commit dfc29b0e3a76729929b532fd71bd8c45ccf449a1 Author: isiah10u419010 Date: Sun Feb 16 07:01:35 2025 +0000 Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' 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..aee02e5 --- /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, [garagesale.es](https://www.garagesale.es/author/agfjulio155/) you can now [release DeepSeek](https://git.pm-gbr.de) [AI](https://www.suntool.top)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](http://yezhem.com:9030) ideas on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://tiptopface.com) that uses support finding out to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial distinguishing feature is its support knowing (RL) action, which was utilized to refine the [model's reactions](http://git.sinoecare.com) beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073259) objectives, eventually improving both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, implying it's [equipped](http://git.gonstack.com) to break down complicated queries and reason through them in a detailed way. This assisted thinking procedure enables the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while [focusing](http://thegrainfather.com) on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be integrated into different workflows such as agents, logical thinking and information interpretation jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, allowing efficient inference by routing inquiries to the most pertinent expert "clusters." This approach permits the model to specialize in different issue domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 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 includes 8 Nvidia H200 [GPUs supplying](https://recrutevite.com) 1128 GB of GPU memory.
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DeepSeek-R1 [distilled](https://www.joboont.in) models bring the reasoning abilities of the main R1 design to more [efficient architectures](https://git.learnzone.com.cn) based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective designs to imitate the behavior 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 [surgiteams.com](https://surgiteams.com/index.php/User:Mirta17E66502287) Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and examine designs against key security requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the [ApplyGuardrail API](https://gst.meu.edu.jo). You can develop several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user [experiences](https://git.electrosoft.hr) and standardizing security controls throughout your generative [AI](https://cvbankye.com) 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 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 circumstances in the AWS Region you are releasing. To request a limit increase, produce a limit boost demand and connect to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:LesliM99556750) see Establish permissions 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 introduce safeguards, avoid harmful material, [raovatonline.org](https://raovatonline.org/author/ajadst45283/) and evaluate models against crucial security criteria. You can execute safety steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and design responses 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 general circulation includes the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for reasoning. After receiving the design's output, another guardrail check is [applied](http://45.67.56.2143030). If the output passes this last check, it's returned as the 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 [occurred](https://aji.ghar.ku.jaldi.nai.aana.ba.tume.dont.tach.me) at the input or output stage. The examples showcased in the following sections show reasoning utilizing 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://www.ahhand.com) 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, choose Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't [support Converse](http://121.40.209.823000) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.
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The design detail page offers necessary details about the capabilities, rates structure, and implementation standards. You can find detailed use instructions, consisting of sample API calls and code bits for combination. The design supports different text generation jobs, including content production, code generation, and question answering, using its support finding out optimization and CoT reasoning capabilities. +The page also consists of deployment choices and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The design ID will be [pre-populated](https://notewave.online). +4. For Endpoint name, enter an endpoint name (between 1-50 [alphanumeric](https://git.pandaminer.com) characters). +5. For Number of circumstances, enter a variety of instances (in between 1-100). +6. For Instance type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and facilities settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption [settings](http://git.spaceio.xyz). For the majority of utilize cases, the default settings will work well. However, for [production](https://gitea.mrc-europe.com) implementations, you might wish to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can experiment with various prompts and adjust design criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, content for reasoning.
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This is an exceptional way to explore the model's thinking and text generation [capabilities](https://grace4djourney.com) before integrating it into your [applications](http://120.48.141.823000). The play area supplies immediate feedback, assisting you comprehend how the model reacts to numerous inputs and letting you fine-tune your triggers for ideal outcomes.
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You can rapidly evaluate the model in the play ground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run [inference utilizing](https://jskenglish.com) guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock [console](http://git.zhiweisz.cn3000) or the API. For the example code to create the guardrail, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1077521) see the GitHub repo. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends a demand to create 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) center with FMs, built-in algorithms, and prebuilt ML options that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you choose 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 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 prompted to [produce](http://8.140.200.2363000) a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design browser displays available models, with details like the company name and model abilities.
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4. Search 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 example, Text Generation). +Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the design
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5. Choose the model card to view the model details page.
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The model details page includes the following details:
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- The model name and service provider details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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[- Model](http://update.zgkw.cn8585) description. +- License details. +- Technical specs. +- Usage standards
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Before you release the design, it's suggested to examine the [model details](https://dev-members.writeappreviews.com) and license terms to verify compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the automatically generated name or create a customized one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of circumstances (default: 1). +Selecting proper circumstances types and counts is crucial for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is [optimized](https://collegetalks.site) for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The deployment process can take a number of minutes to complete.
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When deployment is complete, your endpoint status will change to InService. At this moment, the model is prepared to accept inference demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is complete, you can invoke the design utilizing a [SageMaker runtime](https://japapmessenger.com) client and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the [SageMaker Python](https://corvestcorp.com) SDK
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To start with DeepSeek-R1 [utilizing](https://ifin.gov.so) the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference 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 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as [displayed](https://vhembedirect.co.za) in the following code:
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Clean up
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To avoid undesirable charges, complete the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the design utilizing Amazon Bedrock Marketplace, complete the following steps:
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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 area, locate the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, [select Delete](https://tubechretien.com). +4. Verify the endpoint details to make certain you're [deleting](https://git.molokoin.ru) the proper release: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://isourceprofessionals.com) status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart design you released will sustain costs if you leave it [running](https://teba.timbaktuu.com). 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 using 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 models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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://videofrica.com) business construct innovative services utilizing AWS services and sped up calculate. Currently, he is focused on developing methods for fine-tuning and optimizing the inference efficiency of big language models. In his leisure time, Vivek delights in treking, enjoying movies, and trying different cuisines.
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Niithiyn Vijeaswaran is a [Generative](https://youarealways.online) [AI](https://complete-jobs.co.uk) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://agapeplus.sg) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://www.fightdynasty.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](http://8.141.155.1833000) [AI](https://integramais.com.br) center. She is enthusiastic about constructing solutions that assist customers accelerate their [AI](https://gitlab.henrik.ninja) journey and unlock business worth.
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