8 Critical Considerations for AI Deployments
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The following article is content provided by Intel.
With proprietary platforms, your organization, enterprise architecture, data infrastructure, middleware and application stacks all have to be rearchitected with expert teams to maximize value from CAPEX and OPEX intensive resources. Intel-based solutions provide the most workload flexibility for your organization, including AI. These solutions adapt to your existing organization, deployment context, enterprise architectures, data infrastructures, middleware and application stacks so rearchitecting isn't required. When evaluating AI solutions, make sure you have all the right details to inform your decision. Intel offers the most robust toolkit to support your AI needs. Here are the most important things to keep in mind when considering the implementation of AI solutions across the five stages of AI execution.
When evaluating AI solutions, make sure you have all the right details to inform your decision. Intel offers the most robust toolkit to support your AI needs. Here are the most important things to keep in mind when considering the implementation of AI solutions across the five stages of AI execution.
Data science workflows require highly interactive systems that handle massive volumes of data in memory, using algorithms and tools designed for single-node processing - current GPUs are generally a poor fit for many of these tasks.
- Data preprocessing today is done on a CPU and many practitioners spend a significant amount of their time using the highly popular Pandas library
- Intel's distribution of Modin is an open-source library which accelerates Pandas applications by up to 90x
- PMem can make it possible to load larger datasets into memory without falling back to disk - it can also act as a fast-cache configuration
Data infrastructure is already optimized for Intel and effective ingest. The result is a completely optimized pipeline scaling from PC and workstation to cloud to edge: customers can scale AI everywhere by leveraging the broad, open software ecosystem and unique Intel tools. If you are accessing and processing data then storage and memory are critical - you need a faster storage sub system that doesn't require use of a GPU.
Intel's Habana® Labs Gaudi® provides customers with cost-efficient AI training, ease of use and system scalability – integration of the Gaudi platform eliminates storage bottlenecks and optimizes utilization of AI compute capacity. The Intel Habana® Gaudi® AI Training Processor powers Amazon EC2 DL1 instances, delivering up to 40% better price performance than comparable Nvidia GPU-based training instances, according to AWS testing - this processor is also available for on-premises implementation with the Supermicro X12 Habana Gaudi AI Training Server.
Existing Intel® Xeon® Scalable processors scale well with intermittent training sets during off-peak cycles overnight or on weekends The upcoming launch of 4th Gen Intel® Xeon® Scalable processors (code named Sapphire Rapids) with AMX and BrainFloat16 will deliver even higher performance and scalability.
Intel® Xeon® Scalable Drives Specific 4 Success in Machine Learning Elevate effectiveness of machine learning workloads
Elevate effectiveness of machine learning workloads through the performance of Intel hardware. Deep learning is heavily emphasized in the AI space, but don't overlook machine learning. While deep learning workloads typically require a GPU, other aspects of machine learning can be executed effectively on Intel® Xeon® Scalable.
Machine learning is becoming well established (most data scientists have adopted well established ML methods per 2020 Kaggle Survey); Xeon Scalable excels here.
Built-in acceleration capabilities in 3rd Generation Intel® Xeon® Scalable processors deliver 1.5x greater AI performance than other CPUs across 20 key customer workloads, the majority of which are machine learning workloads.
Built-in acceleration capabilities in 3rd Generation Intel® Xeon® Scalable processors targets higher training and inference performance with the required level of accuracy.
The AI accelerators built into Intel® Xeon® Scalable processors provide 10-to-100x performance improvements for AI frameworks and libraries like Spark for data processing, TensorFlow, PyTorch, Scikit-learn, NumPy and XGBoost.
You don't need to break the bank for effective graph analytics - a single Intel® Xeon® Scalable processor-based server with sufficient memory is a much better choice for large-scale, general-purpose graph analytics.
Get faster analytics insights, up to 2x faster graph analytics computations (Katana Graph) for recommender systems and fraud detection 2x faster on average when using 3rd Gen Intel® Xeon® Scalable Processors with Intel® Optane™ persistent memory 200 series.
AI deployment is about inference, and Intel is the most globally trusted hardware for inference! The performance capabilities of Intel hardware can drive the inferencing success your business operation relies on. Here's why:
Intel® Xeon® Scalable is the only x86 data center CPU with built-in AI acceleration. Utilize Intel® Xeon® Scalable processors for more cost-effective inferencing rather than leveraging new Nvidia hardware that will add deployment and recurring cost.
- Intel® Xeon® Scalable processors have 1.7x higher perf/$, 3.3x higher perf per watt than Nvidia's 4x DGX A100 deployment- CPU-based solutions can provide more cost-effective option for customers.
Let's explore additional performance comparisons:
- 30% higher average AI Performance across 20 workloads with 3rd Gen Intel® Xeon® Scalable processor supporting Intel® DL Boost vs Nvidia A100 GPU12 (Geomean of 20 workloads) without adding the cost and complexity of a GPU.
- Dual socket servers with Next Gen Intel® Xeon® Scalable processors (code-named Sapphire Rapids) can infer over 24k images/second compared with 16k on a Nvidia A30 GPU.
This means Intel can deliver better than 1.5x the performance of Nvidia's mainstream inferencing GPU for 2022, strengthening the recommendation to standardize on Xeon – don't disrupt workflows as the next generation will provide even greater performance.
Instead of creating incremental, complex workloads that you don't need, optimize the Intel® Xeon® Scalable processors you already have installed. Get better end-to-end performance without introducing delays and burden. Customers who care about end-to-end performance can optimize on the Intel CPUs they already have installed – don't introduce unnecessary workloads that create inefficiencies. Integrating non-Intel solutions will require upskilling to learn new technologies. Complexity of non-Intel integration challenges will result in extended latencies.
Write once, use anywhere with Open-Source software. DL/ML framework users can reap all performance and productivity benefits through drop-in acceleration without the need to learn new APIs or low-level foundational libraries as many AI frameworks are already running on Intel.
Don't limit business trajectory and output by locking into a restrictive software development model and partner ecosystem. CUDA-based tools restrict developer choice and lock-in any models created to that platform.
- CUDA created a closed ecosystem that is challenging to grow beyond
- It creates difficult porting without recoding or CUDA engineer support
Intel's end-to-end portfolio of AI tools and framework optimizations for customers is built on the foundation of the open, standards-based, unified oneAPI programming model and constituent libraries. The Intel® oneDNN library is being adopted widely – oneDNN provides the building blocks for deep learning applications with very fast performance across x86_64 processors, and provides a wider breadth of performance optimizations for developers.
Along with developing Intel-optimized distributions for leading AI frameworks, Intel also up-streams optimizations into the main versions of these frameworks, delivering increased performance and productivity to your AI applications even when using default versions of these frameworks.
AI is a complex and varied ecosystem. Intel® provides a product portfolio of performance hardware and Open-Source software to achieve evolving AI needs with maximum performance and cost efficiency for any workload. Intel offers the broadest AI portfolio for customers, including CPUs, FPGAs, VPUs, ASICs, forthcoming discrete GPUs and more, allowing us to position the right hardware for any customer use case.
No matter the AI deployment type, the Intel portfolio provides the hardware and software capabilities you need for success: