Evaluating your SageMaker AI-trained model

The purpose of the evaluation process is to assess trained-model performance against benchmarks or custom dataset. The evaluation process typically involves steps to create evaluation recipe pointing to the trained model, specify evaluation datasets and metrics, submit a separate job for the evaluation, and evaluate against standard benchmarks or custom data. The evaluation process will output performance metrics stored in your Amazon S3 bucket.

Prerequisites

Before you start a evaluation training job, note the following.

Available benchmark tasks

A sample code package is available that demonstrates how to calculate benchmark metrics using the SageMaker model evaluation feature for Amazon Nova. To access the code packages, see sample-Nova-lighteval-custom-task.

Here is a list of available industry standard benchmarks supported. You can specify the following benchmarks in the eval_task parameter.

Available benchmarks for model evaluation

Evaluation specific configurations

Below is a breakdown of the key components in the recipe and guidance on how to modify them for your use cases.

Understanding and modifying your recipes

General run configuration

run:
  name: eval_job_name 
  model_type: amazon.nova-micro-v1:0:128k 
  model_name_or_path: nova-micro/prod 
  replicas: 1 
  data_s3_path: ""

Evaluation configuration

evaluation:
  task: mmlu 
  strategy: zs_cot 
  subtask: abstract_algebra
  metric: accuracy

Inference configuration (optional)

inference:
  max_new_tokens: 2048 
  top_k: -1 
  top_p: 1.0 
  temperature: 0
  top_logprobs: 10

Evaluation recipe examples

Amazon Nova provides four different types of evaluation recipes. All recipes are available in Amazon SageMaker HyperPod recipes GitHub repository.

{
"system":"You are an English major with top marks in class who likes to give minimal word responses: ",
   "query":"What is the symbol that ends the sentence as a question",
   "response":"?"
}{
"system":"You are a pattern analysis specialist who provides succinct answers: ",
   "query":"What is the next number in this series? 1, 2, 4, 8, 16, ?",
   "response":"32"
}{
"system":"You have great attention to detail and follow instructions accurately: ",
   "query":"Repeat only the last two words of the following: I ate a hamburger today and it was kind of dry",
   "response":"of dry"
}{
"system": "Image inference: ",
  "query": "What is the number in the image? Please just use one English word to answer.",
  "response": "two",
  "images": [
    {
      "data": "data:image/png;Base64,iVBORw0KGgoA ..."
    }
  ]
}

evaluation:
  task: gen_qa 
  strategy: gen_qa 
  metric: all

{
"prompt": "What is the most effective way to combat climate change?",
"response_A": "The most effective way to combat climate change is through a combination of transitioning to renewable energy sources and implementing strict carbon pricing policies. This creates economic incentives for businesses to reduce emissions while promoting clean energy adoption.",
"response_B": "We should focus on renewable energy. Solar and wind power are good. People should drive electric cars. Companies need to pollute less."
}
{
"prompt": "Explain how a computer's CPU works",
"response_A": "CPU is like brain of computer. It does math and makes computer work fast. Has lots of tiny parts inside.",
"response_B": "A CPU (Central Processing Unit) functions through a fetch-execute cycle, where instructions are retrieved from memory, decoded, and executed through its arithmetic logic unit (ALU). It coordinates with cache memory and registers to process data efficiently using binary operations."
}
{
"prompt": "How does photosynthesis work?",
"response_A": "Plants do photosynthesis to make food. They use sunlight and water. It happens in leaves.",
"response_B": "Photosynthesis is a complex biochemical process where plants convert light energy into chemical energy. They utilize chlorophyll to absorb sunlight, combining CO2 and water to produce glucose and oxygen through a series of chemical reactions in chloroplasts."
}           

evaluation:
  task: llm_judge
  strategy: judge
  metric: all

{
  "prompt": "What is in the image?",
  "images": [
    {
      "data": "data:image/jpeg;Base64,/9j/2wBDAAQDAwQDAwQEAwQFBAQFBgo..."
    }
  ],
  "response_A": "a dog.",
  "response_B": "a cat.",
} 
{
  "prompt": "How many animals are in each of the images?",
  "images": [
    {
      "data": "data:image/jpeg;Base64,/9j/2wBDAAQDAwQDAwQEAwQFBAQFBgo..."
    },
    {
      "data": "data:image/jpeg;Base64,/DKEafe3gihn..."
    }
  ],
  "response_A": "The first image contains one cat and the second image contains one dog",
  "response_B": "The first image has one aminal and the second has one animal"
}           

evaluation:
  task: mm_llm_judge
  strategy: judge
  metric: all

Running evaluation training jobs

Start a training job using the following sample Jupyter notebook. For more information, see Use a SageMaker AI estimator to run a training job.

Reference tables

Before running the notebook, refer to the following reference tables to select image URI and instance configurations.

Selecting image URI

Selecting instance type and count

Sample notebook

The following sample notebook demonstrates how to run an evaluation training job.

# install python SDK
!pip install sagemaker
 
import os
import sagemaker,boto3
from sagemaker.inputs import TrainingInput
from sagemaker.pytorch import PyTorch

sagemaker_session = sagemaker.Session()
role = sagemaker.get_execution_role()

# Download recipe from https://github.com/aws/sagemaker-hyperpod-recipes/tree/main/recipes_collection/recipes/evaluation/nova to local
# Assume the file name be `recipe.yaml`

# Populate parameters
# input_s3_uri = "s3://<path>/input/" # (Optional) Only used for multi-modal dataset or bring your own dataset s3 location
output_s3_uri= "s3://<path>/output/" # Output data s3 location, a zip containing metrics json and tensorboard metrics files will be stored to this location
instance_type = "instance_type"  # ml.g5.16xlarge as example
instance_count = 1 # The number of instances for inference (set instance_count > 1 for multi-node inference to accelerate evaluation)             
job_name = "your job name"
recipe_path = "recipe path" # ./recipe.yaml as example
image_uri = "708977205387.dkr.ecr.us-east-1.amazonaws.com/nova-evaluation-repo:SM-TJ-Eval-latest" # Do not change

# (Optional) To bring your own dataset and LLM judge for evaluation
# evalInput = TrainingInput(
# s3_data=input_s3_uri,
# distribution='FullyReplicated',
# s3_data_type='S3Prefix'
#)

estimator = PyTorch(
    output_path=output_s3_uri,
    base_job_name=job_name,
    role=role,
    instance_type=instance_type,
    instance_count=instance_count,
    training_recipe=recipe_path,
    sagemaker_session=sagemaker_session,
    image_uri=image_uri
)
estimator.fit()

# If input dataset exist, pass in inputs
# estimator.fit(inputs={"train": evalInput})

Assessing and analyzing evaluation results

After your evaluation job completes successfully, you can assess and analyze the results using the following steps.

Evaluation best practices and troubleshooting

Best practices

The following lists some best practices for the evaluation process.

Troubleshooting

You can use CloudWatch log group /aws/sagemaker/TrainingJobs for training job error logs.

CUDA Out of Memory Error

Issue:

When running model evaluation, you receive the following error:

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate X MiB. 
GPU 0 has a total capacity of Y GiB of which Z MiB is free.

Cause:

This error occurs when you attempt to load a model that requires more GPU memory than what's available on your current instance type.

Solution:

Choose an instance type with more GPU memory. For example, if you use G5.12xlarge (96 GiB GPU memory), upgrade to G5.48xlarge (192 GiB GPU memory)

Prevention:

Before running model evaluation, do the following.

Available subtasks

The following lists available subtasks for model evaluation across multiple domains including MMLU (Massive Multitask Language Understanding), BBH (Big Bench Hard), mathematics, and MMMU (Massive Multi-discipline Multimodal Understanding). These subtasks allow you to assess your model's performance on specific capabilities and knowledge areas.

MMLU

MMLU_SUBTASKS = [
    "abstract_algebra",
    "anatomy",
    "astronomy",
    "business_ethics",
    "clinical_knowledge",
    "college_biology",
    "college_chemistry",
    "college_computer_science",
    "college_mathematics",
    "college_medicine",
    "college_physics",
    "computer_security",
    "conceptual_physics",
    "econometrics",
    "electrical_engineering",
    "elementary_mathematics",
    "formal_logic",
    "global_facts",
    "high_school_biology",
    "high_school_chemistry",
    "high_school_computer_science",
    "high_school_european_history",
    "high_school_geography",
    "high_school_government_and_politics",
    "high_school_macroeconomics",
    "high_school_mathematics",
    "high_school_microeconomics",
    "high_school_physics",
    "high_school_psychology",
    "high_school_statistics",
    "high_school_us_history",
    "high_school_world_history",
    "human_aging",
    "human_sexuality",
    "international_law",
    "jurisprudence",
    "logical_fallacies",
    "machine_learning",
    "management",
    "marketing",
    "medical_genetics",
    "miscellaneous",
    "moral_disputes",
    "moral_scenarios",
    "nutrition",
    "philosophy",
    "prehistory",
    "professional_accounting",
    "professional_law",
    "professional_medicine",
    "professional_psychology",
    "public_relations",
    "security_studies",
    "sociology",
    "us_foreign_policy",
    "virology",
    "world_religions"
]

BBH

BBH_SUBTASKS = [
    "boolean_expressions",
    "causal_judgement",
    "date_understanding",
    "disambiguation_qa",
    "dyck_languages",
    "formal_fallacies",
    "geometric_shapes",
    "hyperbaton",
    "logical_deduction_five_objects",
    "logical_deduction_seven_objects",
    "logical_deduction_three_objects",
    "movie_recommendation",
    "multistep_arithmetic_two",
    "navigate",
    "object_counting",
    "penguins_in_a_table",
    "reasoning_about_colored_objects",
    "ruin_names",
    "salient_translation_error_detection",
    "snarks",
    "sports_understanding",
    "temporal_sequences",
    "tracking_shuffled_objects_five_objects",
    "tracking_shuffled_objects_seven_objects",
    "tracking_shuffled_objects_three_objects",
    "web_of_lies",
    "word_sorting"
]

Math

MATH_SUBTASKS = [
    "algebra",
    "counting_and_probability",
    "geometry",
    "intermediate_algebra",
    "number_theory",
    "prealgebra",
    "precalculus",
]

MMMU

MATH_SUBTASKS = [
    "Accounting",
    "Agriculture",
    "Architecture_and_Engineering",
    "Art",
    "Art_Theory",
    "Basic_Medical_Science",
    "Biology",
    "Chemistry",
    "Clinical_Medicine",
    "Computer_Science",
    "Design",
    "Diagnostics_and_Laboratory_Medicine",
    "Economics",
    "Electronics",
    "Energy_and_Power",
    "Finance",
    "Geography",
    "History",
    "Literature",
    "Manage",
    "Marketing",
    "Materials",
    "Math",
    "Mechanical_Engineering",
    "Music",
    "Pharmacy",
    "Physics",
    "Psychology",
    "Public_Health",
    "Sociology",
]