dxikatherin436/ufidahz
1
0
Code Issues Actions Packages Projects Wiki

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

Katherin Roan 2025-04-13 06:15:41 +08:00
commit 9482864f4f
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 models are available through Amazon Bedrock Marketplace and [Amazon SageMaker](https://disgaeawiki.info) JumpStart. With this launch, you can now deploy DeepSeek [AI](http://42.192.95.179)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions [varying](http://8.134.38.1063000) from 1.5 to 70 billion criteria to develop, experiment, and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) responsibly scale your generative [AI](https://www.scikey.ai) 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 comparable steps to release the distilled variations of the designs also.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](http://129.211.184.184:8090) that [utilizes reinforcement](http://code.istudy.wang) finding out to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its support knowing (RL) action, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) which was utilized to fine-tune the [design's reactions](https://jobs.foodtechconnect.com) beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's equipped to break down complex inquiries and factor through them in a detailed way. This guided thinking [process permits](https://career.agricodeexpo.org) the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured [reactions](http://47.56.181.303000) while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation model that can be integrated into numerous workflows such as representatives, rational reasoning and information interpretation jobs.<br>
<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing inquiries to the most relevant expert "clusters." This approach enables the model to specialize in different problem domains while maintaining overall efficiency. 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 instance to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective 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 designs to imitate the habits and thinking patterns of the larger DeepSeek-R1 design, utilizing it as a .<br>
<br>You can release DeepSeek-R1 design either through SageMaker JumpStart or [Bedrock](http://music.afrixis.com) Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and examine models against key security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user [experiences](http://47.104.60.1587777) and standardizing security controls across your [generative](https://raisacanada.com) [AI](https://jobs.fabumama.com) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, 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 using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit increase, develop a limit boost demand and reach out to your account group.<br>
<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Establish approvals to use guardrails for content filtering.<br>
<br>[Implementing guardrails](https://wino.org.pl) with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to introduce safeguards, avoid hazardous content, and [assess designs](http://121.40.114.1279000) against crucial safety criteria. You can implement security procedures for the DeepSeek-R1 design using the Amazon Bedrock [ApplyGuardrail API](https://japapmessenger.com). This permits you to apply guardrails to examine 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 develop the guardrail, see the GitHub repo.<br>
<br>The general circulation includes 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 reasoning. After getting the design's output, another guardrail check is applied. If the [output passes](https://octomo.co.uk) this last check, it's [returned](https://shiatube.org) as the [outcome](https://git.zzxxxc.com). However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the [intervention](http://ptube.site) and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate inference utilizing this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock [Marketplace](https://gitstud.cunbm.utcluj.ro) provides you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:<br>
<br>1. On the [Amazon Bedrock](https://gitlog.ru) console, choose Model brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.<br>
<br>The model detail page supplies essential details about the design's capabilities, rates structure, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MatthewOconner5) and execution guidelines. You can find detailed use directions, including sample API calls and code bits for integration. The design supports different text generation jobs, consisting of content production, code generation, and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:Carin96U9162) concern answering, using its support discovering optimization and CoT reasoning abilities.
The page also includes release alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, pick Deploy.<br>
<br>You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 [alphanumeric](https://gitlab.vp-yun.com) characters).
5. For Number of circumstances, enter a [variety](https://learninghub.fulljam.com) of instances (in between 1-100).
6. For example type, choose your [instance type](https://sunriji.com). For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:WandaFairbanks9) you can configure innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For the majority of use cases, the default settings will work well. However, for [89u89.com](https://www.89u89.com/author/ramonhaywoo/) production releases, you may want to review these settings to align with your organization's security and [compliance](http://jobsgo.co.za) requirements.
7. Choose Deploy to start using the design.<br>
<br>When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive interface where you can try out various prompts and change model specifications like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For example, material for reasoning.<br>
<br>This is an excellent method to explore the model's thinking and text generation capabilities before integrating it into your applications. The play ground offers immediate feedback, assisting you comprehend how the [model reacts](https://git.thunraz.se) to different inputs and [letting](http://117.50.100.23410080) you tweak your triggers for [optimum outcomes](https://picturegram.app).<br>
<br>You can rapidly check the design 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 reasoning utilizing guardrails with the released DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to carry out reasoning using 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 produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a demand 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](http://8.136.42.2418088) that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](https://vydiio.com) to your usage case, with your information, and release them into production using either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart uses two practical approaches: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the approach that finest fits your requirements.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:<br>
<br>1. On the [SageMaker](http://apps.iwmbd.com) console, pick Studio in the navigation pane.
2. First-time users will be triggered to create a domain.
3. On the [SageMaker Studio](https://one2train.net) console, choose JumpStart in the navigation pane.<br>
<br>The model web browser displays available designs, with details like the service provider name and model abilities.<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card shows key details, including:<br>
<br>- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if suitable), indicating that this model can be [registered](https://wiki.solsombra-abdl.com) with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model<br>
<br>5. Choose the design card to see the model details page.<br>
<br>The model details page consists of the following details:<br>
<br>- The model name and supplier details.
Deploy button to release the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab includes crucial details, such as:<br>
<br>- Model description.
- License details.
- Technical specifications.
- Usage standards<br>
<br>Before you deploy the model, it's recommended to evaluate the [design details](https://git.googoltech.com) and license terms to confirm compatibility with your usage case.<br>
<br>6. Choose Deploy to continue with release.<br>
<br>7. For Endpoint name, utilize the automatically generated name or produce a custom one.
8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the number of circumstances (default: 1).
Selecting appropriate instance types and counts is crucial for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low [latency](https://wkla.no-ip.biz).
10. Review all configurations for accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the design.<br>
<br>The implementation procedure can take a number of minutes to finish.<br>
<br>When deployment is complete, your endpoint status will change to InService. At this point, the model is all set to accept inference requests through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is total, you can invoke the model using a SageMaker runtime customer and incorporate it with your [applications](https://medicalstaffinghub.com).<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To get started with DeepSeek-R1 using the [SageMaker Python](http://111.2.21.14133001) SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a [detailed](https://www.ayc.com.au) code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the model is provided in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
<br>You can run additional demands against the predictor:<br>
<br>Implement guardrails and run reasoning 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 console or the API, and implement it as displayed in the following code:<br>
<br>Tidy up<br>
<br>To avoid undesirable charges, complete the actions in this section to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you released the model utilizing Amazon Bedrock Marketplace, total the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases.
2. In the Managed deployments area, find the endpoint you wish to delete.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're erasing the proper 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 costs if you leave it [running](https://dreamtvhd.com). Use the following code to erase 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, [photorum.eclat-mauve.fr](http://photorum.eclat-mauve.fr/profile.php?id=252314) we explored how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](http://gitea.infomagus.hu) now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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://kaamdekho.co.in) business develop ingenious [solutions utilizing](http://47.103.112.133) AWS services and accelerated calculate. Currently, he is focused on developing methods for fine-tuning and optimizing the inference efficiency of large language designs. In his leisure time, Vivek enjoys hiking, viewing motion pictures, and attempting different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://www.becausetravis.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://39.106.177.160:8756) [accelerators](https://tintinger.org) (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://social.oneworldonesai.com) with the Third-Party Model Science team at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://tagreba.org) hub. She is enthusiastic about constructing services that help clients accelerate their [AI](http://82.19.55.40:443) journey and unlock service value.<br>