commit ea45b20a87415d0c2105c3f7fa929942ccc990ac Author: tomasmundy1660 Date: Wed Feb 19 16:15:25 2025 +1100 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..ae48334 --- /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 models are available through [Amazon Bedrock](http://zaxx.co.jp) Marketplace and Amazon SageMaker JumpStart. With this launch, you can now [deploy DeepSeek](https://cozwo.com) [AI](https://git.ascarion.org)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](https://pl.velo.wiki) [concepts](https://titikaka.unap.edu.pe) on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](http://47.76.210.1863000). You can follow similar actions 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 big language design (LLM) developed by DeepSeek [AI](http://okosg.co.kr) that utilizes support finding out to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial differentiating function is its reinforcement knowing (RL) action, which was used to improve the model's responses beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually boosting both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's geared up to break down intricate queries and reason through them in a detailed manner. This guided reasoning process allows the design to produce more precise, transparent, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:IrwinCambage) and detailed answers. This design integrates RL-based [fine-tuning](https://git.micahmoore.io) with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its [wide-ranging abilities](https://svn.youshengyun.com3000) DeepSeek-R1 has recorded the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as representatives, logical thinking and information analysis jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion criteria, enabling efficient reasoning by routing queries to the most pertinent specialist "clusters." This method allows the design to focus on various issue domains while maintaining overall efficiency. 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 circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 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 on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective models to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.
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You can release DeepSeek-R1 design either through [SageMaker JumpStart](http://git.youkehulian.cn) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we [recommend deploying](https://aijoining.com) this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and assess designs against essential safety criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can [produce](http://www.topverse.world3000) multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](https://afacericrestine.ro) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you [require access](http://xn--jj-xu1im7bd43bzvos7a5l04n158a8xe.com) 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://warleaks.net) SageMaker, and confirm 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 deploying. To ask for a limitation increase, create a limit boost request and connect to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, see [Establish authorizations](https://wiki.team-glisto.com) to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, prevent harmful content, and assess models against essential security requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to [examine](https://cv4job.benella.in) user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create 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](https://lensez.info) 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 out to the model for [reasoning](https://wik.co.kr). After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the last result. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections show [inference utilizing](http://www.vokipedia.de) 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 designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the [navigation](https://git.jackbondpreston.me) pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It does not [support Converse](https://vidy.africa) APIs and other Amazon Bedrock tooling. +2. Filter for [DeepSeek](https://git.adminkin.pro) as a [service provider](https://tiktack.socialkhaleel.com) and choose the DeepSeek-R1 design.
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The design detail page provides essential details about the model's capabilities, rates structure, and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:Janessa98P) execution standards. You can discover detailed usage guidelines, including sample API calls and code bits for integration. The model supports different text generation tasks, including material creation, code generation, and question answering, using its reinforcement learning optimization and CoT thinking capabilities. +The page likewise includes implementation choices and licensing details to help you start with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:ArdisF43895155) pick Deploy.
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You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a variety of instances (in between 1-100). +6. For example type, pick your instance type. For optimum efficiency with DeepSeek-R1, a [GPU-based circumstances](https://wik.co.kr) type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For most use cases, the default settings will work well. However, for production deployments, you might desire to evaluate these [settings](https://gogocambo.com) to line up with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the deployment is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive interface where you can explore various triggers and change model parameters like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, content for inference.
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This is an exceptional way to explore the model's reasoning and text generation capabilities before integrating it into your applications. The playground provides instant feedback, assisting you comprehend how the model reacts to different inputs and letting you fine-tune your prompts for optimal results.
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You can quickly check the model in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example [demonstrates](https://ttemployment.com) how to carry out inference utilizing a released DeepSeek-R1 model through [Amazon Bedrock](https://vybz.live) using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon [Bedrock console](http://120.48.7.2503000) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends out a request to generate 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, built-in algorithms, and prebuilt ML services that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you select the method that finest fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, [choose Studio](http://139.9.60.29) in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model web browser shows available designs, with details like the company name and model capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), indicating that this model can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke 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 model 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 crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you deploy the design, it's to review the [model details](https://git.kawen.site) 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 produce a custom one. +8. For Instance type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting suitable instance types and counts is vital for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for [sustained traffic](http://106.15.120.1273000) and low latency. +10. Review all setups for accuracy. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The implementation process can take several minutes to finish.
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When release is total, your endpoint status will change to [InService](https://git.creeperrush.fun). At this moment, the model is all set to accept inference requests through the [endpoint](https://aijoining.com). You can monitor the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:CarlTabarez70) status details. When the deployment is total, you can invoke 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 utilizing 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](https://pattondemos.com) setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for [releasing](https://dessinateurs-projeteurs.com) the model is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional 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 create 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 Marketplace deployment
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If you deployed the design 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 releases. +2. In the Managed releases area, find the [endpoint](https://loveyou.az) you desire to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the proper implementation: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://git.progamma.com.ua) status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart design you deployed will sustain expenses 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 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. 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 Starting 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://www.youmanitarian.com) business build ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the inference efficiency of big language designs. In his leisure time, Vivek takes pleasure in treking, seeing motion pictures, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://www.haimimedia.cn:3001) Specialist Solutions Architect with the Third-Party Model [Science](https://git.boergmann.it) group at AWS. His location of focus is AWS [AI](http://thinkwithbookmap.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with [generative](https://gitlab.dituhui.com) [AI](http://210.236.40.240:9080) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://schubach-websocket.hopto.org) hub. She is enthusiastic about constructing options that assist customers accelerate their [AI](http://git.appedu.com.tw:3080) journey and unlock business value.
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