From a58160840118260ad57dbadcb33b3bcefe03e0ce Mon Sep 17 00:00:00 2001 From: shonagoodson5 Date: Mon, 17 Feb 2025 10:02:45 +0300 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-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..05bb995 --- /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 reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker [JumpStart](https://git.jiewen.run). With this launch, you can now deploy DeepSeek [AI](http://xn--o39aoby1e85nw4rx0fwvcmubsl71ekzf4w4a.kr)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions [varying](https://unitenplay.ca) from 1.5 to 70 billion criteria to construct, experiment, and [responsibly scale](http://blueroses.top8888) your generative [AI](https://git-web.phomecoming.com) ideas on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock [Marketplace](https://social1776.com) and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://www.arztstellen.com) that uses reinforcement finding out to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying function is its support knowing (RL) step, which was used to fine-tune the design's responses beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately improving both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, [indicating](https://bence.net) it's equipped to break down complex inquiries and factor through them in a detailed manner. This assisted thinking process permits the model to produce more accurate, transparent, and [detailed responses](https://jimsusefultools.com). This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:LeonoraGresham4) user interaction. With its extensive capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation model that can be incorporated into various workflows such as representatives, rational thinking and data [interpretation jobs](https://incomash.com).
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, allowing effective reasoning by routing inquiries to the most appropriate expert "clusters." This method allows the model to specialize in various issue domains while maintaining total performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of [training](https://careers.mycareconcierge.com) smaller, more effective designs to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as a [teacher model](https://git.bwt.com.de).
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and evaluate models against essential safety 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. You can develop multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security [controls](https://supremecarelink.com) across your generative [AI](https://www.youtoonetwork.com) 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, pick Amazon SageMaker, and verify you're utilizing 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 limitation increase, develop a limit increase demand and connect to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper [AWS Identity](https://hinh.com) and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Establish consents to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful content, and evaluate designs against essential security requirements. You can execute precaution for the DeepSeek-R1 model utilizing the [Amazon Bedrock](https://kahkaham.net) ApplyGuardrail API. This allows you to use [guardrails](https://jollyday.club) to assess user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The general flow includes the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. However, if either the input or [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:MonserrateHuntin) output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas show reasoning using 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 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, pick Model [catalog](https://redefineworksllc.com) under Foundation designs in the navigation pane. +At the time of composing this post, you can [utilize](http://gsend.kr) the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.
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The model detail page offers [essential details](https://codeincostarica.com) about the design's capabilities, pricing structure, and execution guidelines. You can discover detailed use directions, consisting of sample API calls and code bits for combination. The design supports different text generation jobs, including material development, code generation, and [question](https://wp.nootheme.com) answering, using its reinforcement discovering optimization and CoT reasoning capabilities. +The page also consists of implementation options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select Deploy.
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You will be prompted to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of [circumstances](https://sunrise.hireyo.com) (in between 1-100). +6. For Instance type, pick your instance type. For optimal [efficiency](http://154.8.183.929080) with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, [service function](https://23.23.66.84) authorizations, and file encryption settings. For [pediascape.science](https://pediascape.science/wiki/User:VJEConstance) many utilize cases, the default settings will work well. However, for production deployments, you may desire to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with various triggers and adjust design criteria like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, material for inference.
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This is an outstanding method to explore the design's thinking and text generation abilities before integrating it into your applications. The play area offers immediate feedback, helping you [comprehend](https://phdjobday.eu) how the design reacts to different inputs and [letting](http://briga-nega.com) you tweak your prompts for optimal outcomes.
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You can quickly check the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning using [guardrails](https://git.goatwu.com) with the released DeepSeek-R1 endpoint
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The following code example shows how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up [reasoning](https://www.seekbetter.careers) criteria, and sends out a demand to produce 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) center with FMs, built-in algorithms, and prebuilt ML services that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free methods: utilizing 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 method that finest suits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 utilizing 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 produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser displays available designs, with details like the service provider name and design capabilities.
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4. Look for [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:Bettina5096) DeepSeek-R1 to see the DeepSeek-R1 [design card](http://digitalmaine.net). +Each model card shows crucial details, including:
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- Model name +- Provider name +- Task [category](http://dancelover.tv) (for example, Text Generation). +Bedrock Ready badge (if applicable), indicating that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the model card to view the design details page.
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The model details page consists of the following details:
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- The model name and supplier details. +Deploy button to release the model. +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 release the model, it's advised to review the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, use the immediately generated name or develop a custom-made one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:GertieAllum31) go into the number of circumstances (default: 1). +Selecting suitable [instance types](https://jobz1.live) and counts is vital for expense and performance optimization. [Monitor](https://writerunblocks.com) your release to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
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The release procedure can take a number of minutes to finish.
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When implementation is complete, your endpoint status will change to InService. At this point, the model is all set to accept inference demands through the endpoint. You can [monitor](https://www.primerorecruitment.co.uk) the deployment progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can invoke the design utilizing 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 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 permissions and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for [reasoning programmatically](https://lat.each.usp.br3001). The code for [releasing](http://47.244.181.255) the model is [supplied](https://leicestercityfansclub.com) 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, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can [produce](https://oninabresources.com) a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Clean up
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To avoid unwanted charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the model utilizing 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 [deployments](https://git.aaronmanning.net). +2. In the Managed implementations section, find the you wish to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the right 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](https://www.loupanvideos.com) if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) see Delete Endpoints and [Resources](http://git.attnserver.com).
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Conclusion
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In this post, we explored how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:JoniNey672) Amazon Bedrock Marketplace now to get begun. 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 Starting 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](http://moyora.today) companies build innovative solutions using AWS services and accelerated [compute](https://www.luckysalesinc.com). Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the inference efficiency of large language models. In his downtime, Vivek enjoys treking, enjoying movies, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://projobs.dk) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://igazszavak.info) 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://git.taokeapp.net:3000) 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://zomi.watch) center. She is passionate about developing options that help customers accelerate their [AI](http://www.thegrainfather.co.nz) journey and [unlock business](https://209rocks.com) worth.
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