From 7dd214fbb6b642c40919bfe8e25e7521455fead2 Mon Sep 17 00:00:00 2001 From: Christa Whitefoord Date: Sun, 9 Feb 2025 04:13:27 +0000 Subject: [PATCH] Add '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..1655a7a --- /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, you can now deploy DeepSeek [AI](https://aggeliesellada.gr)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://www.jobsalert.ai) ideas on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock [Marketplace](https://bibi-kai.com) and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs 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://pyra-handheld.com) that utilizes reinforcement learning to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating function is its reinforcement learning (RL) step, which was used to improve the design's responses beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually improving both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's [equipped](http://8.134.253.2218088) to break down intricate questions and reason through them in a detailed manner. This directed thinking process enables the design to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the market's attention as a versatile text-generation model that can be integrated into numerous workflows such as representatives, rational reasoning and information analysis tasks.
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DeepSeek-R1 utilizes 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 questions to the most relevant professional "clusters." This approach enables the model to specialize in various problem domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of in FP8 format for inference. In this post, we will use an ml.p5e.48 [xlarge instance](http://123.57.66.463000) to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more effective architectures based upon [popular](http://sehwaapparel.co.kr) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient designs to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher design.
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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 releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid [hazardous](https://rocksoff.org) content, and [examine models](https://wavedream.wiki) against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 [deployments](https://starfc.co.kr) on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](https://git.luoui.com:2443) [applications](https://2workinoz.com.au).
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. 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 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 boost demand and connect to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To [Management](https://www.zapztv.com) (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish authorizations 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 present safeguards, avoid [damaging](https://ouptel.com) content, and assess models against key security criteria. You can execute safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use [guardrails](https://ibs3457.com) to examine user inputs and model reactions released on Amazon Bedrock Marketplace and [SageMaker JumpStart](http://www.visiontape.com). You can create a guardrail using 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 circulation 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 check, it's sent out to the model for inference. After getting the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the last result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate reasoning 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, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To [gain access](https://hylpress.net) to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane. +At the time of writing 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 pick the DeepSeek-R1 model.
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The model detail page provides important details about the model's abilities, prices 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 material creation, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:DeniceBales2) code generation, and concern answering, utilizing its reinforcement learning optimization and CoT reasoning abilities. +The page likewise consists of deployment options and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be prompted to set up the implementation 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 Variety of circumstances, get in a number of circumstances (between 1-100). +6. For Instance type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative [security](https://www.activeline.com.au) and [facilities](https://www.valenzuelatrabaho.gov.ph) settings, consisting of virtual private cloud (VPC) networking, service function approvals, and encryption settings. For a lot of use cases, the default settings will work well. However, for [production](http://42.192.130.833000) releases, you may wish to examine these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the implementation is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can experiment with various triggers and adjust model criteria like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For example, material for reasoning.
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This is an excellent method to explore the design's reasoning and text generation [abilities](http://autogangnam.dothome.co.kr) before [integrating](https://social.updum.com) it into your applications. The play area supplies immediate feedback, assisting you understand how the [model reacts](https://git.jzcscw.cn) to numerous inputs and [letting](https://www.activeline.com.au) you tweak your triggers for optimal outcomes.
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You can rapidly check the design in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:SoonWinfrey7778) the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually developed the guardrail, [utilize](https://www.kayserieticaretmerkezi.com) the following code to execute guardrails. The script [initializes](https://git.trov.ar) the bedrock_runtime client, configures inference criteria, and sends a request to create text based on a user prompt.
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Deploy DeepSeek-R1 with [SageMaker](https://test.bsocial.buzz) JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML services that you can [release](http://lophas.com) with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses two hassle-free methods: using the [instinctive SageMaker](http://krasnoselka.od.ua) JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the method 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 release DeepSeek-R1 [utilizing SageMaker](https://gitea.baxir.fr) 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](http://luodev.cn) console, choose JumpStart in the navigation pane.
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The design web browser displays available models, with details like the supplier name and design abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card reveals crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for example, [yewiki.org](https://www.yewiki.org/User:TommyCulbert459) Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to view the model details page.
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The model details page consists of the following details:
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- The design name and company details. +Deploy button to deploy the design. +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 requirements. +- Usage guidelines
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Before you deploy the design, it's suggested to examine the design details and license terms to verify compatibility with your usage case.
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6. [Choose Deploy](https://www.megahiring.com) to proceed with release.
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7. For Endpoint name, use the immediately created name or develop a custom-made one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of instances (default: 1). +Selecting suitable instance types and counts is vital for cost and [efficiency optimization](https://seconddialog.com). Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we strongly advise adhering to SageMaker JumpStart default [settings](https://wiki.dulovic.tech) 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 numerous minutes to finish.
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When implementation is complete, your endpoint status will change to [InService](https://www.diltexbrands.com). At this moment, the model is all set to accept reasoning requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can conjure up the design utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 using the [SageMaker Python](http://www.thynkjobs.com) SDK
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To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and [raovatonline.org](https://raovatonline.org/author/arletha3316/) make certain you have the required AWS consents and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for [inference programmatically](https://publiccharters.org). The code for releasing the design is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run additional 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Tidy up
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To avoid undesirable charges, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:RobertoN34) finish the actions in this section to clean 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, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations. +2. In the Managed deployments area, locate the endpoint you want to erase. +3. Select the endpoint, and on the Actions menu, [choose Delete](http://lophas.com). +4. Verify the endpoint details to make certain you're erasing the correct 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 design you released 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 explored how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](https://baescout.com) 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 Getting going with [Amazon SageMaker](https://gitea.imwangzhiyu.xyz) [JumpStart](https://git.olivierboeren.nl).
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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://www.jobzpakistan.info) business construct ingenious services using AWS services and sped up compute. Currently, he is focused on establishing methods for fine-tuning and optimizing the inference performance of big language models. In his leisure time, Vivek takes pleasure in treking, enjoying films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://clubamericafansclub.com) [Specialist Solutions](http://8.134.61.1073000) Architect with the Third-Party Model Science team at AWS. His [location](https://www.tobeop.com) of focus is AWS [AI](https://www.eruptz.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://flixtube.org) 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 [AI](http://busforsale.ae) center. She is passionate about constructing services that assist customers accelerate their [AI](http://121.40.114.127:9000) journey and unlock service value.
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