commit 11aa3e1a9c371c9fae01938113c7a4f68ccc1f50 Author: oliviakruse818 Date: Sat Feb 22 11:06:39 2025 +0800 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart 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..f0baa81 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are delighted to reveal that [DeepSeek](https://www.activeline.com.au) 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://git.poloniumv.net)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations [ranging](http://kodkod.kr) from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative [AI](http://114.132.230.24:180) ideas on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on [Amazon Bedrock](https://winf.dhsh.de) Marketplace and [SageMaker JumpStart](https://gitlab.digineers.nl). You can follow similar actions to release the distilled variations of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](http://bingbinghome.top:3001) that uses reinforcement finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An [essential distinguishing](https://play.sarkiniyazdir.com) function is its support learning (RL) step, which was utilized to improve the design's actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt better to user [feedback](https://parejas.teyolia.mx) and objectives, eventually improving both significance and clarity. In addition, DeepSeek-R1 utilizes a [chain-of-thought](https://richonline.club) (CoT) method, implying it's equipped to break down intricate queries and factor through them in a detailed way. This directed reasoning [process](http://recruitmentfromnepal.com) [enables](https://stepaheadsupport.co.uk) the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:Lawerence56N) user interaction. With its extensive capabilities DeepSeek-R1 has caught the market's attention as a [versatile text-generation](https://git2.nas.zggsong.cn5001) design that can be integrated into numerous workflows such as agents, logical reasoning and information analysis jobs.
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DeepSeek-R1 uses a Mix of [Experts](https://www.racingfans.com.au) (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, allowing effective inference by routing questions to the most relevant specialist "clusters." This [technique permits](http://www.heart-hotel.com) the design to concentrate on various problem domains while maintaining overall performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for [wavedream.wiki](https://wavedream.wiki/index.php/User:ClaireSparling1) inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more [efficient architectures](https://meebeek.com) based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient designs to simulate the behavior and [reasoning patterns](https://www.cartoonistnetwork.com) of the bigger DeepSeek-R1 model, using it as an instructor model.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and evaluate models against crucial safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, enhancing user [experiences](https://bytes-the-dust.com) and standardizing safety controls across your generative [AI](https://www.contraband.ch) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate you're using 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 ask for [surgiteams.com](https://surgiteams.com/index.php/User:LaureneDortch1) a limit boost, create a limitation boost demand and connect to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Set up permissions to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging content, and evaluate designs against essential security criteria. You can carry out safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using 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 circulation involves the following steps: First, the system gets 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 design for reasoning. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections show inference utilizing this API.
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Deploy DeepSeek-R1 in [Amazon Bedrock](https://realhindu.in) Marketplace
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Amazon [Bedrock Marketplace](https://radiothamkin.com) offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the [Amazon Bedrock](https://youtoosocialnetwork.com) console, select Model catalog under Foundation models 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 APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 design.
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The model detail page provides necessary details about the model's abilities, pricing structure, and application standards. You can discover detailed use instructions, including sample API calls and code bits for combination. The model supports numerous text generation tasks, consisting of content development, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities. +The page also consists of deployment choices and licensing details to assist you get begun with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select Deploy.
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You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a number of circumstances (between 1-100). +6. For Instance type, select your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For most use cases, the default settings will work well. However, for production deployments, you might desire to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can try out different triggers and adjust model parameters like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, content for reasoning.
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This is an excellent method to check out the model's thinking and text generation capabilities before integrating it into your applications. The play area provides immediate feedback, helping you understand how the model responds to various inputs and [letting](https://pakalljobs.live) you fine-tune your triggers for optimum outcomes.
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You can rapidly evaluate the model in the [playground](https://www.youtoonetwork.com) through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce 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 created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends out a request to create [text based](https://www.groceryshopping.co.za) on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial [intelligence](https://precise.co.za) (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use 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 practical techniques: utilizing the user-friendly SageMaker JumpStart UI or executing [programmatically](http://1.14.122.1703000) through the SageMaker Python SDK. Let's [explore](https://employmentabroad.com) both techniques to assist you choose the [approach](http://gitlab.andorsoft.ad) that best fits your needs.
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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, pick JumpStart in the navigation pane.
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The model internet browser shows available models, with details like the [supplier](https://job.honline.ma) name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals essential details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +[Bedrock Ready](http://devhub.dost.gov.ph) badge (if suitable), suggesting that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to view the design details page.
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The design details page consists of the following details:
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- The model name and company details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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[- Model](https://git.dsvision.net) description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you release the design, it's suggested to evaluate the model details and license terms to [confirm compatibility](https://projob.co.il) with your use case.
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6. Choose Deploy to proceed with [release](http://1.14.122.1703000).
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7. For Endpoint name, use the immediately created name or create a custom one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is essential for cost and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for [sustained traffic](https://www.hammerloop.com) and low . +10. Review all configurations for accuracy. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in [location](http://www.asiapp.co.kr). +11. Choose Deploy to release the design.
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The implementation procedure can take several minutes to finish.
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When implementation is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can keep track of the release 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 utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra requests 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 create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To prevent unwanted charges, complete the steps in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. +2. In the Managed deployments section, locate the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting 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 released will [sustain expenses](http://121.4.70.43000) if you leave it [running](http://barungogi.com). Use the following code to erase the [endpoint](http://39.99.158.11410080) if you want 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 checked out how you can access and deploy the DeepSeek-R1 design utilizing Bedrock Marketplace and [SageMaker JumpStart](https://njspmaca.in). Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. 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 Marketplace, and Beginning 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://git.soy.dog) companies build ingenious services utilizing AWS [services](http://gitlab.abovestratus.com) and accelerated calculate. Currently, he is focused on developing methods for fine-tuning and enhancing the inference performance of big language designs. In his leisure time, Vivek delights in treking, seeing movies, and trying various foods.
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Niithiyn Vijeaswaran is a [Generative](https://www.jobcheckinn.com) [AI](http://git.chuangxin1.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://www.fionapremium.com) [accelerators](http://101.132.163.1963000) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://183.221.101.89:3000) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://154.64.253.77:3000) hub. She is passionate about constructing services that assist customers accelerate their [AI](https://pojelaime.net) journey and unlock organization value.
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