From 11cd926c7836d6095804386affb27729ee86e23b Mon Sep 17 00:00:00 2001 From: Anthony Ewart Date: Thu, 20 Feb 2025 03:01:04 +0600 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..2a31cd4 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are thrilled to announce that DeepSeek R1 distilled Llama and Qwen [designs](http://152.136.187.229) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://rejobbing.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to develop, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:Margareta19E) experiment, and [responsibly scale](https://inspirationlift.com) your [generative](http://115.182.208.2453000) [AI](http://www.jobteck.co.in) concepts 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 comparable steps to deploy the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://teachersconsultancy.com) that uses support learning to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A [crucial](https://kandidatez.com) differentiating feature is its support learning (RL) action, which was utilized to improve the design's responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it's equipped to break down complex questions and factor through them in a detailed manner. This guided reasoning procedure enables the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be integrated into different workflows such as agents, logical thinking and data interpretation tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, allowing efficient inference by routing queries to the most relevant professional "clusters." This method enables the model to focus on different [issue domains](https://teachersconsultancy.com) while maintaining total 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 model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design 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 efficient models to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 design, using it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock](http://hjl.me) Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and assess models against [essential security](https://git.cloud.krotovic.com) requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](http://171.244.15.68:3000) 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 instance in the AWS Region you are releasing. To ask for a limit increase, develop a limit boost [request](http://116.203.108.1653000) and reach out to your account group.
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Because you will be deploying this model with [Amazon Bedrock](https://labz.biz) Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Set up consents 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 present safeguards, prevent damaging content, and assess models against essential security [requirements](https://www.facetwig.com). You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce 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 includes the following actions: First, the system receives an input for the design. This input is then processed through the [ApplyGuardrail API](http://deve.work3000). If the input passes the [guardrail](https://hcp.com.gt) check, it's sent to the design for inference. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's [returned](http://ev-gateway.com) as the last outcome. 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 occurred 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](https://duyurum.com) Marketplace provides you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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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 company and choose the DeepSeek-R1 design.
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The model detail page supplies vital details about the design's capabilities, rates structure, and execution guidelines. You can find detailed usage instructions, including sample API calls and code bits for integration. The model supports various text generation tasks, consisting of material creation, code generation, and question answering, utilizing its reinforcement discovering optimization and [CoT thinking](https://git.j.co.ua) capabilities. +The page likewise [consists](https://git-web.phomecoming.com) of release choices and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a number of circumstances (in between 1-100). +6. For example type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is [suggested](https://rocksoff.org). +Optionally, you can configure innovative security and [infrastructure](https://git.rungyun.cn) settings, consisting of virtual private cloud (VPC) networking, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:StefanValentino) service role permissions, and file encryption settings. For [pediascape.science](https://pediascape.science/wiki/User:GitaLemaster) the majority of use cases, the default settings will work well. However, for production releases, you may wish to examine these settings to line up with your company's security and [compliance requirements](https://cagit.cacode.net). +7. Choose Deploy to begin using the model.
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When the implementation is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can explore different prompts and adjust model criteria like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For instance, content for reasoning.
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This is an exceptional method to check out the design's reasoning and text generation abilities before integrating it into your applications. The playground provides immediate feedback, assisting you comprehend how the model reacts to numerous inputs and letting you fine-tune your prompts for ideal results.
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You can quickly evaluate the design in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require 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 perform reasoning utilizing a released DeepSeek-R1 design through [Amazon Bedrock](http://193.30.123.1883500) 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 create 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 customer, sets up reasoning specifications, and sends a request to generate text based upon 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](https://mulaybusiness.com) algorithms, and prebuilt ML services that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models 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 design through SageMaker JumpStart offers two hassle-free approaches: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the technique that finest fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy 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 prompted to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design web browser shows available models, with details like the service provider name and model abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card reveals essential details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, enabling you to use 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 model details page includes the following details:
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- The design name and company details. +Deploy button to deploy the model. +About and [Notebooks tabs](http://1.92.66.293000) with detailed details
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The About tab includes essential details, such as:
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- Model description. +- License [details](https://topcareerscaribbean.com). +- Technical specifications. +- Usage guidelines
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Before you release the design, it's suggested to review the model details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the immediately generated name or develop a customized one. +8. For [Instance type](https://gajaphil.com) ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of instances (default: 1). +Selecting proper instance types and counts is vital for cost and [efficiency optimization](http://internetjo.iwinv.net). 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](http://182.230.209.608418) and [low latency](https://git.bwt.com.de). +10. Review all setups for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. [Choose Deploy](http://47.112.158.863000) to release the design.
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The deployment process can take numerous minutes to complete.
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When deployment is complete, your endpoint status will alter to InService. At this point, the design is all set to accept reasoning demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is total, you can up the model using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get started 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 consents and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for [releasing](http://47.107.153.1118081) the model 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 demands against the predictor:
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Implement [guardrails](http://59.57.4.663000) and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing 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 prevent undesirable charges, complete the steps in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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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 models in the navigation pane, pick Marketplace implementations. +2. In the Managed releases section, find the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're [deleting](https://dalilak.live) the appropriate 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 design you deployed will sustain costs 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 release the DeepSeek-R1 [model utilizing](http://recruitmentfromnepal.com) 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://www.a34z.com) companies develop innovative options using AWS services and accelerated compute. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the reasoning performance of large [language](https://newsfast.online) models. In his leisure time, Vivek enjoys hiking, enjoying films, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://hankukenergy.kr) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://ccconsult.cn:3000) 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](http://78.108.145.23:3000) with the Third-Party Model Science team 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](https://gitea.sitelease.ca:3000) hub. She is enthusiastic about constructing services that assist consumers accelerate their [AI](https://filmcrib.io) journey and unlock organization worth.
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