From 4d3c7fc0675814088b8825f4fed3e67b51420946 Mon Sep 17 00:00:00 2001 From: mavisdominguez Date: Thu, 27 Feb 2025 18:12:40 +0800 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace And Amazon SageMaker JumpStart.-.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md 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..aca5e4c --- /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 announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://ssh.joshuakmckelvey.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](https://aloshigoto.jp) ideas on AWS.
+
In this post, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) we how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the models also.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://jobs.but.co.id) that uses support discovering to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating function is its reinforcement knowing (RL) step, which was used to refine the model's reactions beyond the [standard](https://eliteyachtsclub.com) pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, implying it's geared up to break down complicated questions and factor through them in a detailed way. This guided reasoning process permits the design to produce more accurate, 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 comprehensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation model that can be integrated into various workflows such as agents, sensible thinking and data interpretation jobs.
+
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient reasoning by routing inquiries to the most pertinent specialist "clusters." This [technique](https://peopleworknow.com) permits the model to concentrate on various issue domains 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](http://207.180.250.1143000) an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective architectures based upon [popular](https://www.activeline.com.au) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective models to simulate the behavior 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 advise releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and evaluate models against crucial security criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://wiki.kkg.org) only the ApplyGuardrail API. You can develop multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](http://211.119.124.110:3000) [applications](http://117.50.220.1918418).
+
Prerequisites
+
To deploy the DeepSeek-R1 model, 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, choose Amazon SageMaker, and verify you're [utilizing](https://surmodels.com) ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limitation increase, produce a limitation boost demand and reach out to your account team.
+
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) consents to utilize Amazon Bedrock Guardrails. For instructions, see Set up authorizations to use guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails enables you to [introduce](http://120.79.157.137) safeguards, prevent damaging material, and examine designs against essential safety requirements. You can carry out safety procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use [guardrails](https://git.cooqie.ch) to assess user inputs and [design reactions](https://git.didi.la) released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
+
The basic flow includes the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for [it-viking.ch](http://it-viking.ch/index.php/User:ShannanMullen43) inference. 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 intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate inference using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
+
1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other [Amazon Bedrock](http://34.236.28.152) tooling. +2. Filter for DeepSeek as a [company](http://113.177.27.2002033) and pick the DeepSeek-R1 model.
+
The model detail page provides essential details about the model's abilities, prices structure, and execution standards. You can find detailed use directions, including sample API calls and code bits for combination. The design supports various text generation tasks, including content creation, code generation, and [concern](https://social.mirrororg.com) answering, using its reinforcement finding out optimization and [CoT thinking](https://runningas.co.kr) abilities. +The page also includes implementation choices and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
+
You will be triggered to set up the release 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 Number of circumstances, get in a variety of instances (in between 1-100). +6. For example type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you might desire to evaluate these settings to align with your organization's security and [compliance requirements](https://sugoi.tur.br). +7. Choose Deploy to start using the design.
+
When the deployment is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive interface where you can explore different triggers and change design parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For example, material for inference.
+
This is an exceptional method to explore the design's reasoning and text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, helping you comprehend how the design reacts to different inputs and [letting](http://13.209.39.13932421) you tweak your triggers for optimum outcomes.
+
You can [rapidly evaluate](https://lifefriendsurance.com) the model in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the [endpoint ARN](https://kenyansocial.com).
+
Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
+
The following code example demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a [guardrail utilizing](https://tiptopface.com) the Amazon Bedrock console or the API. For [pediascape.science](https://pediascape.science/wiki/User:TeenaFlinchum7) the example code to create the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to carry out [guardrails](https://tmsafri.com). The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends out a demand to [generate text](https://www.sewosoft.de) based on a user prompt.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can deploy with just a few clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://moyatcareers.co.ke) models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
+
[Deploying](http://xn--9t4b21gtvab0p69c.com) DeepSeek-R1 model through SageMaker JumpStart provides two practical methods: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you pick the technique 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 prompted to produce a domain. +3. On the SageMaker Studio console, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=1008110) pick JumpStart in the navigation pane.
+
The design browser shows available designs, with details like the [provider](https://links.gtanet.com.br) name and model abilities.
+
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card reveals key details, consisting of:
+
- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the model
+
5. Choose the model card to see the model details page.
+
The model details page consists of the following details:
+
- The design name and service provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
+
The About tab includes crucial details, such as:
+
- Model description. +- License details. +- Technical specifications. +- Usage guidelines
+
Before you deploy the design, it's advised to examine the design details and license terms to [verify compatibility](http://13.209.39.13932421) with your usage case.
+
6. Choose Deploy to proceed with deployment.
+
7. For Endpoint name, utilize the immediately produced name or produce a custom-made one. +8. For [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2782175) example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of instances (default: 1). +Selecting appropriate 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 inference is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to [release](https://gogs.tyduyong.com) the model.
+
The implementation procedure can take numerous minutes to finish.
+
When implementation is complete, your [endpoint status](https://gratisafhalen.be) will alter to InService. At this point, the model is all set to accept inference [demands](https://gitlab.kitware.com) through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can [conjure](https://realmadridperipheral.com) up the model using a SageMaker runtime customer and integrate it with your applications.
+
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
+
To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
+
You can run additional demands against the predictor:
+
Implement guardrails and run inference with your [SageMaker JumpStart](https://bence.net) 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, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:EdithJoseph92) and implement it as shown in the following code:
+
Tidy up
+
To avoid undesirable charges, complete the actions in this section to clean up your resources.
+
Delete the Amazon Bedrock Marketplace deployment
+
If you [released](https://vagyonor.hu) the model using Amazon Bedrock Marketplace, total the following steps:
+
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. +2. In the Managed implementations area, locate the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the proper deployment: [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2857223) 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The [SageMaker JumpStart](https://git.guaranteedstruggle.host) design you released will sustain expenses if you leave it running. 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 release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or [Amazon Bedrock](http://183.221.101.893000) [Marketplace](https://www.frigorista.org) now to start. For more details, refer to Use Amazon Bedrock tooling with [Amazon SageMaker](http://119.3.9.593000) 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 is a Lead Specialist Solutions Architect for [Inference](http://47.120.20.1583000) at AWS. He assists emerging generative [AI](https://www.p3r.app) business build ingenious services utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language designs. In his free time, Vivek delights in hiking, enjoying motion pictures, and attempting various cuisines.
+
Niithiyn Vijeaswaran is a Generative [AI](https://goalsshow.com) [Specialist Solutions](https://connect.taifany.com) Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://git.partners.run) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and [Bioinformatics](http://bluemobile010.com).
+
Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://gitea.nasilot.me) with the Third-Party Model [Science](https://git.tool.dwoodauto.com) group at AWS.
+
Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitlab.ngser.com) center. She is enthusiastic about building services that assist consumers accelerate their [AI](http://42.194.159.64:9981) journey and unlock organization worth.
\ No newline at end of file