commit 76d18e6d22cd4e2c48b3453dc760f2132dce46f9 Author: cindibueche305 Date: Fri Feb 28 08:11:11 2025 +0800 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart diff --git a/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md new file mode 100644 index 0000000..54820f4 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are delighted to announce that DeepSeek R1 [distilled Llama](http://git.idiosys.co.uk) and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://hulaser.com)'s first-generation frontier model, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:LesleeTruscott) DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://www.outletrelogios.com.br) ideas on AWS.
+
In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs too.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://igita.ir) that uses reinforcement finding out to boost thinking capabilities through a [multi-stage training](https://heartbeatdigital.cn) procedure from a DeepSeek-V3-Base structure. A key differentiating function is its reinforcement knowing (RL) step, which was used to improve the model's responses beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually boosting both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's geared up to break down complicated inquiries and factor through them in a detailed way. This guided reasoning process enables the design to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation design that can be incorporated into various workflows such as agents, logical thinking and data analysis jobs.
+
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, making it possible for effective reasoning by routing inquiries to the most relevant expert "clusters." This technique permits the model to concentrate on various 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 circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
+
DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient designs to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as a teacher model.
+
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in place. In this blog site, we will [utilize Amazon](http://dibodating.com) Bedrock Guardrails to present safeguards, avoid damaging content, and evaluate designs against essential security criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous [guardrails](http://dev.ccwin-in.com3000) tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](http://dev.shopraves.com) applications.
+
Prerequisites
+
To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, [it-viking.ch](http://it-viking.ch/index.php/User:SheenaWhalen2) pick Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 [xlarge circumstances](http://211.119.124.1103000) in the AWS Region you are deploying. To request a limitation boost, produce a limitation boost demand and connect to your account group.
+
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right [AWS Identity](http://mao2000.com3000) and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Establish consents to utilize guardrails for material filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
[Amazon Bedrock](http://git.ai-robotics.cn) Guardrails enables you to present safeguards, prevent hazardous content, and evaluate models against crucial safety criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design actions released on Amazon Bedrock [Marketplace](http://39.108.86.523000) and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
+
The basic circulation involves the following steps: First, the system gets 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 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 result. 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 happened at the input or output phase. The examples showcased in the following sections show reasoning using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace offers 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:
+
1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can use 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 provider and pick the DeepSeek-R1 design.
+
The design detail page supplies important details about the [design's](http://www.grandbridgenet.com82) abilities, prices structure, and application guidelines. You can discover detailed usage instructions, including sample API calls and code bits for combination. The design supports different text generation tasks, including material creation, code generation, and question answering, using its [support finding](http://kpt.kptyun.cn3000) out optimization and CoT thinking abilities. +The page also includes implementation choices and licensing details to help you get started with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
+
You will be prompted to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, get in a variety of circumstances (in between 1-100). +6. For example type, choose your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [advised](https://igita.ir). +Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
+
When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can try out different prompts and change model criteria like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For instance, material for inference.
+
This is an exceptional way to check out the [model's thinking](https://impactosocial.unicef.es) and text generation capabilities before incorporating it into your applications. The play area provides immediate feedback, helping you understand how the design responds to various inputs and letting you tweak your prompts for [optimum outcomes](https://lazerjobs.in).
+
You can quickly evaluate the design in the play area through the UI. However, [wavedream.wiki](https://wavedream.wiki/index.php/User:BerylFeetham) to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
+
The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through [Amazon Bedrock](http://111.9.47.10510244) 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 create the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends a request to [produce text](http://120.25.165.2073000) based on a user prompt.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production using either the UI or SDK.
+
Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free techniques: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the approach that finest suits your requirements.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
+
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, select JumpStart in the navigation pane.
+
The design browser displays available models, with details like the company name and model capabilities.
+
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals essential details, including:
+
- Model name +- Provider name +- Task classification (for instance, [surgiteams.com](https://surgiteams.com/index.php/User:ShariMadirazza7) Text Generation). +Bedrock Ready badge (if relevant), suggesting that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
+
5. Choose the model card to see the model details page.
+
The design details page includes the following details:
+
- The model name and company details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
+
The About tab consists of important details, such as:
+
- Model description. +- License details. +- Technical specifications. +- Usage standards
+
Before you release the design, it's suggested to evaluate the design details and license terms to validate compatibility with your usage case.
+
6. Choose Deploy to continue with release.
+
7. For Endpoint name, utilize the automatically created name or produce a custom one. +8. For [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:BonnieValle7) example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of circumstances (default: 1). +Selecting proper instance types and counts is crucial for expense and performance optimization. Monitor your deployment 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](http://119.23.72.7). +10. Review all configurations for accuracy. For this model, we [highly advise](https://kollega.by) [adhering](http://www.withsafety.net) to SageMaker JumpStart default [settings](https://2ubii.com) and making certain that network isolation remains in location. +11. [Choose Deploy](https://git.adminkin.pro) to release the model.
+
The implementation procedure can take a number of minutes to finish.
+
When deployment is complete, your endpoint status will alter to InService. At this point, the design is ready to accept inference demands through the endpoint. You can monitor the release development 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.
+
Deploy DeepSeek-R1 utilizing the [SageMaker Python](http://apps.iwmbd.com) SDK
+
To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for [releasing](http://xn--9t4b21gtvab0p69c.com) the design is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
+
You can run extra requests against the predictor:
+
Implement guardrails and run inference with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
+
Tidy up
+
To prevent unwanted charges, complete the actions in this area to tidy up your resources.
+
Delete the Amazon Bedrock Marketplace deployment
+
If you [released](https://git.jzcscw.cn) the model using Amazon Bedrock Marketplace, complete the following actions:
+
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. +2. In the Managed deployments section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're the proper implementation: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart model you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
+
Conclusion
+
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 or Amazon Bedrock Marketplace now to get going. 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.
+
About the Authors
+
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://git.electrosoft.hr) companies build innovative services utilizing AWS services and sped up calculate. Currently, he is focused on establishing techniques for fine-tuning and optimizing the [inference performance](http://47.108.140.33) of big language models. In his [leisure](http://skyfffire.com3000) time, Vivek takes pleasure in treking, enjoying movies, and trying various foods.
+
Niithiyn Vijeaswaran is a Generative [AI](http://xn--o39aoby1e85nw4rx0fwvcmubsl71ekzf4w4a.kr) Specialist Solutions [Architect](http://gitlab.ideabeans.myds.me30000) with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://dokuwiki.stream) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
+
Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](http://cwscience.co.kr) with the Third-Party Model Science group at AWS.
+
Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://digital-field.cn:50443) hub. She is passionate about developing solutions that assist consumers accelerate their [AI](https://truthbook.social) journey and unlock company value.
\ No newline at end of file