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Krrish Dholakia 74a85fac0e docs(aws_sagemaker.md): cleanup sagemaker messages api docs

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import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'

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"
        }
      ]
    }
'

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

response = client.chat.completions.create(model="jumpstart-model", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
    model = "jumpstart-model",
    temperature=0.1
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)

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
)

Set on yaml

model_list:
  - model_name: jumpstart-model
    litellm_params:
      model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614
      temperature: <your-temp>
      top_p: <your-top-p>

Set on request


import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="jumpstart-model", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
],
temperature=0.7,
top_p=1
)

print(response)

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,
)

Set aws_sagemaker_allow_zero_temp on yaml

model_list:
  - model_name: jumpstart-model
    litellm_params:
      model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614
      aws_sagemaker_allow_zero_temp: true

Set temperature=0 on request


import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="jumpstart-model", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
],
temperature=0,
)

print(response)

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
)

Set on yaml

model_list:
  - model_name: jumpstart-model
    litellm_params:
      model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614
      top_k: 1 # 👈 PROVIDER-SPECIFIC PARAM

Set on request


import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="jumpstart-model", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
],
temperature=0.7,
extra_body={
    top_k=1 # 👈 PROVIDER-SPECIFIC PARAM
}
)

print(response)

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
        )

1. Setup config.yaml

model_list:
  - model_name: "sagemaker-model"
    litellm_params:
      model: "sagemaker_chat/jumpstart-dft-hf-textgeneration1-mp-20240815-185614"
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      aws_region_name: os.environ/AWS_REGION_NAME

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": "sagemaker-model",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'

👉 See OpenAI SDK/Langchain/Llamaindex/etc. examples

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 Name	Function Call
Your Custom Huggingface Model	`completion(model='sagemaker/<your-deployment-name>', messages=messages)`
Meta Llama 2 7B	`completion(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 13B	`completion(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 70B	`completion(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}")

14 KiB Raw Blame History

AWS Sagemaker

API KEYS

Usage

Usage - Streaming

LiteLLM Proxy Usage

1. Setup config.yaml

2. Start the proxy

3. Test it

Set temperature, top p, etc.

Allow setting temperature=0 for Sagemaker

Pass provider-specific params

Passing Inference Component Name

Passing credentials as parameters - Completion()

Applying Prompt Templates

Sagemaker Messages API

1. Setup config.yaml

2. Start the proxy

3. Test it

Completion Models

Embedding Models

14 KiB

Raw Blame History