commit a376180ddbc1639e52283848c262f917eb78dabb Author: wilsonweston1 Date: Sun Apr 6 20:23:53 2025 +0300 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart 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..eb4836e --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon [SageMaker JumpStart](http://hmzzxc.com3000). With this launch, you can now deploy DeepSeek [AI](http://94.191.73.38:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://www.ayuujk.com) concepts on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://igita.ir) that utilizes support learning to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial distinguishing function is its reinforcement knowing (RL) step, which was utilized to fine-tune the model's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately improving both [significance](https://raovatonline.org) and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's geared up to break down complicated questions and factor through them in a detailed manner. This guided reasoning process permits the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation model that can be integrated into different workflows such as agents, logical thinking and data interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, enabling effective reasoning by routing questions to the most appropriate specialist "clusters." This technique allows the design to focus on different problem domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective architectures based upon [popular](https://galgbtqhistoryproject.org) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective models to mimic the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we this model with guardrails in location. In this blog site, we will use [Amazon Bedrock](https://prosafely.com) Guardrails to introduce safeguards, avoid [damaging](https://39.105.45.141) content, and [examine designs](https://video.chops.com) against essential security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](https://www.so-open.com) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing 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 releasing. To request a limitation increase, develop a [limitation increase](https://westzoneimmigrations.com) demand and reach out to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Set up permissions to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock [Guardrails permits](https://www.runsimon.com) you to present safeguards, prevent hazardous content, and evaluate designs against essential safety criteria. You can implement security procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic flow involves the following actions: First, the system receives an input for the model. This input is then processed through the [ApplyGuardrail API](https://www.joboptimizers.com). If the input passes the guardrail check, it's sent to the model for reasoning. After receiving the design's output, another guardrail check is used. 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 showing the nature of the intervention and whether it happened at the input or [output stage](https://www.jjldaxuezhang.com). The examples showcased in the following sections show reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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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 steps:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other [Amazon Bedrock](https://xn--v69atsro52ncsg2uqd74apxb.com) tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.
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The model detail page supplies important details about the design's abilities, pricing structure, and implementation guidelines. You can discover detailed use instructions, including sample API calls and code snippets for combination. The design supports various text generation jobs, including content creation, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities. +The page also consists of implementation options and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be prompted to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a variety of circumstances (between 1-100). +6. For example type, select your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can [configure advanced](https://119.29.170.147) security and infrastructure settings, including virtual personal cloud (VPC) networking, service role authorizations, and [encryption settings](http://wiki.lexserve.co.ke). For many 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](https://mobidesign.us). +7. Choose Deploy to begin utilizing the model.
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When the implementation 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 try out various prompts and adjust design parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, material for inference.
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This is an [exceptional method](https://www.personal-social.com) to explore the design's reasoning and text generation capabilities before incorporating it into your applications. The playground supplies instant feedback, helping you understand how the design responds to numerous inputs and letting you tweak your prompts for ideal outcomes.
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You can quickly check the design in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out [reasoning utilizing](http://119.3.70.2075690) a released DeepSeek-R1 model through [Amazon Bedrock](https://livy.biz) using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends out a demand to produce text based on a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](https://bcde.ru) is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML [solutions](https://athleticbilbaofansclub.com) that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical approaches: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the technique that finest fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model browser displays available models, with details like the [company](https://www.openstreetmap.org) name and design abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals crucial details, including:
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- Model name +- [Provider](http://150.158.183.7410080) name +- Task category (for instance, Text Generation). +[Bedrock Ready](https://gogs.2dz.fi) badge (if appropriate), indicating that this model can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the design details page.
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The [design details](http://git.papagostore.com) page includes the following details:
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- The model name and service provider details. +Deploy button to release the model. +About and Notebooks tabs with [detailed](https://thecodelab.online) details
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The About tab includes crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you release the design, it's advised to review the model details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the automatically produced name or [surgiteams.com](https://surgiteams.com/index.php/User:LaureneDortch1) produce a customized one. +8. For Instance type ΒΈ choose an [instance type](https://167.172.148.934433) (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the number of circumstances (default: 1). +Selecting suitable instance types and counts is [essential](https://49.12.72.229) for cost and efficiency optimization. Monitor your [deployment](https://radiothamkin.com) to adjust these [settings](https://gitea.chenbingyuan.com) as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the design.
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The implementation process can take several minutes to complete.
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When deployment is total, your endpoint status will alter to InService. At this moment, the model is all set to accept reasoning demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is total, you can invoke the design using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a [detailed code](http://www.sleepdisordersresource.com) example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra requests against the predictor:
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Implement guardrails and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Clean up
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To prevent unwanted charges, finish the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases. +2. In the Managed implementations area, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [select Delete](http://94.191.100.41). +4. Verify the endpoint details to make certain you're deleting the correct deployment: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart model you released will [sustain expenses](https://161.97.85.50) if you leave it running. Use the following code to erase the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, we [checked](https://spaceballs-nrw.de) out how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or [Amazon Bedrock](https://git.cno.org.co) Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, [SageMaker JumpStart](https://www.eruptz.com) pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions [Architect](https://kol-jobs.com) for Inference at AWS. He assists emerging generative [AI](http://39.99.134.165:8123) business develop [innovative services](http://8.142.152.1374000) using AWS services and accelerated calculate. Currently, he is focused on establishing methods for fine-tuning and enhancing the inference efficiency of large language designs. In his complimentary time, Vivek enjoys hiking, viewing movies, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://63.32.145.226) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://git.junzimu.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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[Jonathan Evans](https://customerscomm.com) is a Professional Solutions Architect working on generative [AI](https://git.whitedwarf.me) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://flixtube.info) center. She is enthusiastic about developing services that help customers accelerate their [AI](https://nextodate.com) journey and unlock business value.
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