abbeyshook7707/wisptales
1
0
Code Issues Actions Packages Projects Wiki

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

Abbey Shook 2025-02-27 09:15:49 +08:00
parent 02fb96cc94
commit 0e3379d578
1 changed files with 93 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

93
DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md Normal file

@ -0,0 +1,93 @@
<br>Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://git.rungyun.cn)['s first-generation](https://git.cno.org.co) frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative [AI](https://jobs.campus-party.org) concepts on AWS.<br>
<br>In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the designs too.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](https://stagingsk.getitupamerica.com) that utilizes reinforcement discovering to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing function is its reinforcement learning (RL) action, which was used to refine the model's reactions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately enhancing both significance and [clearness](https://jobs.cntertech.com). In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, [indicating](https://www.punajuaj.com) it's geared up to break down complicated inquiries and factor through them in a detailed manner. This directed thinking process permits the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as agents, sensible thinking and data interpretation tasks.<br>
<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient reasoning by routing queries to the most appropriate professional "clusters." This approach permits the design to focus on different issue domains while maintaining overall performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more effective architectures based upon [popular](https://git.laser.di.unimi.it) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient designs to imitate the habits and [thinking patterns](https://sugardaddyschile.cl) of the bigger DeepSeek-R1 model, using it as an instructor design.<br>
<br>You can [release](https://techtalent-source.com) DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and evaluate designs against crucial security requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative [AI](https://quikconnect.us) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To examine 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 usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limitation boost, produce a limit boost request and connect to your account team.<br>
<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to use guardrails for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>[Amazon Bedrock](https://friendfairs.com) Guardrails enables you to introduce safeguards, avoid harmful content, and assess models against crucial safety criteria. You can implement [precaution](http://www.zhihutech.com) for the DeepSeek-R1 model utilizing the Amazon Bedrock [ApplyGuardrail API](https://candays.com). This allows you to apply guardrails to [examine](https://ipen.com.hk) user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.<br>
<br>The basic flow involves the following steps: First, the system receives an input for the design. 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 model's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. 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 occurred at the input or output stage. The examples showcased in the following sections demonstrate inference using this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>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, total the following steps:<br>
<br>1. On the [Amazon Bedrock](https://firstcanadajobs.ca) console, select Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other [Amazon Bedrock](http://120.48.7.2503000) tooling.
2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 design.<br>
<br>The model detail page offers essential details about the model's capabilities, pricing structure, and execution guidelines. You can discover detailed usage guidelines, including sample API calls and code bits for integration. The design supports different text generation tasks, consisting of material creation, code generation, and question answering, utilizing its support discovering optimization and CoT thinking abilities.
The page also includes release options and licensing details to help you start with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.<br>
<br>You will be prompted to configure the implementation 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, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:BonnieValle7) get in a number of circumstances (in between 1-100).
6. For example type, choose your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might desire to review these settings to align with your company's security and compliance requirements.
7. Choose Deploy to start utilizing the model.<br>
<br>When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive user interface where you can experiment with different triggers and change 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 outcomes. For example, material for inference.<br>
<br>This is an exceptional method to check out the design's thinking and text generation abilities before integrating it into your applications. The playground offers instant feedback, helping you comprehend how the design reacts to different inputs and letting you [fine-tune](http://129.211.184.1848090) your prompts for ideal outcomes.<br>
<br>You can rapidly test the model in the playground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
<br>Run inference using guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to perform reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop 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 created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends a demand to create text based on a user timely.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) center 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 designs to your usage case, with your information, and deploy them into [production utilizing](https://www.chinami.com) either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient approaches: using the instinctive SageMaker JumpStart UI or executing programmatically through the [SageMaker Python](https://minka.gob.ec) SDK. Let's explore both techniques to help you pick the method that best matches your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be [triggered](https://jobsfevr.com) to create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.<br>
<br>The model browser shows available designs, with details like the company name and model abilities.<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card shows essential details, including:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if relevant), suggesting that this model can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model<br>
<br>5. Choose the model card to see the design details page.<br>
<br>The design details page consists of the following details:<br>
<br>- The design name and provider details.
[Deploy button](http://eliment.kr) to deploy the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab includes essential details, such as:<br>
<br>- Model [description](http://app.ruixinnj.com).
- License details.
- Technical specs.
[- Usage](https://git.micahmoore.io) guidelines<br>
<br>Before you release the design, it's recommended to examine the design details and license terms to confirm compatibility with your use case.<br>
<br>6. Choose Deploy to continue with release.<br>
<br>7. For Endpoint name, use the instantly created name or develop a custom one.
8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the number of circumstances (default: 1).
Selecting proper instance types and counts is important for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the design.<br>
<br>The deployment procedure can take a number of minutes to finish.<br>
<br>When deployment is total, your endpoint status will change to InService. At this moment, the model is ready to accept inference requests through the [endpoint](https://git.tesinteractive.com). You can keep track of the implementation progress on the SageMaker console Endpoints 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](https://securityjobs.africa).<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS [authorizations](https://vidhiveapp.com) and environment setup. The following is a detailed code example that shows how to deploy and [utilize](http://150.158.93.1453000) DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.<br>
<br>You can run additional demands 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 develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:<br>
<br>Tidy up<br>
<br>To avoid [undesirable](https://videobox.rpz24.ir) charges, finish the actions in this section to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace deployment<br>
<br>If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments.
2. In the [Managed implementations](http://47.122.26.543000) area, locate the endpoint you desire to delete.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you're erasing the appropriate implementation: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart design you deployed will sustain expenses if you leave it [running](https://git.dev.advichcloud.com). Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
<br>Conclusion<br>
<br>In this post, we explored how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://git.aionnect.com) or Amazon Bedrock Marketplace now to start. For more details, describe Use [Amazon Bedrock](https://git.rungyun.cn) tooling with Amazon SageMaker JumpStart models, 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://www.execafrica.com) business develop innovative solutions using [AWS services](https://service.aicloud.fit50443) and sped up calculate. Currently, he is focused on developing methods for fine-tuning and enhancing the inference efficiency of big . In his free time, Vivek takes pleasure in treking, viewing movies, and attempting various foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://www.yaweragha.com) Specialist Solutions Architect with the Third-Party Model [Science](https://www.maisondurecrutementafrique.com) team at AWS. His area of focus is AWS [AI](https://videobox.rpz24.ir) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://play.future.al) with the [Third-Party Model](https://pinecorp.com) Science team at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and [tactical partnerships](https://calamitylane.com) for Amazon SageMaker JumpStart, [SageMaker's artificial](https://www.klartraum-wiki.de) intelligence and generative [AI](http://123.60.103.97:3000) hub. She is passionate about developing services that assist consumers accelerate their [AI](https://heatwave.app) journey and unlock business value.<br>