AWS Machine Learning

NOVEMBER 27, 2023

In this post, we dive deep into the new features with the latest release of LMI DLCs, discuss performance benchmarks, and outline the steps required to deploy LLMs with LMI DLCs to maximize performance and reduce costs. To use SmoothQuant, set option.quantize=smoothquan t with engine = DeepSpeed in serving.properties.

Engineering 113

Engineering Benchmark Scripts Advertising 113

AWS Machine Learning

JUNE 17, 2024

PrestoDB is an open source SQL query engine that is designed for fast analytic queries against data of any size from multiple sources. For more information on the TPC-H data, its database entities, relationships, and characteristics, refer to TPC Benchmark H. Twilio needed to implement an MLOps pipeline that queried data from PrestoDB.

Scripts 119

Scripts Engineering Metrics Big data 119

AWS Machine Learning

AUGUST 18, 2022

Machine learning (ML) experts, data scientists, engineers and enthusiasts have encountered this problem the world over. The team’s early benchmarking results show 7.3 The baseline model used in these benchmarking is a multi-layer perceptron neural network with seven dense fully connected layers and over 200 parameters.

Scripts 94

Scripts APIs Benchmark Engineering 94

AWS Machine Learning

JULY 26, 2023

We’ll cover fine-tuning your foundation models, evaluating recent techniques, and understanding how to run these with your scripts and models. As an added bonus, we’ll walk you through a Stable Diffusion deep dive, prompt engineering best practices, standing up LangChain, and more. More of a reader than a video consumer?

Scripts 94

Scripts Engineering Benchmark Best practices 94

AWS Machine Learning

DECEMBER 5, 2023

In this post, we show a high-level overview of how SMDDP works, how you can enable SMDDP in your Amazon SageMaker training scripts, and the performance improvements you can expect. About the Authors Apoorv Gupta is a Software Development Engineer at AWS, focused on building optimal deep learning systems for AWS infrastructure and hardware.

Benchmark 105

Benchmark Engineering Scripts Metrics 105

AWS Machine Learning

NOVEMBER 30, 2023

Note that your model artifacts also include an inference script for preprocessing and postprocessing. If you don’t provide an inference script, the default inference handlers for the container you have chosen will be implemented. Gaurav Bhanderi is a Front End engineer with AI platforms team in SageMaker.

Benchmark 125

Benchmark APIs Scripts Engineering 125

AWS Machine Learning

JULY 15, 2024

Welocalize benchmarks the performance of using LLMs and machine translations and recommends using LLMs as a post-editing tool. We use the custom terminology dictionary to compile frequently used terms within video transcription scripts. in Mechanical Engineering from the University of Notre Dame. Here’s an example.

Engineering 118

Engineering Entertainment Big data APIs 118

AWS Machine Learning

MAY 10, 2023

We cover computer vision (CV), natural language processing (NLP), classification, and ranking scenarios for models and ml.c6g, ml.c7g, ml.c5, and ml.c6i SageMaker instances for benchmarking. You can use the sample notebook to run the benchmarks and reproduce the results. Mohan Gandhi is a Senior Software Engineer at AWS.

Benchmark 93

Benchmark Best practices Engineering Scripts 93

AWS Machine Learning

MAY 29, 2024

The prospect of fine-tuning open source multimodal models like LLaVA are highly appealing because of their cost effectiveness, scalability, and impressive performance on multimodal benchmarks. It sets up a SageMaker training job to run the custom training script from LLaVA. For full parameter fine-tuning, ml.p4d.24xlarge

Scripts 126

Scripts Healthcare Metrics Finance 126

AWS Machine Learning

FEBRUARY 27, 2024

This was the perfect place to start for our prototype—not only would Axfood gain a new AI/ML platform, but we would also get a chance to benchmark our ML capabilities and learn from leading AWS experts. If discrepancies arise, a business logic within the postprocessing script assesses whether retraining the model is necessary.

Best practices 133

Best practices Engineering Scripts Analytics 133

AWS Machine Learning

MAY 22, 2024

Customers can more easily locate products that have correct descriptions, because it allows the search engine to identify products that match not just the general category but also the specific attributes mentioned in the product description. The script also merges the LoRA weights into the model weights after training.

Scripts 131

Scripts Engineering Accountability APIs 131

AWS Machine Learning

APRIL 26, 2024

Code generation DBRX models demonstrate benchmarked strengths for coding tasks. user Write a Python script to read a CSV file containing stock prices and plot the closing prices over time using Matplotlib. The file should have columns named 'Date' and 'Close' for this script to work correctly.

Transportation 122

Transportation Scripts Accountability Benchmark 122

AWS Machine Learning

APRIL 22, 2024

Typically, HyperPod clusters are used by multiple users: machine learning (ML) researchers, software engineers, data scientists, and cluster administrators. To achieve this multi-user environment, you can take advantage of Linux’s user and group mechanism and statically create multiple users on each instance through lifecycle scripts.

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Scripts Engineering Management Benchmark 109

AWS Machine Learning

APRIL 17, 2023

Prompt engineering Prompt engineering refers to efforts to extract accurate, consistent, and fair outputs from large models, such text-to-image synthesizers or large language models. For more information, refer to EMNLP: Prompt engineering is the new feature engineering.

Engineering 95

Engineering Benchmark Scripts industry standards 95

AWS Machine Learning

OCTOBER 31, 2022

Data scientists and machine learning engineers are constantly looking for the best way to optimize their training compute, yet are struggling with the communication overhead that can increase along with the overall cluster size. To get started, follow Modify a PyTorch Training Script to adapt SMPs’ APIs in your training script.

Scripts 76

Scripts Benchmark APIs Engineering 76

AWS Machine Learning

JULY 26, 2023

SageMaker LMI containers provide two ways to deploy the model: A no-code option where we just provide a serving.properties file with the required configurations Bring your own inference script We look at both solutions and go over the configurations and the inference script ( model.py ). The container requires your model.py

Scripts 93

Scripts APIs Benchmark Engineering 93

AWS Machine Learning

DECEMBER 16, 2022

Finally, we’ll benchmark performance of 13B, 50B, and 100B parameter auto-regressive models and wrap up with future work. A ready-to-use training script for GPT-2 model can be found at train_gpt_simple.py. You can find an example in the same training script train_gpt_simple.py. Benchmarking performance. 24xlarge nodes.

Benchmark 82

Benchmark Engineering APIs Scripts 82

AWS Machine Learning

DECEMBER 16, 2022

In this post, we outline the key benefits and pain points addressed by SageMaker Training Managed Warm Pools, as well as benchmarks and best practices. Benchmarks. We performed benchmarking tests to measure job startup latency using a 1.34 Overview of SageMaker Training Managed Warm Pools. When should you use warm pools?

Best practices 90

Best practices Management Engineering Benchmark 90

AWS Machine Learning

MARCH 20, 2023

Refer to the appendix for instance details and benchmark data. Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. Benchmark data The following table compares the cost and relative performance between c5 and c6 instances.

Calibration 91

Calibration Scripts Benchmark APIs 91

Totango

AUGUST 23, 2022

Touchpoints may involve any medium you use to interact with customers, including: Search engine marketing. This may occur through encountering your brand or product through a search engine result, a search engine ad, a social media post, a video, a review on a technology website, word-of-mouth or other means. Blog content.

B2B 122

B2B Journey mapping SaaS Upselling 122

AWS Machine Learning

MARCH 14, 2024

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work.

Best practices 127

Best practices Engineering Chatbots Enterprise 127

AWS Machine Learning

APRIL 27, 2023

Trainium support for custom operators Trainium (and AWS Inferentia2) supports CustomOps in software through the Neuron SDK and accelerates them in hardware using the GPSIMD engine (General Purpose Single Instruction Multiple Data engine). The scalar and vector engines are highly parallelized and optimized for floating-point operations.

APIs 86

APIs Engineering Scripts Benchmark 86

AWS Machine Learning

JANUARY 19, 2024

The concepts illustrated in this post can be applied to applications that use PLM features, such as recommendation systems, sentiment analysis, and search engines. We use the Recognizing Textual Entailment dataset from the GLUE benchmarking suite. He specializes in Generative AI and Machine Learning Data Engineering.

Metrics 120

Metrics Scripts Benchmark Enterprise 120

AWS Machine Learning

NOVEMBER 17, 2022

AlexaTM 20B has shown competitive performance on common natural language processing (NLP) benchmarks and tasks, such as machine translation, data generation and summarization. To use a large language model in SageMaker, you need an inferencing script specific for the model, which includes steps like model loading, parallelization and more.

Scripts 101

Scripts APIs Government Engineering 101

AWS Machine Learning

MARCH 13, 2024

. * The `if __name__ == "__main__"` block checks if the script is being run directly or imported. To run the script, you can use the following command: ``` python hello.py ``` * The output will be printed in the console: ``` Hello, world! Evaluate model on test set, compare to benchmarks, analyze errors and biases.

Benchmark 129

Benchmark Scripts APIs Feedback 129

AWS Machine Learning

OCTOBER 27, 2022

Our benchmarks show up to 46% price performance benefit after enabling heterogeneous clusters in a CPU-bound TensorFlow computer vision model training. AI Engineering, Mobileye. Performance benchmark results. You can build logic in your training script to assign the instance groups to certain training and data processing tasks.

Scripts 82

Scripts Benchmark Metrics Transportation 82

AWS Machine Learning

FEBRUARY 16, 2023

Briefly, this is made possible by an installation script specified by CustomActions in the YAML file used for creating the ParallelCluster (see Create ParallelCluster ). You can invoke neuron-top during the training script run to inspect NeuronCore utilization at each node. Jeffrey Huynh is a Principal Engineer in AWS Annapurna Labs.

Scripts 97

Scripts APIs Benchmark Engineering 97

AWS Machine Learning

MAY 8, 2023

To serve models, Triton supports various backends as engines to support the running and serving of various ML models for inference. With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. Import the ONNX model into TensorRT and generate the TensorRT engine.

Engineering 97

Engineering APIs Best practices Scripts 97

AWS Machine Learning

SEPTEMBER 22, 2023

Gone are the days when you need unnatural prompt engineering to get base models, such as GPT-3, to solve your tasks. The script initiates the SFT model using its current weights and then optimizes them under the guidance of a reward model, so that the resulting RLHF trained model aligns with human preference. yaml ppo_hh.py

Engineering 120

Engineering Feedback industry standards Benchmark 120

AWS Machine Learning

OCTOBER 24, 2022

DL scripts often require boilerplate code, notably the aforementioned double for loop structure that splits the dataset into minibatches and the training into epochs. At the time of this writing, it supports PyTorch and includes 25 techniques—called methods in the MosaicML world—along with standard models, datasets, and benchmarks.

Scripts 90

Scripts Enterprise APIs Benchmark 90

Comm100

JANUARY 21, 2020

Online customers in the pre-purchase stage typically find companies in one of two ways: on social media or through a search engine. Recommended for you: 8 Proactive Chat Best Practices with Ready-to-Use Scripts. Comm100’s 2020 Live Chat Benchmark Report found that 74.5 Ask us about our free shipping codes!”).

B2C 85

B2C B2B Benchmark Best practices 85

AWS Machine Learning

JUNE 6, 2023

The following figure shows a performance benchmark of fine-tuning a RoBERTa model on Amazon EC2 p4d.24xlarge inference with AWS Graviton processors for details on AWS Graviton-based instance inference performance benchmarks for PyTorch 2.0. Run your DLC container with a model training script to fine-tune the RoBERTa model.

Scripts 82

Scripts APIs Benchmark Management 82

AWS Machine Learning

FEBRUARY 12, 2025

Wei-Chih Chen is a Machine Learning Engineer at the AWS Generative AI Innovation Center, where he works on model customization and optimization for LLMs. We convert the samples into the format required by the customization job using the to_customization_format function and save them as train.jsonl.

APIs 76

APIs Management Benchmark Scripts 76

AWS Machine Learning

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

Engineering 124

Engineering Accountability Construction APIs 124

AWS Machine Learning

AUGUST 3, 2022

In this post, we walk you through the benchmarking process and the results we obtained while working on subsampled datasets. Sampling configuration and benchmarking process. This was done by using a custom script designed to create subsampled datasets in which each entity type appears at least k times, within a minimum of n documents.

Benchmark 80

Benchmark APIs Metrics Scripts 80

AWS Machine Learning

JANUARY 13, 2023

For benchmark analysis, we considered the task of predicting the in-hospital mortality of patients [2]. You can place the data in any folder of your choice, as long as the path is consistently referenced in the training script and has access enabled. Import the data loader into the training script. and data_loader.py

Analytics 85

Analytics Healthcare Scripts Accountability 85

AWS Machine Learning

AUGUST 16, 2023

Prepare the BigEarthNet-S2 dataset BigEarthNet-S2 is a benchmark archive that contains 590,325 multispectral images collected by the Sentinel-2 satellite. To train the classifier, we create a SageMaker PyTorch Estimator that runs the training script, eval_linear.py. file: init_distributed_mode function in the util.py

Scripts 85

Scripts Analytics Benchmark Technology 85