commit 64873239384035e1f8b4f3fa4da1bad32e18e93e Author: lilla895284467 Date: Fri Feb 21 10:18:30 2025 +0800 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..c75a359 --- /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](http://gitlab.ideabeans.myds.me30000). With this launch, you can now deploy DeepSeek [AI](http://www.xn--he5bi2aboq18a.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://bogazicitube.com.tr) concepts on AWS.
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In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large [language design](https://git.parat.swiss) (LLM) established by DeepSeek [AI](https://armconnection.com) that uses support learning to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating function is its support learning (RL) action, which was used to refine the model's responses beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually improving both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's geared up to break down complicated queries and factor through them in a detailed manner. This guided reasoning procedure permits the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation model that can be incorporated into different workflows such as representatives, logical thinking and information interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, making it possible for [effective inference](http://git.scdxtc.cn) by routing inquiries to the most appropriate professional "clusters." This [approach permits](https://deadlocked.wiki) the design to focus on various problem domains while maintaining overall effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:Carey3621606) 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, using it as an instructor design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and evaluate designs against [crucial](http://gitz.zhixinhuixue.net18880) safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop [multiple](https://ourehelp.com) [guardrails tailored](https://code.dev.beejee.org) to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](http://209.87.229.34:7080) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify 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 deploying. To ask for a limit boost, develop a limitation increase [request](https://login.discomfort.kz) and connect to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Establish approvals to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful material, and evaluate designs against essential security requirements. You can [execute precaution](https://gitlab.t-salon.cc) for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic flow includes the following steps: First, the system [receives](https://plane3t.soka.ac.jp) 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 getting the design's output, another guardrail check is used. 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 suggesting the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas show reasoning using 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 structure 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, select Model catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.
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The model detail page supplies important details about the model's capabilities, rates structure, and execution standards. You can find detailed use directions, consisting of sample API calls and code snippets for combination. The design supports different text generation jobs, including content production, code generation, and [photorum.eclat-mauve.fr](http://photorum.eclat-mauve.fr/profile.php?id=250144) concern answering, utilizing its reinforcement learning [optimization](https://idemnaposao.rs) and CoT reasoning abilities. +The page likewise includes deployment alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a variety of [instances](https://www.dataalafrica.com) (in between 1-100). +6. For example type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up sophisticated security and infrastructure settings, consisting of [virtual personal](https://git.elder-geek.net) cloud (VPC) networking, service role consents, and encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you might desire to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the deployment is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and adjust model specifications like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For example, content for inference.
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This is an outstanding method to check out the model's thinking and text generation capabilities before incorporating it into your applications. The playground provides instant feedback, [assisting](https://staff-pro.org) you understand how the design reacts to various inputs and letting you tweak your prompts for ideal results.
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You can [rapidly test](http://117.71.100.2223000) 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.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:MaryjoDahl5566) see the GitHub repo. After you have developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_[runtime](https://uedf.org) client, configures inference criteria, and sends a demand to generate text based upon a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you select the technique that best fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1330524) choose Studio in the [navigation pane](https://www.menacopt.com). +2. First-time users will be prompted to a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser displays available designs, with details like the provider name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card reveals key details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), showing that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to see the design details page.
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The design details page consists of the following details:
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- The model name and supplier details. +Deploy button to deploy the design. +About and [Notebooks tabs](http://8.140.205.1543000) with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you release the design, it's suggested to examine the model details and license terms to [validate compatibility](https://hellovivat.com) with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the automatically created name or create a custom one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of circumstances (default: 1). +Selecting suitable circumstances types and counts is essential for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for [accuracy](https://menfucks.com). For this model, we highly suggest sticking to SageMaker JumpStart [default settings](https://cvwala.com) and making certain that network isolation remains in place. +11. Choose Deploy to deploy the design.
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The deployment procedure can take a number of minutes to complete.
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When deployment is total, your endpoint status will change to InService. At this moment, the model is ready to accept inference requests through the endpoint. You can keep an eye on the implementation 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.
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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 needed AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional demands 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 utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Clean up
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To avoid undesirable charges, complete the actions in this area to tidy up your resources.
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Delete the [Amazon Bedrock](http://122.51.46.213) Marketplace release
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If you deployed the design using Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. +2. In the Managed deployments area, find the endpoint you want to erase. +3. Select the endpoint, and on the [Actions](http://62.234.201.16) menu, [pick Delete](http://forum.rcsubmarine.ru). +4. Verify the endpoint details to make certain you're erasing the appropriate implementation: 1. [Endpoint](http://www.fasteap.cn3000) 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 deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you desire to stop sustaining charges. For [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, [disgaeawiki.info](https://disgaeawiki.info/index.php/User:ShayV68172485519) we checked out how you can access and release the DeepSeek-R1 model using 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 Beginning with Amazon SageMaker JumpStart.
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About the Authors
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Vivek [Gangasani](https://aggm.bz) is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://playtube.ann.az) business build innovative solutions using AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference performance of large language designs. In his complimentary time, Vivek enjoys treking, viewing movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://igazszavak.info) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://shiatube.org) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://scfr-ksa.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ShellieGenders) and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.wow-z.com) center. She is enthusiastic about developing solutions that help customers accelerate their [AI](https://www.ynxbd.cn:8888) journey and unlock service worth.
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