velmacote61307
/
whenparked
1
0
Code Issues Packages Projects Wiki

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

Velma Cote 2025-05-28 23:07:22 +08:00
commit b92dc98571
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 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://xn--939a42kg7dvqi7uo.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://scholarpool.com) concepts on AWS.<br>
<br>In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions of the designs also.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://ubereducation.co.uk) that utilizes reinforcement discovering to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3[-Base foundation](https://firemuzik.com). An essential identifying function is its support learning (RL) action, which was used to refine the model's responses beyond the standard pre-training and fine-tuning process. By [integrating](https://youtoosocialnetwork.com) RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's geared up to break down complex questions and factor through them in a detailed way. This directed thinking process [enables](http://www.thekaca.org) the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation design that can be integrated into numerous workflows such as agents, rational reasoning and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:MarieEtter) information interpretation tasks.<br>
<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, allowing effective reasoning by routing inquiries to the most pertinent specialist "clusters." This method enables the model to specialize in various problem domains while maintaining total performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the thinking abilities 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 sized, more efficient models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher model.<br>
<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and assess designs against essential safety criteria. 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](https://git.flandre.net) guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, [enhancing](https://lab.gvid.tv) user experiences and [standardizing safety](https://playvideoo.com) controls throughout your generative [AI](https://tribetok.com) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the [Service Quotas](https://remotejobsint.com) console and under AWS Services, pick Amazon SageMaker, and [confirm](https://okoskalyha.hu) 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 request a limit increase, develop a limit boost [request](https://frce.de) and reach out to your [account team](https://opedge.com).<br>
<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To [Management](http://www.hnyqy.net3000) (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish approvals to use guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to introduce safeguards, avoid harmful material, and examine designs against key security requirements. You can implement security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design actions 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 produce the guardrail, see the GitHub repo.<br>
<br>The general flow involves the following actions: 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 reasoning. After receiving 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 suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate 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 foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:<br>
<br>1. On the Amazon Bedrock console, choose Model brochure under [Foundation models](https://161.97.85.50) in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.<br>
<br>The model detail page provides [vital details](http://1.15.150.903000) about the design's capabilities, prices structure, and application guidelines. You can find detailed usage guidelines, consisting of sample API calls and code bits for combination. The design supports different text generation jobs, consisting of content creation, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT thinking capabilities.
The page also consists of release choices and licensing details to assist you get started with DeepSeek-R1 in your [applications](https://goodinfriends.com).
3. To begin using DeepSeek-R1, select Deploy.<br>
<br>You will be triggered to configure the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For [Variety](https://54.165.237.249) of circumstances, enter a variety of [instances](https://git.sitenevis.com) (between 1-100).
6. For example type, choose your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can configure advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, [service function](http://xn--289an1ad92ak6p.com) approvals, and encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may want to evaluate these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to begin using the design.<br>
<br>When the implementation is total, you can evaluate 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 design parameters like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, material for reasoning.<br>
<br>This is an exceptional way to explore the design's thinking and text generation abilities before integrating it into your applications. The playground provides instant feedback, assisting you understand how the design responds to numerous inputs and letting you fine-tune your triggers for optimal outcomes.<br>
<br>You can quickly evaluate the model in the playground 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 released DeepSeek-R1 endpoint<br>
<br>The following code example shows how to carry out reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends a request to create [text based](https://jobsnotifications.com) upon a user prompt.<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](http://47.119.175.53000) that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into [production utilizing](http://39.99.134.1658123) either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you choose the approach that finest suits your needs.<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, select Studio in the navigation pane.
2. First-time users will be triggered to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.<br>
<br>The model internet browser displays available models, with details like the service provider name and design abilities.<br>
<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each model card shows crucial details, consisting of:<br>
<br>- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if relevant), showing that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model<br>
<br>5. Choose the model card to view the model details page.<br>
<br>The model details page consists of the following details:<br>
<br>- The design name and supplier 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 specs.
- Usage standards<br>
<br>Before you release the model, it's suggested to examine the model details and license terms to confirm compatibility with your usage case.<br>
<br>6. Choose Deploy to [proceed](http://47.112.200.2063000) with deployment.<br>
<br>7. For [Endpoint](https://git.tissue.works) name, [utilize](https://www.bongmedia.tv) the immediately created name or produce a custom one.
8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the variety of circumstances (default: 1).
Selecting suitable circumstances types and counts is vital for cost and efficiency optimization. Monitor your release to adjust 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 design, we highly suggest adhering to SageMaker JumpStart [default settings](http://clipang.com) and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the design.<br>
<br>The deployment process can take numerous minutes to finish.<br>
<br>When deployment is total, your endpoint status will change to InService. At this point, the design is all set to accept reasoning demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will [display](https://www.runsimon.com) pertinent metrics and status details. When the implementation is total, you can invoke the design utilizing a SageMaker runtime client and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need 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. The code for releasing the model is offered in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
<br>You can run additional [demands](https://storymaps.nhmc.uoc.gr) against the predictor:<br>
<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the [Amazon Bedrock](https://www.keeloke.com) console or the API, and execute it as displayed in the following code:<br>
<br>Clean up<br>
<br>To avoid undesirable charges, finish the steps in this section to tidy up your [resources](http://211.91.63.1448088).<br>
<br>Delete the Amazon Bedrock [Marketplace](http://git.armrus.org) deployment<br>
<br>If you deployed the model using Amazon Bedrock Marketplace, total the following steps:<br>
<br>1. On the Amazon [Bedrock](https://git.mitsea.com) console, under Foundation models in the navigation pane, choose Marketplace releases.
2. In the Managed implementations section, find the [endpoint](http://111.8.36.1803000) 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 deleting the right deployment: 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. Use the following code to delete the endpoint if you wish 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon [Bedrock Marketplace](https://git.smartenergi.org) now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, [Amazon Bedrock](https://forum.webmark.com.tr) 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 helps emerging generative [AI](http://gitlab.lecanal.fr) companies build innovative services utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing strategies for [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:CarissaWillett7) fine-tuning and enhancing the inference efficiency of big language models. In his spare time, Vivek enjoys hiking, seeing motion pictures, and trying various cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://git.armrus.org) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://git.lewd.wtf) accelerators (AWS Neuron). He holds a [Bachelor's degree](https://trackrecord.id) in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://1.15.150.90:3000) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.89u89.com) center. She is enthusiastic about developing options that [assist customers](http://www.mitt-slide.com) accelerate their [AI](http://gamebizdev.ru) journey and unlock organization worth.<br>