From 6b1440d1cf6ac2adb1de8e8f6bebf71c262d1b09 Mon Sep 17 00:00:00 2001 From: Andy Burgin Date: Sun, 16 Feb 2025 18:36:22 +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..2080b66 --- /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](https://www.koumii.com) and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://vybz.live)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://www.koumii.com) concepts 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 comparable steps to deploy the distilled variations of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://www.oemautomation.com:8888) that utilizes support [learning](http://1.13.246.1913000) to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A [key identifying](https://peoplesmedia.co) feature is its support knowing (RL) step, which was used to improve the model's reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's geared up to break down complicated questions and factor through them in a detailed manner. This directed thinking procedure allows the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based [fine-tuning](http://git.rabbittec.com) with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the market's attention as a versatile text-generation model that can be incorporated into numerous workflows such as agents, rational thinking and information analysis jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows [activation](https://jobportal.kernel.sa) of 37 billion specifications, making it possible for efficient inference by routing queries to the most pertinent expert "clusters." This approach enables the design to concentrate on various problem domains while maintaining general performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the [thinking capabilities](http://123.111.146.2359070) 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 refers to a procedure of training smaller, more efficient designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we [advise deploying](http://git.risi.fun) this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and assess designs against essential security requirements. At the time of [writing](https://wp.nootheme.com) this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](https://papersoc.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) and confirm you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 [xlarge instance](http://szelidmotorosok.hu) in the AWS Region you are deploying. To ask for a limit boost, produce a limitation increase demand and reach out to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, prevent harmful material, and [examine](https://peoplesmedia.co) models against key safety criteria. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design responses deployed 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 develop the guardrail, see the GitHub repo.
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The basic circulation includes the following actions: First, the system [receives](https://jobsdirect.lk) 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](https://gitlab.tenkai.pl). After receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. However, if either the input or [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:FlorenceGuillen) output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The [examples showcased](https://www.atlantistechnical.com) in the following sections demonstrate reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers 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, [pick Model](https://git.maxwellj.xyz) brochure under Foundation models in the navigation pane. +At the time of composing this post, you can use 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 choose the DeepSeek-R1 model.
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The design detail page supplies vital details about the model's capabilities, rates structure, and execution guidelines. You can find detailed use directions, consisting of sample API calls and code snippets for integration. The design supports different text generation tasks, including material creation, code generation, and concern answering, using its [support finding](https://www.airemploy.co.uk) out optimization and CoT reasoning abilities. +The page likewise includes implementation choices and to assist you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be prompted to configure the [deployment details](https://surgiteams.com) for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in 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, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production deployments, you might desire to review these [settings](https://fotobinge.pincandies.com) to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can explore different triggers and change design specifications like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For example, material for reasoning.
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This is an excellent method to explore the model's thinking and text generation abilities before incorporating it into your applications. The play area supplies instant feedback, assisting you comprehend how the model reacts to numerous inputs and letting you fine-tune your triggers for ideal results.
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You can rapidly evaluate the model in the [playground](https://youtubegratis.com) through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce 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 client, [configures reasoning](https://www.jobindustrie.ma) parameters, and sends out a demand to produce 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 intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can [release](https://recruitment.econet.co.zw) with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient approaches: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the approach that best fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart 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, choose Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model web browser displays available models, with details like the company name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card shows key details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the model
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5. Choose the model card to view the design details page.
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The design details page consists of the following details:
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- The design name and provider 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](https://plamosoku.com) description. +- License details. +- Technical specs. +- Usage guidelines
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Before you release the model, it's suggested to examine the model details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, use the automatically produced name or [produce](http://osbzr.com) a custom one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of instances (default: 1). +Selecting appropriate instance types and counts is essential for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is [enhanced](http://103.205.66.473000) for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. [Choose Deploy](https://1.214.207.4410333) to release the model.
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The release procedure can take several minutes to finish.
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When release is total, your endpoint status will change to InService. At this point, the design is prepared to accept inference demands through the endpoint. You can keep an eye on the implementation development on the SageMaker [console Endpoints](https://pittsburghtribune.org) page, which will show relevant metrics and status details. When the implementation is complete, you can conjure up the model 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 get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1078543) make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for [inference programmatically](https://gitlab.syncad.com). The code for releasing the design is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run extra requests 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, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:AllenHankins0) you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and implement it as revealed in the following code:
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Clean up
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To avoid undesirable charges, finish the actions in this area to clean 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, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments. +2. In the Managed implementations section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're [erasing](https://git.bwt.com.de) the right implementation: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://git.synz.io) 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](https://geohashing.site). Use the following code to erase the endpoint if you wish to stop [sustaining charges](https://git.soy.dog). For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, we checked out 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 get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker 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](https://career.logictive.solutions) Architect for [Inference](http://experienciacortazar.com.ar) at AWS. He assists emerging generative [AI](https://git.pxlbuzzard.com) business construct ingenious services using AWS services and accelerated calculate. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the reasoning performance of large language models. In his downtime, Vivek delights in treking, seeing motion pictures, [raovatonline.org](https://raovatonline.org/author/arletha3316/) and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://jobsleed.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://social.myschoolfriend.ng) 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 working on generative [AI](https://ou812chat.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://blkbook.blactive.com) hub. She is passionate about developing options that assist clients accelerate their [AI](https://joydil.com) [journey](https://pak4job.com) and unlock business value.
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