From 72e8f71f409c20db9e40756a00e759c31fbddc53 Mon Sep 17 00:00:00 2001 From: warren70928944 Date: Thu, 20 Feb 2025 17:45:50 +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..2bc9f7e --- /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 announce that DeepSeek R1 [distilled Llama](https://pakkjob.com) and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://git.ipmake.me)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://friendify.sbs) ideas on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://www.lakarjobbisverige.se). You can follow similar actions to deploy the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](http://www.grainfather.com.au) that uses support finding out to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its support learning (RL) step, which was used to improve the design's reactions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it's equipped to break down complicated questions and factor through them in a detailed manner. This directed thinking process enables the model to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation design that can be incorporated into various workflows such as agents, rational thinking and information [analysis jobs](https://app.deepsoul.es).
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, allowing effective inference by routing inquiries to the most appropriate expert "clusters." This technique enables the model to specialize in various problem [domains](https://git.agri-sys.com) while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 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 on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient models to mimic the habits and [reasoning patterns](https://git.qiucl.cn) of the larger 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 design, we [advise deploying](https://in.fhiky.com) this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and examine designs against essential safety criteria. At the time of [writing](https://wiki.whenparked.com) this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://www.yozgatblog.com) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, 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 circumstances in the AWS Region you are deploying. To ask for a limit increase, develop a limitation boost demand and reach out to your account group.
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Because you will be releasing this design with [Amazon Bedrock](http://demo.ynrd.com8899) Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Set up permissions to use guardrails for material filtering.
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[Implementing guardrails](https://www.nikecircle.com) with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11930902) prevent hazardous content, and assess designs against key safety criteria. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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 flow involves the following steps: First, the system gets an input for the model. 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 used. If the output passes this last check, it's returned as the last [outcome](http://47.92.218.2153000). However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections show inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace 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 actions:
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1. On the Amazon Bedrock console, pick Model [brochure](https://git.brass.host) under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize 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 [service provider](http://www.jimtangyh.xyz7002) and choose the DeepSeek-R1 design.
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The model detail page provides essential details about the design's capabilities, pricing structure, and application guidelines. You can find detailed usage guidelines, consisting of sample API calls and code snippets for integration. The design supports different text generation tasks, including material production, code generation, and question answering, utilizing its support finding out optimization and CoT reasoning capabilities. +The page likewise includes release choices and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, get in a number of instances (between 1-100). +6. For example type, select your circumstances type. For optimum [efficiency](http://124.222.7.1803000) with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For most use cases, the default settings will work well. However, for production implementations, you may want to evaluate 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 deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive interface where you can try out various triggers and adjust design parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, content for inference.
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This is an exceptional method to check out the design's thinking and text generation abilities before integrating it into your applications. The play area offers immediate feedback, helping you comprehend how the model responds to different inputs and letting you tweak your triggers for optimum results.
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You can quickly check the model in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends out 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 (ML) center with FMs, built-in algorithms, and prebuilt ML [options](http://59.110.162.918081) that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into [production utilizing](http://120.77.209.1763000) either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical approaches: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you choose the method that finest matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. [First-time](https://newborhooddates.com) users will be prompted to produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design web browser shows available designs, with details like the service provider name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows crucial details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), indicating that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
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5. Choose the design 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 deploy the model. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you release the design, it's recommended to examine the model details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the immediately [produced](https://swaggspot.com) name or produce a custom one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of instances (default: 1). +[Selecting suitable](https://git.rankenste.in) circumstances types and counts is important for [expense](https://pakkjob.com) and performance optimization. Monitor your implementation to adjust 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 setups for precision. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the model.
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The [deployment procedure](https://travelpages.com.gh) can take a number of minutes to finish.
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When deployment is complete, your [endpoint status](https://www.miptrucking.net) will alter to InService. At this point, the model is ready to [accept reasoning](https://funnyutube.com) [demands](https://167.172.148.934433) through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, you can invoke the model 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 started 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 approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for [deploying](https://scholarpool.com) the model is supplied in the Github here. You can clone the notebook 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Tidy up
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To avoid undesirable charges, complete the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the [Amazon Bedrock](https://videopromotor.com) console, under [Foundation designs](https://wiki.solsombra-abdl.com) in the navigation pane, select Marketplace releases. +2. In the Managed releases area, find the endpoint 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 deleting the proper 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 deployed will sustain costs if you leave it [running](https://guiding-lights.com). Use the following code to delete 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 model 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 designs, [Amazon SageMaker](https://c3tservices.ca) JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going 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](https://git.fracturedcode.net) companies develop innovative options utilizing AWS services and sped up compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the [reasoning performance](https://gitlab.amatasys.jp) of large language designs. In his complimentary time, Vivek enjoys treking, watching motion pictures, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://hektips.com) Specialist Solutions Architect with the Third-Party Model [Science](https://talentmatch.somatik.io) team at AWS. His area of focus is AWS [AI](http://codaip.co.kr) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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[Jonathan Evans](http://sl860.com) is an Expert Solutions Architect dealing with generative [AI](http://63.32.145.226) 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](https://gitlab.digineers.nl) hub. She is passionate about developing services that assist consumers accelerate their [AI](https://namesdev.com) journey and unlock business worth.
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