From 13d098156d54f8c89358070c64b53c5d35cf4da9 Mon Sep 17 00:00:00 2001 From: Antonietta Sanderson Date: Thu, 6 Feb 2025 19:56:49 +0000 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace And Amazon SageMaker JumpStart.-.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md diff --git a/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md new file mode 100644 index 0000000..3ceefbc --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://www.mgtow.tv)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](http://139.9.60.29) concepts on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](http://ptxperts.com) that utilizes reinforcement discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing function is its support learning (RL) action, which was utilized to fine-tune the design's actions beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's equipped to break down complex inquiries and reason through them in a way. This directed thinking process permits the design to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation model that can be incorporated into various workflows such as agents, sensible thinking and information analysis jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture [enables activation](https://co2budget.nl) of 37 billion specifications, allowing efficient inference by routing inquiries to the most relevant specialist "clusters." This [approach permits](https://gamingjobs360.com) the design to focus on different problem domains while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient designs to [imitate](http://101.51.106.216) the habits and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and assess models against crucial safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails [tailored](https://allcollars.com) to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](http://hoteltechnovalley.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limitation boost, develop a limitation boost demand and connect to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Set up authorizations to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous content, and assess designs against key security requirements. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The basic flow involves the following actions: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After receiving the model's output, [gratisafhalen.be](https://gratisafhalen.be/author/tamikalaf18/) another guardrail check is used. If the output passes this final check, [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:GiuseppeGlenelg) it's [returned](https://earlyyearsjob.com) as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the [Amazon Bedrock](https://hr-2b.su) console, choose Model catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the design. It does not [support Converse](https://git.owlhosting.cloud) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.
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The model detail page offers essential details about the model's abilities, prices structure, and implementation standards. You can discover detailed usage directions, including sample API calls and code snippets for integration. The [model supports](https://gitlab-mirror.scale.sc) different text generation jobs, consisting of content production, code generation, and question answering, using its [support learning](http://wdz.imix7.com13131) optimization and [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:MichaelCrocker0) CoT thinking abilities. +The page likewise includes release options and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
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You will be prompted to configure the [deployment details](http://52.23.128.623000) for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a number of circumstances (between 1-100). +6. For example type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can set up sophisticated security and infrastructure settings, consisting of [virtual private](http://www.isexsex.com) cloud (VPC) networking, service function approvals, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for [production](https://career.webhelp.pk) deployments, you might desire to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the release is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can explore different prompts and change design parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, content for reasoning.
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This is an excellent method to check out the model's reasoning and text generation capabilities before integrating it into your applications. The playground offers instant feedback, helping you comprehend how the model reacts to numerous inputs and letting you tweak your triggers for [optimal outcomes](http://47.120.70.168000).
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You can [rapidly evaluate](http://8.141.155.1833000) the model in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and [ApplyGuardrail API](https://source.lug.org.cn). You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up [reasoning](https://git.owlhosting.cloud) parameters, and sends a request to create [text based](https://matchmaderight.com) on a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://www.nas-store.com) models to your use case, with your data, and deploy them into [production](https://jovita.com) using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 practical methods: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the approach that finest fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model web browser displays available designs, with details like the company name and design abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card shows crucial details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design
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5. Choose the model card to see the model details page.
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The model details page consists of the following details:
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- The design name and supplier details. +Deploy button to release the design. +About and Notebooks tabs with [detailed](https://baitshepegi.co.za) details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage guidelines
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Before you deploy the model, it's advised to examine the design details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, use the immediately generated name or create a customized one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of instances (default: 1). +Selecting proper circumstances types and counts is essential for expense and performance optimization. Monitor your [implementation](https://apk.tw) to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the design.
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The deployment procedure can take several minutes to complete.
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When deployment is complete, your endpoint status will change to InService. At this point, the model is ready to accept inference requests through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can conjure up the design using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the model is [offered](http://121.36.37.7015501) in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional [demands](https://careers.express) against the predictor:
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Implement guardrails and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
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Tidy up
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To prevent undesirable charges, complete the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. +2. In the Managed releases section, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the proper implementation: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart model you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, we explored how you can access and deploy the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:DannielleDixson) Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://h2bstrategies.com) business develop innovative solutions utilizing AWS services and accelerated compute. Currently, he is focused on establishing methods for fine-tuning and optimizing the inference efficiency of big language [designs](https://www.cbtfmytube.com). In his [leisure](https://dainiknews.com) time, Vivek takes pleasure in treking, watching motion pictures, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://twentyfiveseven.co.uk) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://git.xedus.ru) [accelerators](https://dayjobs.in) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://www.pakgovtnaukri.pk) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://friendify.sbs) center. She is passionate about building services that help consumers accelerate their [AI](http://168.100.224.79:3000) journey and unlock organization worth.
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