commit 89be112a82d1bc676d4ffc8638fe923af26277f9 Author: brodieferri20 Date: Wed Mar 5 06:22:38 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..db854b1 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, [wavedream.wiki](https://wavedream.wiki/index.php/User:Natalie6866) we are excited 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 release DeepSeek [AI](https://gitlab.innive.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](http://47.92.109.230:8080) concepts on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models too.
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[Overview](https://boonbac.com) of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](http://8.140.200.236:3000) that uses reinforcement learning to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing function is its reinforcement learning (RL) action, which was used to fine-tune the design's reactions beyond the standard pre-training and tweak [process](https://kigalilife.co.rw). By [incorporating](https://gitr.pro) RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually improving both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, meaning it's geared up to break down [intricate inquiries](https://gitea.potatox.net) and reason through them in a detailed way. This directed reasoning procedure enables the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured [actions](https://internship.af) while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, sensible reasoning and data interpretation 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 of 37 billion criteria, making it possible for effective inference by routing queries to the most appropriate professional "clusters." This [approach](https://www.virfans.com) allows the design to focus on different issue domains while maintaining total [effectiveness](https://my-estro.it). DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 [distilled models](http://sujongsa.net) bring the reasoning abilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 model, using 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 releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and examine designs against key safety requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your [generative](https://talktalky.com) [AI](https://www.klartraum-wiki.de) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm 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 you are releasing. To request a limit boost, develop a limitation increase request 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 correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Establish authorizations to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous material, and evaluate designs against key security criteria. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and design responses deployed 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 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 model. This input is then processed through the ApplyGuardrail API. If the [input passes](https://www.careermakingjobs.com) the guardrail check, it's sent out to the model for inference. After getting the model's output, another [guardrail check](https://wiki.piratenpartei.de) is applied. If the [output passes](https://geniusactionblueprint.com) 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 suggesting the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections show inference utilizing 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](https://git.laser.di.unimi.it) designs (FMs) through [Amazon Bedrock](https://134.209.236.143). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the [Amazon Bedrock](https://clickcareerpro.com) console, pick Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.
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The model detail page supplies necessary details about the design's abilities, rates structure, and application guidelines. You can find detailed use instructions, consisting of sample API calls and code snippets for combination. The model supports different text generation tasks, including content production, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking capabilities. +The page also includes deployment choices and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of circumstances (in between 1-100). +6. For example type, select your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [suggested](http://unired.zz.com.ve). +Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service role authorizations, and file encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might desire to review these [settings](https://x-like.ir) to line up with your [organization's security](http://www.origtek.com2999) and compliance requirements. +7. Choose Deploy to begin using the model.
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When the release is complete, you can check 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 try out different prompts and adjust design specifications like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For instance, material for inference.
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This is an outstanding method to explore the model's thinking and text generation abilities before integrating it into your applications. The playground supplies instant feedback, assisting you comprehend how the design reacts to various inputs and letting you fine-tune your triggers for [optimal outcomes](http://47.119.160.1813000).
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You can quickly test the model in the play area 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 released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, [configures inference](http://www.stardustpray.top30009) parameters, and sends a demand to produce text based 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 services that you can deploy 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 using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free approaches: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both [techniques](http://advance5.com.my) to assist you select the method that finest fits 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, [choose Studio](http://test-www.writebug.com3000) in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model web browser displays available designs, with details like the supplier name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals crucial details, consisting of:
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[- Model](https://iklanbaris.id) name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if applicable), indicating that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the [design card](https://blogram.online) to view the [design details](https://www.medexmd.com) page.
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The design details page includes the following details:
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- The model name and company details. +[Deploy button](http://83.151.205.893000) to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you deploy the design, it's advised to review the model details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the immediately created name or produce a custom-made one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of instances (default: 1). +Selecting proper 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 optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The deployment process can take a number of minutes to finish.
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When release is complete, your [endpoint status](https://jobiaa.com) will alter to InService. At this point, the model is all set to accept inference demands through the endpoint. You can monitor the release development on the SageMaker console [Endpoints](https://actu-info.fr) page, which will display appropriate metrics and status details. When the deployment is total, you can conjure up the design using a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the permissions and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run additional demands against the predictor:
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[Implement guardrails](https://www.flughafen-jobs.com) and run inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock [console](http://47.56.181.303000) or the API, and implement it as shown in the following code:
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Clean up
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To [prevent undesirable](https://wiki.monnaie-libre.fr) charges, complete the steps in this area to clean up your resources.
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Delete the [Amazon Bedrock](http://152.136.187.229) Marketplace implementation
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If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. +2. In the Managed implementations section, find 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 correct 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 released will sustain costs if you leave it running. Use the following code to erase the endpoint if you want to stop [sustaining charges](https://accountshunt.com). For more details, see [Delete Endpoints](https://www.dailynaukri.pk) 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart [pretrained](http://139.224.253.313000) 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](http://123.206.9.27:3000) companies construct innovative services using AWS services and sped up calculate. Currently, he is [focused](https://www.joinyfy.com) on developing techniques for fine-tuning and optimizing the reasoning performance of large language designs. In his totally free time, Vivek delights in hiking, watching motion pictures, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://dev.yayprint.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://lesstagiaires.com) accelerators (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](https://git.dev-store.ru) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://wiki.vst.hs-furtwangen.de) hub. She is passionate about building solutions that assist consumers accelerate their [AI](https://bestwork.id) journey and [unlock service](https://www.ausfocus.net) worth.
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