Prompt Flow (Microsoft) A suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, and evaluation to production, deployment, and monitoring. Tutorials Link Weights & Biases(Weights & Biases) A Machine Learn...
What we’re building: an LLM deployment platform The first part of this tutorial demonstrated how to build an application that answers questions about LangSmith’s documentation. This application relies on OpenAI large language model APIs managed with wrappers from LangChain, a popular open-source ...
It also provides tools for issue tracking, documentation, and continuous integration and deployment. Time elapsed: 0.22 GitHub is a web-based platform that is used to manage, store, and share software development projects. It offers a version control system and collaboration tools for developers to...
The E2E LLMOps platform is a comprehensive platform that provides a suite of tools and services for managing the entire LLM lifecycle. This includes tasks such as data management, model training, and model deployment. The E2E LLMOps platform ensures that the LLM infrastructure stack is integrated ...
ClickDeploy. The model deployment requires approximately five minutes. Use WebUI to perform inference Find the deployed service and clickView Web Appin theService Typecolumn. Test the inference performance on the WebUI page. Enter a sentence in the input text box and clickSendto start a conversa...
选择合适的推理选项:例如,我们可以选择使用AWS SageMaker或Google Cloud AI Platform,这些服务可以根据需求动态分配资源,从而在需求较少时降低部署成本。 优化推理性能:我们可以通过使用硬件加速,如GPU或TPU,以及优化推理代码来提高推理性能。例如,我们可以使用TensorRT或OpenVINO这样的库来优化我们的模型,使其能够更有效地在...
# Create a folder to cache the built TRT engines. This is recommended so they don’t have to be built on every deployment call. mkdir -p trt-cache# Run the container, mounting the checkpoint and the cache directorydocker run --rm --net=host \\ --gpus=all \\ -v $(pwd)/nemotron...
Hugging Facehas become a powerhouse in the field of machine learning (ML). Their large collection of pretrained models and user-friendly interfaces have entirely changed how we approach AI/ML deployment and spaces. If you’re interested in looking deeper into the integration of Docker and Hugging...
Building for the future: The enterprise generative AI application lifecycle with Azure AI ByGregory Buehrer, Corporate Vice President, Chief Technology Officer of Azure Machine Learning The enterprise development process requires collaboration, diligent evaluation, risk management, and scaled deployment. By ...
从生命周期来看,部署模型的dev阶段包含了Code development、Unit tests、Integration tests和Model training,staging阶段包含了Continuous deployment,prod阶段包含了Deploy pipelines;而部署代码的dev阶段包含了Code development,staging阶段包含了Unit tests和Integration tests,prod阶段包含了Model training、Continuous deployment和...