commit 25af2c8b0829ede534fb07d4f8aafbb303981cf8 Author: angelikabagot Date: Fri Apr 4 02:08:07 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..d2f9225 --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://116.62.145.60:4000)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](http://82.156.24.193:10098) ideas on AWS.
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In this post, we demonstrate 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 models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) [developed](https://www.mediarebell.com) by DeepSeek [AI](https://157.56.180.169) that uses reinforcement learning to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial differentiating function is its reinforcement learning (RL) action, which was used to fine-tune the model's reactions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, [eventually enhancing](https://studentvolunteers.us) both importance and clearness. In addition, DeepSeek-R1 [utilizes](https://somo.global) a chain-of-thought (CoT) method, implying it's geared up to break down complicated questions and factor through them in a detailed manner. This guided thinking process enables the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, sensible reasoning and data interpretation jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, allowing effective inference by routing inquiries to the most [pertinent](https://asesordocente.com) expert "clusters." This [approach](https://git.frugt.org) allows the design to focus on various issue domains while maintaining overall performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and assess models against [crucial security](https://login.discomfort.kz) requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](http://43.138.236.3:9000) 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 instance in the AWS Region you are deploying. To request a limitation increase, develop a limitation boost demand and [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:ElvisBynum53054) connect to your account team.
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Because you will be releasing this design with [Amazon Bedrock](https://hyg.w-websoft.co.kr) Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Set up approvals to use [guardrails](http://www.haimimedia.cn3001) for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to [introduce](https://gitcq.cyberinner.com) safeguards, prevent harmful material, and examine models against [crucial safety](https://git.toolhub.cc) criteria. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model actions released on [Amazon Bedrock](http://121.36.27.63000) Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock [console](http://engineerring.net) 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 steps: 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 to the model 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 result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the [intervention](https://git.rootfinlay.co.uk) and whether it took place at the input or output stage. The examples showcased in the following sections show reasoning 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, total the following actions:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane. +At the time of writing 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 company and pick the DeepSeek-R1 model.
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The design detail page supplies vital details about the design's capabilities, pricing structure, and implementation standards. You can find detailed usage guidelines, including sample API calls and code bits for combination. The model supports different text generation tasks, consisting of material development, code generation, and concern answering, using its reinforcement discovering optimization and [CoT reasoning](http://gungang.kr) capabilities. +The page also includes deployment alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select Deploy.
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You will be prompted to set up the [release details](https://jobskhata.com) for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a number of instances (in between 1-100). +6. For [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:VetaHavelock69) example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For most use cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing 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 playground to access an interactive interface where you can explore different prompts and adjust design specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For example, material for reasoning.
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This is an outstanding method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The playground offers instant feedback, assisting you understand how the model reacts to various inputs and letting you tweak your triggers for [optimal](http://114.34.163.1743333) results.
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You can quickly test the model in the play area through the UI. However, to invoke the deployed design 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 demonstrates how to perform reasoning using a released 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 [disgaeawiki.info](https://disgaeawiki.info/index.php/User:ShanaBickford13) the example code to create the guardrail, see the [GitHub repo](http://minority2hire.com). After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends out a request to produce text based on a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an [artificial intelligence](https://git.augustogunsch.com) (ML) center with FMs, integrated algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage 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 offers two practical methods: utilizing the intuitive SageMaker [JumpStart](https://shareru.jp) UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you pick the method that best matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model web browser displays available models, with details like the company name and [model capabilities](https://www.cbl.health).
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card [reveals](https://farmjobsuk.co.uk) crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to view the model details page.
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The design details page includes the following details:
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- The model name and company details. +[Deploy button](https://startuptube.xyz) to deploy the design. +About and [Notebooks tabs](https://careers.ebas.co.ke) with detailed details
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The About tab includes crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you deploy the model, it's recommended to review the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For [Endpoint](https://git.we-zone.com) name, utilize the immediately produced name or produce a customized one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of circumstances (default: 1). +Selecting appropriate instance types and counts is crucial for expense and performance 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 configurations for accuracy. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. [Choose Deploy](https://cl-system.jp) to release the design.
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The implementation process can take several minutes to complete.
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When deployment is complete, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GenieFaulding06) your endpoint status will change to [InService](https://bytevidmusic.com). At this point, the model is ready to accept reasoning [requests](https://axeplex.com) through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the [release](https://subemultimedia.com) is complete, you can conjure up the design utilizing a SageMaker runtime client and integrate 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](https://157.56.180.169) SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional requests 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To prevent undesirable charges, finish the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases. +2. In the Managed deployments section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the proper implementation: 1. [Endpoint](https://sodam.shop) 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 deployed 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](https://oninabresources.com) how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1330524) SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, [Amazon Bedrock](https://wiki.trinitydesktop.org) 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](https://hebrewconnect.tv) Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://www.oemautomation.com:8888) business construct ingenious options using AWS services and accelerated compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning performance of large language designs. In his totally free time, Vivek takes pleasure in hiking, seeing motion pictures, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://expertsay.blog) Specialist Solutions with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://git.phyllo.me) 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](https://forum.alwehdaclub.sa) with generative [AI](http://116.62.145.60:4000) with the Third-Party Model Science group 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](https://git.declic3000.com) center. She is passionate about constructing options that help customers accelerate their [AI](https://gitea.cronin.one) journey and unlock organization worth.
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