commit 0073e24367efa8666e27a06b5efa71dc8febb318 Author: tahliah1069136 Date: Sat Feb 22 00:38:23 2025 +0000 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..215fc19 --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://112.48.22.196:3000)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your [AI](http://193.9.44.91) [concepts](https://daystalkers.us) on AWS.
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In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the [distilled variations](https://www.jobzpakistan.info) of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](http://damoa8949.com) that utilizes support learning to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial distinguishing function is its support knowing (RL) step, which was utilized to refine the design's actions beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both relevance and [clearness](https://massivemiracle.com). In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it's equipped to break down complicated queries and reason through them in a detailed way. This guided thinking process allows the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually recorded the industry's attention as a [versatile](https://kollega.by) text-generation design that can be incorporated into different workflows such as representatives, rational thinking and data analysis jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, enabling efficient inference by routing inquiries to the most relevant specialist "clusters." This approach permits the model to focus on different issue domains while [maintaining](http://park8.wakwak.com) general performance. 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 model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient models to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as a [teacher design](https://redmonde.es).
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest [releasing](http://89.251.156.112) this model with guardrails in [location](http://144.123.43.1382023). In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and assess designs against crucial security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can [produce numerous](https://daystalkers.us) guardrails [tailored](https://cruyffinstitutecareers.com) to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](https://www.ubom.com) [applications](https://gogs.koljastrohm-games.com).
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas [console](http://120.55.59.896023) 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](https://kition.mhl.tuc.gr). To ask for a limit boost, develop a limitation increase request 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 correct AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Establish consents to utilize guardrails for [larsaluarna.se](http://www.larsaluarna.se/index.php/User:RosauraYcm) material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails [enables](https://git.lewis.id) you to present safeguards, avoid hazardous material, and evaluate designs against key security requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model actions deployed 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 develop the guardrail, see the GitHub repo.
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The general circulation includes 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 to the design for reasoning. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers 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 actions:
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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 doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.
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The model detail page supplies necessary [details](http://59.57.4.663000) about the model's abilities, prices structure, and application guidelines. You can find detailed use guidelines, consisting of sample API calls and code snippets for integration. The model supports various text generation jobs, consisting of content development, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning capabilities. +The page likewise [consists](https://africasfaces.com) of implementation choices and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For [Endpoint](https://lensez.info) name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For [Variety](https://circassianweb.com) of instances, enter a variety of instances (between 1-100). +6. For Instance type, select your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and [file encryption](https://heli.today) settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may desire to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can explore different triggers and adjust design parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For instance, material for inference.
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This is an exceptional method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play ground supplies instant feedback, helping you comprehend how the design reacts to different inputs and [letting](https://jovita.com) you tweak your triggers for optimum results.
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You can quickly test the model in the [play ground](https://test1.tlogsir.com) through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 model 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 actually produced the guardrail, use the following code to carry out [guardrails](https://moyatcareers.co.ke). The script initializes the bedrock_runtime client, configures reasoning criteria, and sends a request to produce text based on a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center 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 models to your usage case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 convenient methods: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you choose the method that best fits your requirements.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](http://gogs.oxusmedia.com) UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be prompted to develop 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 service provider name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals crucial details, including:
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- Model name +[- Provider](http://175.6.124.2503100) name +- Task classification (for example, Text Generation). +[Bedrock Ready](https://groups.chat) badge (if relevant), suggesting that this model 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 model card to see the [design details](https://wiki.eqoarevival.com) page.
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The model details page consists of the following details:
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- The model name and service provider [details](http://121.42.8.15713000). +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you release the model, it's suggested to review the design details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to proceed with [release](https://subemultimedia.com).
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7. For Endpoint name, utilize the instantly generated name or produce a custom-made one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of instances (default: 1). +Selecting proper circumstances types and counts is crucial for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and [low latency](https://video.chops.com). +10. Review all configurations for precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings 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 several minutes to complete.
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When deployment is complete, your endpoint status will alter to InService. At this point, the design is all set to accept reasoning requests through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the release is complete, you can conjure up the model using a SageMaker runtime [customer](https://handsfarmers.fr) and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To start with DeepSeek-R1 using the [SageMaker Python](http://www.kotlinx.com3000) SDK, you will need 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 design is offered in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run additional demands against the predictor:
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Implement guardrails and run inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Clean up
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To prevent undesirable charges, complete the actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation [designs](http://www.kotlinx.com3000) in the navigation pane, select Marketplace releases. +2. In the Managed implementations section, locate the endpoint you wish 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 appropriate release: 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 deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you wish 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 explored 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 get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
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About the Authors
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[Vivek Gangasani](https://git.flandre.net) is a Lead Specialist Solutions [Architect](https://embargo.energy) for Inference at AWS. He helps emerging generative [AI](http://47.121.121.137:6002) companies build [innovative services](http://www.boutique.maxisujets.net) using AWS services and accelerated calculate. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the reasoning performance of large [language models](https://jobs.quvah.com). In his [totally](https://champ217.flixsterz.com) free time, Vivek takes pleasure in treking, seeing motion pictures, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://minority2hire.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://62.210.71.92) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with [generative](http://47.242.77.180) [AI](http://115.29.48.48:3000) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.netrecruit.al) center. She is passionate about constructing options that assist consumers accelerate their [AI](https://easterntalent.eu) journey and unlock service value.
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