commit 00d291e4f13930b4b16195a4eec1e9b0c68b24cc Author: Alexandria Currie Date: Tue Apr 8 05:00:45 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..821effb --- /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 reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock [Marketplace](https://azaanjobs.com) and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://premiergitea.online:3000)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](http://87.98.157.12:3000) concepts on AWS.
+
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 too.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://60.205.104.179:3000) that uses support learning to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying function is its reinforcement learning (RL) action, which was utilized to improve the model's actions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's equipped to break down [intricate questions](https://git.magicvoidpointers.com) and reason through them in a detailed manner. This directed reasoning process allows the design to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation design that can be integrated into various workflows such as agents, logical thinking and information analysis tasks.
+
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, enabling effective reasoning by routing inquiries to the most appropriate [professional](https://gitlog.ru) "clusters." This approach enables the design to specialize in various problem domains while maintaining general efficiency. 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](https://medhealthprofessionals.com) to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
+
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) 70B). Distillation describes a process of training smaller, more effective designs to simulate the behavior [wiki.whenparked.com](https://wiki.whenparked.com/User:JZKMireya164733) and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor model.
+
You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid [hazardous](https://givebackabroad.org) content, and assess models against essential security requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](http://116.62.159.194) applications.
+
Prerequisites
+
To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas [console](http://git.sysoit.co.kr) and under AWS Services, select Amazon SageMaker, and [confirm](https://portalwe.net) you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 in the AWS Region you are releasing. To request a limit boost, create a limitation increase demand and connect to your account team.
+
Because you will be [deploying](http://218.28.28.18617423) this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish consents to use guardrails for material filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging content, and assess models against key safety requirements. You can execute safety steps for the DeepSeek-R1 design utilizing the Amazon Bedrock [ApplyGuardrail](http://82.156.24.19310098) API. This allows you to apply guardrails to assess user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
+
The basic 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 to the model for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this last check, it's [returned](https://medhealthprofessionals.com) as the final result. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or [output phase](https://mypetdoll.co.kr). The examples showcased in the following areas show inference using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
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, complete the following steps:
+
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 utilize the InvokeModel API to conjure up the model. It doesn't [support Converse](https://git.yingcaibx.com) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [company](http://git.acdts.top3000) and select the DeepSeek-R1 design.
+
The model detail page supplies necessary details about the design's capabilities, rates structure, and execution standards. You can find detailed usage guidelines, consisting of sample API calls and code snippets for integration. The model supports various text [generation](https://mmsmaza.in) tasks, consisting of material creation, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities. +The page likewise consists of implementation choices and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, [surgiteams.com](https://surgiteams.com/index.php/User:StevenRudolph82) pick Deploy.
+
You will be prompted to set up the implementation 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 circumstances, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) enter a variety of instances (in between 1-100). +6. For example type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, service function consents, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
+
When the release is complete, you can check 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 try out different prompts and change design criteria like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, content for reasoning.
+
This is an excellent method to check out the model's thinking and text [generation abilities](http://1.94.127.2103000) before incorporating it into your applications. The play ground offers instant feedback, helping you comprehend how the design reacts to various inputs and letting you fine-tune your prompts for ideal results.
+
You can quickly test the design in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
+
The following code example demonstrates how to carry out reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have produced the guardrail, [utilize](https://xotube.com) the following code to execute guardrails. The [script initializes](https://www.uaehire.com) the bedrock_[runtime](http://83.151.205.893000) client, sets up reasoning parameters, and sends a demand to create text based upon a user prompt.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.
+
Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical techniques: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to help you pick the technique that best matches your requirements.
+
Deploy DeepSeek-R1 through [SageMaker JumpStart](http://git.irunthink.com) UI
+
Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
+
1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
+
The design internet browser displays available models, with [details](https://ransomware.design) like the supplier name and model capabilities.
+
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows essential details, including:
+
- Model name +- Provider name +- Task [category](http://optx.dscloud.me32779) (for example, Text Generation). +Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
+
5. Choose the model card to view the design details page.
+
The design details page consists of the following details:
+
- The design name and company details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
+
The About tab includes important details, such as:
+
- Model description. +- License [details](https://www.informedica.llc). +- Technical requirements. +- Usage standards
+
Before you release the model, it's recommended to [examine](https://thedatingpage.com) the design details and license terms to confirm compatibility with your use case.
+
6. Choose Deploy to proceed with release.
+
7. For Endpoint name, use the immediately generated name or produce a custom one. +8. For Instance type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of instances (default: 1). +Selecting proper circumstances types and counts is important for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, [Real-time reasoning](https://www.opad.biz) is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
+
The implementation process can take numerous minutes to finish.
+
When [release](https://www.mepcobill.site) is complete, your endpoint status will change to InService. At this moment, the model is all set to accept inference demands through the endpoint. You can keep an eye on 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 client and incorporate it with your applications.
+
Deploy DeepSeek-R1 using the SageMaker Python SDK
+
To begin with DeepSeek-R1 using the SageMaker Python SDK, you will [require](http://code.hzqykeji.com) to set up the SageMaker Python SDK and make certain you have the [required AWS](https://www.sealgram.com) permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
+
You can run extra demands against the predictor:
+
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can also utilize 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 displayed 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 deployed the model using Amazon Bedrock Marketplace, total the following steps:
+
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. +2. In the Managed implementations area, locate 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 proper deployment: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://planetdump.com) status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart model you released will sustain expenses if you leave it [running](https://southwestjobs.so). Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.
+
Conclusion
+
In this post, we checked out how you can access and deploy the DeepSeek-R1 model using 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 models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
+
About the Authors
+
[Vivek Gangasani](http://118.89.58.193000) is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](http://207.148.91.145:3000) business construct innovative services utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the reasoning performance of big language models. In his leisure time, Vivek takes pleasure in hiking, [watching](https://video.lamsonsaovang.com) movies, and trying different foods.
+
Niithiyn Vijeaswaran is a Generative [AI](https://git.tedxiong.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://zidra.ru) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is a Specialist Solutions Architect working on generative [AI](http://git.acdts.top:3000) with the Third-Party Model Science team at AWS.
+
Banu Nagasundaram leads product, engineering, and [strategic collaborations](https://sea-crew.ru) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://47.103.91.160:50903) center. She is passionate about constructing services that assist customers accelerate their [AI](https://gl.cooperatic.fr) journey and unlock company value.
\ No newline at end of file