commit 337f3bb335fd1af3e1cfa3ff71a314d2bc611a21 Author: nicholwilliam Date: Tue Mar 11 23:47:50 2025 +0100 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..fb633b2 --- /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 models are available through Amazon Bedrock Marketplace and [Amazon SageMaker](http://123.56.247.1933000) JumpStart. With this launch, you can now deploy DeepSeek [AI](https://jobwings.in)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://nextodate.com) ideas on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://jvptube.net). You can follow similar steps to deploy the distilled versions of the designs also.
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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://sajano.com) that uses support learning to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing [feature](https://www.lightchen.info) is its reinforcement knowing (RL) step, which was utilized to improve the design's reactions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually improving both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's equipped to break down complex queries and reason through them in a detailed way. This guided thinking [process](https://apk.tw) allows the model to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user [interaction](http://121.40.194.1233000). With its comprehensive capabilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation model that can be integrated into various workflows such as representatives, logical reasoning 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 specifications, allowing effective inference by routing questions to the most pertinent expert "clusters." This method permits the model to focus on different issue domains while maintaining total performance. 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 deploy the design. 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 effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as a teacher model.
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You can deploy DeepSeek-R1 model 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, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and evaluate designs against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 [deployments](https://git.dadunode.com) on SageMaker JumpStart and [Bedrock](https://pioneerayurvedic.ac.in) Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails [tailored](http://www.tuzh.top3000) to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://letustalk.co.in) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, [select Amazon](https://git.andert.me) SageMaker, and verify 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 releasing. To request a limit increase, create a limit boost request and connect to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Set up approvals to [utilize guardrails](http://47.111.127.134) for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous content, and examine designs against essential security criteria. You can execute security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon [Bedrock console](https://git.chirag.cc) or the API. For the example code to develop the guardrail, see the GitHub repo.
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The general circulation includes the following steps: First, the system gets 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 model 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 intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or [output stage](http://kodkod.kr). The examples showcased in the following sections show 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 foundation](https://consultoresdeproductividad.com) designs (FMs) through [Amazon Bedrock](https://firefish.dev). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It does not [support Converse](http://175.178.153.226) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
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The model detail page supplies important details about the [model's](https://git.lewd.wtf) abilities, rates structure, and implementation standards. You can find detailed use guidelines, consisting of sample API calls and code bits for combination. The design supports numerous text generation jobs, consisting of content production, code generation, and question answering, using its reinforcement learning optimization and CoT reasoning capabilities. +The page also includes deployment options and licensing details to assist you get going 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](https://dessinateurs-projeteurs.com) for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an [endpoint](http://git.mutouyun.com3005) name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a number of instances (between 1-100). +6. For Instance type, choose your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service role permissions, and encryption [settings](https://wolvesbaneuo.com). For the majority of use cases, the default settings will work well. However, for production deployments, you might want to evaluate these settings to line up with your company'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 abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with different triggers and adjust model parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For instance, content for inference.
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This is an excellent way to explore the design's thinking and text generation abilities before incorporating it into your applications. The playground provides instant feedback, helping you understand how the model reacts to different inputs and letting you fine-tune your triggers for optimum results.
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You can quickly test the model in the [play ground](https://source.brutex.net) through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon [Bedrock console](http://209.141.61.263000) or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, [configures reasoning](https://esunsolar.in) criteria, and sends a request to [generate text](https://medicalstaffinghub.com) based on 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 services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 design through SageMaker JumpStart offers two hassle-free approaches: utilizing the [instinctive SageMaker](https://actv.1tv.hk) JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you pick the technique that best fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 using 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](http://182.92.251.553000) pane.
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The design browser shows available models, with details like the provider name and design abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card reveals essential details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, allowing 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 company details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the design, it's advised to review the model details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the immediately created name or create a custom one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting appropriate circumstances types and counts is important for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, [Real-time reasoning](https://seedvertexnetwork.co.ke) is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The release process can take a number of minutes to complete.
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When implementation is total, your endpoint status will alter to InService. At this moment, the model is prepared to requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is total, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a [detailed code](https://www.alkhazana.net) example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional demands 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To [prevent unwanted](https://zidra.ru) charges, complete the steps in this section to tidy 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, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11926441) choose Marketplace implementations. +2. In the Managed deployments section, find the endpoint you want to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the correct deployment: 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 deployed will [sustain expenses](https://dev.clikviewstorage.com) if you leave it running. Use the following code to delete the endpoint if you desire 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to 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 is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://maxmeet.ru) business build ingenious options using AWS services and accelerated compute. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the inference efficiency of big language models. In his spare time, Vivek enjoys hiking, enjoying movies, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://hyg.w-websoft.co.kr) [Specialist Solutions](https://www.jgluiggi.xyz) Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://gogs.gzzzyd.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](http://175.24.174.173:3000) with the Third-Party Model [Science](https://git.wo.ai) group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](http://xingyunyi.cn3000) [AI](http://git.aiotools.ovh) hub. She is passionate about constructing options that assist consumers accelerate their [AI](https://rassi.tv) journey and unlock company worth.
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