commit 35c57f0c6924b57a9491d04ef5759d4745e41501 Author: latishaspyer81 Date: Tue Feb 18 04:06:51 2025 +0330 Update '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..b0bc389 --- /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 and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://175.178.71.89:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](http://git.baobaot.com) concepts on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps 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) developed by DeepSeek [AI](http://youtubeer.ru) that utilizes reinforcement discovering to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing function is its reinforcement learning (RL) step, which was utilized to improve the design's actions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both relevance and [clarity](http://hybrid-forum.ru). In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it's geared up to break down complex queries and reason through them in a detailed manner. This assisted thinking process allows the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be integrated into various workflows such as representatives, sensible thinking and data interpretation tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, [allowing efficient](https://noaisocial.pro) reasoning by routing inquiries to the most pertinent professional "clusters." This approach enables the model to specialize in different problem domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and [yewiki.org](https://www.yewiki.org/User:MaisieRoldan5) 70B). Distillation describes a process of training smaller sized, more efficient models to mimic the habits and thinking patterns of the larger DeepSeek-R1 model, utilizing 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 advise releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and examine models against crucial security [requirements](https://www.arztstellen.com). At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only 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 safety controls throughout your generative [AI](http://202.164.44.246:3000) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To inspect 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 usage](https://socialsnug.net). Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limitation increase, develop a limitation boost demand and connect to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS [Identity](https://scode.unisza.edu.my) and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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[Amazon Bedrock](https://gogs.koljastrohm-games.com) Guardrails enables you to present safeguards, prevent harmful content, and examine designs against [crucial safety](https://www.cittamondoagency.it) requirements. You can execute safety [measures](https://tartar.app) for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic circulation includes the following steps: First, the system gets an input for the design. This input is then processed through the [ApplyGuardrail API](http://stay22.kr). If the input passes the [guardrail](https://nursingguru.in) check, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) it's sent out to the design for reasoning. After getting the model's output, [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:MyraCollocott58) another guardrail check is used. If the output passes this final check, it's returned as the final outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the [intervention](http://118.89.58.193000) and whether it took place at the input or output stage. The examples showcased in the following areas 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, complete the following steps:
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1. On the Amazon Bedrock console, select [Model catalog](http://47.92.26.237) under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.
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The model detail page supplies important details about the design's abilities, prices structure, and application standards. You can discover detailed usage instructions, consisting of sample API calls and code bits for integration. The model supports numerous text generation tasks, including material production, code generation, and concern answering, using its reinforcement finding out optimization and CoT reasoning abilities. +The page likewise consists of [deployment options](https://git.saphir.one) and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select Deploy.
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You will be triggered to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a variety of circumstances (between 1-100). +6. For example type, select your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you might wish to examine these settings to align with your company's security and compliance requirements. +7. [Choose Deploy](http://youtubeer.ru) to begin using the design.
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When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with various triggers and change model criteria like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, material for reasoning.
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This is an excellent way to check out the [model's thinking](https://www.thempower.co.in) and text generation abilities before incorporating it into your applications. The play ground provides instant feedback, assisting you understand how the design reacts to different inputs and letting you tweak your prompts for optimal outcomes.
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You can rapidly check the model in the play ground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, [configures inference](https://equipifieds.com) specifications, and sends a [request](http://111.2.21.14133001) 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 solutions that you can [release](https://haitianpie.net) with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses two hassle-free techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's [explore](http://194.87.97.823000) both approaches to help you select the technique that best fits 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 using SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design web browser shows available designs, with details like the provider name and [design abilities](https://zenabifair.com).
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card reveals key details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if relevant), suggesting that this design can be [registered](http://archmageriseswiki.com) with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to see the model details page.
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The [model details](http://charmjoeun.com) page includes the following details:
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- The design name and provider details. +Deploy button to release the design. +About and Notebooks tabs with [detailed](http://git.taokeapp.net3000) details
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The About tab includes crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you release the model, it's suggested to review the model details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the automatically produced name or create a custom one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of circumstances (default: 1). +Selecting proper circumstances types and counts is vital 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 optimized for [sustained traffic](https://3flow.se) 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 seclusion remains in location. +11. Choose Deploy to release the model.
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The deployment procedure can take a number of minutes to complete.
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When implementation is total, your endpoint status will alter to [InService](http://47.103.112.133). At this point, the model is ready to accept reasoning requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is complete, you can invoke the model using a SageMaker runtime client and [surgiteams.com](https://surgiteams.com/index.php/User:ZakNeff06884) integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for [deploying](http://www.fasteap.cn3000) the design is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run extra 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock [console](http://git.vimer.top3000) or the API, and [execute](https://propveda.com) it as shown in the following code:
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Clean up
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To prevent unwanted charges, complete 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 released the model 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 releases. +2. In the Managed releases area, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [pick Delete](https://nujob.ch). +4. Verify the endpoint details to make certain you're erasing the right 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 design you [released](https://git.chirag.cc) will sustain costs if you leave it running. Use the following code to delete the endpoint if you want 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 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use [Amazon Bedrock](https://www.arztsucheonline.de) tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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](https://palsyworld.com) at AWS. He helps emerging generative [AI](http://forum.altaycoins.com) business build ingenious options using AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the inference efficiency of big language models. In his totally free time, Vivek takes [pleasure](https://www.tqmusic.cn) in hiking, seeing motion pictures, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://wellandfitnessgn.co.kr) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://optimiserenergy.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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[Jonathan Evans](https://firstcanadajobs.ca) is a Specialist Solutions Architect working on generative [AI](https://skillfilltalent.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's [artificial intelligence](https://git.runsimon.com) and generative [AI](https://git.augustogunsch.com) center. She is enthusiastic about developing services that help customers accelerate their [AI](https://haitianpie.net) journey and unlock service worth.
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