)
  • Manage S3 storage for batch input/output files

(Proxy Admin) Usage​

Here's how to give developers access to your Bedrock Batch models.

1. Setup config.yaml​

  • Specify mode: batch for each model: Allows developers to know this is a batch model
  • Configure S3 bucket and AWS credentials for batch operations
litellm_config.yaml
model_list:
  - model_name: "bedrock-batch-claude"
    litellm_params:
      model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
      #########################################################
      ########## batch specific params ########################
      s3_bucket_name: litellm-proxy
      s3_region_name: us-west-2
      s3_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      s3_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      aws_batch_role_arn: arn:aws:iam::888602223428:role/service-role/AmazonBedrockExecutionRoleForAgents_BB9HNW6V4CV
      # Optional: Custom KMS encryption key for S3 output
      # s3_encryption_key_id: arn:aws:kms:us-west-2:123456789012:key/12345678-1234-1234-1234-123456789012
    model_info: 
      mode: batch # 👈 SPECIFY MODE AS BATCH, to tell user this is a batch model

Required Parameters:

ParameterDescription
s3_bucket_nameS3 bucket for batch input/output files
s3_region_nameAWS region for S3 bucket
s3_access_key_idAWS access key for S3 bucket
s3_secret_access_keyAWS secret key for S3 bucket
aws_batch_role_arnIAM role ARN for Bedrock batch operations. Bedrock Batch APIs require an IAM role ARN to be set.
mode: batchIndicates to LiteLLM this is a batch model

Optional Parameters:

ParameterDescription
s3_encryption_key_idCustom KMS encryption key ID for S3 output data. If not specified, Bedrock uses AWS managed encryption keys.

2. Create Virtual Key​

create_virtual_key.sh
curl -L -X POST 'https://{PROXY_BASE_URL}/key/generate' \
-H 'Authorization: Bearer ${PROXY_API_KEY}' \
-H 'Content-Type: application/json' \
-d '{"models": ["bedrock-batch-claude"]}'

You can now use the virtual key to access the batch models (See Developer flow).

(Developer) Usage​

Here's how to create a LiteLLM managed file and execute Bedrock Batch CRUD operations with the file.

1. Create request.jsonl​

  • Check models available via /model_group/info
  • See all models with mode: batch
  • Set model in .jsonl to the model from /model_group/info
bedrock_batch_completions.jsonl
{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are an unhelpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}}

Expectation:

  • LiteLLM translates this to the bedrock deployment specific value (e.g. bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0)

2. Upload File​

Specify target_model_names: "<model-name>" to enable LiteLLM managed files and request validation.

model-name should be the same as the model-name in the request.jsonl

bedrock_batch.py
from openai import OpenAI

client = OpenAI(
    base_url="http://0.0.0.0:4000",
    api_key="sk-1234",
)

# Upload file
batch_input_file = client.files.create(
    file=open("./bedrock_batch_completions.jsonl", "rb"), # {"model": "bedrock-batch-claude"} <-> {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0"}
    purpose="batch",
    extra_body={"target_model_names": "bedrock-batch-claude"}
)
print(batch_input_file)

Where is the file written?:

The file is written to S3 bucket specified in your config and prepared for Bedrock batch inference.

3. Create the batch​

bedrock_batch.py
...
# Create batch
batch = client.batches.create( 
    input_file_id=batch_input_file.id,
    endpoint="/v1/chat/completions",
    completion_window="24h",
    metadata={"description": "Test batch job"},
)
print(batch)

FAQ​

Where are my files written?​

When a target_model_names is specified, the file is written to the S3 bucket configured in your Bedrock batch model configuration.

What models are supported?​

LiteLLM only supports Bedrock Anthropic Models for Batch API. If you want other bedrock models file an issue here.

How do I use a custom KMS encryption key?​

If your S3 bucket requires a custom KMS encryption key, you can specify it in your configuration using s3_encryption_key_id. This is useful for enterprise customers with specific encryption requirements.

You can set the encryption key in 2 ways:

  1. In config.yaml (recommended):
model_list:
  - model_name: "bedrock-batch-claude"
    litellm_params:
      model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
      s3_encryption_key_id: arn:aws:kms:us-west-2:123456789012:key/12345678-1234-1234-1234-123456789012
      # ... other params
  1. As an environment variable:
export AWS_S3_ENCRYPTION_KEY_ID=arn:aws:kms:us-west-2:123456789012:key/12345678-1234-1234-1234-123456789012

Further Reading​