From 2e6c95b107f9e104d9f9bf72788ce5209f36c039 Mon Sep 17 00:00:00 2001 From: carltoncostant Date: Thu, 29 May 2025 09:36:12 +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..ccc1d5e --- /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 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://www.activeline.com.au)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and [responsibly scale](http://git.permaviat.ru) your generative [AI](http://116.62.118.242) 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 steps to deploy the distilled versions of the models as well.
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[Overview](https://one2train.net) of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://zapinacz.pl) that uses support learning to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing function is its support learning (RL) action, which was used to fine-tune the design's reactions beyond the [basic pre-training](https://jobs.360career.org) and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it's geared up to break down intricate queries and factor through them in a detailed manner. This guided reasoning process permits the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing 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 integrated into numerous workflows such as representatives, sensible reasoning and data interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, allowing efficient inference by routing questions to the most relevant specialist "clusters." This approach enables the model to specialize in different problem domains while [maintaining](https://carvidoo.com) total efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. 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 supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective designs to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and examine models against crucial safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and [yewiki.org](https://www.yewiki.org/User:Nichol9618) use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](https://ready4hr.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for [endpoint usage](http://home.rogersun.cn3000). 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 increase demand and reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper [AWS Identity](https://theglobalservices.in) and Gain Access To Management (IAM) consents to use Amazon Bedrock [Guardrails](https://git.sortug.com). For directions, see Establish approvals to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock [Guardrails](https://grailinsurance.co.ke) allows you to present safeguards, prevent hazardous material, and examine models against crucial safety requirements. You can implement security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock [console](https://jktechnohub.com) or the API. For the example code to [produce](https://thevesti.com) the guardrail, see the GitHub repo.
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The general circulation involves the following actions: 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 receiving the design's output, another guardrail check is applied. If the [output passes](http://114.132.230.24180) 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 phase. The examples showcased in the following areas show inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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[Amazon Bedrock](http://zhangsheng1993.tpddns.cn3000) Marketplace offers you access to over 100 popular, emerging, and specialized structure models (FMs) through [Amazon Bedrock](https://mypungi.com). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.
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The design detail page offers [essential details](https://ramique.kr) about the design's capabilities, prices structure, and implementation guidelines. You can discover detailed usage guidelines, [including](http://165.22.249.528888) sample API calls and code bits for integration. The design supports different text generation tasks, consisting of material creation, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking abilities. +The page likewise includes release options and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be triggered to set up the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a variety of circumstances (between 1-100). +6. For example type, choose your circumstances type. For ideal efficiency with DeepSeek-R1, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:MitziCarandini3) a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you might desire to evaluate 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 deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive interface where you can experiment with various prompts and change model specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For example, material for reasoning.
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This is an outstanding way to explore the design's reasoning and text generation capabilities before [integrating](https://git.es-ukrtb.ru) it into your applications. The play area supplies instant feedback, [helping](https://acrohani-ta.com) you comprehend how the model reacts to numerous inputs and letting you [fine-tune](http://ev-gateway.com) your prompts for .
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You can quickly test the model in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a released DeepSeek-R1 design 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 create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends a demand to create [text based](https://www.findnaukri.pk) upon 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, integrated algorithms, and prebuilt ML solutions that you can release with just a few clicks. With [SageMaker](https://jobz1.live) JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into [production](http://www.book-os.com3000) using either the UI or SDK.
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[Deploying](http://charge-gateway.com) DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free methods: using the intuitive SageMaker JumpStart UI or executing programmatically through the [SageMaker Python](https://audioedu.kyaikkhami.com) SDK. Let's check out both [techniques](https://pedulidigital.com) to assist you select the method that finest matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release 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 create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design browser displays available designs, with details like the company name and model capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card shows essential details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +[Bedrock Ready](http://turtle.pics) badge (if suitable), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to [conjure](https://chat-oo.com) up the design
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5. Choose the model card to view the design details page.
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The model details page [consists](http://www.xn--v42bq2sqta01ewty.com) of the following details:
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- The design name and service provider details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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- Model [description](http://121.42.8.15713000). +- License details. +- Technical specs. +- Usage standards
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Before you release the design, it's recommended to review the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the immediately produced name or develop a custom one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is vital for expense and efficiency optimization. [Monitor](https://www.suyun.store) your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the model.
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The implementation procedure can take a number of minutes to complete.
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When implementation is total, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MargieBergin53) your endpoint status will alter to [InService](http://103.254.32.77). At this moment, the design is all set to accept inference demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can conjure up the design utilizing a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for [inference programmatically](http://aircrew.co.kr). The code for releasing the design is provided in the Github here. You can clone the note pad and range 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and [implement](https://ofebo.com) it as shown 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 deployed the model utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, [kigalilife.co.rw](https://kigalilife.co.rw/author/gertiebnj04/) under Foundation models in the navigation pane, select Marketplace implementations. +2. In the Managed implementations section, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:LeandroBozeman) find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the right release: 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 sustain costs if you leave it running. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete [Endpoints](https://gitea.jessy-lebrun.fr) 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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](https://cv4job.benella.in) is a Lead Specialist Solutions Architect for Inference at AWS. He [assists emerging](http://107.172.157.443000) generative [AI](https://executiverecruitmentltd.co.uk) [companies build](http://orcz.com) ingenious options utilizing AWS services and accelerated compute. Currently, he is focused on establishing strategies for fine-tuning and optimizing the inference efficiency of large language designs. In his complimentary time, Vivek delights in treking, seeing movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://asixmusik.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://blogville.in.net) 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 dealing with generative [AI](https://muwafag.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11983461) engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://usa.life) hub. She is passionate about constructing services that help consumers accelerate their [AI](https://code.webpro.ltd) journey and unlock organization worth.
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