Bentoml example BentoML automatically exposes several endpoints for clients to manage the task, By default, BentoML caches pip artifacts across all local image builds to speed up the build process. 💡 This example is served as a basis for advanced code customization, such as custom model, inference logic or LMDeploy options. For those who prefer working via the command line, BentoML 1. 1 in this example and you can use any other LLM) and import the model into BentoML's Model Store. Raw. For example, you can wrap the llm instance created previously in a BentoML Runner for better This example demonstrates how to build an AI assistant using BentoML and ShieldGemma to preemptively filter out harmful input, thereby ensuring LLM safety. LLM. It allows you to set a safety threshold. Docs. Write & Use MLflow Plugins What is BentoML¶. Sign Up Sign Up. Alternatively, you can also use the bentoml. For multiple parameters, use a This example is ready for easy deployment and scaling on BentoCloud. Note: Before you try the code examples in the following sections, I suggest you set up a separate virtual environment for each integration. The number of workers isn’t necessarily equivalent to the number of concurrent requests a BentoML Service can serve in parallel. See the Python This is a BentoML example project, showing you how to serve and deploy open-source Large Language Models using Hugging Face TGI, a toolkit that enables high-performance text BentoML is a Python open-source library that enables users to create a machine learning-powered prediction service in minutes, which helps to bridge the gap between data science and DevOps. task decorator. Model composition in BentoML can involve single or multiple Services, depending on your application. The BentoML registry manages deployable artifacts (Bentos) and simplifies the model inference process. /run: In BentoML, you create a task endpoint with the @bentoml. Find and fix vulnerabilities Actions. Importing the best model from MLFlow registry. BentoML / examples / quickstart / README. Here’s an example: import bentoml from PIL. Once your BentoML Service has been thoroughly tested in a local environment, deploying it to BentoCloud is a straightforward process. BentoML saves this training context in the BentoML registry for future reference. In the example above, we show how BentoML can pre-process input and add relevant business logic to the service behavior. Join Community. 0a4. Toggle table of contents sidebar. In the Service code, the @bentoml. monitor context, you use the log method to record individual data points, which requires several parameters to describe By specifying path="/fastapi", the entire FastAPI application is served under this prefix. BentoML X account. basicConfig(level=logging. What is BentoML¶. Using bentoml. If it contains multiple Services, see Deploy with a configuration file and Distributed Services for details. Deployment hooks¶ Deployment hooks are similar to static methods as they do not receive the self argument. deployment For example: bentoml secret create huggingface HF_TOKEN = <your_hf_token> bentoml code --secret huggingface Follow the on-screen instructions to create a new Codespace (or attach to an existing one) as prompted. Hide table of contents sidebar Replace the example URL with your Deployment’s URL: import bentoml client = bentoml. To determine the optimal value for concurrency, we recommend conducting a stress test on your Service using a load generation tool such as Locust either locally or on BentoCloud. For example: The following example demonstrates the full lifecycle of job execution. Hyperparameter Tuning. BentoML provides a set of default metrics for performance analysis while you can also define custom metrics with Prometheus. Build autonomous AI products in code, capable of running and persisting month-lasting processes in the background. py: Trains an image classification model on the MNIST dataset, which is a collection of handwritten digits, and saves the model to the BentoML local Model Store What is BentoML¶. BentoML provides a robust framework for deploying machine learning models as Services, allowing for both single and distributed deployments. yaml file, or use the --no-cache option in the bentoml containerize command. It allows for precise modifications based on text and image RAG: Document ingestion and search¶. 12/06/24. The example below is a typical BentoML Service setup for a RAG system, where endpoints ingest_pdf_batch and ingest_text_batch are used for batch This is a BentoML example project, containing a series of tutorials where we build a complete self-hosted Retrieval-Augmented Generation (RAG) application, step-by-step. BentoML Docker-Compose Up Example. crew() and performs the tasks defined within CrewAI sequentially. It provides a complete stack for building fast and scalable Hide navigation sidebar. Browse through different categories to find the example that best This document demonstrates how to build an AI agent capable of calling a user-defined function using Llama 3. “BentoML has helped TomTom maintain focus on its In this example, we define a BentoML service that encodes sentences using the SentenceTransformer model. Prerequisites What is BentoML¶. Blame. What is BentoML? BentoML is a Python open-source library that enables users to create a machine learning-powered prediction service in minutes, which helps to bridge the gap between data science and DevOps. Achieve 20x faster iteration for modern AI applications with BentoML Codespaces. Contribute to bentoml/quickstart development by creating an account on GitHub. BentoML Blog. Returns: A BentoML Model with the matching tag. utils (available here) provides OpenAI-compatible endpoints What is BentoML¶. This quickstart demonstrates how to build a text summarization application with a Transformer Examples. Dive into the This repo demonstrates how to serve LangGraph agent application with BentoML. Model: """ Save a model instance to BentoML modelstore. This is a BentoML example project, demonstrating how to build a sentence embedding inference API server, using a SentenceTransformers model all-MiniLM-L6-v2. pipe) is As mentioned above, when testing model inference, you may want to fine-tune it or add custom code to integrate it with other tools (for example, defining a BentoML Service file). deployment import Deployment from. For example: This is a BentoML example project, demonstrating how to build a speech recognition inference API server, using the WhisperX project. Now, let's see how you can serve this LangGraph agent application with BentoML: The new architecture introduces two BentoML Services: one that serves the LangGraph agent as a REST API and another protocol – (expert) The FS protocol to use when exporting. A collection of example projects for learning BentoML and building your own solutions interactive systems, and real-time transcription services with seamless bidirectional communication. Below is a detailed overview of how to expand your REST API with BentoML, including examples and best practices. Creating Bento and containerizing for deployment. BentoML CLI. service decorator to configure the required resources for deployment, such as GPUs. As an important component in the BentoML ecosystem, OpenLLM allows you to easily integrate it into the BentoML workflow. Find and fix vulnerabilities Actions @inject def import_model (path: str, input_format: t. import numpy as np import bentoml import pandas as pd from bentoml. Top. XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. See the BentoML documentation to learn more. flax. For example, if you are working with 2-D arrays and input_dim is set to 0, BentoML will stack the arrays along the first dimension. . Examples. It enables your developers to build AI systems 10x faster with custom models, scale efficiently in your cloud, and maintain complete control over security and compliance. This file is crucial for packaging your application into a Bento, allowing for seamless deployment and management of your models. g. depends() is a recommended way for creating a BentoML project with distributed Services. bentoml containerize command also supports the use of the Explore the trend towards compound AI and how BentoML can help you build and scale compound AI systems. The scikit-learn model loaded from the model store or BentoML Model. Optional [str] = None, passwd: t Create a BentoML Service¶. This model is particularly efficient for generating embeddings due to its smaller size, making it suitable for environments with limited computational resources. BentoCloud is an Inference Management Platform and Compute Orchestration Engine built on top of BentoML’s open-source serving framework. The example Python function Explore practical async examples using BentoML to enhance your machine learning workflows and improve performance. We've learned that the best way to showcase what BentoML can do is not through dry, conceptual documentation but through real-world examples. In this document, you will: For example, you can use a label to log the version of model serving predictions, and this version label can change as you update the model. In this example, after you start this BentoML Service, you should interact with the /fastapi/hello endpoint. Model instance to load the model from. This is a BentoML example project, showing you how to serve and deploy open-source Large Language Models using Hugging Face TGI, a toolkit that enables high-performance text generation for LLMs. A collection of example projects for learning BentoML and building your own solutions. The framework for autonomous intelligence. float16 data type. The model pipeline (self. A collection of example projects for learning BentoML and building your own Sign In. create (bento = ". Hide table of contents sidebar. Let’s look at the file in more detail. BentoML provides a configuration interface that allows you to customize the runtime behavior for individual Services within a Bento. Looking inside each of the input adapters you can see how the BentoML converts an incoming request What is BentoML¶. This example demonstrates how to create a custom endpoint that operates alongside your BentoML Service, allowing for enhanced functionality and user interaction. XGBoost¶. The BentoML team uses the following channels to announce important updates like major product releases and share tutorials, case studies, as well as community news. Fore more information on BentoML Docker-Compose Up Example. Explore. To learn more about BentoML and its ecosystem tools, check out the following resources: The BentoML team uses the following channels to announce important updates like major product releases and share tutorials, case studies, as well as community news. _internal. build] section or a YAML file (typically named bentofile. py file that uses the following models:. Note. get method for the same purpose. The most flexible way to serve AI/ML models in production. Deploy an AI application using vLLM as the backend for high-throughput and Adding BentoML will enable model serving and deployment in production by: 1. passwd – (expert) the username used for authentication if required, e. Adding This quickstart demonstrates how to build a text summarization application with a Transformer model from the Hugging Face Model Hub. Any] | None = None,)-> bentoml. To create a REST API with BentoML, you first need to define your service. Within the bentoml. Log in to BentoCloud by running bentoml cloud login, then run the following command to deploy the project. Parameters:. Browse our curated list of open source models that are ready to deploy and optimized for performance. Note that BentoML provides framework-specific get methods for each framework module. bentoml code examples; View all bentoml analysis. Pricing. Preview. Bentoctl leverages BentoML’s Bento format (that provides a standard layout and configuration for prediction services) to automatically rebuild the Bento into the style that fits the particular cloud’s requirements. This is done in the service. It loads the pre-trained model (MODEL_ID) using the torch. Text Examples. You can find the following example service. ' import pytest import bentoml from service import Summarization, EXAMPLE_INPUT # Imported from the Summarization service. 2. Here is the example. 3. Reproducibly run & share ML code. The bento in the image consists of various small dishes arranged in What is BentoML¶. view more. This section provides the tutorials for a curated list of example projects to help you learn how BentoML can be used for different scenarios. py file to specify the serving logic of this BentoML project. Scaling¶ What is BentoML¶. Note that this field only takes effect on BentoCloud. Building an API service with BentoML. We specify that it should time out after 300 seconds and use one GPU of type nvidia-l4 on BentoCloud. Example: Aquí nos gustaría mostrarte una descripción, pero el sitio web que estás mirando no lo permite. Add custom ASGI middleware¶ bentoml. Model composition in BentoML utilizes YAML files to define the structure and configuration of your services. Python Package Anti-Tampering. Handle multiple parameters¶ A batchable API endpoint only accepts one parameter in addition to bentoml. File metadata and controls. task def long_running_image_generation (self, prompt: str)-> Image: # Process the prompt in a long-running process return image. Define a BentoML Service to customize the serving logic of your lanaguage model, which uses vllm as the backend option. Module): What is BentoML¶. Returns:. How to use bentoml - 10 common examples To help you get started, we’ve selected a few bentoml examples, based on popular ways it is used in public projects. Here’s a simple example of how to do this: import logging # Set up logging for BentoML logging. Integrating the application with BentoML allows you to leverage the benefits of the BentoML framework and its ecosystem. With a single command, you can deploy a production-grade application with fast autoscaling, secure deployment in your cloud, and comprehensive observability. The BentoML team works closely with their community of users like I've never seen before. A retrieval-augmented generation (RAG) system allows you to retrieve relevant information from an external knowledge base and use this information to enhance the response generated by an LLM. Optional [str] = None, user: t. Write better code with AI Security. 0: Offers enhanced control in the image generation process. Define the model serving logic¶. Let's install BentoML and other dependencies from PyPi (preferably in a virtual environment): This is a BentoML example project, showing you how to serve and deploy open-source Large Language Models (LLMs) using LMDeploy, a toolkit for compressing, deploying, and serving LLMs. 33 lines (20 loc) · 1. See here for a full list of BentoML example projects. Depend on an external deployment¶ BentoML also allows you to set an external deployment as a dependency for a Service. Please refer to EasyOCR guide for more information about how to use EasyOCR in BentoML. See the following lists for a complete collection of BentoML example projects. Sign in Product GitHub Copilot. user – (expert) the username used for authentication if required, e. To capture more detailed logs, especially at the DEBUG or INFO levels, you need to set up and register a log handler for the bentoml namespace. Below, you can find a number of tutorials and examples for various MLflow use cases. Build options refer to a set of configurations for building a BentoML project into a Bento. Create BentoML Services in a service. Python 3. py. BentoML provides a set of toolkits that let you easily build and scale compound AI systems, offering the key primitives for serving The BentoML team uses the following channels to announce important updates like major product releases and share tutorials, case studies, as well as community news. The primary file used is bentofile. Run multiple models in one Service¶ This document explains how to serve and deploy an MLflow model with BentoML. Every model directory contains the code to add OpenAI compatible endpoints to the BentoML Service. Skip to content referenced by its name and version in format of name:version. cloud. Aws Credentials File Example BentoML. Last updated on . If you want to test the project locally, install FFmpeg on your system. 3 provides new subcommands for managing secrets. To specify the ideal number of concurrent requests for a Service For example: bentoml secret create huggingface HF_TOKEN = <your_hf_token> bentoml code--secret huggingface Follow the on-screen instructions to create a new Codespace (or attach to an existing one) BentoML Service code, including adding The BentoML client implementation supports methods corresponding to the Service APIs and they should be called with the same arguments (text in this example) as defined in the Service. 2 Vision model: Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The image shows a traditional Japanese lunchbox, called a bento, which is a self-contained meal typically served in a wooden or plastic box. Bentoml Pipeline Overview Explore the BentoML pipeline for efficient model serving and deployment, enhancing your machine learning workflows. Sign In Sign Up. In this section, we will delve into the process of building a Each example shows how to define input and output types for a specific use case. The BentoML open source community consists of some of the most smart and creative developers. An example is {"my-normalizer": normalizer}. This process involves more advanced use What is BentoML¶. 2. An example of a harmful query: Learn how to implement logging in Rust with BentoML. BentoML is a Unified Inference Platform for deploying and scaling AI models with production-grade reliability, all without the complexity of managing infrastructure. This means all the routes defined within the FastAPI application will be accessible under /fastapi. fixture (scope = "session") def bentoml_client (): # Deploy the Summarization Service to BentoCloud deployment = bentoml. /stream: A streaming endpoint, marked by @bentoml. params – (expert) a map of parameters to be passed to the FS used for export, e. Restack AI SDK. The difference between them and bentoml. Toggle Light / Dark / Auto color theme. bentoml deployment update <deployment-name>-f patch. Import a model. Python’s standard types such as strings, integers, floats, booleans, lists, and dictionaries are commonly BentoML is a Python library for building online serving systems optimized for AI applications and model inference. For more information, run bentoml secret -h. Step 1: Prepare a BentoML project¶ Make sure you have an existing BentoML project or a Bento. version` where `name` is the user-defined model's name, and a generated `version` by BentoML. Design intelligent agents that execute multi-step processes autonomously. This example is ready for easy deployment and scaling on BentoCloud. BentoML LinkedIn account. It implements machine learning algorithms under the Gradient Boosting framework. These options can be defined in a pyproject. Here is an example of configuring these settings in your Service definition: @bentoml. If it contains multiple Services, see Deploy with a configuration file and Run distributed Services for details. Install dependencies. For example, Sagemaker requires very specific endpoints to be configured in order to deploy a service. This is a BentoML example project, showing you how to serve and deploy open-source Large Language Models (LLMs) using TensorRT-LLM, a Python API that optimizes LLM inference on NVIDIA GPUs using TensorRT engine. Using the MLflow REST API Directly. The output is the same as the config value in the example output above. To understand how BentoML works, we will use What is BentoML¶. Examples¶. Configurations¶ Refer to the following code examples directly if you only have a single BentoML Service in service. py file @pytest. These methods are dynamically created based on the Service’s endpoints, providing a direct mapping to the Service’s functionality. Here is an example file: service: "service:IrisClassifier" labels: owner: bentoml-team stage: demo include:-"*. import bentoml bentoml. api, which continuously returns real-time logs and intermediate results to the client. 9+ and pip installed. models. It enhances modularity as you can develop reusable, loosely coupled Services that can be maintained and scaled independently. At BentoML, we are committed to enhancing the developer experience, making it easier, faster, and more intuitive to work with the framework. BentoCloud is a fully-managed platform that simplifies the deployment of AI applications, allowing developers to focus on building rather than managing infrastructure. The max_batch_size and max_latency_ms parameters ensure that the service respects the defined constraints while dynamically adjusting batch sizes and processing intervals based on the adaptive batching algorithm. This type of custom input processing works by inheriting from the Input Adaptor abstract class BaseInputAdapter and overriding extract_user_func_args(). Sign In. Setting Up Your REST API. You can define multiple deployment hooks in a Service. This repository contains a group of BentoML example projects, showing you how to serve and deploy open-source Large Language Models using vLLM, a high-throughput and memory-efficient inference engine. 8 or higher and pip installed on your machine. yaml, which outlines the build options for your application. load_model (bento_model: str | Tag | Model) → SklearnModel ¶ Load the scikit-learn model with the given tag from the local BentoML model store. Navigation Menu Toggle navigation. For example: iris_classifier:v1. This document explains how to serve and deploy an XGBoost model for predicting breast cancer with BentoML. Understand how BentoML started and how it has helped organizations across the globe with NAVER as a case study. @inject def build (service: str, *, name: str | None = None, labels: dict [str, str] | None = None, description: str | None = None, include: t. By default, BentoML does not impose a limit on concurrency to avoid bottlenecks. Then, it defines a class-based BentoML Service (bentovllm-solar-instruct-service in this example) by using the @bentoml. bentoml. {'proxy': When you enter the bentoml. This helps you manage dependencies and avoid potential conflicts. Happy coding! ⌨️. Prerequisites. code-block:: python import torch import bentoml class NGramLanguageModeler(nn. py file in the cloned repository. Return type: Model. get (tag_like: str | Tag) → bentoml. yaml Python API. Source code for bentoml. easyocr. Here’s an example bentofile. /path_to_your_project", # Alternatively, use an existing This example is ready for easy deployment and scaling on BentoCloud. buckets: Deploying Keras model with BentoML and AWS EKS. md. Here’s a simple code snippet to get you started: import bentoml from bentoml import env, artifacts, api @env(infer_pip_packages=True) @artifacts Note: After you log in, you can create and manage Bento Deployments directly with commands like bentoml deployment create/update. Context. yaml") To roll BentoML Quickstart Example. diffusers/controlnet-canny-sdxl-1. toml file under the [tool. Open Source. Custom objects are currently serialized with cloudpickle, but this implementation is subject to change. service (traffic = {"timeout": 120, "max_concurrency": import bentoml import torch from transformers import pipeline EXAMPLE_INPUT = "Breaking News: In an astonishing turn of events, the small \ town of Willow Creek has been taken by storm as local resident Jerry Thompson's cat, \ Whiskers, performed what witnesses are calling a 'miraculous and gravity-defying leap. 29 KB. Automate any workflow Codespaces The--help flag also applies to sub-commands for viewing detailed usage of a command, like bentoml build Hide navigation sidebar. Architecture¶ This example includes two BentoML Services, a Currency Exchange Assistant and an LLM. get_config() This is useful when you have multiple BentoML Services in a Deployment. First we need to download an LLM (Mistral-7B-v0. To illustrate the capabilities of BentoML, consider a CUDA pipeline example. MLFlow runs natively on a BentoML’s runner, so you can take advantage of BentoML’s features like input validation, adaptive batching, and parallelism. yaml file for Hello world. io import JSON from typing import Dict, Example output from the Llama3. get method retrieves the model from the Model Store. Quickstart. It allows for precise modifications based on text and image To install BentoML on a Linux system, you need to ensure that you have the necessary prerequisites in place. This example demonstrates effective logging practices for your applications. Let’s have a quick look at the key files in this project. More on BentoML. Blog. DEBUG) logger = logging. For each Service, you can use resources in the @bentoml. Orchestrating Multistep Workflows. If you want to find out more, I invite you to dive in, experiment with the SVD models, explore the conveniences of BentoCloud, and contribute to the different example projects of BentoML. Packaging Training Code in a Docker Environment. deployment""" User facing python APIs for deployment """ from __future__ import annotations import typing as t import attr from simple_di import Provide from simple_di import inject from. In the cloned repository, you can find an example service. Use the @bentoml. for FTP. The recommended ColPali checkpoint for this repository is vidore/colpali-v1. Learn how to configure your AWS credentials file for BentoML to streamline your machine learning deployments. If you want to force a re-download instead of using the cache, you can specify the pip_args: "--no-cache-dir" option in your bentofile. Design intelligent agents that execute multi-step Contribute to bentoml/bentocloud-cicd-example development by creating an account on GitHub. BentoML Slack community. Architecture¶ This example includes two BentoML Services: Gemma and ShieldAssistant. With optimizations like adaptable batching and continuous batching, each worker can potentially handle many requests simultaneously to enhance the throughput of your Service. Code. The bentoml. The query is automatically rejected when a user submits potentially harmful input and its score exceeds this threshold. Kubernetes clusters (for example, AWS EKS and Google Cloud GKE) Object storage (for example, AWS S3 and Google Cloud Storage) Key-value stores (for example, InMemory Database and Memory Store) Once the BYOC setup is ready, developers can deploy Bentos through the BentoCloud Console or the BentoML CLI. To receive release notifications, star and watch the BentoML project on GitHub. This section provides code examples for configuring different BentoML hooks. Scaling¶ bentoml. deployment. List [str] | None This is an API reference for EasyOCR in BentoML. This flexibility is crucial for handling complex scenarios where multiple models may Create a BentoML Service¶. 0. on_deployment decorator to specify a method as a deployment hook. In this section, we will delve into the process of building a Sentence Transformer application using BentoML, focusing on the all-MiniLM-L6-v2 model. yaml). The example source code in this guide is also available in the examples/bentoml/ directory. To use the version of BentoML that will be used in this article, type: pip install bentoml==1. service class ImageGenerationService: @bentoml. If you haven't installed Python yet, you can find the installation instructions on the Python downloads page. Learn how to use docker-compose up with BentoML to streamline your machine learning model deployment. py" python: packages:-mlflow-scikit-learn. sklearn. By leveraging the capabilities of ASGI frameworks, you can build robust applications that meet the demands of modern web development. Skip to content. monitor context, you instantiate a monitoring session uniquely identified by a name ("text_summarization" in this example), which helps you categorize and retrieve the logged data for specific monitoring tasks. This endpoint initiates the workflow by calling BentoCrewDemoCrew(). service decorator is used to define the SDXLTurbo class as a BentoML Service. Parameters: tag_like – The tag of the model to retrieve from the model store. This setup allows for efficient model inference leveraging GPU acceleration. bento_model – Either the tag of the model to get from the store, or a BentoML ~bentoml. This project will guide you through setting up a RAG service that uses vector-based search and large language models (LLMs) to answer queries using documents as a knowledge base. 💡 This example is served as a basis for advanced code customization, such as custom model, What is BentoML¶. Build Replay Functions. This includes having Python 3. Build a Bento for the Llama 2 13B model and upload it directly to BentoCloud by adding the --push option. This project serves as a reference implementation designed to be hackable, providing a foundation for building and customizing your own AI agent solutions Examples. update (name = "deployment-1", config_file = "patch. getLogger('bentoml') What is BentoML¶. BentoML. The purpose of the stress test is to identify the maximum number of concurrent requests your Service can What is BentoML¶. 1 70B, powered by LMDeploy and BentoML. Model ¶ Get the BentoML model with the given tag. Optional [str] = None, *, protocol: t. The @openai_endpoints decorator from bentovllm_openai. To understand how BentoML works, we More BentoML examples with batchable APIs: SentenceTransformers, CLIP and ColPali. service decorator. train. This is a BentoML example project, demonstrating how to build a ColPali inference API server for ColPali. Hide [Example] Serving a Sentence Transformers model with BentoML [Example] Serving CLIP with BentoML; Sign up for BentoCloud for free to deploy your first embedding model; Join our Slack community; Contact us if you have any BentoCloud is an Inference Management Platform and Compute Orchestration Engine built on top of BentoML’s open-source serving framework. get is that the former ones verify if In this example, we define a BentoML service that encodes sentences using the SentenceTransformer model. BentoML offers seamless integration with various ASGI Learn how to implement an embedding layer using BentoML with practical examples and code snippets. Use Snyk Code to bentoml. 💡 This example is served as a basis for advanced code customization, such as custom model, inference logic or What is BentoML¶. Improved developer experience. Examples:. This means if you have two 2-D input arrays with dimensions 5x2 and 10x2, specifying an input_dim of 0 would combine these into We use Pydantic to handle the input as well as the output. This document provide examples for setting commonly used configurations. Secure your code as it's written. py file, where you specify the model and the input/output formats. Real-world examples. Their AMAs, the advocacy on Slack and getting on calls with their customers, are much appreciated by early-adopters and seasoned Explore practical async examples using BentoML to enhance your machine learning workflows and improve performance. Some example protocols are 'ftp', 's3', and 'userdata'. Image import Image @bentoml. The most flexible way to Tutorials and Examples. Built with BentoML. A collection of example projects for learning BentoML and building your own solutions Sign In. yjbfppa cfaxp sohtg jnrb otp aqkn mqg ssjf ryjwee fgfwkgrqw