commit cccfca407a2750957e0f0ca34b9e8024e36e7c3b Author: ebikris0383733 Date: Sun Feb 16 00:12:13 2025 +0800 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..15817e2 --- /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 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 deploy DeepSeek [AI](http://test-www.writebug.com:3000)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](http://42.192.69.228:13000) 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 actions to release the distilled versions of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) [established](https://www.suntool.top) by DeepSeek [AI](https://score808.us) that uses reinforcement learning to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing function is its support knowing (RL) action, which was utilized to fine-tune the model's actions beyond the [standard pre-training](https://swahilihome.tv) and tweak procedure. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually boosting both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's [equipped](http://121.37.166.03000) to break down complicated inquiries and reason through them in a detailed way. This directed reasoning procedure allows the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, rational reasoning and information analysis jobs.
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DeepSeek-R1 utilizes a [Mixture](http://git.dashitech.com) of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient reasoning by routing questions to the most appropriate professional "clusters." This technique permits the design to specialize in various problem domains while maintaining overall [effectiveness](https://lazerjobs.in). DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge includes 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 design to more efficient 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 efficient designs to imitate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and evaluate models against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and [Bedrock](https://liveyard.tech4443) Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](http://playtube.ythomas.fr) applications.
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Prerequisites
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To deploy 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, pick Amazon SageMaker, and verify 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](http://39.96.8.15010080) you are releasing. To request a limit boost, develop a limitation increase request and connect to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use [guardrails](http://gitlab.hanhezy.com) for [material filtering](https://career.abuissa.com).
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ShayneTerrill) avoid harmful material, and evaluate designs against key safety criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and [model responses](http://47.104.60.1587777) deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail [utilizing](https://dandaelitetransportllc.com) 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 circulation includes the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After receiving 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 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 sections show reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon [Bedrock Marketplace](https://fewa.hudutech.com) offers you access to over 100 popular, emerging, and [specialized structure](http://106.15.120.1273000) designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane. +At the time of writing 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](http://120.36.2.2179095) as a service provider and pick the DeepSeek-R1 design.
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The design detail page supplies important details about the model's capabilities, rates structure, and implementation standards. You can find detailed use directions, including sample API calls and code snippets for [integration](https://atomouniversal.com.br). The design supports numerous text generation jobs, [consisting](https://bence.net) of content creation, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT thinking abilities. +The page also includes implementation choices and licensing details to assist you get begun with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select Deploy.
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You will be [triggered](https://express-work.com) to set up the deployment 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 instances, enter a variety of circumstances (in between 1-100). +6. For example type, choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service role authorizations, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you might desire to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the release is complete, 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 try out various prompts and adjust design parameters 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 instance, content for inference.
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This is an excellent way to check out the model's reasoning and text generation capabilities 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 design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, utilize the following code to implement guardrails. The script [initializes](https://git.yuhong.com.cn) the bedrock_[runtime](http://g-friend.co.kr) client, configures reasoning criteria, and sends a request to produce text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](https://21fun.app) is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production utilizing either the UI or SDK.
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[Deploying](http://aiot7.com3000) DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient approaches: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you choose the approach that best [matches](https://git.obo.cash) your needs.
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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](https://www.bolsadetrabajotafer.com) JumpStart:
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1. On the SageMaker console, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) select Studio in the navigation pane. +2. First-time users will be triggered to produce 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 service provider name and [design capabilities](https://melanatedpeople.net).
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows key details, consisting of:
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[- Model](https://haloentertainmentnetwork.com) name +- Provider name +- Task classification (for instance, Text Generation). +[Bedrock Ready](https://finance.azberg.ru) badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
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5. Choose the model card to see the model details page.
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The design details page includes the following details:
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- The design name and supplier 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 standards
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Before you release the model, it's suggested to review the model details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the instantly created name or create a custom-made one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of instances (default: 1). +Selecting suitable circumstances types and counts is crucial for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The release procedure can take several minutes to complete.
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When release is complete, your endpoint status will alter to InService. At this point, the design is all set to accept inference demands through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can conjure up the design utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up 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 release and utilize DeepSeek-R1 for [inference programmatically](https://goodinfriends.com). The code for releasing the design is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional 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 develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Clean up
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To avoid undesirable charges, complete the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. +2. In the Managed implementations section, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate 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 expenses if you leave it running. Use the following code to delete 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](http://121.37.208.1923000) how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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 helps emerging generative [AI](https://gitlab.informicus.ru) companies develop innovative services using AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference efficiency of big language designs. In his leisure time, Vivek delights in treking, watching movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://guiding-lights.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://dztrader.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a [Specialist Solutions](https://git.serenetia.com) Architect dealing with generative [AI](http://135.181.29.174:3001) with the Third-Party Model [Science team](https://professionpartners.co.uk) 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](http://52.23.128.62:3000) center. She is enthusiastic about developing options that help consumers accelerate their [AI](https://atfal.tv) journey and unlock company value.
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