AWS Machine Learning Blog

MAY 25, 2023

The code for modules resided in multiple notebooks, and running these notebooks was manual, with no orchestration tool in place. py files), and the SageMaker platform is used to build a Docker image with BYO (bring your own) containers. A Docker image is prepared using this code and pushed to Amazon ECR for serverless computing.

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AWS Machine Learning Blog

AUGUST 3, 2023

Docker in your development environment (local machine or a SageMaker notebook instance where you are deploying the solution from). This folder contains two main files: a Dockerfile that defines the Docker image that will run in our Lambda function, and the app.py The AWS Cloud Development Kit (AWS CDK) installed.

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Smart Data Collective

OCTOBER 8, 2023

3 Public Repositories Public repositories such as GitHub and Docker are treasure troves for developers, offering an abundance of resources. To safeguard against this, organizations should employ strong key protection measures such as hardware security modules (HSMs). However, they also pose a considerable risk.

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Dataconomy

JUNE 9, 2023

Setting up Pyroscope is a breeze, regardless of whether you are using Docker, Linux, or seeking documentation for Ruby or Go. Pyroscope is compatible with macOS, Linux, and Docker, making it accessible across various platforms. In a matter of minutes, you can gain valuable insights and identify areas of improvement.

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DataRobot Blog

JUNE 16, 2022

DataRobot Provider Modules. If you’re using the Astro CLI tool to run Airflow, make sure to place the input files in the include/ directory which is accessible to the Airflow application inside the docker container. The graph view of the DAG looks like this: Note: If you are running Airflow in a Docker container (e.g.,

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Heartbeat

DECEMBER 27, 2023

Photo by Ian Taylor on Unsplash This article will comprehensively create, deploy, and execute machine learning application containers using the Docker tool. Lastly, it will discuss the best practices when working with Docker and the future of containerization. What is Docker? Docker is the most popular container technology.

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AWS Machine Learning Blog

NOVEMBER 8, 2023

MONAI Deploy is one of the key modules within MONAI (Medical Open Network for Artificial Intelligence) developed by a consortium of academic and industry leaders, including NVIDIA. package with a defined directory structure into a Docker container. To create a SageMaker model, you will need to load a model.tar.gz The model.tar.gz

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Flipboard

NOVEMBER 30, 2023

ModelBuilder supports use cases when bringing your own containers that are extended from our pre-built Docker containers. If a pre-built SageMaker container doesn’t fulfill all your requirements, you can extend the existing image to accommodate your needs. its "predict" method will be invoked in the endpoint. In this case, numpy==1.26.1

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AWS Machine Learning Blog

APRIL 11, 2023

whl ) of the modules that you install through pip. If there isn’t space on our hard drive or if we want to keep a Docker image as small as possible, we can use this flag so the command runs to completion with minimal memory usage. The no-cache-dir flag will disable the cache. Caching helps store the installation files (.whl

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AWS Machine Learning Blog

FEBRUARY 1, 2023

When a training job is launched using TorchX, the launch command uses the provided TorchX distributed DDP module for Trainium to configure the overall training job and then run the appropriate torchrun commands on each of the PyTorch worker pods. Each NeuronCore can be accessed as a unique PyTorch XLA device in the training script.

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The MLOps Blog

APRIL 5, 2023

For example, Modularizing a natural language processing (NLP) model for sentiment analysis can include separating the word embedding layer and the RNN layer into separate modules, which can be packaged and reused in other NLP models to manage code and reduce duplication and computational resources required to run the model.

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IBM Journey to AI blog

OCTOBER 17, 2023

Kyma’s modular design enables users to select and customize modules based on their specific business needs. Modules can be enabled or disabled as required, giving users control over the infrastructure expenses associated with their chosen modules. Kyma and Docker simplify application deployment.

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Heartbeat

NOVEMBER 6, 2023

There are two significant methods of installing Airflow: Installing on your local laptop via PyPi Installing with Docker and Docker Compose Installing Airflow with Docker and Docker Compose The approach for installing Airflow in this tutorial is using docker. to build the image.

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Hacker News

FEBRUARY 8, 2024

Note that you can do good things with containers like Docker (see below), but there are a lot of images over 350 MB on the Docker Hub. There are very good modules that likely are more secure than what you could type in on your own.) This frequently entails effectively shipping a complete computer disk image.

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AWS Machine Learning Blog

FEBRUARY 20, 2024

When selecting the Docker image, consider the following settings: framework (Hugging Face), task (inference), Python version, and hardware (for example, GPU). __dict__[WAV2VEC2_MODEL].get_model(dl_kwargs={"model_dir": We recommend using the following image: 763104351884.dkr.ecr.

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Flipboard

FEBRUARY 15, 2023

We also have to modify the path inside codebuild-buildspec.yml , as shown in the sample repository: run-pipeline --module-name pipelines.customer_churn.pipeline The ModelDeploy folder has the CloudFormation templates for the deployment pipeline. See the following code: # Estimator Instance count and instance type.

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The MLOps Blog

MARCH 28, 2023

Most of the time, these templates have all the modules that integrate with their experiment tracking tool, neptune.ai. When it comes to pipelines for training, they use the Terraform module to create CI/CD that will initialize the pipelines, test them, and deploy them to SageMaker (Pipelines) if the tests pass.

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Shreyansh Singh

JANUARY 24, 2021

After my last post on deploying Machine Learning and Deep Learning models using FastAPI and Docker, I wanted to explore a bit more on deploying deep learning models. My last post discussed a server-side method for deploying the model. I used a pretrained ResNet50 model (trained on Imagnet dataset) for this.

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Dataconomy

APRIL 28, 2023

Its primary functions include packaging and organizing docker containers to support the maintenance of an entire machine learning system. Cortex integrates with open-source projects such as Docker, Kubernetes, TensorFlow Serving, and TorchServe, and can seamlessly collaborate with any machine learning libraries or tools.

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The MLOps Blog

AUGUST 4, 2023

It is a simple monorepo holding just one project, or module: a hand-written digits classifier called mnist , after the famous dataset it uses. Alternatively, we could have replaced it with mnist: to run only against the mnist module. Take testing. You’ve made a change in a utility function you maintain, ran the tests, and all’s green.

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Towards AI

AUGUST 17, 2023

Considerations: Selective Promotion: The company’s application landscape is composed of multiple modules and teams with different timelines. Therefore, it is necessary to support the promotion of images for only selected modules in each release. Source: Image by the author.

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AWS Machine Learning Blog

JULY 3, 2023

In this module, the best-so-far model trained for a particular task (for example, pedestrian detection or lane segmentation) is used to generate high-quality labels. Downstream modules such as the training or validation modules can use these labels as is.

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AWS Machine Learning Blog

JANUARY 24, 2024

For helmenabledmodules , make sure tex2vec-aws and generative-aws are present in the list of enabled modules within Weaviate. In this step, you configure the relevant modules to enable the usage of Cohere language models hosted on Amazon Bedrock natively from within the Weaviate vector database. It must be at least v.16.8.0.

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AWS Machine Learning Blog

JUNE 6, 2023

Download a Deep Learning Container AWS DLCs are available as Docker images in Amazon Elastic Container Registry Public , a managed AWS container image registry service that is secure, scalable, and reliable. container with GPU support in us-west-2 #STEP 2.6 - pull the latest DLC PyTorch image docker pull 763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-ec2

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The MLOps Blog

MAY 10, 2023

Docker is one of the tools which allows you to do this. Saving trained model with pickle The pickle module can be used to serialize and deserialize the Python objects. For saving the ML models used as a pickle file, you need to use the Pickle module that already comes with the default Python installation.

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AWS Machine Learning Blog

DECEMBER 13, 2023

modules = find_all_linear_names(model) ## Setting up LoRA configuration lora_r = 64 # Alpha parameter for LoRA scaling lora_alpha = 16 # Dropout probability for LoRA layers lora_dropout = 0.1 . # include linear layers to apply LoRA to. Please refer to this post for more information about using a DJL NeuronX container for inference.

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Flipboard

MARCH 27, 2023

installed on your system, you can download all the required dependencies in the PyTorch nightly binaries with docker. sudo apt install -y nvidia-docker2 $ sudo systemctl restart docker $ docker pull ghcr.io/pytorch/pytorch-nightly pytorch/pytorch-nightly $ docker run --gpus all -it ghcr.io/pytorch/pytorch-nightly:latest

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AWS Machine Learning Blog

APRIL 12, 2023

Parameters, data, and code dependencies between each step were removed, and shared modules for the shared components across the steps was created. We could re-use the previous Sagemaker Python SDK code to run the modules individually into Sagemaker Pipeline SDK based runs.

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AWS Machine Learning Blog

SEPTEMBER 5, 2023

The third component is a trainable module called Q-Former, a lightweight transformer that connects the frozen image encoder with the frozen LLM. To prepare the Docker image and model file, you need to retrieve the Docker image of DJLServing, package the inference script and configuration files as a model.tar.gz

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AWS Machine Learning Blog

MAY 8, 2024

Reusable components – Employing containerized environments, such as Docker images, and custom modules promoted the bring your own code approach within Dialog Axiata’s ML pipelines. Monitoring and alerting – Monitoring tools and alert systems provided ongoing success by keeping track of the model and pipeline status.

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The MLOps Blog

MARCH 14, 2023

So for our project, we created a document containing information about each specific module that we have worked on for example, data collection, data preprocessing and exploration, modeling, deployment, monitoring, etc. It was very hard for us to remember in words when and how what component would be used by which process.

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Heartbeat

MAY 29, 2023

Workflow Deploying LLMs to production involves several steps, including containerization using Docker and Kubernetes, and API design using Flask. Containerize the LLM using Docker: The next step is to containerize the LLM using Docker. This involves training the LLM on a large dataset and optimizing it for the desired NLP task.

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Becoming Human

MAY 15, 2023

As I mentioned above, Python is a vast language which means it has rich community support, and you can find various types of libraries and modules as per your work. Docker , a containerization platform, enables developers to package applications and dependencies in a portable container.

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phData

FEBRUARY 7, 2024

In your custom code, you can also call code packaged in JAR files using Java or Scala, and import modules from Python files or third-party packages if using Python. As a result, the data doesn’t need to be transferred to the client in order for the function to process the data.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

These jobs can be run immediately or on a recurring time schedule without the need for data workers to refactor code as Python modules. Image URI – The Docker image behind the notebook job step. Here you can pass in the local path to the input notebook.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

framework/processing/ – This directory contains a Python script that creates a SageMaker Processing job based on the specified Docker image and entry point script. The training script does the following: Load the files locally from local container paths using the NumPy load module. Set hyperparameters for the training algorithm.

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AWS Machine Learning Blog

JUNE 12, 2023

Behind the scenes, the SageMaker SDK uses AWS Deep Learning Containers (DLCs), which are a set of prebuilt Docker images for training and serving models offered by SageMaker. The integration makes it easier to customize Hugging Face models on domain-specific use cases.

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