<!channel> Hello everyone! We are thrilled to announce the release of BentoML 1.4! This version introduces several new features and improvements to accelerate your iteration cycle and enhance the overall developer experience. Below are the key highlights of 1.4, and you can find more details in the release blog post. 🚀 20x faster iteration with Codespaces ◦ Introduced BentoML Codespaces, a development platform built on BentoCloud ◦ Added the

bentoml code

command for creating a Codespace ◦ Auto-sync of local changes to the cloud environment ◦ Access to a variety of powerful cloud GPUs ◦ Real-time logs and debugging through the cloud dashboard ◦ Eliminate dependency headaches and ensure consistency between dev and prod environments 🐍 New Python SDK for runtime configurations ◦ Added

bentoml.images.PythonImage

for defining the Bento runtime environment in Python instead of using

bentofile.yaml

or

pyproject.toml

◦ Support customizing runtime configurations (e.g., Python version, system packages, and dependencies) directly in the

service.py

file ◦ Introduced context-sensitive

run()

method for running custom build commands ◦ Backward compatible with existing

bentofile.yaml

and

pyproject.toml

configurations ⚡ Accelerated model loading ◦ Implemented build-time model downloads and parallel loading of model weights using safetensors to reduce cold start time and improve scaling performance. See the documentation to learn more. ◦ Added

bentoml.models.HuggingFaceModel

for loading models from HF. It supports private model repositories and custom endpoints ◦ Added

bentoml.models.BentoModel

for loading models from BentoCloud and the Model Store 🌍 External deployment dependencies ◦ Extended

bentoml.depends()

to support external deployments ◦ Added support for calling BentoCloud Deployments via name or URL ◦ Added support for calling self-hosted HTTP AI services outside BentoCloud ⚠️ Legacy Service API deprecation ◦ The legacy

bentoml.Service

API (with runners) is now officially deprecated and is scheduled for removal in a future release. We recommend you use the

@bentoml.service

decorator. Note that: •

1.4

remains fully compatible with Bentos created by

1.3

. • The BentoML documentation has been updated with examples and guides for

1.4

. 🙏 As always, we appreciate your continued support!