From ae0b9159fbc01a71df38e0c90c2bcde6c80e898e Mon Sep 17 00:00:00 2001 From: Andreas Zwar Date: Thu, 20 Feb 2025 03:25:59 +0800 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-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..9cbbd42 --- /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 reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and [Amazon SageMaker](http://123.207.206.1358048) JumpStart. With this launch, you can now release DeepSeek [AI](http://gogs.dev.fudingri.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](https://tube.leadstrium.com) [concepts](https://www.klaverjob.com) 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](https://tawtheaf.com) to deploy the [distilled versions](http://39.100.139.16) 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) developed by DeepSeek [AI](https://funitube.com) that uses support finding out to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating feature is its support knowing (RL) action, which was used to [fine-tune](http://106.55.61.1283000) the design's reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's geared up to break down complex inquiries and reason through them in a detailed way. This directed reasoning procedure allows the model to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured [actions](https://www.thempower.co.in) while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation model that can be integrated into [numerous workflows](http://66.112.209.23000) such as representatives, sensible thinking and information analysis jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, making it possible for efficient reasoning by routing inquiries to the most pertinent expert "clusters." This technique permits the design to focus on various problem domains while maintaining total performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 [xlarge instance](http://154.40.47.1873000) to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of [GPU memory](http://154.8.183.929080).
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more effective architectures based upon [popular](https://source.coderefinery.org) open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to simulate the behavior and [reasoning patterns](https://athleticbilbaofansclub.com) of the larger DeepSeek-R1 model, utilizing it as a teacher model.
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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 advise releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and evaluate designs against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](https://chumcity.xyz) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're using 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 limitation boost, create a limitation increase request and connect to your account team.
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Because you will be releasing this design with [Amazon Bedrock](https://forsetelomr.online) Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Establish permissions to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid harmful material, and assess designs against essential security criteria. You can carry out security procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model reactions released 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 create the guardrail, see the GitHub repo.
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The basic circulation involves the following actions: First, the system gets an input for the model. This input is then processed through the [ApplyGuardrail API](https://h2bstrategies.com). If the input passes the guardrail check, it's sent out to the design for [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:RondaJop19310) inference. After receiving the model's output, another guardrail check is applied. If the output passes this final 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 took place at the input or output phase. The examples showcased in the following sections show inference utilizing 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](https://joinwood.co.kr) 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 designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 model.
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The model detail page supplies necessary details about the model's capabilities, prices structure, and execution guidelines. You can discover detailed usage guidelines, consisting of [sample API](https://dev.fleeped.com) calls and code snippets for [combination](http://compass-framework.com3000). The design supports various text generation jobs, consisting of content production, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking capabilities. +The page likewise consists of deployment choices and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be prompted 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 (between 1-50 [alphanumeric](http://www.withsafety.net) characters). +5. For Number of circumstances, enter a variety of circumstances (in between 1-100). +6. For Instance type, pick your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up advanced security and [facilities](https://nexthub.live) settings, consisting of virtual personal cloud (VPC) networking, service function consents, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you might wish to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive interface where you can experiment with different triggers and change design specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For instance, material for inference.
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This is an exceptional method to check out the design's reasoning and text generation capabilities before incorporating it into your [applications](http://119.45.49.2123000). The play area offers immediate feedback, assisting you understand how the model responds to different inputs and letting you fine-tune your prompts for ideal results.
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You can rapidly evaluate the model in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends a request to generate 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 [services](http://121.5.25.2463000) that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you pick the method that finest matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 [utilizing SageMaker](http://175.178.113.2203000) JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design web browser shows available designs, with details like the provider name and model capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows essential details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if relevant), showing that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the model 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 includes essential 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 suggested to evaluate the design details and license terms to [verify compatibility](https://git.kicker.dev) with your use case.
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6. [Choose Deploy](http://git.chilidoginteractive.com3000) to continue with implementation.
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7. For Endpoint name, utilize the immediately produced name or develop a customized one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:LeonardoDullo29) Initial instance count, get in the variety of instances (default: 1). +Selecting suitable instance types and counts is crucial for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the design.
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The release process can take a number of minutes to complete.
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When release is total, your endpoint status will change to InService. At this point, the model is prepared to accept reasoning demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the release is complete, you can invoke the design utilizing a SageMaker runtime client and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To start with DeepSeek-R1 [utilizing](https://git.xjtustei.nteren.net) the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that [demonstrates](https://git.the-kn.com) how to release 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 run 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 predictor
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Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Tidy up
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To prevent undesirable charges, finish the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model 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 implementations area, find the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, [select Delete](https://pakkjob.com). +4. Verify the endpoint details to make certain you're erasing the right 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](https://asesordocente.com) will sustain costs if you leave it running. Use the following code to erase the endpoint if you want 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 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](https://www.klaverjob.com) business construct ingenious solutions utilizing AWS services and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) sped up calculate. Currently, he is focused on developing techniques for fine-tuning and optimizing the inference efficiency of big language models. In his downtime, Vivek takes pleasure in treking, seeing films, and [gratisafhalen.be](https://gratisafhalen.be/author/rebbeca9609/) trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.jigmedatse.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://tjoobloom.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](http://gagetaylor.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://source.lug.org.cn) center. She is enthusiastic about developing solutions that help customers accelerate their [AI](http://106.15.48.132:3880) journey and [yewiki.org](https://www.yewiki.org/User:MaisieRoldan5) unlock company value.
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