commit a9523e9c839051775db893697d35d1b5b7dffcc5 Author: Anke Repass Date: Fri Feb 7 01:03:38 2025 +0100 Add 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..ea046a0 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are delighted to announce that DeepSeek R1 distilled Llama and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Arturo0965) Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://h2bstrategies.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](http://120.79.75.202:3000) ideas on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://jobsdirect.lk) that utilizes reinforcement finding out to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key identifying feature is its support learning (RL) step, which was used to improve the model's responses beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's geared up to break down complex queries and factor through them in a detailed manner. This assisted reasoning process enables the design to produce more accurate, [yewiki.org](https://www.yewiki.org/User:EdwinaMcintire3) transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, rational reasoning and information analysis tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) [architecture](http://123.111.146.2359070) and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, [allowing effective](http://121.40.209.823000) inference by routing questions to the most appropriate professional "clusters." This approach allows the design to concentrate on different problem domains while maintaining total performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 [xlarge circumstances](https://www.philthejob.nl) to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 [distilled designs](https://git.progamma.com.ua) bring the [reasoning capabilities](http://112.126.100.1343000) 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 describes a procedure of training smaller sized, more efficient models to imitate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as an instructor design.
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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 suggest [releasing](https://git.l1.media) this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and examine designs against crucial safety criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several [guardrails tailored](https://clickcareerpro.com) to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://sossdate.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate 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 releasing. To request a limitation boost, create a limit boost request 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 directions, see Set up authorizations to utilize guardrails for [material](https://trabaja.talendig.com) 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 material, and evaluate models against key safety requirements. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create 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 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 used. If the output passes this final check, it's returned as the last outcome. 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 areas demonstrate reasoning [utilizing](https://git.dsvision.net) 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 designs (FMs) through [Amazon Bedrock](https://subamtv.com). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, select Model catalog under [Foundation models](http://13.213.171.1363000) in the navigation pane. +At the time of [writing](http://101.231.37.1708087) this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.
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The design detail page offers necessary details about the design's abilities, prices structure, and application guidelines. You can discover detailed use guidelines, consisting of sample API calls and code bits for combination. The model supports numerous text generation tasks, [consisting](http://busforsale.ae) of material creation, code generation, and question answering, utilizing its support finding out optimization and CoT thinking capabilities. +The page also consists of release options and licensing details to help you begin with DeepSeek-R1 in your [applications](http://112.126.100.1343000). +3. To start using DeepSeek-R1, pick Deploy.
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You will be prompted to set up the release details for [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:ClaraKimbrell) DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, go into a number of circumstances (between 1-100). +6. For Instance type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, service function consents, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you might desire to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the implementation is total, you can evaluate 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 try out different prompts and adjust model specifications like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For example, content for reasoning.
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This is an outstanding way to check out the and text generation capabilities before integrating it into your applications. The playground provides instant feedback, helping you comprehend how the model responds to various inputs and letting you tweak your prompts for optimum outcomes.
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You can quickly check the model in the play ground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you [require](https://www.ksqa-contest.kr) to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning 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 the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_[runtime](https://littlebigempire.com) client, configures inference criteria, and sends out a demand to create 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) hub with FMs, integrated algorithms, and prebuilt ML services that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two practical techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both [methods](https://teengigs.fun) to help you choose the approach that best matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release 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 create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design internet browser displays available designs, with details like the supplier name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals essential details, including:
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- Model name +[- Provider](http://git.cqbitmap.com8001) name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if suitable), [suggesting](https://octomo.co.uk) that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon [Bedrock](https://gurjar.app) APIs to invoke the model
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5. Choose the model card to see the model details page.
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The [model details](https://govtpakjobz.com) page consists of the following details:
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- The design name and service provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical specs. +[- Usage](https://www.globalshowup.com) guidelines
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Before you deploy the design, it's recommended to evaluate the [model details](https://gitea.easio-com.com) and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, utilize the automatically produced name or develop a customized one. +8. For Instance type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting proper instance types and counts is important for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the design.
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The release process can take numerous minutes to finish.
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When implementation is total, your endpoint status will change to [InService](https://uptoscreen.com). At this point, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:FlorianHoutz6) the design is all set to accept reasoning demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the design utilizing a SageMaker runtime client and [integrate](https://jobsekerz.com) it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing 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 prevent undesirable charges, finish 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 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, select Marketplace deployments. +2. In the Managed deployments section, locate the endpoint you want to erase. +3. Select the endpoint, and on the Actions menu, [choose Delete](https://funitube.com). +4. Verify the endpoint details to make certain you're erasing the correct 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 expenses 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 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 designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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](https://git.gday.express) generative [AI](http://8.137.58.20:3000) business build ingenious options using AWS services and sped up [calculate](https://jobflux.eu). Currently, he is focused on establishing strategies for fine-tuning and optimizing the inference performance of large language models. In his totally free time, Vivek enjoys hiking, enjoying motion pictures, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://elitevacancies.co.za) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://www.arztstellen.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [Science](http://106.52.242.1773000) and Bioinformatics.
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[Jonathan Evans](https://git.foxarmy.org) is an Expert Solutions Architect working on generative [AI](https://www.k4be.eu) with the Third-Party Model Science team at AWS.
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[Banu Nagasundaram](https://asicwiki.org) leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://connectzapp.com) center. She is enthusiastic about building options that help customers accelerate their [AI](https://gitlab.zogop.com) journey and unlock service value.
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