From 6bd6557e3fec4eca88f248c07cd36a726cc041e1 Mon Sep 17 00:00:00 2001 From: Benjamin Minnick Date: Thu, 6 Feb 2025 22:37:59 +0000 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..1fae1c9 --- /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 deploy DeepSeek [AI](https://fondnauk.ru)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative [AI](https://www.meetyobi.com) ideas on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](https://www.schoenerechner.de) that utilizes support learning to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its reinforcement knowing (RL) action, which was utilized to refine the model's actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's [equipped](http://94.191.73.383000) to break down intricate queries and factor through them in a detailed manner. This assisted thinking procedure enables the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on [interpretability](https://addismarket.net) and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the market's attention as a versatile text-generation design that can be integrated into various workflows such as representatives, sensible thinking and information interpretation tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, making it possible for effective inference by routing queries to the most appropriate specialist "clusters." This method permits the design to concentrate on different problem domains while [maintaining](https://rootsofblackessence.com) overall performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release 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 reasoning abilities of the main R1 model to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient models to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and examine models against essential security criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://git.mista.ru) applications.
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Prerequisites
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To deploy 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, choose Amazon SageMaker, and verify 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 deploying. To ask for a limitation increase, produce a limitation boost request and connect 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 Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, prevent hazardous material, and assess designs against key security requirements. You can implement safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to [apply guardrails](http://82.157.77.1203000) to examine user inputs and design actions [deployed](https://gitlab.healthcare-inc.com) on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://4realrecords.com). You can create a [guardrail](https://bartists.info) using the Amazon Bedrock [console](http://116.62.118.242) or the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic circulation includes 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 out to the design for reasoning. After getting 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 intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas demonstrate inference using 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 structure](https://afrocinema.org) 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 use 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 service provider and choose the DeepSeek-R1 model.
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The design detail page supplies essential [details](https://www.indianpharmajobs.in) about the model's capabilities, rates structure, and [application standards](http://www.cl1024.online). You can discover detailed usage instructions, including sample API calls and code bits for combination. The model supports various text generation jobs, consisting of material production, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking capabilities. +The page likewise includes release options and [it-viking.ch](http://it-viking.ch/index.php/User:Heath0421670) licensing details to help you get going with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be triggered to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a variety of circumstances (between 1-100). +6. For example type, pick your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can set up advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role approvals, and encryption settings. For many use 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 using the model.
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When the deployment is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can try out various triggers and change model criteria like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, content for inference.
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This is an excellent method to explore the model's reasoning and [raovatonline.org](https://raovatonline.org/author/terryconnor/) text generation abilities before incorporating it into your applications. The playground supplies immediate feedback, helping you comprehend how the design reacts to various inputs and [letting](http://94.224.160.697990) you fine-tune your triggers for ideal outcomes.
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You can [rapidly check](http://www.kotlinx.com3000) the design in the play ground through the UI. However, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:MichaelCrocker0) to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need 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 shows how to carry out inference using a released DeepSeek-R1 model through [Amazon Bedrock](https://givebackabroad.org) utilizing the invoke_model and ApplyGuardrail API. 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](http://awonaesthetic.co.kr). After you have produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends a request to create text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through [SageMaker JumpStart](http://forum.infonzplus.net) offers two hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the method that finest matches your .
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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 produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design internet browser displays available models, with details like the company name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows key details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), indicating that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to see the model details page.
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The model details page includes the following details:
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- The model name and supplier details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you release the model, it's recommended to evaluate the model details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, use the automatically produced name or produce a custom-made one. +8. For example [type ΒΈ](http://yezhem.com9030) pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the number of circumstances (default: 1). +Selecting appropriate circumstances types and counts is vital for cost and [efficiency optimization](https://jobwings.in). Monitor your release to change 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 highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the model.
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The implementation procedure can take numerous minutes to complete.
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When [implementation](http://103.254.32.77) 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 display appropriate metrics and status details. When the deployment is total, you can conjure up the design using 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 start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for [inference programmatically](https://recrutementdelta.ca). The code for deploying the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run additional demands 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 develop 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 avoid undesirable charges, complete 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, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments. +2. In the Managed deployments section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [choose Delete](https://git.obo.cash). +4. Verify the endpoint details to make certain you're erasing the correct release: 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 deployed will sustain costs if you leave it [running](https://videofrica.com). Use the following code to delete the [endpoint](https://git.easytelecoms.fr) 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 checked out how you can access and release the DeepSeek-R1 model 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 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 assists emerging generative [AI](https://lovelynarratives.com) companies construct innovative services using AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning performance of large language designs. In his totally free time, Vivek delights in hiking, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:FranklinBreillat) seeing films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://chancefinders.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://www.ch-valence-pro.fr) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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[Jonathan Evans](http://125.ps-lessons.ru) is an Expert Solutions Architect working on generative [AI](https://willingjobs.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://8.130.72.63:18081) hub. She is enthusiastic about [building options](https://iamzoyah.com) that help customers accelerate their [AI](http://123.249.110.128:5555) journey and unlock business value.
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