commit 2e8f00ceccdf21588188885660f9bf3ffca072c3 Author: jewelhoag37132 Date: Thu Mar 13 03:07:23 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..1edb650 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon [Bedrock Marketplace](https://www.armeniapedia.org) and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://koreaeducation.co.kr)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion parameters to build, experiment, and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1118767) responsibly scale your generative [AI](https://pompeo.com) concepts on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://gitlab.vog.media). You can follow similar steps to deploy the distilled versions of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://git.iovchinnikov.ru) that utilizes support discovering to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating feature is its support knowing (RL) step, which was used to improve the design's actions beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:TammieOfficer) goals, eventually enhancing both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's equipped to break down intricate questions and factor through them in a detailed way. This assisted thinking procedure enables the model to produce more accurate, transparent, and [detailed answers](https://source.brutex.net). This model combines RL-based fine-tuning with CoT capabilities, aiming to [produce structured](http://h2kelim.com) actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as agents, rational thinking and information [interpretation](https://bytes-the-dust.com) tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion [parameters](https://eelam.tv) in size. The MoE architecture enables activation of 37 billion parameters, allowing effective inference by routing questions to the most relevant professional "clusters." This [technique permits](https://www.locumsanesthesia.com) the design to focus on different issue domains while maintaining total performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 [distilled](http://101.33.234.2163000) designs bring the reasoning capabilities of the main R1 model to more effective architectures based upon [popular](https://bestremotejobs.net) open models like Qwen (1.5 B, 7B, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to mimic the [behavior](https://git.polycompsol.com3000) and thinking patterns of the bigger DeepSeek-R1 design, utilizing 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 model, we recommend deploying this model with guardrails in [location](https://sosmed.almarifah.id). In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and evaluate models against key safety criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user [experiences](http://47.103.91.16050903) and [standardizing safety](https://git.magesoft.tech) controls across your generative [AI](http://git.1473.cn) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're 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 limitation increase, create a limitation increase request and reach out to your account team.
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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) permissions to use Amazon Bedrock Guardrails. For instructions, see Establish permissions 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 hazardous material, and examine designs against key safety requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to [examine](http://101.43.151.1913000) user inputs and model reactions 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 general flow 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 design for inference. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the final result. However, if either the input or output is intervened by the guardrail, a [message](http://162.19.95.943000) is returned suggesting the nature of the [intervention](https://hatchingjobs.com) and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) whether it took place at the input or output phase. The examples showcased in the following areas show inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [provider](https://git.electrosoft.hr) and pick the DeepSeek-R1 model.
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The design detail page supplies important details about the design's capabilities, rates structure, and application guidelines. You can find detailed use directions, including sample API calls and code snippets for combination. The model supports numerous text generation jobs, consisting of material production, code generation, and concern answering, using its [reinforcement discovering](http://34.236.28.152) optimization and CoT thinking abilities. +The page also consists of deployment options and licensing details to assist you get begun with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select 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, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=995449) go into an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a number of instances (between 1-100). +6. For Instance type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [suggested](http://orcz.com). +Optionally, you can set up advanced security and facilities settings, [consisting](http://82.156.184.993000) of virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for [production](https://223.130.175.1476501) releases, you might want to examine these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can try out different triggers and adjust model parameters like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For example, material for reasoning.
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This is an excellent method to check out the model's thinking and text generation capabilities before incorporating it into your applications. The play ground provides instant feedback, assisting you comprehend how the design reacts to various inputs and letting you tweak your prompts for [optimum](https://flixtube.org) results.
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You can rapidly evaluate the design in the playground through the UI. However, to invoke the deployed design 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 how to carry out inference using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and [ApplyGuardrail API](http://xn--ok0b850bc3bx9c.com). You can create a guardrail utilizing the [Amazon Bedrock](https://maarifatv.ng) console or the API. For the example code to develop 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](https://gitea.nasilot.me) customer, configures reasoning criteria, and sends out a demand to create [text based](https://napolifansclub.com) 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) center with FMs, integrated algorithms, and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1337957) prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, 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: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the approach that best suits 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio 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 design internet browser shows available designs, with details like the [provider](https://site4people.com) name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows key details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +[Bedrock Ready](https://tokemonkey.com) badge (if suitable), indicating that this model can be [registered](https://textasian.com) with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the [model card](https://natgeophoto.com) to view the design details page.
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The design details page includes the following details:
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- The design name and company 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. +- License details. +- Technical specs. +- Usage standards
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Before you deploy the model, 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 proceed with release.
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7. For Endpoint name, use the automatically generated name or create a custom one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of circumstances (default: 1). +Selecting proper [instance types](https://applykar.com) and counts is important for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that [network seclusion](http://115.159.107.1173000) remains in place. +11. Choose Deploy to deploy the model.
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The deployment process can take several minutes to finish.
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When implementation is total, your endpoint status will alter to InService. At this point, the model is ready to accept inference demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is complete, you can invoke the design utilizing a SageMaker runtime client and incorporate 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 set up the SageMaker Python SDK and make certain you have the required AWS authorizations 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 releasing the model is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement guardrails and run inference with your [SageMaker JumpStart](https://job-maniak.com) predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:MarshallEscamill) execute it as [revealed](https://wiki.dulovic.tech) in the following code:
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Tidy up
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To avoid [undesirable](https://genzkenya.co.ke) charges, finish the steps in this section to tidy up your [resources](https://geetgram.com).
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Delete the Amazon Bedrock Marketplace release
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If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations. +2. In the Managed implementations section, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the right 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 design you deployed will sustain costs if you leave it running. Use the following code to erase 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 release 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 designs, SageMaker JumpStart pretrained models, Amazon SageMaker [JumpStart](https://youtubegratis.com) Foundation Models, Amazon Bedrock Marketplace, and Getting started 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://git.getmind.cn) business construct innovative services utilizing AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning performance of big language models. In his downtime, Vivek delights in hiking, [enjoying motion](https://source.ecoversities.org) pictures, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://gogs.k4be.pl) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://cwscience.co.kr) 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 dealing with generative [AI](https://kryza.network) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.execafrica.com) center. She is enthusiastic about developing services that assist consumers accelerate their [AI](http://git.bkdo.net) journey and unlock company worth.
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