{"repo":"aws/sagemaker-training-toolkit","free":true,"listed":false,"github":"https://github.com/aws/sagemaker-training-toolkit","clone":"git clone https://github.com/aws/sagemaker-training-toolkit.git","description":"Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.","language":"Python","stars":530,"topics":["aws","sagemaker","training","docker","python","machine-learning","deep-learning"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"SageMaker Training Toolkit Train machine learning models within a Docker container using Amazon SageMaker. :books: Background Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. If you use a prebuilt SageMaker Docker image for training, this library may already be included. For more information, see the Amazon SageMaker Developer Guide sections on using Docker containers for training. :hammer and wrench: Installation To install this library in your Docker image, add the following line to your Dockerfile: :computer: Usage The following are brief how-to guides. For complete, working examples of custom training containers built with the SageMaker Training Toolkit, please see the example notebooks. Create a Docker image and train a model 1. Write a training script (eg. train.py ). 2. Define a container with a Dockerfile that includes the training script and any dependencies. The training script must be located in the /opt/ml/code directory. The environment variable S","default_branch":null,"files":null,"tree":[],"storefront":"/r/aws","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/aws/sagemaker-training-toolkit/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}