From 9aea0e17b51a05e2d5d3a9d41f94c69814c7ffcf Mon Sep 17 00:00:00 2001 From: Allison Macalister Date: Thu, 27 Feb 2025 01:54:53 +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..afa98e2 --- /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://47.92.26.237)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](http://macrocc.com:3000) ideas on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://tyciis.com) that utilizes support finding out to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating [function](https://clinicial.co.uk) is its support learning (RL) step, which was used to fine-tune the design's reactions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, indicating it's equipped to break down intricate inquiries and reason through them in a detailed way. This directed thinking process permits the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation model that can be integrated into various workflows such as agents, rational thinking and [data analysis](http://git.daiss.work) jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, making it possible for efficient inference by routing queries to the most appropriate specialist "clusters." This method permits the design to focus on various problem domains while maintaining general effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking [abilities](https://nuswar.com) of the main R1 design to more effective architectures based upon [popular](https://sosmed.almarifah.id) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient designs to mimic the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as a teacher design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and evaluate designs against key security criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous [guardrails tailored](https://svn.youshengyun.com3000) to various usage cases and use them to the DeepSeek-R1 model, [improving](http://1.15.150.903000) user experiences and standardizing safety controls across your generative [AI](http://forum.rcsubmarine.ru) 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 inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. 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, develop a limitation increase request and reach out to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) permissions to use [Amazon Bedrock](http://118.190.145.2173000) Guardrails. For instructions, see Establish consents to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent harmful material, and assess designs against key safety requirements. You can carry out security measures for the DeepSeek-R1 model utilizing the [Amazon Bedrock](https://git.jerrita.cn) ApplyGuardrail API. This enables you to use guardrails to examine user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a [guardrail utilizing](http://www.boutique.maxisujets.net) the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic flow includes the following actions: First, the system receives an input for the model. 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 output, another guardrail check is used. If the output passes this last check, it's returned as the 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 happened at the input or output stage. The examples showcased in the following sections demonstrate reasoning utilizing 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 designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, choose 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 does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.
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The design detail page supplies vital details about the design's abilities, rates structure, and execution standards. You can find detailed use guidelines, including sample API calls and code bits for combination. The model supports different text generation jobs, consisting of material creation, code generation, and concern answering, using its reinforcement learning optimization and CoT reasoning capabilities. +The page also consists of deployment alternatives and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select 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, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a variety of circumstances (between 1-100). +6. For example type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and infrastructure settings, including virtual [private](https://bikapsul.com) cloud (VPC) networking, service role authorizations, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you may wish to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive interface where you can explore various triggers and adjust model parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For instance, material for reasoning.
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This is an outstanding way to explore the design's reasoning and text generation capabilities before incorporating it into your applications. The play ground supplies immediate feedback, assisting you comprehend how the design reacts to various inputs and letting you [fine-tune](https://www.jobzalerts.com) your triggers for ideal results.
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You can quickly test the design in the play area through the UI. However, to conjure up the released model 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 demonstrates how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073364) the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:JosephSecrest8) sets up reasoning parameters, and sends out a demand to create 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 (ML) hub with FMs, built-in algorithms, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:IslaVandermark) and prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://www.florevit.com) models to your use 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 offers 2 hassle-free methods: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to assist you choose the approach that best fits your [requirements](https://amore.is).
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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 triggered to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model browser shows available designs, with details like the company name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows essential details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), showing that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to view the model details page.
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The design details page consists of the following details:
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- The design name and service provider details. +Deploy button to release the model. +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 requirements. +- Usage standards
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Before you deploy the design, it's advised to examine the model details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, utilize the automatically created name or develop a custom-made one. +8. For example type ΒΈ choose a [circumstances type](https://calciojob.com) (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of instances (default: 1). +Selecting suitable instance types and counts is crucial for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for [accuracy](https://www.rozgar.site). For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that [network seclusion](http://gitlab.hanhezy.com) remains in location. +11. Choose Deploy to deploy the model.
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The release process can take several minutes to complete.
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When release is total, your endpoint status will alter to [InService](https://www.niveza.co.in). At this moment, the design is prepared to accept inference demands through the endpoint. You can monitor 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 model using a SageMaker runtime client and incorporate 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 using the SageMaker Python SDK, you will need to install the [SageMaker](https://gitlab.optitable.com) 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 [reasoning programmatically](http://47.94.142.23510230). The code for [deploying](https://rami-vcard.site) the design is offered in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra 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 produce a guardrail using the Amazon Bedrock console or the API, and execute it as [displayed](https://gitea.mrc-europe.com) in the following code:
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Clean up
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To avoid undesirable charges, complete the [actions](https://gitea.lelespace.top) in this area to tidy 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, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace [implementations](https://git.gday.express). +2. In the Managed releases section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the correct 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](https://sugardaddyschile.cl). Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see [Delete Endpoints](https://www.hue-max.ca) and Resources.
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Conclusion
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In this post, we [checked](https://janhelp.co.in) out 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 get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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://equijob.de) business construct innovative services using AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the inference efficiency of big language models. In his spare time, Vivek enjoys treking, seeing films, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://thunder-consulting.net) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://sanaldunyam.awardspace.biz) 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://62.234.201.16) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.uzavr.ru) hub. She is passionate about constructing options that help customers accelerate their [AI](http://162.14.117.234:3000) journey and [wiki.myamens.com](http://wiki.myamens.com/index.php/User:RachelSantos0) unlock company value.
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