🍱 Hi <!channel>, we have just released BentoML v1.0.2 with a number of features and bug fixes requested by the community. • Added support for custom model versions, e.g.

bentoml.tensorflow.save_model("model_name:1.2.4", model)

. • Fixed PyTorch Runner payload serialization issue due to tensor not on CPU.

TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first

• Fixed Transformers GPU device assignment due to kwargs handling. • Fixed excessive Runner thread spawning issue under high load. • Fixed PyTorch Runner inference error due to saving tensor during inference mode.

RuntimeError: Inference tensors cannot be saved for backward. To work around you can make a clone to get a normal tensor and use it in autograd.

• Fixed Keras Runner error when the input has only a single element. • Deprecated the

validate_json

option in JSON IO descriptor and recommended specifying validation logic natively in the Pydantic model. 🎨 We added an examples directory and in it you will find interesting sample projects demonstrating various applications of BentoML. We welcome your contribution if you have a project idea and would like to share with the community. 💡 We continue to update the documentation on every release to help our users unlock the full power of BentoML. • Did you know BentoML service supports mounting and calling runners from custom FastAPI and Flask apps? • Did you know IO descriptor supports input and output validation of schema, shape, and data types?