commit 3aa4593698b7b227a0005f8a934e5ae53436ef8d Author: Alphonse Hoffnung Date: Fri Feb 21 03:51:19 2025 +0800 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..0153af4 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://medhealthprofessionals.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and [properly scale](http://gagetaylor.com) your generative [AI](https://tintinger.org) concepts on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions 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://rna.link) that utilizes support discovering to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating function is its support knowing (RL) action, which was utilized to improve the model's actions beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, implying it's equipped to break down complex queries and reason through them in a detailed way. This assisted reasoning procedure allows the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user [interaction](https://aceme.ink). With its extensive abilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation model that can be [integrated](https://servergit.itb.edu.ec) into numerous workflows such as representatives, logical thinking and information interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, making it possible for efficient inference by routing inquiries to the most relevant specialist "clusters." This approach enables the model to specialize in various issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more [effective architectures](https://music.michaelmknight.com) based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective models to simulate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as a teacher model.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and evaluate designs against key security criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](http://109.195.52.92:3000) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you [require access](https://test.bsocial.buzz) to an ml.p5e [instance](https://blablasell.com). To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for [endpoint](https://git.chartsoft.cn) use. 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, create 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 appropriate AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Set up authorizations to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging material, and assess designs against key [safety requirements](https://jobs.but.co.id). You can carry out security measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail [utilizing](https://gitlab.dangwan.com) 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 flow involves the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](http://barungogi.com) check, it's sent to the design for inference. After receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the [outcome](http://47.92.109.2308080). However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas show inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure 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 models in the [navigation pane](https://sansaadhan.ipistisdemo.com). +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 provider and choose the DeepSeek-R1 model.
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The design detail page offers important details about the model's capabilities, pricing structure, and application guidelines. You can discover detailed use directions, consisting of sample API calls and code bits for integration. The design supports various text generation tasks, consisting of material production, code generation, and question answering, utilizing its support finding out optimization and CoT reasoning capabilities. +The page also consists of release choices and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be prompted to configure the implementation 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, go into a variety of instances (between 1-100). +6. For Instance type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can [configure innovative](https://pierre-humblot.com) security and infrastructure settings, including virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For many use cases, the default settings will work well. However, for production implementations, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:CynthiaCrombie) you may wish to examine these settings to align with your organization's security and [oeclub.org](https://oeclub.org/index.php/User:JacquelynTinker) compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can explore various triggers and change design criteria like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For example, content for reasoning.
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This is an exceptional way to explore the design's reasoning and text generation abilities before integrating it into your applications. The playground offers immediate feedback, helping you comprehend how the design reacts to different inputs and letting you tweak your triggers for ideal outcomes.
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You can quickly test the design in the playground through the UI. However, to invoke the [released design](https://pakallnaukri.com) programmatically with any [Amazon Bedrock](https://gitea.joodit.com) APIs, you require 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 demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock [console](https://www.teamswedenclub.com) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends out a request to create text based on 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 algorithms, and prebuilt ML services that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://bestremotejobs.net) models to your usage case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient approaches: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's [explore](http://test-www.writebug.com3000) both approaches to assist you select the technique that finest suits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, choose 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 browser displays available models, with details like the supplier name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows key details, consisting of:
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- Model name +- Provider name +- Task [category](https://healthcarestaff.org) (for example, Text Generation). +Bedrock Ready badge (if applicable), showing 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 model 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 supplier 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 description. +- License details. +- Technical specs. +- Usage standards
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Before you deploy the model, it's suggested to review the model details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the instantly produced name or create a customized one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of instances (default: 1). +Selecting proper circumstances types and counts is vital for expense and efficiency optimization. Monitor your deployment to adjust 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 precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The release procedure can take a number of minutes to finish.
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When deployment is total, your endpoint status will change to InService. At this moment, the model is prepared to accept reasoning demands through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, you can conjure up the design utilizing a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get going with DeepSeek-R1 using the SageMaker Python SDK, you will require 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 shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is provided 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 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 develop a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Clean up
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To prevent unwanted charges, finish the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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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, [select Marketplace](http://39.108.93.0) implementations. +2. In the Managed implementations section, locate the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the right deployment: 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. Use the following code to erase the endpoint if you wish 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 explored how you can access and release the DeepSeek-R1 design utilizing Bedrock [Marketplace](https://www.elcel.org) and SageMaker JumpStart. Visit [SageMaker JumpStart](https://titikaka.unap.edu.pe) in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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](https://www.alkhazana.net) for Inference at AWS. He assists emerging generative [AI](http://139.224.253.31:3000) business build ingenious services utilizing AWS services and accelerated calculate. Currently, he is focused on establishing methods for fine-tuning and optimizing the [inference performance](https://radi8tv.com) of big language models. In his spare time, Vivek delights in treking, viewing films, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://jobsdirect.lk) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](http://101.42.248.1083000) of focus is AWS [AI](https://zudate.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and .
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://play.sarkiniyazdir.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://180.76.133.253:16300) center. She is passionate about constructing options that assist customers accelerate their [AI](http://stream.appliedanalytics.tech) journey and [yewiki.org](https://www.yewiki.org/User:TommyCulbert459) unlock service value.
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