From f2227edf30aad1214c71a10e7e398e9c79c28838 Mon Sep 17 00:00:00 2001 From: maricruzseese6 Date: Fri, 4 Apr 2025 07:54:47 +0800 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..70f59d4 --- /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](http://193.105.6.1673000) R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://gitea.scalz.cloud)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](https://git.k8sutv.it.ntnu.no) concepts 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 similar steps to deploy the distilled variations of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://mobishorts.com) that uses support discovering to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying function is its support knowing (RL) action, which was used to improve the design's actions beyond the basic pre-training and tweak process. By [integrating](https://abstaffs.com) RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately improving both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, implying it's geared up to break down intricate questions and factor through them in a [detailed](http://busforsale.ae) way. This assisted thinking process permits the model to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be integrated into various workflows such as agents, logical reasoning and information interpretation tasks.
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DeepSeek-R1 [utilizes](https://oyotunji.site) a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, enabling effective [reasoning](https://www.majalat2030.com) by routing questions to the most appropriate professional "clusters." This method permits the design to specialize in various issue domains while maintaining total [effectiveness](https://gitlab.ui.ac.id). DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more [effective architectures](https://www.hrdemployment.com) based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient models to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as a teacher design.
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You can [release](https://bartists.info) DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this design with guardrails in place. In this blog site, we will use Amazon Bedrock [Guardrails](https://mobishorts.com) to present safeguards, prevent hazardous material, and examine designs against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 [deployments](http://git.nextopen.cn) on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](http://190.117.85.58:8095) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas [console](https://impactosocial.unicef.es) and under AWS Services, pick Amazon SageMaker, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:JanelleJevons) 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 boost, produce a limitation boost request and connect to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:ColinStoddard) make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Set up approvals to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous material, and assess designs against crucial safety requirements. You can execute safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and design actions deployed 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 produce the guardrail, [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:TerriPiper3180) see the GitHub repo.
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The basic circulation involves the following actions: First, the system receives an input for the model. This input is then processed through the [ApplyGuardrail API](https://tottenhamhotspurfansclub.com). If the input passes the [guardrail](https://git.k8sutv.it.ntnu.no) check, it's sent to the design for inference. After getting the [design's](http://gpis.kr) output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output stage. The [examples](https://193.31.26.118) 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](https://gitea.freshbrewed.science) gives you access to over 100 popular, emerging, and specialized foundation models (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, pick Model brochure under Foundation designs in the navigation pane. +At the time of [writing](https://site4people.com) this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
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The model detail page offers vital details about the design's capabilities, pricing structure, and execution standards. You can find detailed use directions, including sample API calls and code bits for [wiki.myamens.com](http://wiki.myamens.com/index.php/User:FrederickLegg5) combination. The model supports different text generation tasks, including content production, code generation, and question answering, using its [support discovering](http://180.76.133.25316300) optimization and CoT reasoning abilities. +The page likewise consists of deployment alternatives and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick 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, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a number of circumstances (in between 1-100). +6. For example type, select your circumstances type. For ideal 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 cloud (VPC) networking, service role authorizations, and [oeclub.org](https://oeclub.org/index.php/User:MichelleL34) file encryption settings. For most use cases, the default settings will work well. However, for production releases, you may want to examine these [settings](https://socipops.com) to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the deployment is total, you can check DeepSeek-R1's capabilities 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 adjust model criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For example, content for reasoning.
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This is an outstanding method to explore the design's thinking and text generation capabilities before integrating it into your applications. The play area offers instant feedback, assisting you understand how the design reacts to various inputs and letting you tweak your prompts for ideal results.
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You can quickly check the design in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out inference using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the [Amazon Bedrock](http://ggzypz.org.cn8664) console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends out a request to produce 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) center with FMs, built-in algorithms, and prebuilt ML services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical techniques: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to help you pick the approach that finest suits your needs.
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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 [triggered](https://localjobpost.com) to develop a domain. +3. On the SageMaker Studio console, [pick JumpStart](http://codaip.co.kr) in the navigation pane.
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The design web browser shows available designs, with details like the provider name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows essential details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the design details page.
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The design details page consists of the following details:
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- The model name and supplier details. +Deploy button to deploy 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 specs. +- Usage standards
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Before you release the model, it's suggested to evaluate the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, utilize the instantly generated name or produce a customized one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is vital for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, [ratemywifey.com](https://ratemywifey.com/author/martyhayes/) Real-time reasoning is [selected](http://hualiyun.cc3568) by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this model, we highly suggest adhering to SageMaker JumpStart default [settings](http://hjl.me) and making certain that network seclusion remains in location. +11. Choose Deploy to release the model.
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The release process can take a number of minutes to complete.
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When deployment is total, your [endpoint status](https://blessednewstv.com) will change to InService. At this moment, the model is prepared to [accept reasoning](https://jobsinethiopia.net) demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can conjure up the model 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 start with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is [offered](https://inspirationlift.com) in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional requests 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 create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as [revealed](http://dev.onstyler.net30300) in the following code:
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Tidy up
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To prevent unwanted charges, complete the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation [designs](https://git.markscala.org) in the navigation pane, [pick Marketplace](https://gitea.shoulin.net) implementations. +2. In the Managed releases area, locate the [endpoint](https://howtolo.com) you wish to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the right release: 1. [Endpoint](https://heli.today) 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. Use the following code to delete the endpoint if you want 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](http://139.199.191.19715000) or [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:DonnyLathrop) Amazon Bedrock Marketplace now to begin. For more details, refer to Use [Amazon Bedrock](https://code.jigmedatse.com) tooling with Amazon SageMaker JumpStart models, 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://221.229.103.55:63010) companies construct ingenious services using AWS services and sped up calculate. Currently, he is focused on establishing techniques for fine-tuning and optimizing the reasoning performance of large language designs. In his spare time, Vivek delights in hiking, watching motion pictures, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://edujobs.itpcrm.net) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://git.valami.giize.com) [accelerators](https://47.100.42.7510443) (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](https://gitlab.internetguru.io) 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](http://git.scdxtc.cn) intelligence and generative [AI](https://play.future.al) hub. She is passionate about developing services that assist customers accelerate their [AI](https://jobs.fabumama.com) journey and unlock service value.
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