commit 8172315dd097d35a9c246a7214bbec3d0b86378e Author: rainao43987460 Date: Sat Apr 5 18:50:25 2025 +0000 Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' 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..902d6c3 --- /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 announce that DeepSeek R1 [distilled Llama](https://gitea.scubbo.org) and Qwen models are available through Amazon Bedrock [Marketplace](https://hcp.com.gt) and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://123.60.173.13:3000)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your [generative](https://c-hireepersonnel.com) [AI](http://docker.clhero.fun:3000) concepts on AWS.
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In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock [Marketplace](http://forum.rcsubmarine.ru) and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs too.
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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://jobpile.uk) that utilizes reinforcement learning to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying function is its reinforcement knowing (RL) step, which was utilized to improve the model's responses beyond the basic [pre-training](https://hireteachers.net) and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually improving both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, [meaning](http://8.218.14.833000) it's geared up to break down intricate questions and reason through them in a detailed way. This [assisted thinking](https://portalwe.net) process allows the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to create [structured reactions](https://phpcode.ketofastlifestyle.com) while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation design that can be [incorporated](https://gitlab.reemii.cn) into numerous workflows such as agents, sensible reasoning and data analysis jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, [allowing efficient](http://124.71.134.1463000) reasoning by routing inquiries to the most pertinent specialist "clusters." This approach permits the design to specialize in various problem domains 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 release the design. ml.p5e.48 xlarge comes with 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 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 smaller, more effective models to imitate the habits and thinking patterns of the larger DeepSeek-R1 model, utilizing it as an instructor model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and evaluate models against key safety criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, [improving](https://socipops.com) user experiences and standardizing security controls across your generative [AI](https://git.xedus.ru) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e [instance](https://collegestudentjobboard.com). To check if you have quotas for P5e, open the [Service Quotas](https://git.learnzone.com.cn) console and under AWS Services, choose Amazon SageMaker, and [confirm](https://frce.de) you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limit boost, produce a limit increase request and reach out 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](http://61.174.243.2815863) and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid harmful content, and assess models against crucial safety requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design actions released 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 develop the guardrail, see the GitHub repo.
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The general flow involves the following actions: First, the system receives an input for the design. This input is then processed through the [ApplyGuardrail API](https://edu.shpl.ru). If the input passes the guardrail check, it's sent out to the model for [reasoning](https://onsanmo.co.kr). After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the [intervention](https://stnav.com) and whether it occurred at the input or output phase. 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](https://hellovivat.com) gives you access to over 100 popular, emerging, and specialized foundation designs (FMs) through [Amazon Bedrock](http://szyg.work3000). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, select Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.
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The model detail page offers necessary details about the model's abilities, pricing structure, and application guidelines. You can discover detailed use instructions, consisting of sample API calls and code snippets for combination. The design supports different text generation jobs, consisting of material development, code generation, and concern answering, using its support discovering optimization and CoT reasoning capabilities. +The page likewise consists of deployment choices and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a variety of circumstances (in between 1-100). +6. For example type, select your [instance type](https://hellovivat.com). For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you might want to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can experiment with different prompts and change model parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, content for reasoning.
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This is an excellent way to check out the model's reasoning and text generation abilities before integrating it into your applications. The play area provides instant feedback, helping you comprehend how the model reacts to various inputs and letting you fine-tune your triggers for ideal outcomes.
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You can rapidly check the model in the play area through the UI. However, to conjure up the deployed 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](https://jobdd.de) DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference utilizing a [deployed](https://portalwe.net) DeepSeek-R1 design through Amazon Bedrock using the invoke_model and [ApplyGuardrail API](https://mysazle.com). You can produce a [guardrail utilizing](https://support.mlone.ai) the [Amazon Bedrock](https://git.blinkpay.vn) console or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends out a request to create [text based](https://www.myad.live) 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, integrated algorithms, and prebuilt ML [services](https://firemuzik.com) that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the method that finest matches your requirements.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](http://hybrid-forum.ru) UI
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Complete the following steps to deploy 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, choose JumpStart in the navigation pane.
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The design web browser shows available designs, with details like the supplier name and model capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals essential details, consisting of:
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- Model name +- [Provider](https://git.didi.la) name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if relevant), showing that this model 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 design 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 release the design. +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 specifications. +- Usage guidelines
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Before you release the model, it's advised to evaluate the model details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the automatically produced name or produce a customized one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of instances (default: 1). +Selecting suitable instance types and counts is vital for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for [sustained traffic](http://cgi3.bekkoame.ne.jp) and low latency. +10. Review all setups for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart default [settings](https://baescout.com) and making certain that [network seclusion](http://demo.qkseo.in) remains in location. +11. Choose Deploy to deploy the model.
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The release process can take several minutes to finish.
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When release is complete, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will [display](https://baescout.com) pertinent metrics and status details. When the release is total, you can conjure up the model using a SageMaker runtime client and integrate 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 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or [pediascape.science](https://pediascape.science/wiki/User:MarylouXrp) the API, and implement it as displayed in the following code:
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Clean up
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To prevent undesirable charges, complete the actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the design utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed releases area, find the endpoint you want to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're [deleting](https://git.zyhhb.net) 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](https://www.cartoonistnetwork.com) design you deployed will sustain costs if you leave it running. Use the following code to erase 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 using 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 models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with [Amazon SageMaker](http://8.136.197.2303000) 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.lolilove.rs) business build innovative solutions utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference efficiency of large language designs. In his leisure time, Vivek delights in treking, watching motion pictures, and trying different cuisines.
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Niithiyn Vijeaswaran is a [Generative](http://110.42.178.1133000) [AI](http://rackons.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://laboryes.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://talentsplendor.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and [strategic partnerships](https://git.satori.love) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://git.indep.gob.mx) center. She is passionate about developing solutions that help clients accelerate their [AI](http://jobasjob.com) journey and unlock organization worth.
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