Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

2025-02-21 22:55:57 -05:00 · 2025-02-21 22:55:57 -05:00 · 31a279bbea
commit 31a279bbea
1 changed files with 93 additions and 0 deletions
--- a/JumpStart.-.md
+++ b/JumpStart.-.md
@ -0,0 +1,93 @@
 <br>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 [AI](https://online-learning-initiative.org)'s first-generation frontier design, DeepSeek-R1, along with the distilled [versions varying](http://www.withsafety.net) from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://195.216.35.156) ideas on AWS.<br>
 <br>In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs too.<br>
 <br>Overview of DeepSeek-R1<br>
 <br>DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](http://forum.altaycoins.com) that uses support discovering to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its reinforcement learning (RL) action, which was utilized to refine the model's responses beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's equipped to break down intricate queries and reason through them in a detailed manner. This directed reasoning process permits the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the market's attention as a flexible text-generation design that can be integrated into various workflows such as agents, rational reasoning and information interpretation jobs.<br>
 <br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient inference by routing inquiries to the most relevant specialist "clusters." This [technique enables](https://hylpress.net) the design to focus on different problem domains while maintaining overall [efficiency](https://code.52abp.com). DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br>
 <br>DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more efficient models to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.<br>
 <br>You can release DeepSeek-R1 design either through SageMaker JumpStart or  [photorum.eclat-mauve.fr](http://photorum.eclat-mauve.fr/profile.php?id=263362) Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest [deploying](https://hylpress.net) this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and assess models against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://jobwings.in) [applications](https://vlogloop.com).<br>
 <br>Prerequisites<br>
 <br>To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're [utilizing](https://www.grandtribunal.org) ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limit boost, create a limit increase demand and reach out to your account group.<br>
 <br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper [AWS Identity](https://xotube.com) and Gain Access To Management (IAM) approvals to utilize Amazon [Bedrock Guardrails](https://git.nothamor.com3000). For directions, see Establish authorizations to use guardrails for content [filtering](https://4kwavemedia.com).<br>
 <br>Implementing guardrails with the ApplyGuardrail API<br>
 <br>Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous content, and examine models against crucial safety requirements. You can carry out safety procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the [GitHub repo](https://deepsound.goodsoundstream.com).<br>
 <br>The basic flow includes the following actions: 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 out to the model for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in 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 areas show inference using this API.<br>
 <br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
 <br>Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:<br>
 <br>1. On the Amazon Bedrock console, select 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 as a service provider and pick the DeepSeek-R1 model.<br>
 <br>The design detail page provides important details about the [design's](https://workmate.club) abilities, pricing structure, and implementation guidelines. You can find detailed use guidelines, including sample API calls and [code bits](http://120.77.213.1393389) for combination. The model supports different text generation tasks, consisting of content production, code generation, and concern answering, using its [support learning](https://rhabits.io) optimization and CoT thinking capabilities.
 The page also consists of [release choices](https://www.sparrowjob.com) and licensing details to assist you begin with DeepSeek-R1 in your applications.
 3. To begin using DeepSeek-R1, choose Deploy.<br>
 <br>You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
 4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
 5. For Number of circumstances, get in a number of instances (between 1-100).
 6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based [instance type](http://code.hzqykeji.com) like ml.p5e.48 xlarge is recommended.
 Optionally, you can configure innovative security and facilities settings, including virtual personal cloud (VPC) networking, service function consents, and encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you might want to review these settings to align with your organization's security and compliance requirements.
 7. Choose Deploy to start utilizing the model.<br>
 <br>When the implementation is complete, you can evaluate 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 different prompts and adjust model parameters like temperature level and maximum length.
 When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, material for reasoning.<br>
 <br>This is an excellent way to explore the design's thinking and text generation abilities before incorporating it into your applications. The play area offers instant feedback, assisting you comprehend how the design reacts to various inputs and letting you fine-tune your triggers for ideal outcomes.<br>
 <br>You can rapidly test the model in the [playground](https://www.jobsalert.ai) through the UI. However, to invoke the deployed design [programmatically](http://haiji.qnoddns.org.cn3000) with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
 <br>Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint<br>
 <br>The following code example demonstrates how to perform inference using a [deployed](https://sameday.iiime.net) DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a request to create text based upon a user timely.<br>
 <br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
 <br>SageMaker JumpStart is an [artificial intelligence](https://git.songyuchao.cn) (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can deploy 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.<br>
 <br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient approaches: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the approach that best suits your needs.<br>
 <br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
 <br>Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:<br>
 <br>1. On the SageMaker console, pick 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.<br>
 <br>The design internet browser displays available models, with details like the provider name and design capabilities.<br>
 <br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
 Each design card shows crucial details, including:<br>
 <br>- Model name
 - Provider name
 - Task category (for instance, Text Generation).
 Bedrock Ready badge (if relevant), showing that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design<br>
 <br>5. Choose the design card to see the design details page.<br>
 <br>The design details page includes the following details:<br>
 <br>- The design name and supplier details.
 Deploy button to deploy the model.
 About and Notebooks tabs with detailed details<br>
 <br>The About tab consists of important details, such as:<br>
 <br>- Model description.
 - License details.
 - Technical requirements.
 - Usage standards<br>
 <br>Before you release the model, it's [suggested](https://media.izandu.com) to examine the model details and license terms to confirm compatibility with your use case.<br>
 <br>6. Choose Deploy to continue with deployment.<br>
 <br>7. For Endpoint name, use the immediately generated name or develop a custom one.
 8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
 9. For  [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) Initial instance count, get in the variety of circumstances (default: 1).
 Selecting proper instance 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 chosen by default. This is optimized for sustained traffic and low latency.
 10. Review all setups for accuracy. For this design, we strongly advise adhering to [SageMaker JumpStart](http://haiji.qnoddns.org.cn3000) default settings and making certain that network isolation remains in location.
 11. Choose Deploy to deploy the design.<br>
 <br>The deployment process can take several minutes to finish.<br>
 <br>When implementation is complete, your [endpoint status](https://www.lizyum.com) will alter to InService. At this moment, the model is all set to accept reasoning requests through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will display pertinent metrics and [status details](http://ncdsource.kanghehealth.com). When the implementation is total, you can conjure up the design utilizing a SageMaker runtime customer and incorporate it with your applications.<br>
 <br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
 <br>To get started with DeepSeek-R1 utilizing the SageMaker Python SDK,  [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1093497) you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.<br>
 <br>You can run extra requests against the predictor:<br>
 <br>Implement guardrails and run inference with your SageMaker JumpStart predictor<br>
 <br>Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the [Amazon Bedrock](https://allcollars.com) console or the API, and execute it as displayed in the following code:<br>
 <br>Clean up<br>
 <br>To prevent unwanted charges, finish the actions in this area to clean up your resources.<br>
 <br>Delete the Amazon Bedrock Marketplace deployment<br>
 <br>If you released the model using Amazon Bedrock Marketplace, total the following steps:<br>
 <br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations.
 2. In the Managed releases area, find the endpoint you desire to delete.
 3. Select the endpoint, and on the Actions menu, pick Delete.
 4. Verify the endpoint details to make certain you're erasing the proper deployment: 1. Endpoint name.
 2. Model name.
 3. Endpoint status<br>
 <br>Delete the SageMaker JumpStart predictor<br>
 <br>The SageMaker JumpStart model you released will sustain costs if you leave it . Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, see [Delete Endpoints](http://www.hanmacsamsung.com) and Resources.<br>
 <br>Conclusion<br>
 <br>In this post, we checked out 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 begun. For more details, describe 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.<br>
 <br>About the Authors<br>
 <br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://ofebo.com) business develop innovative options utilizing AWS services and sped up calculate. Currently, he is focused on developing techniques for  [raovatonline.org](https://raovatonline.org/author/terryconnor/) fine-tuning and optimizing the reasoning performance of large language models. In his downtime, Vivek takes pleasure in hiking, watching films, and attempting various foods.<br>
 <br>Niithiyn Vijeaswaran is a Generative [AI](https://git.mhurliman.net) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://www.hanmacsamsung.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
 <br>Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://oakrecruitment.uk) with the Third-Party Model Science group at AWS.<br>
 <br>Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://123.111.146.235:9070) hub. She is passionate about developing services that assist consumers accelerate their [AI](https://git.pandaminer.com) journey and unlock organization value.<br>