Determined AI
O que é Determined AI?
Plataforma de treinamento de aprendizado profundo de código aberto que permite aos cientistas de dados treinar modelos melhores, com ajuste de hiperparâmetros incorporado e treinamento distribuído
Como usar Determined AI?
1. Instale a plataforma Determined AI 2. Configure seus dados e modelo 3. Defina hiperparâmetros e opções de treinamento 4. Inicie o treinamento do modelo 5. Acompanhe e analise os resultados do experimento
Principais recursos da Determined AI
Ajuste de Hiperparâmetros
Treinamento Distribuído
Acompanhamento de Experimento
Casos de uso da Determined AI
Treinamento de Modelos de Aprendizado Profundo Populares
Arte Generativa
Visão Computacional
Perguntas frequentes de Determined AI
Quais frameworks de aprendizado profundo o Determined AI suporta?
Posso usar o Determined AI na nuvem ou em infraestrutura local?
O Determined AI suporta a exportação de modelos para sistemas de implementação downstream?
Determined AI E-mail de suporte e contato de atendimento ao cliente e contato de reembolso, etc.
Mais contato, visite a página de contato(https://www.determined.ai/contact/)
Determined AI Empresa
Determined AI Nome da empresa: Determined AI .
Linkedin de Determined AI
Link de Linkedin de Determined AI: https://www.linkedin.com/company/determined-ai
Twitter de Determined AI
Link de Twitter de Determined AI: https://twitter.com/DeterminedAI
Github de Determined AI
Link de Github de Determined AI: https://github.com/determined-ai/determined
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Lunch & Learn: Batch Inference!
Determined's new Batch Processing API allows users to treat Batch Inference as an experiment within their Determined workflow. We talked with two of the lead engineers - Ran Fu and Sze Wai Yuen - about data parallel inference in this month's Lunch & Learn session. Check out the official Batch Inference Blog here: https://www.determined.ai/blog/batch-processing-api Read the Batch Processing API documentation here: https://docs.determined.ai/latest/model-dev-guide/batch-processing/batch-process-api-ug.html#torch-batch-processing-ug
Open-Source Spotlight - Determined - Isha Ghodgaonkar
Determined: a deep-learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. 00:00 Introduction 00:45 Determined WebUI walkthrough and where you can deploy 02:35 What is a Determined Experiment? 03:13 How Determined AI solves the problems of a ML engineer 06:47 Determined vs MLFlow 08:50 Collaboration features 11:12 How to use Determined using PyTorch API 13:53 Code walkthrough 19:33 What is Preemption? 24:13 Model training vs. model serving 25:51: Q/A about the tool 27:28 Advice for the audience Links: - Slack Community: https://join.slack.com/t/determined-community/shared_invite/zt-23xsp1p1d-vAlq6e5aaF8s9ZYxp~Al~w - Homepage: https://www.determined.ai/ - Quickstart guide: https://docs.determined.ai/latest/tutorials/quickstart-mdldev.html Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html
Introducing: GenAI studio in Determined!
Today we launched GenAI studio, a playground feature of Determined for ML enthusiasts to work on foundational AI models. This is the recording of our launch webinar, but you can check all of the pertinent information on our blog here: https://www.determined.ai/blog/genai-studio-launch -- Have you experienced challenges during your LLM journey including: Accessing trendy open-source models? Designing effective prompts for your use case? And saving them? Evaluating models to make tradeoff decisions between accuracy, speed, and cost? Finetuning your model quickly and safely? If these LLM challenges resonate with you, join us for the first look at Determined generative AI studio, the new tool for validating your LLM ideas on Day 1, accelerating your LLM journey, and gaining more LLMOps productivity. The no-code experience includes: Self-hosting Llama2, Falcon, and MPT models on your cloud. Making your prompt engineering work reproducible. Dataset-oriented model evaluation. One-button finetuning. Monitoring finetuning as an experiment. Integration with Huggingface. In addition, Determined generative AI studio includes a Python API, for those who want greater control of the LLM workflow.
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