commit d1d0a49839efaa7aabff7da3deffc2012c9e491e Author: tammymurr19365 Date: Thu Feb 6 18:27:48 2025 +0300 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..14ca75f --- /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 Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://kurva.su)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](http://139.224.213.4:3000) ideas on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to [release](http://recruitmentfromnepal.com) the distilled versions of the designs 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](https://mypungi.com) that utilizes support discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying function is its support learning (RL) step, which was used to refine the design's actions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt more to user feedback and objectives, ultimately enhancing both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it's equipped to break down intricate queries and reason through them in a detailed way. This assisted reasoning procedure allows the design to produce more accurate, transparent, and [detailed responses](https://eet3122salainf.sytes.net). This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, rational reasoning and information interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture [enables](https://cn.wejob.info) activation of 37 billion specifications, enabling efficient inference by routing inquiries to the most appropriate specialist "clusters." This method permits the design to concentrate on various issue domains while maintaining 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 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 design to more efficient 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 sized, more effective designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 model, 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 design, we suggest releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and assess models against essential security requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://yourecruitplace.com.au) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate 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, produce a limit increase request and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) reach out 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 directions, see Set up consents to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful material, and [evaluate designs](http://128.199.125.933000) against key safety requirements. You can implement security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and [design reactions](http://bingbinghome.top3001) deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or [pediascape.science](https://pediascape.science/wiki/User:KristanLightfoot) the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic flow involves the following steps: [fishtanklive.wiki](https://fishtanklive.wiki/User:GiuseppeXve) 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 out to the design for inference. After getting the design's output, another guardrail check is applied. 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 suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon [Bedrock Marketplace](https://git.lotus-wallet.com) provides you access to over 100 popular, emerging, and specialized structure 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](https://digital-field.cn50443) console, select Model catalog under [Foundation](https://sowjobs.com) models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for [DeepSeek](https://plamosoku.com) as a [provider](https://yourecruitplace.com.au) and pick the DeepSeek-R1 design.
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The model detail page provides necessary details about the [model's](https://scm.fornaxian.tech) capabilities, rates structure, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ShellieGenders) and application guidelines. You can find detailed usage instructions, consisting of sample API calls and code snippets for integration. The design supports different text generation tasks, including material production, code generation, and concern answering, using its reinforcement finding out optimization and CoT reasoning abilities. +The page likewise includes release alternatives and licensing details to help you get started with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of circumstances (between 1-100). +6. For example type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [recommended](http://60.204.229.15120080). +Optionally, you can set up advanced security and infrastructure settings, consisting of [virtual private](http://120.26.108.2399188) cloud (VPC) networking, service role permissions, and encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you might desire to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can try out various prompts and adjust design criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For instance, content for reasoning.
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This is an exceptional way to check out the design's thinking and text generation capabilities before integrating it into your applications. The play ground offers immediate feedback, helping you understand how the model reacts to various inputs and letting you fine-tune your prompts for optimum outcomes.
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You can [rapidly](https://gitea.robertops.com) test the model in the play area through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require 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 carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 implement guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends out a demand to create text 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, built-in algorithms, and prebuilt ML options 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 data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free approaches: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the technique that finest suits your needs.
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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, [pick Studio](https://audioedu.kyaikkhami.com) in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the [navigation pane](https://www.ksqa-contest.kr).
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The model internet browser shows available models, with details like the provider name and design abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows essential details, including:
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- Model name +- [Provider](https://eschoolgates.com) name +- Task category (for example, Text Generation). +Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to see the model details page.
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The model details page includes the following details:
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- The design name and company details. +Deploy button to release 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 requirements. +- Usage guidelines
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Before you deploy the design, it's recommended to review the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, use the instantly created name or create a custom-made one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of circumstances (default: 1). +Selecting proper instance types and counts is vital for expense and performance optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to [release](http://mooel.co.kr) the design.
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The deployment procedure can take several minutes to finish.
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When implementation is complete, your endpoint status will alter to InService. At this point, the model is all set to accept reasoning requests through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the [implementation](https://lafffrica.com) is complete, you can invoke the model using a [SageMaker runtime](https://harborhousejeju.kr) client and integrate 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 require to install the [SageMaker Python](http://autogangnam.dothome.co.kr) SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook 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 [inference](https://ehrsgroup.com) 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 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 prevent unwanted charges, finish the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the design using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. +2. In the Managed implementations area, find the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, choose 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](https://ravadasolutions.com) you released will sustain costs if you leave it [running](https://talentlagoon.com). Use the following code to erase 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 checked out how you can access and release 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 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://124.70.149.18:10880) companies develop ingenious solutions using AWS services and accelerated calculate. Currently, he is [focused](https://www.garagesale.es) on developing strategies for fine-tuning and optimizing the reasoning efficiency of big language designs. In his complimentary time, Vivek enjoys hiking, enjoying motion pictures, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://hektips.com) Specialist Solutions Architect with the Third-Party Model [Science](https://edge1.co.kr) group at AWS. His area of focus is AWS [AI](https://mastercare.care) 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 working on generative [AI](http://88.198.122.255:3001) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://repo.bpo.technology) center. She is enthusiastic about building solutions that help consumers accelerate their [AI](https://gitlab.oc3.ru) journey and unlock company value.
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