aws-neuron

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optimum-neuron-cache

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Several 7b models are available in the cache, and a snippet to deploy each of them on SageMaker is available in the model card (Deploy > Amazon SageMaker > AWS Inferentia & Trainium).

Here is for instance the sniipet to deploy zephyr-7b-beta:

import json
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri

try:
    role = sagemaker.get_execution_role()
except ValueError:
    iam = boto3.client("iam")
    role = iam.get_role(RoleName="sagemaker_execution_role")["Role"]["Arn"]

# Hub Model configuration. https://huggingface.co/models
hub = {
    "HF_MODEL_ID": "HuggingFaceH4/zephyr-7b-beta",
    "HF_NUM_CORES": "8",
    "HF_BATCH_SIZE": "1",
    "HF_SEQUENCE_LENGTH": "4096",
    "HF_AUTO_CAST_TYPE": "bf16",  
    "MAX_BATCH_SIZE": "1",
    "MAX_INPUT_LENGTH": "3686",
    "MAX_TOTAL_TOKENS": "4096",
}


# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
    image_uri=get_huggingface_llm_image_uri("huggingface-neuronx", version="0.0.20"),
    env=hub,
    role=role,
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
    initial_instance_count=1,
    instance_type="ml.inf2.24xlarge",
    container_startup_health_check_timeout=1800,
    volume_size=512,
)

# send request
predictor.predict(
    {
        "inputs": "What is is the capital of France?",
        "parameters": {
            "do_sample": True,
            "max_new_tokens": 128,
            "temperature": 0.7,
            "top_k": 50,
            "top_p": 0.95,
        }
    }
)

Alternatively, you can also export them locally in your ec2 environment following the instructions here and using the same configuration parameters as the cached version:

Works for me, thank you. Don't forget to upgrade the Sagemaker SDK with 'pip install sagemaker --upgrade'. For the record, I used 2.214.0.

where do you see the available models for aws inf2? im looking for llama2 7b chat for "ml.inf2.xlarge"

You can see the list of cached models here:

https://huggingface.co/aws-neuron/optimum-neuron-cache/tree/main/inference-cache-config

Alternatively, you can use the optimum-cli neuron cache lookup command to look for a specific model and see the cached configurations.

Since you want to deploy on an ml.inf2.xlarge, you need to select a configuration with 2 cores.

The following configuration is available:

batch_size: 1
sequence_length: 4096
num_cores: 2
auto_cast_type: fp16

You can adapt the snippet from the model card (Deploy/Amazon Sagemaker/AWS Inferentia & Trainium).

Hi @dacorvo - can i use that model cache with djl serving too?
see: https://docs.djl.ai/docs/demos/aws/sagemaker/large-model-inference/sample-llm/tnx_rollingbatch_deploy_llama_7b_int8.html

if not what steps i need to do in that case?