From 1c27500b9a019c7a9ae33ad33abcddd02bae8831 Mon Sep 17 00:00:00 2001 From: Arianne Valdez Date: Thu, 29 May 2025 16:45:50 +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..36d4ade --- /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 reveal 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://jobpile.uk)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://gitea.scalz.cloud) concepts on AWS.
+
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the [distilled versions](https://ramique.kr) of the designs too.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://www.keyfirst.co.uk) that uses reinforcement finding out to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing function is its support learning (RL) step, which was utilized to improve the model's responses beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, eventually improving both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's equipped to break down [complicated inquiries](http://114.116.15.2273000) and reason through them in a detailed manner. This guided thinking procedure enables the design to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be incorporated into numerous workflows such as agents, sensible reasoning and data interpretation jobs.
+
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion criteria, allowing effective inference by routing questions to the most pertinent specialist "clusters." This approach allows the design to specialize in different issue domains while maintaining overall efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of [GPU memory](http://121.199.172.2383000).
+
DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective designs to mimic the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a teacher model.
+
You can deploy DeepSeek-R1 model either through [SageMaker JumpStart](http://git.thinkpbx.com) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and evaluate models against [key security](https://home.zhupei.me3000) criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous [guardrails tailored](http://47.92.26.237) to different usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://git.kitgxrl.gay) applications.
+
Prerequisites
+
To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're using 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 request a limit increase, produce a limit increase request and connect to your account group.
+
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the [correct AWS](https://howtolo.com) Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Establish approvals to utilize guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging material, and assess models against crucial safety requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock [ApplyGuardrail](http://fridayad.in) API. This allows you to apply guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
+
The general flow involves the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for reasoning. After receiving the model's output, another guardrail check is applied. If the [output passes](http://tigg.1212321.com) this last check, it's returned as the last result. However, if either the input or [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MaurineMyers) output is intervened by the guardrail, a message is [returned indicating](https://wiki.monnaie-libre.fr) the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate inference utilizing this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
[Amazon Bedrock](https://ozoms.com) Marketplace offers you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
+
1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to invoke the design. It does not [support Converse](https://git.tea-assets.com) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.
+
The design detail page provides vital details about the design's capabilities, prices structure, and [execution standards](http://www.xn--80agdtqbchdq6j.xn--p1ai). You can discover detailed use instructions, consisting of sample API calls and code bits for combination. The design supports numerous text generation tasks, consisting of content production, code generation, and question answering, utilizing its support discovering optimization and CoT reasoning capabilities. +The page also consists of implementation choices and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select Deploy.
+
You will be triggered to configure 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 Variety of circumstances, go into a number of circumstances (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 suggested. +Optionally, you can configure advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you might desire to review these settings to line up with your [organization's security](https://amigomanpower.com) and compliance requirements. +7. Choose Deploy to begin utilizing the design.
+
When the implementation is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with various prompts and adjust model specifications like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, material for inference.
+
This is an exceptional method to check out the model's thinking and text generation abilities before integrating it into your applications. The play area offers instant feedback, helping you understand how the model responds to numerous inputs and letting you tweak your prompts for [optimal outcomes](https://git.vhdltool.com).
+
You can quickly test the model in the play ground through the UI. However, to invoke 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 reasoning](https://www.keyfirst.co.uk) utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the [Amazon Bedrock](https://lifestagescs.com) console or the API. For the example code to create the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up [inference](http://43.143.245.1353000) specifications, and sends a demand to generate text based on a user timely.
+
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 just a few clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](http://194.67.86.1603100) to your usage case, with your data, and deploy them into production using either the UI or SDK.
+
Deploying DeepSeek-R1 model through SageMaker JumpStart uses two practical techniques: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you select the [approach](http://175.178.153.226) that best suits your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
+
1. On the SageMaker console, select Studio in the [navigation pane](https://kennetjobs.com). +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
+
The [model browser](http://wiki.lexserve.co.ke) displays available models, with details like the [service provider](https://hub.bdsg.academy) name and [design abilities](https://linuxreviews.org).
+
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals key details, including:
+
- Model name +- Provider name +- Task [category](https://source.brutex.net) (for instance, Text Generation). +Bedrock Ready badge (if appropriate), suggesting that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
+
5. Choose the design card to view the model details page.
+
The model details page consists of the following details:
+
- The design name and . +Deploy button to release the design. +About and Notebooks tabs with detailed details
+
The About tab consists of essential details, such as:
+
- Model description. +- License details. +- Technical specs. +- Usage guidelines
+
Before you release the design, it's recommended to examine the model details and license terms to validate compatibility with your use case.
+
6. Choose Deploy to proceed with deployment.
+
7. For Endpoint name, use the instantly produced name or create a customized one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the number of instances (default: 1). +Selecting appropriate instance types and counts is essential for expense and performance optimization. Monitor your [release](http://doc.folib.com3000) to adjust 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 configurations for accuracy. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the design.
+
The release procedure can take numerous minutes to finish.
+
When release is total, your endpoint status will alter to InService. At this point, the model is ready to accept reasoning demands through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is total, you can conjure up the model using a SageMaker runtime customer and integrate it with your applications.
+
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
+
To start 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 consents and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for [deploying](https://git.jerrita.cn) the model is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
+
You can run additional requests against the predictor:
+
Implement guardrails and run reasoning 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:
+
Tidy up
+
To avoid undesirable charges, finish the steps in this area to clean up your resources.
+
Delete the Amazon Bedrock Marketplace deployment
+
If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:
+
1. On the Amazon Bedrock console, under Foundation designs 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, choose Delete. +4. Verify the endpoint details to make certain you're erasing the proper implementation: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart design you deployed will sustain costs 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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](http://zhangsheng1993.tpddns.cn:3000) companies construct innovative options utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the reasoning efficiency of big language designs. In his spare time, Vivek delights in treking, viewing films, and attempting various foods.
+
Niithiyn Vijeaswaran is a Generative [AI](http://39.99.224.27:9022) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://fridayad.in) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is a [Specialist](https://jimsusefultools.com) Solutions Architect working on generative [AI](https://git.polycompsol.com:3000) 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://timviec24h.com.vn) center. She is enthusiastic about constructing options that help customers accelerate their [AI](https://git.bloade.com) journey and unlock business worth.
\ No newline at end of file