From 2f5506f2a0dc1fe1ec45168c7566f98e0175e3a1 Mon Sep 17 00:00:00 2001 From: Anibal Dresdner Date: Fri, 14 Feb 2025 19:31:43 +0000 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..6c2ab5c --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://101.200.241.6:3000)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative [AI](https://git.fafadiatech.com) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the [distilled variations](https://gitlab.xfce.org) of the models 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://kigalilife.co.rw) that utilizes reinforcement finding out to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying function is its reinforcement learning (RL) step, which was utilized to fine-tune the model's responses beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually improving both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's geared up to break down intricate inquiries and reason through them in a detailed manner. This guided reasoning process allows the design to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the industry's attention as a [versatile text-generation](http://revoltsoft.ru3000) model that can be integrated into numerous workflows such as agents, sensible thinking and data interpretation jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) [architecture](https://melanatedpeople.net) and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:DamionNobles) is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, making it possible for efficient inference by routing inquiries to the most appropriate specialist "clusters." This approach allows the model to specialize in different problem domains while maintaining overall efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of .
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DeepSeek-R1 distilled models 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 refers to a process of training smaller sized, more efficient models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and examine designs against crucial safety criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security [controls](http://www.hakyoun.co.kr) across your generative [AI](http://www.xn--9m1b66aq3oyvjvmate.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need 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 validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit boost, produce a limitation increase demand and reach out to your account team.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Establish authorizations to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful material, and assess models against essential security requirements. You can carry out security procedures for the DeepSeek-R1 design using the Amazon Bedrock [ApplyGuardrail](https://gitlab.cranecloud.io) API. This allows you to apply guardrails to evaluate user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a [guardrail utilizing](https://pakkjob.com) the Amazon Bedrock console or the API. For the example code to create the guardrail, see the [GitHub repo](http://fcgit.scitech.co.kr).
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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](https://ambitech.com.br). If the input passes the guardrail check, it's sent out 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 result. However, if either the input or output is stepped in 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 reasoning 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 designs (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, select Model catalog under Foundation models in the navigation pane. +At the time of writing this post, you can [utilize](https://casajienilor.ro) the [InvokeModel API](http://git.itlym.cn) to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.
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The design detail page supplies vital details about the model's capabilities, rates structure, and application guidelines. You can find detailed usage directions, consisting of sample API calls and code bits for integration. The model supports various text [generation](http://hitbat.co.kr) jobs, consisting of content creation, code generation, and question answering, utilizing its support discovering optimization and CoT reasoning capabilities. +The page likewise includes release options and licensing [details](https://salesupprocess.it) to help you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a variety of instances (in between 1-100). +6. For [disgaeawiki.info](https://disgaeawiki.info/index.php/User:Mari220954) example type, pick your [circumstances type](https://xnxxsex.in). For ideal efficiency 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, including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may want to examine these settings to line up with your [company's security](https://abilliontestimoniesandmore.org) and compliance requirements. +7. Choose Deploy to start using the model.
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When the deployment is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can experiment with various prompts and adjust design criteria like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For example, content for reasoning.
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This is an excellent way to check out the model's thinking and text generation abilities before incorporating it into your applications. The play area supplies immediate feedback, assisting you understand how the design reacts to different inputs and letting you fine-tune your prompts for optimum outcomes.
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You can rapidly evaluate the design in the playground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a [guardrail utilizing](https://www.social.united-tuesday.org) the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends out a demand to produce 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) hub with FMs, built-in algorithms, and prebuilt ML options that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical methods: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the technique 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 using SageMaker 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, choose JumpStart in the navigation pane.
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The model web browser displays available designs, with details like the provider name and design abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows key details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), suggesting that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the [model card](http://101.43.151.1913000) to see the model details page.
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The design details page [consists](https://www.joboont.in) of 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 consists](https://fleerty.com) of important details, such as:
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- Model [description](https://repo.serlink.es). +- License details. +- Technical specifications. +- Usage guidelines
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Before you release the model, it's advised to evaluate the model details and license terms to verify 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 produced name or develop a custom one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting appropriate circumstances types and counts is vital for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The deployment procedure can take several minutes to finish.
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When release is complete, your endpoint status will change to InService. At this moment, the design is ready to accept reasoning demands through the endpoint. You can monitor the release development on the SageMaker [console Endpoints](https://rrallytv.com) page, which will show appropriate metrics and [status details](https://gitlab.thesunflowerlab.com). When the implementation is complete, you can invoke the model 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 begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required 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 model is offered in the Github here. You can clone the note pad 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 reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:ArleenBabbidge) you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Clean up
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To [prevent undesirable](https://kewesocial.site) charges, finish the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed 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, pick Marketplace releases. +2. In the Managed implementations section, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate 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 deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint 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 deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](http://git.yoho.cn) or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with [Amazon SageMaker](https://git.saidomar.fr) JumpStart designs, 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](http://osbzr.com) business construct ingenious services utilizing AWS services and sped up [compute](https://git.jackyu.cn). Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language models. In his complimentary time, Vivek takes pleasure in treking, seeing motion pictures, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://fujino-mori.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://www.hrdemployment.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://learninghub.fulljam.com) with the Third-Party Model Science team at AWS.
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[Banu Nagasundaram](https://www.trappmasters.com) leads item, engineering, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:JohnetteTonkin7) and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://qstack.pl:3000) center. She is [enthusiastic](http://git.z-lucky.com90) about developing options that help customers accelerate their [AI](https://castingnotices.com) journey and unlock organization value.
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