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Offline Batch Inference: Comparing Ray, Apache Spark, and SageMaker Amog Kamsetty Email: [email protected] Twitter: @AmogKamsetty

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Talk Overview - What is Batch inference and why does it matter?

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Talk Overview - What is Batch inference and why does it matter? - Challenges with Batch inference

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Talk Overview - What is Batch inference and why does it matter? - Challenges with Batch inference - Exploring current solution space

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Talk Overview - What is Batch inference and why does it matter? - Challenges with Batch inference - Exploring current solution space - Comparing three solutions

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Talk Overview - What is Batch inference and why does it matter? - Challenges with Batch inference - Exploring current solution space - Comparing three solutions - Sagemaker Batch Transform - Apache Spark - Ray Datasets

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What is Offline Batch Inference?

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Challenges 1. Managing compute infrastructure

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Challenges 1. Managing heterogeneous compute infrastructure

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Challenges 1. Managing heterogeneous compute infrastructure 2. Utilizing all resources in the cluster

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Challenges 1. Managing heterogeneous compute infrastructure 2. Utilizing all resources in the cluster 3. Efficient data transfer storage -> CPU RAM -> GPU RAM

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Challenges 1. Managing heterogeneous compute infrastructure 2. Utilizing all resources in the cluster 3. Efficient data transfer storage -> CPU RAM -> GPU RAM 4. Developer experience

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What does the industry recommend?

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What does the industry recommend?

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What does the industry recommend?

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What does the industry recommend?

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What does the industry recommend?

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What does the industry recommend?

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What does the industry recommend?

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What does the industry recommend?

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What does the industry recommend?

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Approach 1: Batch Services AWS Batch, GCP Batch, Azure Batch Partially handle Challenge #1 (managed infra) No heterogeneous clusters Don’t handle Challenges #2, #3, #4

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Approach 1: Batch Services AWS Batch, GCP Batch, Azure Batch Partially handle Challenge #1 (managed infra) No heterogeneous clusters Don’t handle Challenges #2, #3, #4 What about Modal Labs?

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Approach 2: Online Inference Solutions Bento ML, Ray Serve, Sagemaker Batch Transform ● Abstracts away infra complexities ● Abstractions for model packaging ● Framework integrations

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Approach 2: Online Inference Solutions Bento ML, Ray Serve, Sagemaker Batch Transform ● Abstracts away infra complexities ● Abstractions for model packaging ● Framework integrations Unnecessary complexities for offline inference Starting HTTP Server, sending requests over network… Hard to saturate GPUs BentoML integrates with Spark for offline inference

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Approach 3: Distributed Data Systems Spark, Ray Data Designed to handle Map operations on large datasets Native support for ● Scaling across clusters ● Data partitioning and batching ● I/O layer, connect to data sources

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Image Classification: - SageMaker Batch Transform - Apache Spark - Ray Data

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Benchmark Pretrained ResNet 50 model on ImageNet data 1. Reading images from S3 2. Simple CPU preprocessing (resizing, cropping, normalization) 3. Model inference on GPU 10 GB, 300 GB

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10 GB

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SageMaker Batch Transform Addresses Challenge #1 partially- abstracts away infra management But, Sagemaker Batch Transform uses architecture for online serving ● Starts HTTP Server, deploys model as endpoint ● Each image sent as a request to the server ● Cannot batch across multiple files ● Max payload size is 100 MB -> cannot saturate GPUs! Poor developer UX, difficult debugging

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Comparing Ray Data and Spark Challenge #2: Utilizing all resources in the cluster

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Comparing Ray Data and Spark Challenge #2: Utilizing all resources in the cluster for CPU+GPU workloads

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Comparing Ray Data and Spark Challenge #2: Utilizing all resources in the cluster for CPU+GPU workloads

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Comparing Ray Data and Spark Challenge #2: Utilizing all resources in the cluster for CPU+GPU workloads Challenge #3: Efficient data transfer for multi-dimensional tensors - Numpy+Pyarrow, no Pandas overhead - No JVM<>Pyarrow overhead

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Comparing Ray Data and Spark Challenge #2: Utilizing all resources in the cluster for CPU+GPU workloads Challenge #3: Efficient data transfer for multi-dimensional tensors - Numpy+Pyarrow, no Pandas overhead - No JVM<>Pyarrow overhead Challenge #4: Developer Experience - Ray is Python first - Easier debugging, better stack traces

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300 GB Let’s see this live!

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Does Ray Data Scale to 10 TB? 40 GPU cluster Throughput: 11,580.958 img/sec 90%+ GPU utilization

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Summary Ray Data outperforms Sagemaker and Spark for offline batch inference

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Summary Ray Data outperforms Sagemaker and Spark for offline batch inference Ray Data meets all 4 challenges 1. Abstracts away compute infrastructure management, supports heterogeneous clusters.

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Summary Ray Data outperforms Sagemaker and Spark for offline batch inference Ray Data meets all 4 challenges 1. Abstracts away compute infrastructure management, supports heterogeneous clusters. 2. Streams data from cloud storage -> CPU -> GPU, utilizing all cluster resources.

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Summary Ray Data outperforms SageMaker and Spark for offline batch inference Ray Data meets all 4 challenges 1. Abstracts away compute infrastructure management, supports heterogeneous clusters. 2. Streams data from cloud storage -> CPU -> GPU, utilizing all cluster resources. 3. Support for multi-dimensional tensors with zero-copy exchange

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Summary Ray Data outperforms Sagemaker and Spark for offline batch inference Ray Data meets all 4 challenges 1. Abstracts away compute infrastructure management, supports heterogeneous clusters. 2. Streams data from cloud storage -> CPU -> GPU, utilizing all cluster resources. 3. Support for multi-dimensional tensors with zero-copy exchange 4. Python native, making it easy to develop and debug.

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