From f148e77d0e1e760a9a4a759621ee8610463fa759 Mon Sep 17 00:00:00 2001 From: stefan59l23599 Date: Sun, 13 Apr 2025 03:04:56 +0800 Subject: [PATCH] Add '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..d2dea0d --- /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 announce that DeepSeek R1 [distilled Llama](https://younivix.com) and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://saathiyo.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://gitea.joodit.com) concepts on AWS.
+
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the models as well.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://114jobs.com) that utilizes support finding out to improve thinking abilities through a multi-stage training [procedure](https://git.137900.xyz) from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its reinforcement learning (RL) step, which was used to fine-tune the model's responses beyond the standard pre-training and fine-tuning procedure. By [integrating](https://lonestartube.com) RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's equipped to break down intricate questions and factor through them in a detailed way. This assisted thinking process enables the design to produce more precise, transparent, and [detailed responses](http://118.195.226.1249000). This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation model that can be incorporated into different workflows such as agents, rational reasoning and data analysis jobs.
+
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, allowing efficient inference by routing questions to the most relevant professional "clusters." This approach permits the model to [specialize](https://dramatubes.com) in different problem domains while maintaining total efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the [thinking abilities](https://scm.fornaxian.tech) 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 describes a procedure of training smaller, more [effective models](https://kaymack.careers) to mimic the behavior and [thinking patterns](https://nuswar.com) of the larger DeepSeek-R1 design, using it as a teacher design.
+
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in location. In this blog, we will [utilize Amazon](https://nakshetra.com.np) Bedrock Guardrails to introduce safeguards, prevent harmful content, and evaluate models against key safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](https://watch-wiki.org) applications.
+
Prerequisites
+
To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To inspect 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 use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limitation increase, [produce](http://37.187.2.253000) a limit boost request and connect to your account team.
+
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For guidelines, see Set up consents to use guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails permits you to introduce safeguards, prevent hazardous material, and assess designs against crucial security criteria. You can implement safety procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design actions deployed 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 create the guardrail, see the GitHub repo.
+
The basic flow [involves](https://itconsulting.millims.com) the following steps: 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 to the model for inference. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the last outcome. However, if either the input or output is stepped in 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 areas demonstrate reasoning utilizing 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 catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.
+
The design detail page offers necessary details about the model's abilities, rates structure, and implementation guidelines. You can find detailed usage instructions, including sample API calls and code snippets for integration. The model supports different text generation tasks, consisting of content development, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT reasoning capabilities. +The page also consists of deployment alternatives and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select Deploy.
+
You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be [pre-populated](https://www.sealgram.com). +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of instances (between 1-100). +6. For example type, select your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and facilities 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 deployments, you might wish to evaluate these settings to align with your [organization's security](https://www.activeline.com.au) and compliance requirements. +7. Choose Deploy to start using the model.
+
When the deployment is total, you can check 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 try out various prompts and adjust model specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, material for reasoning.
+
This is an outstanding method to explore the design's thinking and text generation abilities before integrating it into your applications. The play ground offers immediate feedback, assisting you understand how the model responds to different inputs and letting you fine-tune your triggers for optimal results.
+
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 need to get the endpoint ARN.
+
Run reasoning using guardrails with the released DeepSeek-R1 endpoint
+
The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends out a demand to create text based upon a user timely.
+
Deploy DeepSeek-R1 with [SageMaker](http://jobee.cubixdesigns.com) JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can [release](http://39.101.160.118099) with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
+
Deploying DeepSeek-R1 design through [SageMaker JumpStart](http://git.pushecommerce.com) offers two hassle-free methods: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker [Python SDK](http://www.withsafety.net). Let's check out both approaches to assist you select the method that best fits 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, choose Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
+
The design browser shows available designs, with details like the service provider name and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:DanielePurton9) design abilities.
+
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals essential details, consisting of:
+
- Model name +- [Provider](http://ufiy.com) name +- Task category (for instance, Text Generation). +[Bedrock Ready](https://gantnews.com) badge (if applicable), [indicating](http://turtle.tube) that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
+
5. Choose the design card to view the design details page.
+
The design details page consists of the following details:
+
- The model name and provider details. +Deploy button to release the design. +About and [Notebooks tabs](https://www.guidancetaxdebt.com) with detailed details
+
The About tab consists of essential details, such as:
+
- Model description. +- License details. +- Technical requirements. +- Usage guidelines
+
Before you release the model, it's recommended to review the model details and license terms to confirm compatibility with your usage case.
+
6. Choose Deploy to continue with release.
+
7. For Endpoint name, utilize the instantly produced name or produce a customized one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of circumstances (default: 1). +Selecting suitable instance types and counts is essential for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups 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 release the model.
+
The [deployment process](https://wiki.communitydata.science) can take a number of minutes to complete.
+
When release is complete, your endpoint status will change to InService. At this point, the model is ready to accept inference requests through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can conjure up the design using a SageMaker runtime client and integrate it with your applications.
+
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
+
To get going 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 consents and environment setup. The following is a detailed code example that [demonstrates](https://jobboat.co.uk) how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for [releasing](https://nuswar.com) the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
+
You can run additional demands against the predictor:
+
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker [JumpStart predictor](http://218.28.28.18617423). You can produce a guardrail using the [Amazon Bedrock](https://git.sortug.com) console or the API, and implement it as displayed in the following code:
+
Tidy up
+
To prevent undesirable charges, finish the steps in this area to clean up your resources.
+
Delete the Amazon Bedrock Marketplace release
+
If you deployed the model using Amazon Bedrock Marketplace, total the following actions:
+
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. +2. In the Managed implementations area, find 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 implementation: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the [SageMaker JumpStart](https://music.lcn.asia) predictor
+
The SageMaker JumpStart model you deployed 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 explored how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and [SageMaker](https://lab.chocomart.kz) JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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](https://codeincostarica.com) [generative](https://www.yiyanmyplus.com) [AI](https://wiki.communitydata.science) companies build ingenious options using AWS services and accelerated calculate. Currently, he is focused on [developing methods](https://video.lamsonsaovang.com) for fine-tuning and optimizing the inference performance of big language models. In his leisure time, Vivek takes pleasure in hiking, watching movies, and attempting different foods.
+
Niithiyn Vijeaswaran is a Generative [AI](https://letustalk.co.in) [Specialist Solutions](https://supardating.com) [Architect](http://121.37.166.03000) with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://47.107.126.107:3000) accelerators (AWS Neuron). He holds a in Computer Science and Bioinformatics.
+
Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://www.onlywam.tv) with the Third-Party Model Science team at AWS.
+
Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon [SageMaker](https://ubereducation.co.uk) JumpStart, SageMaker's artificial intelligence and generative [AI](https://mychampionssport.jubelio.store) hub. She is passionate about developing options that assist clients accelerate their [AI](http://git.zhongjie51.com) journey and unlock company value.
\ No newline at end of file