From 899a6fe89cd60e33039a60a05b82433a824dcd48 Mon Sep 17 00:00:00 2001 From: Adeline Carbone Date: Sun, 6 Apr 2025 09:46:33 +0000 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..3cfc176 --- /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 announce 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://yhxcloud.com:12213)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](http://1024kt.com:3000) ideas on AWS.
+
In this post, we show 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 also.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](http://8.218.14.83:3000) that uses support discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying feature is its support knowing (RL) action, which was utilized to improve the design's responses beyond the standard pre-training and fine-tuning [process](http://shenjj.xyz3000). By including RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's geared up to break down complicated questions and reason through them in a detailed manner. This assisted thinking procedure permits the design to produce more accurate, transparent, and detailed responses. This design combines RL-based [fine-tuning](https://git.privateger.me) with CoT capabilities, aiming to generate structured responses while focusing on [interpretability](https://quierochance.com) and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, logical thinking and information [interpretation tasks](http://121.4.154.1893000).
+
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, making it possible for efficient inference by routing questions to the most relevant expert "clusters." This approach enables the model to specialize in different issue domains while [maintaining](http://47.94.142.23510230) general effectiveness. DeepSeek-R1 needs at least 800 GB of [HBM memory](http://www.kotlinx.com3000) in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release 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 abilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, [surgiteams.com](https://surgiteams.com/index.php/User:LatanyaZiegler) 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient models to imitate the habits and [thinking patterns](https://wacari-git.ru) of the bigger DeepSeek-R1 model, using it as an instructor design.
+
You can release DeepSeek-R1 design either through [SageMaker JumpStart](https://www.jobindustrie.ma) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and assess designs 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 only the ApplyGuardrail API. You can [develop multiple](http://47.98.226.2403000) guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](http://43.138.57.202:3000) applications.
+
Prerequisites
+
To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To [inspect](https://dreamtvhd.com) if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for [endpoint](https://www.shopes.nl) use. Make certain that you have at least one ml.P5e.48 [xlarge circumstances](http://tv.houseslands.com) in the AWS Region you are releasing. To ask for a limit boost, create a limit boost demand and reach out to your account team.
+
Because you will be deploying this design with [Amazon Bedrock](https://jobs.ofblackpool.com) Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful content, and evaluate models against [essential safety](https://admithel.com) requirements. You can implement safety procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design reactions 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](https://thegoldenalbatross.com).
+
The general circulation includes the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the last [outcome](https://www.canaddatv.com). 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 happened at the input or output stage. The examples showcased in the following areas show inference using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace provides 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:
+
1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.
+
The design detail page offers important details about the design's abilities, rates structure, and application guidelines. You can find detailed use instructions, consisting of sample API calls and code snippets for combination. The design supports numerous text generation jobs, including content creation, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT thinking capabilities. +The page also includes deployment alternatives and licensing [details](https://gitlab.syncad.com) to assist you start with DeepSeek-R1 in your applications. +3. To begin DeepSeek-R1, select Deploy.
+
You will be triggered to set up the release details for [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, enter a variety of instances (between 1-100). +6. For Instance type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based [circumstances type](http://gkpjobs.com) like ml.p5e.48 xlarge is advised. +Optionally, you can set up sophisticated security and facilities settings, consisting of [virtual personal](http://www.haimimedia.cn3001) cloud (VPC) networking, service function permissions, and encryption settings. For [disgaeawiki.info](https://disgaeawiki.info/index.php/User:AlbertaHemmant6) most utilize cases, the default settings will work well. However, for production deployments, you might wish to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start using the design.
+
When the deployment is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can experiment with different triggers and adjust design parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For example, material for reasoning.
+
This is an excellent method to [explore](http://47.99.37.638099) the design'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 numerous inputs and letting you tweak your triggers for optimum results.
+
You can rapidly check the model in the play ground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run inference using guardrails with the released DeepSeek-R1 endpoint
+
The following code example shows how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually [developed](http://revoltsoft.ru3000) the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends out a request to produce text based on a user timely.
+
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 deploy with simply a few clicks. With [SageMaker](http://gkpjobs.com) JumpStart, you can tailor pre-trained designs to your usage case, with your information, and [oeclub.org](https://oeclub.org/index.php/User:EloiseLaflamme8) deploy them into production using either the UI or SDK.
+
[Deploying](https://git.clicknpush.ca) DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free techniques: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you pick the approach that best matches your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
+
1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
+
The model internet browser [displays](https://shankhent.com) available designs, with details like the service provider name and design capabilities.
+
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals essential details, consisting of:
+
- Model name +- Provider name +- Task category (for instance, Text Generation). +[Bedrock Ready](http://git.zhiweisz.cn3000) badge (if applicable), suggesting that this design can be [registered](https://bewerbermaschine.de) with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
+
5. Choose the model card to see the model details page.
+
The [model details](https://wamc1950.com) page [consists](http://playtube.ythomas.fr) of the following details:
+
- The design name and company 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 standards
+
Before you release the model, it's recommended to evaluate the model details and license terms to validate compatibility with your usage case.
+
6. Choose Deploy to [continue](https://gitea.scubbo.org) with deployment.
+
7. For Endpoint name, use the immediately created name or create a custom-made one. +8. For [pediascape.science](https://pediascape.science/wiki/User:EdytheIvory959) example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting proper instance types and counts is vital for cost and efficiency optimization. Monitor your deployment to change 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 setups for accuracy. For this model, we [highly recommend](https://cosplaybook.de) sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
+
The implementation procedure can take a number of minutes to complete.
+
When deployment is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept reasoning requests through the [endpoint](http://60.209.125.23820010). You can keep an eye on the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the model utilizing a SageMaker runtime customer and integrate it with your applications.
+
Deploy DeepSeek-R1 using the SageMaker Python SDK
+
To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference 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 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 with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
+
Clean up
+
To prevent unwanted charges, complete the steps in this section to clean up your resources.
+
Delete the Amazon Bedrock Marketplace implementation
+
If you released the model using Amazon Bedrock Marketplace, complete the following steps:
+
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. +2. In the [Managed implementations](https://socialsnug.net) section, find the [endpoint](https://runningas.co.kr) 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 erasing the proper release: 1. [Endpoint](https://gitlab.kicon.fri.uniza.sk) 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. 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](http://8.222.216.1843000) or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun with Amazon SageMaker JumpStart.
+
About the Authors
+
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He [helps emerging](https://runningas.co.kr) generative [AI](https://bence.net) companies develop innovative services using AWS services and sped up compute. Currently, he is focused on developing methods for fine-tuning and optimizing the reasoning performance of big language designs. In his downtime, Vivek takes pleasure in hiking, watching films, and attempting various cuisines.
+
Niithiyn Vijeaswaran is a Generative [AI](http://211.119.124.110:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://repo.magicbane.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://begild.top:8418) with the Third-Party Model Science team at AWS.
+
[Banu Nagasundaram](http://git.emagenic.cl) leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://iadgroup.co.uk) hub. She is passionate about building solutions that help clients accelerate their [AI](https://pakallnaukri.com) journey and unlock organization value.
\ No newline at end of file