dianex48142061
/
site-4people
1
0
Code Issues Packages Projects Wiki

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

master
Diane Brooks 2025-06-01 12:23:30 +00:00
commit 1fe804da2b
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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://edurich.lk)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://strimsocial.net) concepts 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 steps to deploy the distilled versions of the models too.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](http://www.carnevalecommunity.it) that utilizes reinforcement learning to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying feature is its [support](https://git.alexhill.org) learning (RL) step, which was used to improve the model's actions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately improving both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's geared up to break down complicated inquiries and factor through them in a detailed way. This directed thinking process allows the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its [extensive capabilities](https://git.mm-music.cn) DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as agents, sensible thinking and information analysis tasks.<br>
<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, allowing efficient inference by routing queries to the most pertinent professional "clusters." This technique enables the model to concentrate on various issue domains while maintaining overall effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more effective architectures based upon [popular](https://peopleworknow.com) open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient models to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, using it as an instructor model.<br>
<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock Marketplace](http://121.40.209.823000). 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, avoid hazardous content, and examine designs against essential safety criteria. At the time of [writing](https://www.anetastaffing.com) this blog site, for DeepSeek-R1 implementations 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 across your generative [AI](https://git.liubin.name) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 model, you need 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 verify you're utilizing 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 . To ask for a limitation increase, produce a limit boost demand and connect to your account group.<br>
<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous content, and evaluate models against essential security criteria. You can carry out security procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a [guardrail](http://xn--jj-xu1im7bd43bzvos7a5l04n158a8xe.com) using 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](https://sowjobs.com) an input for the design. This input is then processed through the [ApplyGuardrail API](http://git.andyshi.cloud). If the input passes the [guardrail](http://gsrl.uk) check, it's sent out to the design for inference. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output phase. 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 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 actions:<br>
<br>1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other [Amazon Bedrock](https://gogs.jublot.com) tooling.
2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.<br>
<br>The design detail page provides essential details about the design's capabilities, prices structure, and implementation standards. You can find detailed usage instructions, consisting of sample API calls and code snippets for integration. The design supports various text generation jobs, consisting of material development, code generation, and question answering, using its support learning optimization and CoT reasoning abilities.
The page also includes implementation alternatives and [licensing](https://vacaturebank.vrijwilligerspuntvlissingen.nl) details to assist you start with DeepSeek-R1 in your applications.
3. To begin 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](https://git.goatwu.com) name, enter an endpoint name (between 1-50 [alphanumeric](https://code.agileum.com) characters).
5. For Number of instances, enter a number of circumstances (in between 1-100).
6. For example type, pick your instance type. For [optimum](https://sharingopportunities.com) performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For a lot of 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 to start utilizing the design.<br>
<br>When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive interface where you can try out various triggers and adjust design criteria like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For instance, material for inference.<br>
<br>This is an outstanding way to check out 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 model reacts to various inputs and letting you tweak your prompts for optimal results.<br>
<br>You can quickly evaluate the design in the play ground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a [guardrail](https://jmusic.me) using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends a demand to create text based upon a user prompt.<br>
<br>Deploy DeepSeek-R1 with [SageMaker](http://120.55.59.896023) 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 few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into production using either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through [SageMaker JumpStart](https://gitea.v-box.cn) provides two [convenient](https://twoplustwoequal.com) methods: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the technique that best matches your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions 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 create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
<br>The design browser displays available designs, with details like the provider name and model capabilities.<br>
<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card reveals essential details, consisting of:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if appropriate), showing that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design<br>
<br>5. Choose the design card to see the model details page.<br>
<br>The design details page includes the following details:<br>
<br>- The design name and provider details.
Deploy button to release the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of essential details, such as:<br>
<br>- Model description.
- License details.
- Technical specifications.
- Usage guidelines<br>
<br>Before you release the design, it's suggested to evaluate the model details and license terms to confirm compatibility with your use case.<br>
<br>6. [Choose Deploy](https://git.wun.im) to continue with implementation.<br>
<br>7. For Endpoint name, use the immediately generated name or produce a custom-made one.
8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial [instance](https://www.jr-it-services.de3000) count, enter the number of [circumstances](https://git.tool.dwoodauto.com) (default: 1).
Selecting proper circumstances types and counts is essential for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for precision. For this design, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to release the model.<br>
<br>The deployment procedure can take numerous minutes to finish.<br>
<br>When deployment is total, your endpoint status will alter to InService. At this point, the model is prepared to accept inference requests through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can conjure up the model 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 going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is [offered](https://thematragroup.in) in the Github here. You can clone the notebook and range 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](http://121.36.27.63000) the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:<br>
<br>Clean up<br>
<br>To avoid unwanted charges, finish the actions in this section to tidy up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you released the design using Amazon Bedrock Marketplace, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) complete the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments.
2. In the Managed releases section, find the endpoint you want 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 appropriate 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 erase the endpoint if you desire 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 deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. 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 Starting with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>[Vivek Gangasani](https://www.dpfremovalnottingham.com) is a Lead Specialist [Solutions Architect](https://social.mirrororg.com) for Inference at AWS. He assists emerging generative [AI](http://parasite.kicks-ass.org:3000) business develop ingenious services utilizing AWS services and sped up compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the reasoning performance of big language designs. In his totally free time, Vivek delights in hiking, viewing movies, and attempting various foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://vidacibernetica.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](https://freeads.cloud) of focus is AWS [AI](http://bh-prince2.sakura.ne.jp) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>[Jonathan Evans](http://gitpfg.pinfangw.com) is a Specialist Solutions Architect dealing with generative [AI](http://slfood.co.kr) with the Third-Party Model Science team at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial [intelligence](https://git.fracturedcode.net) and generative [AI](http://yanghaoran.space:6003) center. She is passionate about [developing services](https://clik.social) that assist clients accelerate their [AI](https://edurich.lk) [journey](https://csmsound.exagopartners.com) and [unlock organization](https://dev-members.writeappreviews.com) worth.<br>