AWS Machine Learning Blog

OCTOBER 11, 2023

The decode phase includes the following: Completion – After the prefill phase, you have a partially generated text that may be incomplete or cut off at some point. The decode phase is responsible for completing the text to make it coherent and grammatically correct. The default is 32.

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AWS Machine Learning Blog

SEPTEMBER 25, 2023

Large language models (LLMs) used to generate text sequences need immense amounts of computing power and have difficulty accessing the available high bandwidth memory (HBM) and compute capacity. The inference server handles this automatically, so no client-side code changes are needed.

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PyImageSearch

FEBRUARY 6, 2023

If you are a regular PyImageSearch reader and have even basic knowledge of Deep Learning in Computer Vision, then this tutorial should be easy to understand. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or requires a degree in computer science?

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AWS Machine Learning Blog

MAY 31, 2024

It’s built on causal decoder-only architecture, making it powerful for auto-regressive tasks. Discover Falcon 2 11B in SageMaker JumpStart You can access the FMs through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. After deployment is complete, you will see that an endpoint is created.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

In this post, we walk through how to use the Titan Image Generator and Titan Multimodal Embeddings models via the AWS Python SDK. Code examples are provided in Python, and JavaScript (Node.js) is also available in this GitHub repository. For Python scripts, you can use the AWS SDK for Python (Boto3).

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AWS Machine Learning Blog

JANUARY 8, 2024

The first allows you to run a Python script from any server or instance including a Jupyter notebook; this is the quickest way to get started. When the script ends, a completion status along with the time taken will be returned to the SageMaker studio console. We provide you with two different solutions for this use case.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python. The SageMaker Python SDK is a high-level Python API that abstracts some of the steps and configuration, and makes it easier to deploy models. For that, we are offering improvements in the Python SDK.

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AWS Machine Learning Blog

MAY 31, 2023

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. One of the primary reasons that customers are choosing a PyTorch framework is its simplicity and the fact that it’s designed and assembled to work with Python. xlarge instance.

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AWS Machine Learning Blog

MAY 9, 2023

In addition, you can now use Application Auto Scaling with provisioned concurrency to address inference traffic dynamically based on target metrics or a schedule. In this post, we discuss what provisioned concurrency and Application Auto Scaling are, how to use them, and some best practices and guidance for your inference workloads.

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AWS Machine Learning Blog

AUGUST 1, 2023

SageMaker endpoints also have auto scaling features and are highly available. For this post, we use the following GitHub sample , which uses Amazon SageMaker Studio with foundation models (Stable Diffusion), prompts, computer vision techniques, and a SageMaker endpoint to generate new images from existing images.

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AWS Machine Learning Blog

JUNE 13, 2023

In addition, all SageMaker real-time endpoints benefit from built-in capabilities to manage and monitor models, such as including shadow variants , auto scaling , and native integration with Amazon CloudWatch (for more information, refer to CloudWatch Metrics for Multi-Model Endpoint Deployments ). 2xlarge instances.

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Flipboard

AUGUST 17, 2023

Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml Launch SageMaker Studio Complete the following steps to launch your SageMaker Studio domain: On the SageMaker console, choose Domains in the navigation pane.

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Towards AI

FEBRUARY 13, 2023

It also overcomes complex challenges in speech recognition and computer vision, such as creating a transcript of a sound sample or a description of an image. Our model gets a prompt and auto-completes it. NLP doesn’t just deal with written text. Cool, we learned what NLP is in this section. Note that this […]

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AWS Machine Learning Blog

MAY 4, 2023

For ultra-large models that don’t fit into a single accelerator, data flows directly between accelerators with NeuronLink, bypassing the CPU completely. You can use a SageMaker notebook or an Amazon Elastic Compute Cloud (Amazon EC2) instance to compile the model. These endpoints are fully managed and support auto scaling.

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AWS Machine Learning Blog

FEBRUARY 20, 2024

This time-consuming process must be completed before content can be dubbed into another language. SageMaker asynchronous endpoints support upload sizes up to 1 GB and incorporate auto scaling features that efficiently mitigate traffic spikes and save costs during off-peak times. __dict__[WAV2VEC2_MODEL].get_model(dl_kwargs={"model_dir":

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NVIDIA

JUNE 7, 2024

In early trials, cuOpt delivered routing solutions in 10 seconds , achieving a 90% reduction in cloud costs and enabling technicians to complete more service calls daily. The company found that data scientists were having to remove features from algorithms just so they would run to completion.

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AWS Machine Learning Blog

JULY 24, 2024

Einstein has a list of over 60 features, unlocked at different price points and segmented into four main categories: machine learning (ML), natural language processing (NLP), computer vision, and automatic speech recognition. This is particularly valuable given the current market shortages of high-end GPUs.

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AWS Machine Learning Blog

MARCH 28, 2024

LangChain is an open source Python library designed to build applications with LLMs. When you create an AWS account, you get a single sign-on (SSO) identity that has complete access to all the AWS services and resources in the account. There was no monitoring, load balancing, auto-scaling, or persistent storage at the time.

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AWS Machine Learning Blog

JUNE 13, 2023

LMI DLCs are a complete end-to-end solution for hosting LLMs like Falcon-40B. You can monitor the status of the endpoint by calling DescribeEndpoint , which will tell you when everything is complete. His expertise lies in Deep Learning in the domains of Natural Language Processing (NLP) and Computer Vision.

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AWS Machine Learning Blog

MAY 25, 2023

Prerequisites Complete the following prerequisite steps: If you’re a first-time user of QuickSight in your AWS account, sign up for QuickSight. amazonaws.com/ :latest Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Download the CloudFormation template kendrablog-sam-template.yml.

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Marktechpost

NOVEMBER 16, 2024

Scrapy A powerful, open-source Python framework called Scrapy was created for highly effective web scraping and data extraction. This tool’s AI-powered auto-detect functionality makes data collecting as simple as point-and-click by automatically recognizing data fields on the majority of websites. Puppeteer A robust Node.js

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Mlearning.ai

JULY 22, 2023

that comply to YOLOv5 with specific requirement on model output, which easily got mess up thru conversion of model from PyTorch > ONNX > Tensorflow > TensorflowJS) Computer Vision Annotation Tool (CVAT) CVAT is build by Intel for doing computer vision annotation which put together openCV, OpenVino (to speed up CPU inference).

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AWS Machine Learning Blog

SEPTEMBER 11, 2023

It’s an auto-regressive language model that uses an optimized transformer architecture. Discover models You can access the foundation models through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. Eiffel Tower: No trip to Paris is complete without a visit to the iconic Eiffel Tower.

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PyImageSearch

OCTOBER 2, 2023

in their paper Auto-Encoding Variational Bayes. Auto-Encoding Variational Bayes. With that, we’ve completed the training of a variational autoencoder on the Fashion-MNIST dataset. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? The torch.nn

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AWS Machine Learning Blog

DECEMBER 14, 2023

Complete the following steps to edit an existing space: On the space details page, choose Stop space. Reconfigure the compute, storage, or runtime. EFS mounts provide a solid alternative for sharing Python environments like conda or virtualenv across multiple workspaces. Choose Create JupyterLab space. Choose Create space.

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Viso.ai

MARCH 29, 2024

About us: At viso.ai, we’ve built the end-to-end machine learning infrastructure for enterprises to scale their computer vision applications easily. Viso Suite, the end-to-end computer vision solution What is Streamlit? Streamlit is a Python-based library specifically developed for machine learning engineers.

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Pickl AI

JANUARY 23, 2023

It supports languages like Python and R and processes the data with the help of data flow graphs. It is an open-source framework that is written in Python and can efficiently operate on both GPUs and CPUs. Keras supports a high-level neural network API written in Python. Cons Low level computation cannot be handled by keras.

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AWS Machine Learning Blog

APRIL 16, 2024

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code. For Secret type , choose Other type of secret.

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PyImageSearch

JUNE 5, 2023

trainInput = trainInput.map( read_train_example, num_parallel_calls=AUTO).shuffle( repeat() trainOutput = trainOutput.map( read_train_example, num_parallel_calls=AUTO).shuffle( batch(config.INFER_BATCH_SIZE) testOutput = testOutput.map(read_test_example, num_parallel_calls=AUTO).shuffle( Let us open the train.py

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

You can also split your training data and model across all nodes for parallel processing, fully using the cluster’s compute and network infrastructure. Moreover, you have full control over the execution environment, including the ability to easily install and customize virtual Python environments for each project.

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The MLOps Blog

JUNE 27, 2023

For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., Can you see the complete model lineage with data/models/experiments used downstream? Auto-annotation tools such as Meta’s Segment Anything Model and other AI-assisted labeling techniques.

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AWS Machine Learning Blog

MARCH 6, 2023

Although this post focuses on LLMs, most of its best practices are relevant for any kind of large-model training, including computer vision and multi-modal models, such as Stable Diffusion. The AWS Python SDK Boto3 may also be combined with Torch Dataset classes to create custom data loading code.

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AWS Machine Learning Blog

JULY 18, 2023

What is Llama 2 Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Discover models You can access the foundation models through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. In this post, we walk through how to use Llama 2 models via SageMaker JumpStart.

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AWS Machine Learning Blog

APRIL 17, 2023

SageMaker LMI containers includes model download optimization by using the s5cmd library to speed up the model download time and container startup times, and eventually speed up auto scaling on SageMaker. It has faster loading and uses multi-process loading on Python. It supports AOT compilation and uses CPU to partition the model.

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Google Research AI blog

FEBRUARY 28, 2023

Dataset Description Auto-Arborist A multiview urban tree classification dataset that consists of ~2.6M UGIF A multi-lingual, multi-modal UI grounded dataset for step-by-step task completion on the smartphone. We also continued to release sustainability data via Data Commons and invite others to use it for their research.

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Dlabs.ai

JANUARY 16, 2023

The lines are then parsed into pythonic dictionaries. We decided to analyze the images in the following ways: Split the image into tiles and use tiles as input to a computer vision model Extract histograms of colors in images Tile generation The raw TIFF input images are too big to use for processing directly.

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The MLOps Blog

JANUARY 19, 2024

But nowadays, it is used for various tasks, ranging from language modeling to computer vision and generative AI. You can find all the code in two Colab notebooks: Fine-tuning Model selection Related post How to Version and Organize ML Experiments That You Run in Google Colab Read more We will use Python 3.10 in our codes.

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