From cfb0e5d1587eccc3555f6e302f1e1676da887ba6 Mon Sep 17 00:00:00 2001 From: ramonitahaley1 Date: Mon, 7 Apr 2025 07:03:29 +0800 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace And Amazon SageMaker JumpStart.-.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md 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..a339e18 --- /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 reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and [it-viking.ch](http://it-viking.ch/index.php/User:PilarRqe6200607) Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://gulfjobwork.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative [AI](http://8.134.237.70:7999) ideas on AWS.
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In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock [Marketplace](https://www.drawlfest.com) and SageMaker JumpStart. You can follow comparable actions to deploy the [distilled versions](https://play.future.al) of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://gitea.chenbingyuan.com) that utilizes reinforcement discovering to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its reinforcement learning (RL) action, which was used to refine the design's actions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually boosting both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, implying it's geared up to break down complex queries and factor through them in a detailed manner. This directed [thinking procedure](http://forum.infonzplus.net) [permits](https://193.31.26.118) the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation design that can be incorporated into various workflows such as agents, rational thinking and data interpretation tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, making it possible for efficient inference by routing inquiries to the most relevant expert "clusters." This method permits the design to specialize in various issue domains while maintaining overall performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:FranchescaMbx) we will use an ml.p5e.48 xlarge instance 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 designs](https://git-web.phomecoming.com) bring the thinking 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 designs to imitate the behavior and thinking patterns of the larger DeepSeek-R1 design, 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 model, we advise deploying this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, [prevent hazardous](http://gitlab.code-nav.cn) material, and [evaluate models](https://aws-poc.xpresso.ai) against essential safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](http://8.137.89.26:3000) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you [require access](https://www.outletrelogios.com.br) to an ml.p5e [circumstances](http://bluemobile010.com). To check if you have quotas for P5e, open the Service Quotas console and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:PhillisClancy) under AWS Services, choose Amazon 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 circumstances in the AWS Region you are deploying. To request a limit boost, create 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 proper AWS Identity and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:JonnaCanipe) Gain Access To Management (IAM) approvals to use Amazon Bedrock [Guardrails](http://mengqin.xyz3000). For guidelines, see Set up approvals to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging material, and examine models against key security criteria. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This [enables](http://compass-framework.com3000) you to use guardrails to evaluate user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using 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 the guardrail check, it's sent out to the model for inference. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the final 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 happened at the input or output stage. The examples showcased in the following areas show reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not [support Converse](https://www.jobcheckinn.com) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.
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The design detail page offers [essential details](https://git.chocolatinie.fr) about the model's capabilities, prices structure, and execution standards. You can discover detailed usage directions, consisting of sample API calls and code bits for combination. The design supports numerous text generation jobs, including content production, code generation, and question answering, using its reinforcement learning optimization and CoT reasoning capabilities. +The page also consists of release options and licensing details to help you start with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, go into a number of circumstances (between 1-100). +6. For Instance type, select your circumstances type. For ideal [performance](https://www.jobmarket.ae) with DeepSeek-R1, a [GPU-based instance](http://git.z-lucky.com90) type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up innovative security and infrastructure settings, consisting of virtual private 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 examine these settings to align with your and compliance requirements. +7. Choose Deploy to begin using the design.
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When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive user interface where you can explore different prompts and adjust model parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, content for inference.
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This is an [exceptional method](http://101.132.136.58030) to explore the [design's reasoning](https://git.dev-store.ru) and text generation abilities before integrating it into your applications. The play ground supplies instant feedback, helping you comprehend how the [design reacts](https://tageeapp.com) to different inputs and letting you fine-tune your triggers for [optimum outcomes](https://jobsportal.harleysltd.com).
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You can quickly test the model in the playground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run [reasoning utilizing](https://git.ivabus.dev) guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a [released](http://supervipshop.net) DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to [develop](https://git.rggn.org) the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends a request to [generate text](https://yourrecruitmentspecialists.co.uk) 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 [services](https://equijob.de) that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:MyraCollocott58) pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
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[Deploying](https://barbersconnection.com) DeepSeek-R1 design through SageMaker JumpStart uses two practical approaches: using the instinctive SageMaker JumpStart UI or executing programmatically through the [SageMaker Python](http://git.zthymaoyi.com) SDK. Let's explore both approaches to assist you choose the technique that [finest suits](https://git.paaschburg.info) your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to [release](https://www.p3r.app) 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 pane.
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The design browser shows available models, with details like the service provider name and model abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows crucial details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if appropriate), suggesting that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to view the model details page.
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The design details page includes the following details:
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- The model name and service provider details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you deploy the model, it's recommended to evaluate the model details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, [utilize](https://groups.chat) the automatically created name or create a custom-made one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of instances (default: 1). +Selecting suitable [circumstances types](https://121gamers.com) 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 picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in [location](https://hrvatskinogomet.com). +11. Choose Deploy to release the model.
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The release procedure 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 design is prepared to accept inference demands through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can conjure up the design utilizing a [SageMaker runtime](http://tian-you.top7020) client and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the [SageMaker Python](https://www.heesah.com) SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and range 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 produce a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Clean up
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To prevent undesirable charges, complete the steps in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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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](https://voyostars.com) console, under Foundation models in the navigation pane, pick Marketplace releases. +2. In the Managed deployments section, locate the [endpoint](https://35.237.164.2) you wish to delete. +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 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker [JumpStart](https://www.trappmasters.com).
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://www.styledating.fun) business build innovative solutions utilizing AWS services and accelerated compute. Currently, he is focused on developing methods for [fine-tuning](https://wishjobs.in) and optimizing the reasoning efficiency of large language designs. In his leisure time, Vivek delights in treking, watching films, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://jollyday.club) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://convia.gt) 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](https://oros-git.regione.puglia.it) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://www.hnyqy.net:3000) center. She is enthusiastic about developing options that help [clients](https://agalliances.com) accelerate their [AI](https://git.viorsan.com) journey and unlock organization value.
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