From 1088785895596e5ab62f06261b6afff56b774e1f Mon Sep 17 00:00:00 2001 From: gregpapst7312 Date: Tue, 8 Apr 2025 10:02:33 +0000 Subject: [PATCH] Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-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..25d88dc --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are thrilled to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://www.sealgram.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](http://114.132.245.203:8001) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow [comparable actions](http://mangofarm.kr) to deploy the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://www.postajob.in) that uses reinforcement finding out to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial [distinguishing function](https://www.hirecybers.com) is its reinforcement learning (RL) action, which was utilized to refine the design's responses beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it's equipped to break down complicated questions and reason through them in a detailed way. This assisted thinking procedure permits the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as agents, logical thinking and information analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture [permits activation](https://gitea.xiaolongkeji.net) of 37 billion parameters, allowing effective reasoning by routing questions to the most pertinent professional "clusters." This approach enables the model to concentrate on different issue domains while maintaining total effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more [efficient architectures](https://ai.ceo) based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective designs to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as a teacher design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we [recommend deploying](https://ashawo.club) this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, [pediascape.science](https://pediascape.science/wiki/User:LoriHsu3056) avoid hazardous content, and assess models against essential security criteria. At the time of writing this blog site, for [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:BrianneDupree41) DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://kerjayapedia.com) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) under AWS Services, pick 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](http://git.zthymaoyi.com) you are releasing. To ask for a limitation increase, [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:LeonoreMuse6) create a limitation boost request and connect to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous material, and examine designs against key safety requirements. You can carry out safety procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This [enables](https://rami-vcard.site) you to apply guardrails to examine user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The [basic circulation](https://laboryes.com) involves the following steps: First, the system receives an input for the model. 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 receiving the design's output, another guardrail check is used. If the [output passes](https://tangguifang.dreamhosters.com) 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 happened at the input or output stage. The [examples showcased](https://linkpiz.com) in the following areas demonstrate [inference](https://professionpartners.co.uk) using 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 models (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 catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It does not [support Converse](https://samman-co.com) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and pick the DeepSeek-R1 design.
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The design detail page offers important details about the design's capabilities, rates structure, and implementation guidelines. You can find detailed use instructions, including sample API calls and code bits for combination. The model supports different text generation tasks, consisting of material production, code generation, and concern answering, utilizing its support learning optimization and CoT thinking capabilities. +The page also consists of release options and licensing details to help you get started with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose 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, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a variety of [instances](http://47.93.56.668080) (in between 1-100). +6. For Instance type, select your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For the majority of use cases, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:GayKastner43699) the default settings will work well. However, for production releases, you might desire to review these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can explore various triggers and adjust design criteria like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For example, content for inference.
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This is an exceptional way to explore the model's thinking and text generation abilities before [incorporating](https://www.shwemusic.com) it into your applications. The play area provides immediate feedback, helping you comprehend how the design responds to various inputs and letting you tweak your triggers for ideal results.
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You can rapidly check the model in the [playground](https://pioneerayurvedic.ac.in) through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_[runtime](http://120.79.7.1223000) customer, sets up [reasoning](https://redmonde.es) criteria, and sends a demand to produce text based upon a user timely.
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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 solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://partyandeventjobs.com) models to your use case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to assist you choose the technique 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 release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design internet browser shows available models, with details like the service provider name and model capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows essential details, consisting of:
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- Model name +- [Provider](https://nepaxxtube.com) name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to see the model details page.
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The model details page includes the following details:
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- The design name and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:PhillisClancy) provider details. +Deploy button to deploy the design. +About and Notebooks tabs with [detailed](http://47.97.161.14010080) details
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The About tab includes essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you release the design, it's recommended to examine the model details and license terms to confirm 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 automatically created name or [produce](https://www.hi-kl.com) a custom one. +8. For example [type ΒΈ](https://oeclub.org) select an instance type (default: ml.p5e.48 xlarge). +9. For Initial [circumstances](http://tv.houseslands.com) count, enter the number of circumstances (default: 1). +Selecting appropriate instance types and counts is crucial for expense and performance optimization. Monitor your release 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 model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the model.
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The deployment procedure can take a number of minutes to finish.
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When deployment is total, your endpoint status will change to InService. At this moment, the model is all set to accept reasoning demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can [conjure](https://githost.geometrx.com) up the design utilizing a SageMaker runtime customer and incorporate 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 set up the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the note pad 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, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:Bonny80V58) you can likewise use the ApplyGuardrail API with your [SageMaker](https://u-hired.com) JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Clean up
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To avoid undesirable charges, complete the [actions](https://meephoo.com) in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments. +2. In the Managed implementations section, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, choose 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 design you deployed will if you leave it [running](http://121.37.208.1923000). Use the following code to delete 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](http://161.97.176.30) how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart [Foundation](https://eastcoastaudios.in) 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://bihiring.com) companies construct ingenious services using AWS services and sped up compute. Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference performance of large language designs. In his downtime, Vivek delights in hiking, viewing films, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://115.236.37.105:30011) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://ivytube.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a [Specialist Solutions](https://gitlab.bzzndata.cn) Architect working on generative [AI](https://skillfilltalent.com) 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://bpx.world) center. She is passionate about [developing options](http://39.99.158.11410080) that help consumers accelerate their [AI](http://git.pushecommerce.com) journey and unlock company worth.
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