Code for the online course "Deployment of Machine Learning Models" - deploying-machine-learning-models/Dockerfile at master · vk00226/deploying-machine-learning-models
Steps to deploy a machine learning model with Flask and Docker We will use Docker as a container to deploy the Flask app. A container is similar to a virtual machine except that it does not have its own resources. Rather, it shares them with the host machine. This helps to deploy the ...
As part of creating the service, model management also creates and stores an image. To get details on the image, use the id specified in the output. You can download and use that image to deploy to a Docker host. You can create an image separately, without creating the s...
To get started, I train a simple binary classifier usingscikit-learnin a notebook session of the OCI Data Science service. The business problem itself and the quality of the model don’t really matter, and the model is only used for illustrative purposes. Any binary classifier would do fine...
Basic understanding of machine learning models and Docker Storing models in Scaleway Object Storage In this step, we will create an Object Storage bucket on Scaleway and upload your machine-learning model. If you have already created and uploaded your model to an Object Storage bucket, you can ...
This section uses AWS SAM to build, test, and deploy a Docker image containing a pre-trained digit classifier model on Lambda: Update orinstall AWS SAM. AWS SAM CLI v1.24.1 or later is required to use the machine learning templates. ...
Inference on GPU and CPU: As alluded to earlier, Triton serves inferences from models on both GPU and CPU. Triton can be used in public clouds, on-premises, data centers, and on the edge. Triton can run as a Docker container, on bare metal, or inside a virtual machine in a virtualiz...
Amazon SageMaker AI makes extensive use of Docker containers for build and runtime tasks. SageMaker AI provides pre-built Docker images for its built-in algorithms and the supported deep learning frameworks used for training and inference. Using containers, you can train machine learning algorithms ...
functions:ml_model:image:name:appimagetimeout:90memorySize:4096environment:TORCH_HOME:/tmp/.ml_cacheTRANSFORMERS_CACHE:/tmp/.ml_cache/huggingface Since we're using the AWS provider, all definedfunctionswill be AWS Lambda functions. We specified which Docker image will be used, which will be push...
In the R case, we Dockerize your model using plumber and its associated Docker image. See the included Dockerfile and plumber script for more details.Deploying your own modelTo deploy your own model, do the following:Place model in scripts folder...