From e87cf4e7893e93514cb8573d21d4bbd40a20867b Mon Sep 17 00:00:00 2001 From: pkcjonelle0397 Date: Tue, 8 Apr 2025 00:06:39 +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..fe10308 --- /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](http://163.228.224.1053000) Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://selfyclub.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://mypungi.com) ideas on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://www.szkis.cn:13000) that uses reinforcement finding out to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3[-Base structure](https://git.tanxhub.com). A key identifying feature is its support learning (RL) action, which was used to fine-tune the design's reactions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, indicating it's geared up to break down complicated queries and reason through them in a detailed way. This guided reasoning process allows the design to produce more precise, transparent, and detailed answers. This model combines RL-based [fine-tuning](http://recruitmentfromnepal.com) with CoT abilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the [market's attention](https://git.mm-music.cn) as a flexible text-generation model that can be integrated into numerous workflows such as agents, rational thinking and data analysis jobs.
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DeepSeek-R1 utilizes a Mix of [Experts](https://gitea.offends.cn) (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, enabling effective inference by routing inquiries to the most relevant specialist "clusters." This method permits the model to focus on various issue domains while maintaining overall effectiveness. DeepSeek-R1 needs 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 release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:JayVachon971) Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective models to mimic the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.
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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 advise deploying this design with [guardrails](https://burlesquegalaxy.com) in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and evaluate 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 develop multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](https://login.discomfort.kz) 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 examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limit boost, create a limitation boost demand and reach out to your account group.
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Because you will be deploying this design with [Amazon Bedrock](https://ssconsultancy.in) Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful content, and evaluate designs against crucial security criteria. You can carry out security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design reactions 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 general circulation involves the following actions: 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 applied. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or [output stage](http://119.3.9.593000). The examples showcased in the following areas demonstrate inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through [Amazon Bedrock](https://git.youxiner.com). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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1. On the [Amazon Bedrock](https://www.rotaryjobmarket.com) console, select Model catalog under Foundation models in the navigation pane. +At the time of [writing](http://47.101.139.60) this post, you can utilize the InvokeModel API to [conjure](https://kaykarbar.com) up the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.
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The model detail page offers important details about the model's abilities, rates structure, and application standards. You can find detailed use instructions, consisting of sample API calls and code bits for combination. The design supports numerous text generation jobs, consisting of content creation, code generation, and concern answering, using its support finding out optimization and CoT reasoning abilities. +The page also consists of release options and licensing details to help you get going with DeepSeek-R1 in your . +3. To begin utilizing DeepSeek-R1, select Deploy.
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You will be triggered to configure the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For [surgiteams.com](https://surgiteams.com/index.php/User:ZakNeff06884) Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, get in a number of circumstances (between 1-100). +6. For example type, choose your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service role authorizations, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may desire to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the release is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive interface where you can experiment with various triggers and adjust design specifications like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, content for reasoning.
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This is an exceptional way to explore the design's thinking and text generation capabilities before incorporating it into your applications. The playground offers immediate feedback, assisting you understand how the design reacts to numerous inputs and letting you tweak your prompts for ideal results.
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You can rapidly check the model in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using 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, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends a demand to create 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) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient approaches: utilizing the instinctive SageMaker JumpStart UI or [carrying](http://47.120.16.1378889) out programmatically through the SageMaker Python SDK. Let's check out both techniques to help you pick the approach that best matches 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 using 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 create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design browser shows available designs, with details like the service provider name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card shows key details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if relevant), [wiki.eqoarevival.com](https://wiki.eqoarevival.com/index.php/User:LizetteValenzuel) indicating that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the design 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 deploy 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](https://crossdark.net). +- License details. +- Technical specifications. +- Usage guidelines
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Before you release the design, it's recommended to examine the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the instantly produced name or create a custom-made one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting suitable [instance](https://jobskhata.com) types and counts is vital for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the design.
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The release procedure can take numerous minutes to complete.
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When deployment is total, your endpoint status will alter to InService. At this moment, the design is prepared to accept inference [demands](http://git.guandanmaster.com) through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is complete, you can invoke the design using a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will [require](https://busanmkt.com) 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 demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:HattieArmstrong) deploying the model is offered in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra requests against the predictor:
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Implement guardrails and run inference 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 develop a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Clean up
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To avoid unwanted charges, finish the steps in this section to clean up your resources.
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Delete the [Amazon Bedrock](https://connectzapp.com) 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 designs in the navigation pane, select Marketplace deployments. +2. In the Managed implementations area, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the appropriate release: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the [SageMaker JumpStart](http://wcipeg.com) predictor
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The SageMaker JumpStart model you deployed will [sustain expenses](http://digitalmaine.net) if you leave it running. Use the following code to delete the [endpoint](https://igit.heysq.com) 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 model using Bedrock Marketplace and SageMaker JumpStart. 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 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 assists emerging generative [AI](http://121.5.25.246:3000) business build innovative services utilizing AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning efficiency of big language models. In his totally free time, Vivek takes [pleasure](http://www.stardustpray.top30009) in treking, viewing motion pictures, and [attempting](https://git.7vbc.com) various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://gitea.gumirov.xyz) Specialist Solutions Architect with the [Third-Party Model](https://kod.pardus.org.tr) Science team at AWS. His area of focus is AWS [AI](https://www.jobseeker.my) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a [Professional](https://www.paknaukris.pro) Solutions Architect dealing with generative [AI](http://www.thegrainfather.com.au) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://jotshopping.com) [AI](https://www.thempower.co.in) center. She is enthusiastic about building options that assist consumers accelerate their [AI](https://deprezyon.com) journey and unlock organization value.
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