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AWS Sagemaker

LiteLLM supports All Sagemaker Huggingface Jumpstart Models

tip

We support ALL Sagemaker models, just set model=sagemaker/<any-model-on-sagemaker> as a prefix when sending litellm requests

API KEYS​

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

Usage​

import os 
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
            model="sagemaker/<your-endpoint-name>", 
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            temperature=0.2,
            max_tokens=80
        )

Usage - Streaming​

Sagemaker currently does not support streaming - LiteLLM fakes streaming by returning chunks of the response string

import os 
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", 
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            temperature=0.2,
            max_tokens=80,
            stream=True,
        )
for chunk in response:
    print(chunk)

LiteLLM Proxy Usage​

Here's how to call Sagemaker with the LiteLLM Proxy Server

1. Setup config.yaml​

model_list:
  - model_name: jumpstart-model
    litellm_params:
      model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614
      aws_access_key_id: os.environ/CUSTOM_AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/CUSTOM_AWS_SECRET_ACCESS_KEY
      aws_region_name: os.environ/CUSTOM_AWS_REGION_NAME

All possible auth params: 

aws_access_key_id: Optional[str],
aws_secret_access_key: Optional[str],
aws_session_token: Optional[str],
aws_region_name: Optional[str],
aws_session_name: Optional[str],
aws_profile_name: Optional[str],
aws_role_name: Optional[str],
aws_web_identity_token: Optional[str],

2. Start the proxy​

litellm --config /path/to/config.yaml

3. Test it​

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "jumpstart-model",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'

Set temperature, top p, etc.​

import os
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
  model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
  messages=[{ "content": "Hello, how are you?","role": "user"}],
  temperature=0.7,
  top_p=1
)

Allow setting temperature=0 for Sagemaker​

By default when temperature=0 is sent in requests to LiteLLM, LiteLLM rounds up to temperature=0.1 since Sagemaker fails most requests when temperature=0

If you want to send temperature=0 for your model here's how to set it up (Since Sagemaker can host any kind of model, some models allow zero temperature)

import os
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
  model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
  messages=[{ "content": "Hello, how are you?","role": "user"}],
  temperature=0,
  aws_sagemaker_allow_zero_temp=True,
)

Pass provider-specific params​

If you pass a non-openai param to litellm, we'll assume it's provider-specific and send it as a kwarg in the request body. See more

import os
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
  model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
  messages=[{ "content": "Hello, how are you?","role": "user"}],
  top_k=1 # 👈 PROVIDER-SPECIFIC PARAM
)

Passing Inference Component Name​

If you have multiple models on an endpoint, you'll need to specify the individual model names, do this via model_id. 

import os 
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
            model="sagemaker/<your-endpoint-name>", 
            model_id="<your-model-name",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            temperature=0.2,
            max_tokens=80
        )

Passing credentials as parameters - Completion()​

Pass AWS credentials as parameters to litellm.completion

import os 
from litellm import completion

response = completion(
            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            aws_access_key_id="",
            aws_secret_access_key="",
            aws_region_name="",
)

Applying Prompt Templates​

To apply the correct prompt template for your sagemaker deployment, pass in it's hf model name as well. 

import os 
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", 
            messages=messages,
            temperature=0.2,
            max_tokens=80,
            hf_model_name="meta-llama/Llama-2-7b",
        )

You can also pass in your own custom prompt template

Sagemaker Messages API​

Use route sagemaker_chat/* to route to Sagemaker Messages API

model: sagemaker_chat/<your-endpoint-name>
import os
import litellm
from litellm import completion

litellm.set_verbose = True # 👈 SEE RAW REQUEST

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
            model="sagemaker_chat/<your-endpoint-name>", 
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            temperature=0.2,
            max_tokens=80
        )

Completion Models​

tip

We support ALL Sagemaker models, just set model=sagemaker/<any-model-on-sagemaker> as a prefix when sending litellm requests

Here's an example of using a sagemaker model with LiteLLM

Model NameFunction Call
Your Custom Huggingface Modelcompletion(model='sagemaker/<your-deployment-name>', messages=messages)
Meta Llama 2 7Bcompletion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b', messages=messages)
Meta Llama 2 7B (Chat/Fine-tuned)completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b-f', messages=messages)
Meta Llama 2 13Bcompletion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-13b', messages=messages)
Meta Llama 2 13B (Chat/Fine-tuned)completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-13b-f', messages=messages)
Meta Llama 2 70Bcompletion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b', messages=messages)
Meta Llama 2 70B (Chat/Fine-tuned)completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b-b-f', messages=messages)

Embedding Models​

LiteLLM supports all Sagemaker Jumpstart Huggingface Embedding models. Here's how to call it: 

from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = litellm.embedding(model="sagemaker/<your-deployment-name>", input=["good morning from litellm", "this is another item"])
print(f"response: {response}")