AWS Machine Learning Blog

APRIL 5, 2023

Generate embeddings of product images using a SageMaker batch transform job. Use Amazon Simple Storage Service (Amazon S3) to store the raw text (product description) and images (product images) and image embedding generated by the SageMaker batch transform jobs. The job takes around 10 minutes. unsqueeze(0).to(device)

Metadata 88

Metadata ML Neural Network Python 88

AWS Machine Learning Blog

MAY 1, 2024

Canvas offers a Python Jupyter notebook detailing your fine-tuning job, alleviating concerns about vendor lock-in associated with no-code tools and enabling detail sharing with data science teams for further validation and deployment. The Amazon Titan answer is incorrect: “The A100 has 80 GB of unified memory. Human: What is NVDIMM?

Algorithm 104

Algorithm LLM Software Engineer Large Language Models 104

AWS Machine Learning Blog

JUNE 1, 2023

Make sure that you have a minimum of one GPU instance (for example, ml.p3.2xlarge for single GPU training, or ml.p3.16xlarge ) for the distributed training job. Make sure that you have a minimum of one GPU instance (for example, ml.p3.2xlarge ) for running batch prediction with processing jobs. Define a training job.

Auto-complete 77

Auto-complete ML Algorithm Machine Learning 77

Heartbeat

APRIL 10, 2023

This language model was trained on a 300 billion word (~570 GB) dataset and fine-tuned on GPT-3.5 It is now possible to see job postings for this role with an annual salary between $250k — $335k! ? For example, maybe you want to analyze customer reviews for a restaurant separately according to taste, location, service, speed and price.

Prompt Engineer 83

Prompt Engineer Prompt Engineering OpenAI ChatGPT 83

AWS Machine Learning Blog

NOVEMBER 14, 2023

SageMaker Experiments is also used to view and compare Llama2 fine-tuning job logs (training loss/test loss/etc.). Taking the llama2 7b model as an example, the base model is approximately 13 GB and the fine-tuned model is 13.6 GB of adapter weights, which is a 95% space savings. For instructions, refer to Creating a bucket. .

LLM 92

LLM ML Natural Language Processing Machine Learning 92

AWS Machine Learning Blog

APRIL 16, 2024

These connections are used by AWS Glue crawlers, jobs, and development endpoints to access various types of data stores. You can use these connections for both source and target data, and even reuse the same connection across multiple crawlers or extract, transform, and load (ETL) jobs.

Data Scientist 93

Data Scientist Generative AI ML Machine Learning 93

AWS Machine Learning Blog

JULY 31, 2023

Solution overview In this solution, scientists can interactively launch protein folding experiments, analyze the 3D structure, monitor the job progress, and track the experiments in Amazon SageMaker Studio. Data is automatically saved to a specified S3 bucket location. The job logs are kept in Amazon CloudWatch for monitoring.

Algorithm 77

Algorithm ML Machine Learning Neural Network 77

AWS Machine Learning Blog

SEPTEMBER 5, 2023

SageMaker asynchronous inference queues incoming requests and processes them asynchronously, making this option ideal for requests with large payload sizes up to 1 GB, long processing times, and near-real-time latency requirements. Upon processing, SageMaker places the result in the Amazon S3 location.

Auto-complete 96

Auto-complete Python Computer Vision Large Language Models 96

AWS Machine Learning Blog

SEPTEMBER 6, 2023

You can then choose Train to start the training job on a SageMaker ML instance. Choose I have read and accept EULA and AUP to start the fine-tuning job. Fine-tuning technique Language models such as Llama are more than 10 GB or even 100 GB in size. Therefore, you can use the cheaper instance for training (ml.g5.2xlarge).

Auto-complete 100

Auto-complete Machine Learning ML Python 100

AWS Machine Learning Blog

APRIL 3, 2023

You can run real-time analysis for small workloads or you can start asynchronous analysis jobs for large document sets. The Kindle Scribe has the most storage options of all Kindle devices: choose from 8 GB, 16 GB, or 32 GB to suit your level of reading and writing. nn what are the key features of new Kindle?

Natural Language Processing 77

Natural Language Processing NLP Prompt Engineer Prompt Engineering 77

AWS Machine Learning Blog

FEBRUARY 1, 2023

Call the video moderation API in an AWS Lambda function (or customized script on premises) with the video file location as a parameter. You can either implement a heartbeat logic to check the moderation job status until it completes, or use Amazon Simple Notification Service (Amazon SNS) to implement an event-driven pattern.

ML 66

ML Natural Language Processing Machine Learning Computer Vision 66

Marktechpost

JULY 24, 2023

After initial training, the climaX may be fine-tuned to perform a wide range of climate and weather jobs, including those that involve atmospheric variables and different time and space scales. With Cloud TPU v4 technology, GraphCast can produce a 10-day prediction (35 GB of data) in under 60 seconds.

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network ML 52

Heartbeat

NOVEMBER 6, 2023

If the development of the execution is a success, the scheduler will trigger the next job in the DAG since the second task depends on executing the first task. You might need to extract the weather and metadata information about the location, after which you will combine both for transformation. This type of execution is shown below.

ETL 52

ETL Python Metadata Deep Learning 52

The MLOps Blog

AUGUST 4, 2023

Because just throwing what used to be separate repositories into one folder does not do the job. This is relative to the build file’s location, that is: even if we had Python files outside of the mnist/src directory, these sources only capture the contents of the mnist/src folder. Enough of the theory! files in the directory.

ML 52

ML Machine Learning Python Software Engineer 52

AWS Machine Learning Blog

JANUARY 30, 2023

Solution overview When a training job using LightGBM is started with multiple instances, we first create a Dask cluster. If your predictors include categorical features, you can provide a JSON file named cat_index.json in the same location as your training data. This follows the convention of the SageMaker XGBoost algorithm.

Algorithm 81

Algorithm Categorization Natural Language Processing Machine Learning 81

AWS Machine Learning Blog

APRIL 17, 2023

Large models complicate this process because loading a 350 GB model such as BLOOM-176B can take tens of minutes, which materially impacts endpoint startup time. Secondly, we get the model weights downloaded under the /tmp location on the container and referenceable by the environment variable “ model_dir ”.

Prompt Engineer 79

Prompt Engineer Prompt Engineering Deep Learning Large Language Models 79

AWS Machine Learning Blog

MAY 30, 2023

The cost of SageMaker real-time endpoints is based on the per instance-hour consumed for each instance while the endpoint is running, the cost of GB-month of provisioned storage (EBS volume), as well as the GB data processed in and out of the endpoint instance, as outlined in Amazon SageMaker Pricing.

Auto-complete 68

Auto-complete ML Machine Learning Computer Vision 68

AWS Machine Learning Blog

MAY 30, 2023

In this post, we focus on data preprocessing using Amazon SageMaker Processing and Amazon SageMaker Data Wrangler jobs. In this post, we analyze the pricing factors and provide cost optimization guidance for SageMaker Processing and Data Wrangler jobs. A high number of failed jobs is common when developing new MLOps pipelines.

ML 65

ML ETL Machine Learning Computer Vision 65

AWS Machine Learning Blog

AUGUST 2, 2023

Finally, in postprocessing, we package the fine-tuned LoRA weights with the inference script and configuration files (tar.gz) and upload them to an S3 bucket location for SageMaker MMEs. SageMaker will dynamically load and cache the model from the Amazon S3 location based on the inference traffic to each model. deepspeed0.8.3-cu117"

Generative AI 68

Generative AI Computer Vision Auto-complete Inference Engine 68

AWS Machine Learning Blog

MARCH 19, 2024

put_object(Body=s3_data, Bucket=bucket, Key=input_key) Create and run batch inference jobs in Amazon Bedrock Batch inference job creation requires an Amazon Bedrock client. We specify the S3 input and output paths and give each invocation job a unique name: # Create Bedrock client bedrock = boto3.client("bedrock")

Auto-complete 106

Auto-complete NLP Prompt Engineer Prompt Engineering 106

AWS Machine Learning Blog

JANUARY 9, 2023

Here, the inference request is processed with either a scheduled or event-based trigger of a batch inference job. Asynchronous inference – This is designed for workloads that don’t have sub-second latency requirements, payload sizes up to 1 GB, and processing times of up to 15 minutes. Inference latency.

ML 74

ML Auto-complete Auto-classification Deep Learning 74