Scaling LLM Experimentation with SageMaker Pipelines and MLflow

Author(s): Saleh Alkhalifa

Originally published on Towards AI.

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Large language models (LLMs) are transforming NLP tasks across a wide variety of industries, but often require customization to excel in specific domains. This article summarizes how Amazon SageMaker and MLflow can help streamline LLM fine-tuning and evaluation, providing scalable solutions for model experimentation at the enterprise level.

Evaluate pre-trained models to find the best fit for your use case using Amazon SageMaker JumpStart or SageMaker Clarify. Compare models at scale using SageMaker Pipelines.

Hugging Face Token: Access datasets and models.SageMaker IAM Role: Ensure necessary permissions for creating and managing resources.MLflow Tracking Server:mlflow_arn="arn:aws:sagemaker:<region>:<account_id>:mlflow-tracking-server/<tracking_server_name>" mlflow.set_tracking_uri(mlflow_arn) mlflow.set_experiment("experiment_name")

Thats it! Once you have all these basic requirements complete, you are ready to start.

Track training and evaluation data for reproducibility. We use MLFlow to manage our workflows here. In this step, we set up MLFlow and then import the dataset of interest (this would generally be internal company data).

from datasets import load_datasetimport pandas as pdmlflow.set_tracking_uri(mlflow_arn)mlflow.set_experiment("experiment_name")dataset = load_dataset("HuggingFaceH4/no_robots", split="train")df_train = pd.DataFrame(dataset)training_data = mlflow.data.from_pandas(df_train)mlflow.log_input(training_data, context="training")

Next, we will use Parameter-Efficient Fine-Tuning (PEFT) to customize LLMs efficiently. For this we will take advantage of the transformers library from PyPi.

from transformers import Trainer, TrainingArgumentstrainer = Trainer( model=model, train_dataset=lm_train_dataset, eval_dataset=lm_test_dataset,… Read the full blog for free on Medium.

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Published via Towards AI