From 1f3e38be9cb50ece48908e12107083de22d0b27d Mon Sep 17 00:00:00 2001 From: abdulchristian Date: Thu, 10 Apr 2025 02:29:21 +0800 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-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..9ba94c1 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are thrilled to reveal that [DeepSeek](https://gitea.linkensphere.com) R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://praca.e-logistyka.pl)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](http://it-viking.ch) concepts on AWS.
+
In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the designs as well.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a large (LLM) developed by DeepSeek [AI](http://47.97.178.182) that uses reinforcement learning to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying function is its support knowing (RL) step, which was utilized to fine-tune the design's actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's geared up to break down complicated queries and factor through them in a detailed way. This guided reasoning procedure permits the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation design that can be integrated into numerous workflows such as agents, sensible [reasoning](http://www.youly.top3000) and [data analysis](https://dainiknews.com) tasks.
+
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion [parameters](http://skupra-nat.uamt.feec.vutbr.cz30000) in size. The MoE architecture enables activation of 37 billion specifications, allowing effective reasoning by routing queries to the most pertinent specialist "clusters." This [technique enables](https://www.kritterklub.com) the model to specialize in various issue domains while maintaining general 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 instance to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as an [instructor design](https://vazeefa.com).
+
You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and examine models against essential security criteria. At the time of composing this blog site, for DeepSeek-R1 releases on [SageMaker JumpStart](http://football.aobtravel.se) and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, [enhancing](https://www.tcrew.be) user experiences and standardizing security controls across your generative [AI](https://gitea.gm56.ru) applications.
+
Prerequisites
+
To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas [console](http://121.43.99.1283000) and under AWS Services, select Amazon SageMaker, and verify you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limit increase, produce a limit increase request and connect to your account team.
+
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, see Set up permissions to utilize guardrails for material filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful material, and evaluate models against essential security requirements. You can execute security procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to [apply guardrails](http://www.xn--1-2n1f41hm3fn0i3wcd3gi8ldhk.com) to evaluate user inputs and model actions [released](http://gitlab.xma1.de) on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
+
The general flow includes the following actions: 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 inference. After receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate reasoning using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace provides 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 actions:
+
1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.
+
The design detail page provides vital details about the design's abilities, rates structure, and execution guidelines. You can discover detailed usage guidelines, including sample API calls and code snippets for combination. The design supports numerous text generation jobs, consisting of content development, code generation, and question answering, utilizing its reinforcement learning optimization and CoT thinking abilities. +The page likewise includes release choices and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
+
You will be prompted to configure the deployment details for DeepSeek-R1. The model ID will be [pre-populated](http://gitlab.fuxicarbon.com). +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, go into a variety of instances (between 1-100). +6. For example type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up innovative security and facilities settings, consisting of [virtual private](https://b52cum.com) cloud (VPC) networking, service function approvals, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you may want to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
+
When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive user interface where you can explore various prompts and change design parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For instance, material for inference.
+
This is an excellent way to explore the model's reasoning and text generation capabilities before integrating it into your applications. The playground offers immediate feedback, helping you understand how the design reacts to various inputs and letting you fine-tune your triggers for ideal outcomes.
+
You can quickly check the design in the playground through the UI. However, to conjure up the deployed model 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 shows how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a demand to generate text based upon a user prompt.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial [intelligence](https://git.nosharpdistinction.com) (ML) center with FMs, integrated algorithms, [surgiteams.com](https://surgiteams.com/index.php/User:Wanda46F48) and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production using either the UI or SDK.
+
Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free methods: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you select the approach that finest matches your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:
+
1. On the SageMaker console, choose Studio in the navigation pane. +2. [First-time](https://wiki.armello.com) users will be [prompted](https://git.panggame.com) to [develop](http://201.17.3.963000) a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
+
The model internet browser shows available models, with details like the supplier name and design capabilities.
+
4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows [crucial](https://sos.shinhan.ac.kr) details, consisting of:
+
- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to [conjure](https://www.kritterklub.com) up the model
+
5. Choose the model card to view the design details page.
+
The design details page consists of the following details:
+
- The model name and service provider details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
+
The About tab consists of important details, such as:
+
- [Model description](https://club.at.world). +- License details. +- Technical requirements. +- Usage guidelines
+
Before you deploy the model, it's recommended to review the model details and license terms to confirm compatibility with your use case.
+
6. Choose Deploy to proceed with release.
+
7. For Endpoint name, use the automatically created name or develop a customized one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting suitable circumstances types and counts is crucial for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the design.
+
The deployment process can take a number of minutes to finish.
+
When deployment is total, your [endpoint status](https://cannabisjobs.solutions) will change to InService. At this point, the design is prepared to accept reasoning [requests](https://getstartupjob.com) through the endpoint. You can monitor the implementation progress on the [SageMaker](https://www.jobs-f.com) console Endpoints page, which will display appropriate metrics and status details. When the deployment is total, you can invoke the design utilizing a SageMaker runtime customer and integrate it with your applications.
+
Deploy DeepSeek-R1 utilizing the [SageMaker Python](https://jobportal.kernel.sa) SDK
+
To start with DeepSeek-R1 utilizing the [SageMaker Python](https://www.blatech.co.uk) SDK, you will require to install the [SageMaker Python](https://minka.gob.ec) SDK and make certain you have the required AWS permissions and [environment setup](https://mastercare.care). The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is offered 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](https://git.kimcblog.com) with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as [displayed](https://gemma.mysocialuniverse.com) in the following code:
+
Tidy up
+
To avoid [undesirable](https://etrade.co.zw) charges, finish the steps in this area to clean up your resources.
+
Delete the Amazon Bedrock Marketplace release
+
If you deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
+
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments. +2. In the Managed implementations area, locate the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the proper release: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart design 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 checked out how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. 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 is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://kommunalwiki.boell.de) business construct ingenious options using AWS services and sped up calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the reasoning efficiency of big language models. In his downtime, Vivek delights in hiking, watching movies, and trying different foods.
+
Niithiyn Vijeaswaran is a Generative [AI](http://gitlab.fuxicarbon.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://git.nikmaos.ru) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is a Specialist Solutions [Architect](https://ec2-13-237-50-115.ap-southeast-2.compute.amazonaws.com) working on generative [AI](https://neejobs.com) with the Third-Party Model Science group at AWS.
+
Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://social1776.com) hub. She is passionate about constructing options that assist consumers accelerate their [AI](https://lidoo.com.br) journey and unlock business worth.
\ No newline at end of file