From 0d25690b1988762b944170b948aee241b1a370f0 Mon Sep 17 00:00:00 2001 From: barbaraspielvo Date: Fri, 7 Feb 2025 03:26:11 +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..afb4822 --- /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 and Qwen designs are available through Amazon Bedrock Marketplace and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:LindseyWalstab9) Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://samman-co.com)'s first-generation frontier design, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:LoreenErtel66) DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://realmadridperipheral.com) ideas on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release 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](https://rna.link) (LLM) developed by DeepSeek [AI](http://szfinest.com:6060) that [utilizes reinforcement](https://job.firm.in) finding out to boost reasoning [capabilities](https://www.keeloke.com) through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing feature is its [support](http://git.njrzwl.cn3000) knowing (RL) action, which was utilized to refine the model's reactions beyond the basic pre-training and fine-tuning process. By [incorporating](http://124.221.76.2813000) RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both [relevance](http://git.maxdoc.top) and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's equipped to break down complex inquiries and reason through them in a detailed manner. This assisted reasoning process enables the design to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually [recorded](https://www.pickmemo.com) the market's attention as a flexible text-generation design that can be incorporated into different workflows such as representatives, logical thinking and data analysis tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion parameters, enabling effective inference by routing questions to the most pertinent expert "clusters." This technique allows the design to focus on various issue domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of [HBM memory](http://jialcheerful.club3000) in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to [release](http://47.108.182.667777) the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a [process](https://notitia.tv) of training smaller, more effective models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using 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 model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and assess models against essential safety requirements. 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 produce numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](http://bristol.rackons.com) applications.
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Prerequisites
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To deploy 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, pick Amazon SageMaker, and validate 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 releasing. To request a limit increase, develop a limit boost demand and reach out to your account team.
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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 (IAM) consents to use Amazon Bedrock Guardrails. For instructions, see Set up authorizations to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, avoid hazardous material, and examine models against key safety requirements. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock [console](https://rami-vcard.site) or the API. For the example code to develop 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 to the design for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this last check, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:Princess3594) it's returned as the [final result](https://pakallnaukri.com). 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 occurred at the input or output stage. The examples showcased in the following areas 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 structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.
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The design detail page offers essential details about the design's abilities, pricing structure, and implementation guidelines. You can find detailed usage guidelines, including sample API calls and code bits for combination. The model supports different text generation tasks, including material production, code generation, and question answering, utilizing its [reinforcement discovering](https://fishtanklive.wiki) optimization and CoT reasoning abilities. +The page also includes release choices and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To [start utilizing](https://dooplern.com) DeepSeek-R1, select 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 (in between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a variety of circumstances (between 1-100). +6. For Instance type, select your instance type. For optimum performance with DeepSeek-R1, a [GPU-based circumstances](https://fishtanklive.wiki) type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, [yewiki.org](https://www.yewiki.org/User:LucianaChau79) and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you may wish to examine these settings to line up with your [organization's security](https://gitlab.profi.travel) and compliance requirements. +7. Choose Deploy to start using the design.
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When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can experiment with different triggers and change model criteria like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, content for reasoning.
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This is an outstanding way to check out the design's thinking and [links.gtanet.com.br](https://links.gtanet.com.br/jacquelinega) text generation abilities before incorporating it into your applications. The play area offers immediate feedback, assisting you understand how the design reacts to different inputs and letting you fine-tune your prompts for [optimum outcomes](https://intunz.com).
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You can rapidly check the model in the play ground 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 reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a [released](https://gitlab.wah.ph) DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, and sends a request to generate text based upon a user prompt.
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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](http://47.108.69.3310888) with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical methods: [utilizing](https://careers.jabenefits.com) the intuitive SageMaker JumpStart UI or [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:Margareta19E) executing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you pick the technique that finest suits your requirements.
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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 users will be triggered to create a domain. +3. On the [SageMaker Studio](https://my.beninwebtv.com) console, select JumpStart in the navigation pane.
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The model browser shows available designs, with details like the provider name and model capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 [model card](http://fridayad.in). +Each [model card](https://bytes-the-dust.com) reveals crucial details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if relevant), indicating that this model can be registered with Amazon Bedrock, permitting you to use [Amazon Bedrock](https://www.oradebusiness.eu) APIs to invoke the model
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5. Choose the model card to see the design details page.
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The model details page [consists](http://115.182.208.2453000) of the following details:
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- The design name and company details. +Deploy button to release the model. +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 specs. +- Usage guidelines
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Before you release the model, it's advised to evaluate the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the automatically created name or produce a custom one. +8. For Instance type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of instances (default: 1). +Selecting appropriate circumstances types and [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:Bettina5096) counts is vital for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to [release](https://www.nairaland.com) the design.
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The release process can take a number of minutes to finish.
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When release is total, your endpoint status will change to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can monitor the [release progress](https://howtolo.com) on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can invoke the design using 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 begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS authorizations and [environment setup](https://oerdigamers.info). The following is a detailed code example that [demonstrates](https://paksarkarijob.com) how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad 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 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 using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To prevent unwanted charges, finish 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 deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments. +2. In the Managed deployments area, locate the [endpoint](https://accc.rcec.sinica.edu.tw) 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 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 deployed will [sustain costs](http://www.xn--739an41crlc.kr) if you leave it [running](https://hr-2b.su). Use the following code to erase 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 model using [Bedrock Marketplace](https://jobs.colwagen.co) and . Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [SageMaker JumpStart](https://social.stssconstruction.com) pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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](https://fydate.com) [AI](https://gitea.thuispc.dynu.net) business construct innovative services [utilizing](https://puzzle.thedimeland.com) AWS services and sped up compute. Currently, he is concentrated on developing techniques for [fine-tuning](https://magnusrecruitment.com.au) and optimizing the reasoning performance of big language models. In his free time, Vivek takes pleasure in hiking, seeing movies, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.numa.jku.at) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://castingnotices.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://ai.ceo) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitea.cronin.one) hub. She is enthusiastic about building options that assist customers accelerate their [AI](https://galgbtqhistoryproject.org) journey and unlock organization worth.
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