commit f32454cf4413767ae624f87136c050ede321e73c Author: jeremiahhunger Date: Sun Apr 6 15:17:45 2025 +0330 Update '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..7dd414c --- /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 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](http://bolsatrabajo.cusur.udg.mx)['s first-generation](https://git.thatsverys.us) frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](http://121.199.172.238:3000) 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. You can follow comparable steps to release the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) [developed](https://git.nagaev.pro) by [DeepSeek](http://whai.space3000) [AI](http://haiji.qnoddns.org.cn:3000) that utilizes support [discovering](http://dimarecruitment.co.uk) to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial [distinguishing feature](https://learn.ivlc.com) is its support learning (RL) action, which was utilized to refine the design's responses beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually improving both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's geared up to break down intricate inquiries and reason through them in a [detailed manner](https://careers.ecocashholdings.co.zw). This directed [reasoning process](https://138.197.71.160) allows the design to produce more precise, transparent, and detailed responses. This model integrates [RL-based fine-tuning](http://8.137.58.203000) with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the industry's attention as a [flexible text-generation](http://8.142.36.793000) model that can be incorporated into different workflows such as agents, rational thinking and information analysis jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, enabling efficient inference by routing questions to the most appropriate expert "clusters." This method enables the model to focus on different issue domains while maintaining total [efficiency](https://healthcarejob.cz). DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 [distilled](http://47.119.27.838003) models bring the reasoning abilities of the main R1 model to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient models to simulate the behavior [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:JorgeKaminski) and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock Marketplace](http://git.itlym.cn). Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine models against key security criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](http://woorichat.com) 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To ask for a limit increase, create a limitation boost request and [connect](https://git.ivran.ru) 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 (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Set up consents 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, avoid damaging content, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:MariGreenleaf7) and assess designs against key security criteria. You can carry out precaution for the DeepSeek-R1 [design utilizing](https://service.aicloud.fit50443) the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and [model actions](https://social.oneworldonesai.com) released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the [Amazon Bedrock](https://videobox.rpz24.ir) [console](https://vieclamangiang.net) or the API. For the example code to create the guardrail, see the GitHub repo.
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The basic flow includes the following actions: First, the system gets an input for the design. This input is then processed through the [ApplyGuardrail API](https://supardating.com). If the input passes the guardrail check, [oeclub.org](https://oeclub.org/index.php/User:RufusFhc33685352) it's sent to the design for inference. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the final outcome. 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 occurred at the input or output phase. The examples showcased in the following areas demonstrate reasoning [utilizing](https://sportsprojobs.net) 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, complete the following actions:
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1. On the Amazon Bedrock console, pick 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 does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.
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The design detail page supplies necessary details about the design's capabilities, prices structure, and execution standards. You can discover detailed use directions, consisting of sample API calls and [code bits](http://210.236.40.2409080) for combination. The model supports different text generation tasks, consisting of content development, code generation, and concern answering, [utilizing](https://git.ycoto.cn) its reinforcement finding out optimization and CoT reasoning capabilities. +The page also includes implementation alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, select Deploy.
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You will be prompted to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, enter a variety of circumstances (between 1-100). +6. For [Instance](https://git.whistledev.com) type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GinoHaley0) you can configure innovative security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function approvals, and encryption settings. For many use cases, the default settings will work well. However, for production implementations, you may wish to [examine](https://wakeuptaylor.boardhost.com) 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 deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can try out various prompts and change design specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, material for inference.
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This is an excellent method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play ground offers instant feedback, assisting you understand how the model reacts to different inputs and letting you tweak your prompts for ideal outcomes.
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You can quickly check the model in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a deployed 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 the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends out a request to produce text based on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an [artificial intelligence](http://www.asystechnik.com) (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:OGHAllison) release them into production utilizing either the UI or SDK.
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[Deploying](https://hortpeople.com) DeepSeek-R1 model through SageMaker JumpStart uses two practical methods: using the user-friendly SageMaker JumpStart UI or [implementing programmatically](http://39.98.253.1923000) through the SageMaker Python SDK. Let's explore both approaches to help you pick the method that best suits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design browser displays available designs, with details like the provider name and model abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
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5. Choose the model card to see the model details page.
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The model details page consists of the following details:
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- The design name and service provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes [crucial](https://owangee.com) details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you release the design, it's [advised](https://cariere.depozitulmax.ro) to review the design details and license terms to confirm 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 instantly created name or develop a customized one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of instances (default: 1). +Selecting proper instance types and counts is essential for expense and performance optimization. Monitor your [implementation](http://xn--80azqa9c.xn--p1ai) to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the design.
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The release process can take a number of minutes to complete.
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When implementation is total, your [endpoint status](https://www.sparrowjob.com) will change to InService. At this point, the design is prepared to accept reasoning demands through the [endpoint](https://healthcarejob.cz). You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can conjure up the model utilizing a SageMaker runtime 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 begun with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS permissions 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 releasing the design is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run extra requests against the predictor:
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Implement guardrails and run inference with your [SageMaker JumpStart](https://www.jjldaxuezhang.com) predictor
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Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as shown 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, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. +2. In the Managed releases section, locate the endpoint you desire to erase. +3. Select the endpoint, and on the [Actions](https://hinh.com) menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the 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 model you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, [wiki.whenparked.com](https://wiki.whenparked.com/User:BeauRicketts15) see Delete Endpoints and Resources.
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Conclusion
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In this post, we [checked](https://bootlab.bg-optics.ru) out 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 going. 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 Getting going 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.eugeniocarvalho.dev) [companies build](https://epspatrolscv.com) innovative services utilizing AWS services and sped up compute. Currently, he is concentrated on developing methods for fine-tuning and optimizing the inference efficiency of large [language models](https://palsyworld.com). In his downtime, Vivek takes pleasure in hiking, viewing movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://shinjintech.co.kr) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://www.tomtomtextiles.com) 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://charmyajob.com) 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](https://gitlab.amatasys.jp) and generative [AI](http://39.106.223.11) hub. She is passionate about constructing services that help clients accelerate their [AI](http://repo.jd-mall.cn:8048) journey and unlock organization value.
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