From 19221e8b6cf3256a09cc23bcc021fe3a0b29b101 Mon Sep 17 00:00:00 2001 From: Alva Sutcliffe Date: Sun, 9 Feb 2025 21:13:16 +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..640634c --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1324171) you can now deploy DeepSeek [AI](http://moyora.today)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative [AI](https://ozgurtasdemir.net) ideas on AWS.
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In this post, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:DominiqueCurmi) we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models 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](http://hellowordxf.cn) that utilizes reinforcement finding out to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing function is its [support knowing](https://tube.denthubs.com) (RL) step, which was used to refine the design's reactions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, eventually boosting both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's equipped to break down [intricate questions](https://gitea.ecommercetools.com.br) and factor through them in a detailed manner. This guided thinking process enables the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured [actions](http://106.15.120.1273000) while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the [market's attention](http://git.the-archive.xyz) as a versatile text-generation model that can be into numerous workflows such as representatives, sensible thinking and [data analysis](https://gitea.joodit.com) tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, allowing effective reasoning by routing queries to the most pertinent professional "clusters." This method permits the design to specialize in different issue domains while maintaining overall efficiency. 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 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 design to more effective architectures 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, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:TawnyaWhitley87) more effective designs to mimic the [behavior](http://hualiyun.cc3568) and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock [Guardrails](http://filmmaniac.ru) to present safeguards, avoid hazardous material, and examine designs against essential security criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the [ApplyGuardrail API](https://sneakerxp.com). You can develop numerous guardrails tailored to different usage cases and [ratemywifey.com](https://ratemywifey.com/author/ollieholtze/) use them to the DeepSeek-R1 model, [improving](http://ods.ranker.pub) user experiences and standardizing security controls across your generative [AI](http://194.67.86.160:3100) 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 and under AWS Services, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:CarriBurnside4) pick Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 [xlarge instance](http://omkie.com3000) in the AWS Region you are deploying. To request a [limitation](https://www.referall.us) boost, produce 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 right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Set up approvals to use guardrails for material filtering.
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[Implementing](https://cdltruckdrivingcareers.com) guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and [assess designs](https://www.menacopt.com) against essential safety criteria. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to [produce](http://www.hydrionlab.com) the guardrail, see the GitHub repo.
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The basic 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 model for inference. After receiving 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 output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas show [reasoning](https://39.105.45.141) using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock [Marketplace](https://cdltruckdrivingcareers.com) 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, total the following actions:
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1. On the [Amazon Bedrock](https://git.lab.evangoo.de) console, pick Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.
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The design detail page provides important [details](https://bd.cane-recruitment.com) about the model's capabilities, rates structure, and implementation standards. You can discover detailed use instructions, consisting of sample API calls and code snippets for combination. The design supports numerous text generation tasks, consisting of content creation, code generation, and question answering, using its reinforcement finding out optimization and CoT reasoning capabilities. +The page also includes deployment choices and [licensing details](https://home.42-e.com3000) to help you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be triggered to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, get in a [variety](http://xiaomu-student.xuetangx.com) of circumstances (in between 1-100). +6. For example type, select your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you may wish to review these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can explore different triggers and change design criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, content for reasoning.
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This is an exceptional way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play area supplies immediate feedback, assisting you comprehend how the model reacts to various inputs and letting you tweak your prompts for optimal results.
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You can quickly evaluate the model in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends a demand 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](https://portalwe.net) (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 convenient techniques: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the method that finest fits 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, choose Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design internet browser displays available designs, with details like the [service provider](http://git.superiot.net) name and model capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows essential details, including:
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- Model name +- [Provider](https://inspirationlift.com) name +- Task [classification](http://git.oksei.ru) (for instance, Text Generation). +[Bedrock Ready](https://just-entry.com) badge (if appropriate), showing that this model 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 model card to see the model [details](http://51.75.64.148) page.
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The design details page [consists](https://miggoo.com.br) of the following details:
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- The design name and supplier details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes important 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 suggested to [evaluate](http://lespoetesbizarres.free.fr) the model details and [it-viking.ch](http://it-viking.ch/index.php/User:KarenSteinberger) license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, utilize the automatically generated name or produce a custom-made one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +[Selecting suitable](https://www.linkedaut.it) instance types and counts is important for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we strongly suggest sticking to SageMaker JumpStart [default settings](https://xremit.lol) and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The implementation process can take numerous minutes to complete.
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When [implementation](http://211.119.124.1103000) 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 keep track of the [implementation development](https://wiki.idealirc.org) on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is total, you can invoke the design using a SageMaker runtime client and incorporate it with your [applications](https://cbfacilitiesmanagement.ie).
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the [SageMaker Python](http://103.197.204.1633025) SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for [inference programmatically](https://just-entry.com). The code for releasing the design is offered in the Github here. You can clone the note pad 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 reasoning 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](https://teengigs.fun) using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To avoid undesirable charges, complete the actions 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 design utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. +2. In the Managed deployments area, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate release: 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 released will sustain costs if you leave it running. Use the following code to erase the endpoint if you desire to stop sustaining charges. For more details, see Delete [Endpoints](https://www.refermee.com) and Resources.
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Conclusion
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In this post, we explored 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 start. 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](http://git.befish.com) [AI](https://git.micg.net) business develop ingenious options utilizing AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the inference efficiency of large language designs. In his totally free time, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11864354) Vivek delights in treking, enjoying movies, and [attempting](https://sound.co.id) various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://volunteering.ishayoga.eu) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://www.mizmiz.de) 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://c3tservices.ca) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://inspirationlift.com) center. She is enthusiastic about building solutions that assist customers accelerate their [AI](https://test.gamesfree.ca) journey and unlock organization worth.
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