commit baee01bfa46dcf957d57b042ea3715f19d47980b Author: chadrobeson170 Date: Thu Feb 6 17:08:11 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..495e4f5 --- /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 [release DeepSeek](https://git.lewis.id) [AI](https://git.manu.moe)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative [AI](https://groups.chat) 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 actions to release 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 design (LLM) established by DeepSeek [AI](https://medifore.co.jp) that [utilizes support](https://www.com.listatto.ca) learning to improve reasoning [capabilities](https://members.mcafeeinstitute.com) through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial differentiating feature is its reinforcement knowing (RL) action, which was used to refine the design's reactions beyond the standard pre-training and [fine-tuning process](https://medea.medianet.cs.kent.edu). By incorporating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's geared up to break down intricate questions and reason through them in a detailed manner. This directed thinking procedure enables the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create [structured actions](https://www.greenpage.kr) while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the [market's attention](https://connectworld.app) as a flexible text-generation design that can be integrated into numerous workflows such as representatives, rational reasoning and data analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, allowing effective inference by routing queries to the most appropriate specialist "clusters." This approach allows the design to concentrate on different issue domains while [maintaining](https://git.yuhong.com.cn) overall [performance](https://jobedges.com). DeepSeek-R1 needs a minimum of 800 GB of [HBM memory](https://thefreedommovement.ca) in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of [GPU memory](https://www.soundofrecovery.org).
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more [effective architectures](https://myclassictv.com) based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:Princess3594) 70B). Distillation refers to a process of training smaller, more effective models to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and evaluate models against key security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](https://endhum.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, 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 confirm you're utilizing ml.p5e.48 xlarge for [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073113) endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limitation increase, create a limitation boost demand and reach out 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](https://hitechjobs.me) To Management (IAM) permissions to use Amazon Bedrock Guardrails. For guidelines, see Establish permissions to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, avoid [harmful](http://xn--ok0b74gbuofpaf7p.com) material, and assess designs against essential security requirements. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:FidelBatt531106) the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic [circulation involves](http://123.111.146.2359070) 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 to the design for inference. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the [intervention](https://gigen.net) and whether it happened at the input or output phase. The examples showcased in the following areas show [reasoning](https://wik.co.kr) 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, complete the following steps:
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1. On the Amazon Bedrock console, select Model catalog under [Foundation designs](https://essencialponto.com.br) in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [service provider](https://video.chops.com) and pick the DeepSeek-R1 model.
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The model detail page supplies vital details about the design's abilities, rates structure, and implementation guidelines. You can discover detailed usage directions, consisting of sample API calls and code bits for combination. The design supports different text generation tasks, consisting of material creation, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking abilities. +The page likewise consists of release alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be prompted to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 [alphanumeric](https://rna.link) characters). +5. For Number of circumstances, get in a number of circumstances (between 1-100). +6. For example type, select your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service function authorizations, and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:HunterY514213) encryption settings. For most use cases, the default settings will work well. However, for production implementations, you might desire to review these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can experiment with different triggers and change design criteria like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For example, content for inference.
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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 supplies instant feedback, assisting you comprehend how the model reacts to different inputs and letting you fine-tune your prompts for optimum outcomes.
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You can quickly evaluate the design in the [playground](https://heatwave.app) through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and [ApplyGuardrail API](http://152.136.126.2523000). 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. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:Margareta19E) configures inference specifications, and sends out a request to generate text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an [artificial](http://146.148.65.983000) intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can [release](https://avpro.cc) with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free approaches: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the approach that finest suits your [requirements](https://yourmoove.in).
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release 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 prompted to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design web browser displays available models, with details like the company name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if applicable), indicating that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to see 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 company details. +Deploy button to release 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 specifications. +- Usage standards
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Before you deploy the model, it's suggested to evaluate the model details and license terms to verify compatibility with your use case.
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6. Choose Deploy to [continue](http://47.100.81.115) with release.
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7. For Endpoint name, utilize the instantly produced 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, go into the number of instances (default: 1). +Selecting appropriate instance types and counts is vital for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the design.
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The implementation process can take numerous minutes to complete.
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When release is total, your endpoint status will alter to InService. At this point, the model is all set to accept inference requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is total, you can conjure up the design utilizing a runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the [SageMaker Python](https://www.jungmile.com) SDK
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To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:PHZIsis067429) 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](https://nojoom.net) predictor
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Similar to Amazon Bedrock, you can also utilize 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 shown in the following code:
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Clean up
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To avoid undesirable charges, finish the steps in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under [Foundation models](https://jobs.campus-party.org) in the navigation pane, pick Marketplace implementations. +2. In the Managed releases section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the right 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 model you released 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 deploy the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. 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 pretrained designs, Amazon SageMaker JumpStart [Foundation](http://101.132.73.143000) Models, Amazon Bedrock Marketplace, [yewiki.org](https://www.yewiki.org/User:WinifredHassell) and Starting 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](http://jatushome.myqnapcloud.com8090) at AWS. He assists emerging generative [AI](https://fcschalke04fansclub.com) business develop ingenious services utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the reasoning efficiency of large language models. In his downtime, Vivek enjoys hiking, seeing movies, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.ascarion.org) Specialist Solutions Architect with the Third-Party Model [Science](https://wiki.awkshare.com) team at AWS. His location of focus is AWS [AI](http://sl860.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://careers.ecocashholdings.co.zw) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://47.105.162.154) center. She is passionate about developing options that help customers accelerate their [AI](https://www.beyoncetube.com) journey and unlock organization worth.
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