commit 3009590bed5d5defbf4d741704b80405313d01b3 Author: tiffaniezr0898 Date: Wed Feb 19 09:57:44 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..3a751f3 --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://mysazle.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:RenateGovan1811) responsibly scale your generative [AI](https://www.sewosoft.de) ideas on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://mcn-kw.com) that utilizes reinforcement learning to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying function is its support knowing (RL) step, which was utilized to fine-tune the design's reactions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, [implying](https://git.purplepanda.cc) it's geared up to break down [complicated questions](http://42.192.95.179) and factor through them in a detailed manner. This directed thinking process allows the model to produce more accurate, transparent, and detailed answers. This design integrates [RL-based fine-tuning](https://www.cbl.aero) with CoT capabilities, aiming to produce structured reactions while focusing on [interpretability](https://corevacancies.com) and user [interaction](https://www.workinternational-df.com). With its comprehensive capabilities DeepSeek-R1 has recorded the market's attention as a [flexible text-generation](https://gl.vlabs.knu.ua) design that can be integrated into numerous workflows such as representatives, rational thinking and information analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion [criteria](http://git.qwerin.cz) in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient reasoning by routing questions to the most appropriate expert "clusters." This method permits the model to specialize in various issue domains while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model 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 models to mimic the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as an instructor design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and examine models against essential security requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:JanessaDamron28) Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](http://www.maxellprojector.co.kr) 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 inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for [endpoint](https://gitlab.dituhui.com) use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are [releasing](http://42.192.69.22813000). To ask for a limit boost, produce a limit increase demand 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 appropriate AWS Identity and Gain Access To [Management](http://git.tederen.com) (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, 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 permits you to introduce safeguards, prevent harmful content, and evaluate designs against key security requirements. You can carry out security procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and design reactions 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 develop the guardrail, see the GitHub repo.
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The basic [flow involves](https://yourgreendaily.com) the following steps: 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 to the design for reasoning. After receiving the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate 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 foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, select Model brochure under Foundation models in the navigation pane. +At the time of [writing](https://flixtube.info) this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and pick the DeepSeek-R1 design.
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The design detail page provides necessary details about the model's capabilities, prices structure, and execution standards. You can discover detailed usage directions, including sample API calls and code snippets for combination. The design supports numerous text generation tasks, [wiki.whenparked.com](https://wiki.whenparked.com/User:HarveyMintz4360) including material development, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking capabilities. +The page also consists of deployment options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to configure 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 [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) Number of instances, get in a variety of instances (in between 1-100). +6. For Instance type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For most use cases, the default settings will work well. However, for production implementations, you may desire to examine these settings to line up with your organization'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 area. +8. Choose Open in playground to access an interactive user interface where you can experiment with different prompts and adjust design specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, content for reasoning.
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This is an excellent way to explore the design's reasoning and text generation abilities before integrating it into your applications. The playground supplies immediate feedback, assisting you understand how the model reacts to various inputs and [letting](http://1.14.71.1033000) you tweak your triggers for optimum outcomes.
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You can rapidly check the design in the playground through the UI. However, to conjure up the [deployed model](https://grailinsurance.co.ke) 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 shows how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to [produce](http://lty.co.kr) the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_[runtime](https://kaymack.careers) client, sets up reasoning parameters, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:JerriRabinovitch) and sends 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 solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free approaches: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you pick the technique that best fits your requirements.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](https://glhwar3.com) UI
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Complete the following steps to release 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 to develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model web browser displays available designs, with details like the service provider name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows key details, consisting of:
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- Model name +- Provider name +- Task classification (for example, [yewiki.org](https://www.yewiki.org/User:AlfonzoMiramonte) Text Generation). +Bedrock Ready badge (if appropriate), [ratemywifey.com](https://ratemywifey.com/author/xjstrudi716/) suggesting that this design can be registered with Amazon Bedrock, [permitting](https://sondezar.com) you to utilize Amazon Bedrock APIs to conjure up the design
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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 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](http://47.97.159.1443000). +- License details. +- Technical specs. +- Usage guidelines
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Before you release the design, it's suggested to examine the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, utilize the instantly generated name or produce a custom-made one. +8. For [Instance type](https://bnsgh.com) ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of [instances](http://www.carnevalecommunity.it) (default: 1). +[Selecting suitable](http://www.shopmento.net) [circumstances types](http://1.117.194.11510080) and counts is vital for cost and efficiency 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 [location](https://git.rankenste.in). +11. Choose Deploy to deploy the design.
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The implementation procedure can take a number of minutes to complete.
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When deployment is complete, your endpoint status will alter to InService. At this moment, the design is ready to accept inference demands through the endpoint. You can keep an eye on the implementation development on the [SageMaker](http://git.anyh5.com) console Endpoints page, which will display relevant metrics and status details. When the implementation is total, you can conjure up the model 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 install the SageMaker Python SDK and make certain you have the required AWS consents 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 design is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional demands against the predictor:
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Implement guardrails and run reasoning 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 utilizing the Amazon Bedrock console or the API, and implement it as [displayed](https://www.cbl.aero) in the following code:
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Tidy up
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To [prevent undesirable](https://gitea.daysofourlives.cn11443) charges, finish the steps in this area to clean 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 designs in the navigation pane, select Marketplace implementations. +2. In the Managed deployments area, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the correct release: 1. Endpoint name. +2. Model name. +3. [Endpoint](http://116.198.225.843000) 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 designs, 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](http://httelecom.com.cn:3000) business build ingenious services using AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and [enhancing](https://sangha.live) the inference performance of large [language](http://47.107.92.41234) models. In his spare time, Vivek delights in hiking, seeing movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://114.111.0.104:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://jobstoapply.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 [AI](http://ipc.gdguanhui.com:3001) with the Third-Party Model Science group 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](http://47.97.6.9:8081) center. She is passionate about constructing services that help customers [accelerate](https://git.iovchinnikov.ru) their [AI](https://community.cathome.pet) journey and unlock organization worth.
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