christopherrab/youtubeer
1
0
Code Issues Actions Packages Projects Wiki

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

Christopher Rabin 2025-02-07 18:13:55 +08:00
commit 4c7c2eb3ae
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 thrilled 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](https://natgeophoto.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to construct, experiment, and [properly scale](https://pivotalta.com) your generative [AI](https://dhivideo.com) ideas on AWS.<br>
<br>In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the models also.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language model (LLM) established by [DeepSeek](https://baitshepegi.co.za) [AI](https://git.mm-music.cn) that utilizes support finding out to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying function is its reinforcement knowing (RL) step, which was used to fine-tune the model's reactions beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, eventually enhancing both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's equipped to break down complicated queries and factor through them in a detailed way. This guided thinking procedure permits the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a flexible text-generation design that can be incorporated into different workflows such as agents, logical reasoning and information [interpretation](https://kiwiboom.com) tasks.<br>
<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing questions to the most relevant specialist "clusters." This method allows the model to specialize in various issue domains while maintaining total performance. 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 to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor design.<br>
<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock [Guardrails](https://geniusactionblueprint.com) to present safeguards, prevent damaging content, and [examine](https://www.ksqa-contest.kr) designs against essential safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and [Bedrock](https://chancefinders.com) Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your [generative](https://degroeneuitzender.nl) [AI](http://luodev.cn) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 model, you need access to an ml.p5e [circumstances](https://git.kairoscope.net). To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate 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 you are deploying. To ask for a limitation increase, create a limitation increase demand and connect to your account team.<br>
<br>Because you will be [deploying](https://cheere.org) this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock [Guardrails](https://gitea.potatox.net). For guidelines, see Set up authorizations to utilize guardrails for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging material, and evaluate designs against crucial security requirements. You can implement safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model reactions deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](http://tanpoposc.com). You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.<br>
<br>The basic circulation involves 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 out to the design for inference. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, [wavedream.wiki](https://wavedream.wiki/index.php/User:DeliaGarrett5) a message is returned showing the nature of the intervention and whether it happened at the input or [output phase](https://0miz2638.cdn.hp.avalon.pw9443). The examples showcased in the following areas show reasoning utilizing this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure [designs](http://git.meloinfo.com) (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br>
<br>1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock [tooling](http://187.216.152.1519999).
2. Filter for [DeepSeek](https://www.pkjobshub.store) as a [company](https://lastpiece.co.kr) and pick the DeepSeek-R1 design.<br>
<br>The model detail page supplies necessary details about the design's capabilities, rates structure, and execution standards. You can find detailed use guidelines, consisting of sample API calls and code snippets for integration. The design supports numerous text generation jobs, including content development, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT thinking abilities.
The page likewise includes deployment options and [raovatonline.org](https://raovatonline.org/author/roxanalechu/) licensing details to help you get going with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.<br>
<br>You will be triggered to configure the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of instances, go into a number of circumstances (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 suggested.
Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function approvals, and encryption settings. For most use cases, the default settings will work well. However, for production implementations, you might wish to examine these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to begin using the model.<br>
<br>When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive user interface where you can experiment with different triggers and change model parameters like temperature and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For instance, material for inference.<br>
<br>This is an exceptional way to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The playground supplies instant feedback, helping you [comprehend](https://whotube.great-site.net) how the model reacts to different inputs and letting you tweak your prompts for optimum results.<br>
<br>You can quickly check the design in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run inference using guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example shows how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create 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](http://122.51.230.863000) the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends a request to generate text based upon a user prompt.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production using either the UI or SDK.<br>
<br>[Deploying](https://my.buzztv.co.za) DeepSeek-R1 design through SageMaker JumpStart uses 2 practical techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you choose the method that best matches your requirements.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to deploy 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, pick JumpStart in the navigation pane.<br>
<br>The model browser displays available designs, with details like the supplier name and model capabilities.<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card reveals crucial details, consisting of:<br>
<br>- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design<br>
<br>5. Choose the design card to see the design details page.<br>
<br>The design details page consists of the following details:<br>
<br>- The design name and service provider details.
Deploy button to release the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of crucial details, such as:<br>
<br>- Model description.
- License details.
- Technical [requirements](https://sowjobs.com).
- Usage standards<br>
<br>Before you deploy the model, it's suggested to evaluate the design details and license terms to confirm compatibility with your use case.<br>
<br>6. Choose Deploy to proceed with implementation.<br>
<br>7. For Endpoint name, [utilize](https://empleos.contatech.org) the automatically created name or create a custom-made one.
8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the variety of circumstances (default: 1).
Selecting appropriate [circumstances types](https://git.ashcloudsolution.com) and counts is crucial for expense and performance optimization. Monitor your implementation 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 precision. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the design.<br>
<br>The [implementation procedure](http://maitri.adaptiveit.net) can take numerous minutes to complete.<br>
<br>When deployment is total, your endpoint status will change to InService. At this point, the design is all set to accept inference [demands](http://tanpoposc.com) through the [endpoint](https://saathiyo.com). You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, you can invoke the model using a SageMaker runtime client and integrate it with your applications.<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To begin 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 authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for [inference programmatically](https://www.noagagu.kr). The code for releasing the model is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.<br>
<br>You can run additional requests against the predictor:<br>
<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:<br>
<br>Clean up<br>
<br>To prevent undesirable charges, complete the actions in this section to clean up your [resources](https://recruitment.nohproblem.com).<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you deployed the design using [Amazon Bedrock](https://samisg.eu8443) Marketplace, total the following steps:<br>
<br>1. On the Amazon [Bedrock](https://topstours.com) console, under Foundation designs in the navigation pane, choose Marketplace releases.
2. In the Managed releases section, locate the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're deleting the right implementation: 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 expenses if you leave it running. Use the following code to erase the endpoint if you desire to stop [sustaining charges](https://gitea-working.testrail-staging.com). For more details, see Delete Endpoints and Resources.<br>
<br>Conclusion<br>
<br>In this post, we explored how you can access and deploy the DeepSeek-R1 model utilizing 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, SageMaker JumpStart pretrained models, [Amazon SageMaker](https://telecomgurus.in) JumpStart [Foundation](http://www.jimtangyh.xyz7002) Models, Amazon Bedrock Marketplace, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:CharleyRudall29) and Starting 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](http://101.42.41.254:3000) business construct ingenious options using AWS services and accelerated calculate. Currently, he is focused on developing methods for fine-tuning and optimizing the inference performance of big language designs. In his totally free time, Vivek takes pleasure in treking, seeing movies, and attempting various foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://hellowordxf.cn) [Specialist Solutions](http://129.211.184.1848090) Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://62.178.96.192:3000) 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://myjobasia.com) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://property.listatto.ca) center. She is passionate about building services that assist customers accelerate their [AI](https://minka.gob.ec) journey and unlock service worth.<br>