From 547966cda222caa99a1c181ab7cd375438957e88 Mon Sep 17 00:00:00 2001 From: juanmasten404 Date: Fri, 7 Feb 2025 02:06:37 +0800 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..d044aee --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:DaleneCollins99) we are excited to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://career.ltu.bg)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](http://encocns.com:30001) ideas on AWS.
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In this post, we show how to start with DeepSeek-R1 on [Amazon Bedrock](http://124.222.181.1503000) Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the [distilled variations](https://c-hireepersonnel.com) of the designs too.
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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://154.64.253.77:3000) that uses support discovering to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating feature is its support knowing (RL) step, which was used to fine-tune the model's responses beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, eventually boosting both importance and clarity. In addition, [wavedream.wiki](https://wavedream.wiki/index.php/User:AdriannaBranch) DeepSeek-R1 uses a chain-of-thought (CoT) approach, implying it's geared up to break down intricate queries and factor through them in a detailed manner. This assisted reasoning procedure permits the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be integrated into numerous workflows such as representatives, logical thinking and information interpretation tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by routing queries to the most appropriate expert "clusters." This technique enables the design to concentrate on different problem domains while maintaining total efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of [GPU memory](https://git.guaranteedstruggle.host).
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DeepSeek-R1 [distilled models](https://gitlab.reemii.cn) bring the [reasoning abilities](http://45.55.138.823000) of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective designs to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as an instructor design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and evaluate designs against [essential security](https://tjoobloom.com) criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](http://112.124.19.38:8080) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, 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, choose Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limitation boost, create a [limit increase](https://15.164.25.185) request and reach out to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Set up consents to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous material, and assess models against crucial security requirements. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock [console](https://gitea.v-box.cn) or the API. For the example code to create the guardrail, see the GitHub repo.
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The general flow involves the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for inference. After receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [provider](https://gitea.daysofourlives.cn11443) and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:FidelBatt531106) choose the DeepSeek-R1 model.
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The model detail page offers vital details about the model's capabilities, pricing structure, and application standards. You can find detailed usage guidelines, consisting of sample API calls and code bits for combination. The design supports numerous text generation tasks, consisting of material development, code generation, and [question](https://maibuzz.com) answering, using its reinforcement discovering optimization and CoT thinking abilities. +The page likewise includes implementation choices 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 set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, go into a number of circumstances (in between 1-100). +6. For [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Homer93G479471) example type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service role approvals, and encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to align with your company's security and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:CharleyRudall29) compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive user interface where you can explore various prompts and change design specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, content for inference.
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This is an exceptional way to check out the model's reasoning and text generation abilities before incorporating it into your applications. The [play ground](http://47.101.46.1243000) offers immediate feedback, assisting you [comprehend](https://warleaks.net) how the design reacts to various inputs and letting you tweak your prompts for ideal outcomes.
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You can rapidly test the design in the playground through the UI. However, to invoke the released design [programmatically](https://uniondaocoop.com) with any Amazon Bedrock APIs, you need to get the [endpoint ARN](https://www.genbecle.com).
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Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform inference using a deployed DeepSeek-R1 design through [Amazon Bedrock](https://git.guaranteedstruggle.host) using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a demand to generate text based on a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an [artificial](https://git.pm-gbr.de) intelligence (ML) hub with FMs, [built-in](http://47.90.83.1323000) algorithms, and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://xotube.com) models to your usage case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient methods: utilizing the instinctive SageMaker [JumpStart](https://vishwakarmacommunity.org) UI or implementing programmatically through the [SageMaker Python](http://git.agdatatec.com) SDK. Let's explore both [techniques](https://ansambemploi.re) to assist you select the method that finest suits 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, select Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design internet browser shows available models, with details like the company name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows essential details, including:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if suitable), indicating that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to see the design details page.
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The [model details](https://centerdb.makorang.com) page includes the following details:
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- The model name and service provider [details](http://git.magic-beans.cn3000). +Deploy button to deploy the model. +About and [Notebooks tabs](https://www.gc-forever.com) with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you deploy the design, it's [advised](https://nurseportal.io) to examine the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to [continue](http://git.baige.me) with release.
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7. For Endpoint name, utilize the instantly produced name or produce a customized one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting suitable instance types and counts is essential for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to [release](http://27.154.233.18610080) the design.
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The implementation process can take several minutes to complete.
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When implementation is complete, your endpoint status will change to InService. At this moment, the model is all set to accept reasoning demands through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can conjure up the design utilizing a SageMaker runtime customer and incorporate it with your [applications](http://40th.jiuzhai.com).
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS permissions and [environment setup](http://47.108.239.2023001). The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for [inference programmatically](https://spm.social). The code for releasing the design is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run extra demands 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 also 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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Clean up
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To prevent unwanted charges, finish the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace [implementations](https://www.sc57.wang). +2. In the Managed implementations area, find the endpoint you want to erase. +3. Select the endpoint, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:JuniorBowser22) and on the Actions menu, select Delete. +4. Verify the [endpoint details](http://www.xn--he5bi2aboq18a.com) to make certain you're [deleting](https://git.morenonet.com) the right 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 design you released will sustain costs 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](http://82.156.184.993000) and Resources.
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Conclusion
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In this post, we checked out how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](https://mobidesign.us) now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, [Amazon Bedrock](https://code.flyingtop.cn) Marketplace, and Getting started 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 assists emerging generative [AI](https://git.bubblesthebunny.com) companies construct innovative options utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning performance of big language designs. In his leisure time, Vivek takes [pleasure](https://app.zamow-kontener.pl) in hiking, watching motion pictures, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://botcam.robocoders.ir) Specialist Solutions Architect with the Third-Party Model [Science](https://iamzoyah.com) group at AWS. His area of focus is AWS [AI](https://git.szrcai.ru) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on [AI](http://124.221.76.28:13000) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [surgiteams.com](https://surgiteams.com/index.php/User:KaseyDees635) generative [AI](https://suprabullion.com) center. She is enthusiastic about [developing services](https://git.cyu.fr) that help customers accelerate their [AI](https://git.lodis.se) journey and unlock service value.
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