commit ba378ad3d7834ecefe504f414250a9acc0669bb8 Author: leslieokq05900 Date: Sun Jun 1 23:28:00 2025 +0000 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..6ed0f75 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://www.hxgc-tech.com:3000)'s first-generation [frontier](http://jenkins.stormindgames.com) model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://spiritustv.com) concepts on AWS.
+
In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the designs also.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://git.o-for.net) that uses reinforcement discovering to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3[-Base structure](https://saopaulofansclub.com). An essential differentiating function is its reinforcement knowing (RL) action, which was used to improve the design's actions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both relevance and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:CindaFriday) clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's equipped to break down intricate queries and factor through them in a [detailed manner](https://awaz.cc). This directed reasoning [procedure](https://gitea.tmartens.dev) allows the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, logical thinking and information interpretation jobs.
+
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient reasoning by routing queries to the most pertinent expert "clusters." This method permits the design to concentrate on different problem [domains](https://chosenflex.com) while maintaining total 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 instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more efficient architectures based upon [popular](https://myclassictv.com) 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 effective models to imitate the habits and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.
+
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we [advise deploying](https://topdubaijobs.ae) this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and assess models against key security requirements. At the time of [composing](https://silverray.worshipwithme.co.ke) this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](https://bucket.functionary.co) applications.
+
Prerequisites
+
To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate 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 limitation increase, produce a limitation boost demand and connect to your account team.
+
Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish consents to utilize guardrails for material filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails permits you to introduce safeguards, avoid harmful material, and assess models against crucial security requirements. You can carry out security steps for the DeepSeek-R1 design using the [Amazon Bedrock](https://www.kenpoguy.com) ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design responses released on Amazon Bedrock Marketplace 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 flow includes 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](https://ixoye.do) check, it's sent out to the model for [inference](https://gitea.thuispc.dynu.net). After [receiving](https://sparcle.cn) the model's output, another [guardrail check](https://51.68.46.170) is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is [returned](https://gitea.tmartens.dev) showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate reasoning using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
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, total the following steps:
+
1. On the Amazon Bedrock console, select Model [brochure](https://tayseerconsultants.com) under Foundation models in the navigation pane. +At the time of composing this post, you can use the [InvokeModel API](http://106.52.215.1523000) to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.
+
The model detail page supplies important details about the model's abilities, pricing structure, and implementation guidelines. You can find detailed usage instructions, including sample API calls and code snippets for [combination](https://www.uaehire.com). The design supports different text generation tasks, consisting of material production, code generation, and question answering, using its support finding out optimization and CoT thinking abilities. +The page likewise includes deployment alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
+
You will be triggered to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, go into a variety of circumstances (between 1-100). +6. For Instance type, pick your instance type. For [wiki.myamens.com](http://wiki.myamens.com/index.php/User:AndreHeiden8) optimal [efficiency](https://www.jigmedatse.com) with DeepSeek-R1, a type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and infrastructure settings, including virtual [private](http://www.andreagorini.it) cloud (VPC) networking, service role permissions, and file encryption settings. For many use cases, the default settings will work well. However, for production releases, you might wish to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start using the model.
+
When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can explore different prompts and adjust design criteria like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For instance, content for inference.
+
This is an [excellent](https://teachersconsultancy.com) way to check out the design's reasoning and text generation abilities before integrating 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 triggers for optimum outcomes.
+
You can rapidly evaluate the design in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you [require](http://13.209.39.13932421) to get the endpoint ARN.
+
Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
+
The following code example demonstrates how to perform inference utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock [console](http://112.48.22.1963000) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, [utilize](https://git.qiucl.cn) the following code to carry out guardrails. The script initializes the bedrock_[runtime](http://47.93.16.2223000) client, sets up reasoning parameters, and sends a request to create text based on a user prompt.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with simply 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 using either the UI or SDK.
+
Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the approach that best suits your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:
+
1. On the SageMaker console, choose 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 web browser displays available designs, with details like the supplier name and model abilities.
+
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows crucial details, consisting of:
+
- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if relevant), [suggesting](http://git.andyshi.cloud) that this model can be signed up with Amazon Bedrock, allowing you to use [Amazon Bedrock](https://git.qoto.org) APIs to conjure up the model
+
5. Choose the model card to see the design details page.
+
The model details page consists of the following details:
+
- The model name and provider details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
+
The About tab includes essential details, such as:
+
- Model description. +- License details. +- Technical specifications. +- Usage guidelines
+
Before you deploy the design, it's recommended to review the design details and license terms to verify compatibility with your use case.
+
6. Choose Deploy to proceed with release.
+
7. For Endpoint name, use the automatically produced name or develop a custom-made one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is important for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the model.
+
The implementation procedure can take numerous minutes to finish.
+
When deployment is complete, your endpoint status will alter to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display relevant [metrics](https://openedu.com) and status details. When the release is total, you can conjure up the [design utilizing](https://git.nosharpdistinction.com) a SageMaker runtime client and integrate it with your applications.
+
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
+
To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
+
You can run extra requests against the predictor:
+
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:ElizbethMcclanah) execute it as shown in the following code:
+
Clean up
+
To avoid unwanted charges, complete the steps in this area to tidy up your resources.
+
Delete the Amazon Bedrock Marketplace release
+
If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:
+
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, [pick Marketplace](https://casajienilor.ro) implementations. +2. In the Managed releases area, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, [select Delete](http://116.62.145.604000). +4. Verify the endpoint details to make certain you're erasing the appropriate implementation: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart model you released will sustain expenses if you leave it [running](https://foris.gr). Use the following code to erase the endpoint if you desire 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 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 designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.
+
About the Authors
+
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://120.77.209.176:3000) business build ingenious solutions using [AWS services](https://git.the.mk) and accelerated [calculate](https://eastcoastaudios.in). Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference performance of big language designs. In his downtime, Vivek delights in treking, enjoying films, and attempting different cuisines.
+
Niithiyn Vijeaswaran is a Generative [AI](http://www.andreagorini.it) Specialist Solutions Architect with the Third-Party Model [Science](https://dooplern.com) group at AWS. His area of focus is AWS [AI](https://git.hitchhiker-linux.org) [accelerators](http://31.184.254.1768078) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
+
Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://yaseen.tv) with the Third-Party Model Science team at AWS.
+
Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://git.gonstack.com) center. She is enthusiastic about building services that assist consumers accelerate their [AI](https://ipmanage.sumedangkab.go.id) journey and unlock service worth.
\ No newline at end of file