finleyz5546930
/
atlantistechnical
1
0
Code Issues Packages Projects Wiki

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

master
Finley Huntingfield 2025-04-12 12:19:05 +08:00
commit 8630b25742
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 100644

@ -0,0 +1,93 @@
<br>Today, we are excited to reveal that DeepSeek R1 distilled Llama and [Qwen designs](https://parejas.teyolia.mx) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=1098885) you can now release DeepSeek [AI](https://git.augustogunsch.com)'s first-generation frontier design, DeepSeek-R1, together with the [distilled](https://asesordocente.com) versions varying from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](http://152.136.232.113:3000) ideas on AWS.<br>
<br>In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to [release](http://thinkwithbookmap.com) the distilled versions of the models also.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](http://193.30.123.188:3500) that utilizes reinforcement discovering to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key identifying feature is its reinforcement knowing (RL) action, which was used to refine the model's responses beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's equipped to break down complicated queries and factor through them in a detailed way. This guided thinking process enables the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, rational reasoning and information analysis jobs.<br>
<br>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, allowing effective inference by routing inquiries to the most pertinent [specialist](https://gst.meu.edu.jo) "clusters." This [approach enables](https://hireforeignworkers.ca) the design to specialize in various issue domains while maintaining overall effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to [release](http://wiki.myamens.com) the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more efficient architectures based on 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 efficient models to imitate the [behavior](https://profesional.id) and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor design.<br>
<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and examine designs against key security requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](http://git.chuangxin1.com) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 model, you need 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 confirm you're utilizing 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 releasing. To ask for a limitation increase, create a limitation increase request and [connect](https://energypowerworld.co.uk) to your [account](https://git.itk.academy) group.<br>
<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Establish consents to use guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous material, and evaluate designs against key safety criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and design responses deployed 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.<br>
<br>The general circulation 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 check, it's sent to the design for reasoning. After getting the design's output, another guardrail check is applied. 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 indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate inference using this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace gives 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 actions:<br>
<br>1. On the [Amazon Bedrock](http://git.chilidoginteractive.com3000) console, pick Model catalog under Foundation models in the navigation pane.
At the time of writing this post, you can utilize 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 company and select the DeepSeek-R1 design.<br>
<br>The model detail page offers vital details about the model's capabilities, prices structure, and application standards. You can find detailed usage directions, consisting of sample API calls and code snippets for integration. The design supports different text generation tasks, consisting of material production, code generation, and question answering, utilizing its support finding out optimization and CoT thinking abilities.
The page also includes release alternatives and licensing details to help you start with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, choose Deploy.<br>
<br>You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, enter a variety of instances (in between 1-100).
6. For example type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can configure sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may want to examine these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to start using the model.<br>
<br>When the release is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive user interface where you can try out different prompts and change design criteria like temperature level and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For example, content for reasoning.<br>
<br>This is an exceptional method to explore the design's thinking and text generation capabilities before incorporating it into your applications. The play area provides immediate feedback, assisting you comprehend how the design reacts to [numerous inputs](https://strimsocial.net) and letting you fine-tune your triggers for optimal results.<br>
<br>You can rapidly evaluate the design in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you [require](http://8.137.54.2139000) to get the .<br>
<br>Run inference using guardrails with the released DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to perform inference utilizing a [released](https://git.fafadiatech.com) DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends a demand to produce text based upon a user prompt.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can [tailor pre-trained](http://www.book-os.com3000) designs to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you select the method that finest matches your requirements.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
<br>The design web browser shows available models, with details like the company name and design capabilities.<br>
<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card reveals crucial details, consisting of:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if applicable), showing that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design<br>
<br>5. Choose the design card to see the design details page.<br>
<br>The design details page consists of the following details:<br>
<br>- The design name and service provider details.
Deploy button to deploy the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab includes essential details, such as:<br>
<br>- Model [description](https://cello.cnu.ac.kr).
- License details.
- Technical specifications.
- Usage standards<br>
<br>Before you release the model, it's advised to evaluate the model details and license terms to validate compatibility with your use case.<br>
<br>6. Choose Deploy to proceed with implementation.<br>
<br>7. For Endpoint name, use the instantly produced name or produce a custom one.
8. For Instance type [¸ select](https://www.askmeclassifieds.com) an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the variety of instances (default: 1).
Selecting proper circumstances types and counts is crucial for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. [Choose Deploy](https://noaisocial.pro) to deploy the design.<br>
<br>The implementation process can take several minutes to finish.<br>
<br>When deployment is total, your endpoint status will change to InService. At this point, the design is ready to accept reasoning [requests](https://spiritustv.com) through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Yolanda31R) you can conjure up the design using a SageMaker runtime customer and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS approvals and [environment](https://wik.co.kr) setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the note pad and range from SageMaker Studio.<br>
<br>You can run extra demands against the predictor:<br>
<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:<br>
<br>Tidy up<br>
<br>To prevent unwanted charges, complete the steps in this section to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace deployment<br>
<br>If you deployed 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, select Marketplace releases.
2. In the Managed implementations area, locate 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 erasing the [correct](https://www.jobcreator.no) release: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart model you released will [sustain expenses](http://git.chuangxin1.com) 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.<br>
<br>Conclusion<br>
<br>In this post, we checked out how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit [SageMaker JumpStart](https://git.mxr612.top) in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with [Amazon SageMaker](https://www.telewolves.com) JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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](http://artpia.net) companies develop ingenious solutions using AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the inference efficiency of large language models. In his leisure time, Vivek enjoys hiking, seeing movies, and trying different cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://talentrendezvous.com) Specialist Solutions Architect with the Third-Party Model [Science team](https://www.menacopt.com) at AWS. His location of focus is AWS [AI](https://git.easytelecoms.fr) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://www.jooner.com) with the Third-Party Model Science team at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://gitlab.sybiji.com) hub. She is enthusiastic about constructing services that assist customers accelerate their [AI](https://aji.ghar.ku.jaldi.nai.aana.ba.tume.dont.tach.me) journey and [unlock company](https://hortpeople.com) worth.<br>