commit 229d9e24494d761f0df0c91521f24afd1a6b50d0 Author: letabermudez6 Date: Wed Mar 12 04:43:15 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..73c62a2 --- /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 and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://elektro.jobsgt.ch)'s first-generation frontier design, DeepSeek-R1, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:CheriI281603) in addition to the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](http://gitlab.ileadgame.net) ideas on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions 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 large language model (LLM) [developed](https://social.acadri.org) by DeepSeek [AI](https://www.suntool.top) that uses reinforcement discovering to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its support knowing (RL) step, which was utilized to fine-tune the model's reactions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually improving both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's geared up to break down complex questions and reason through them in a detailed manner. This guided reasoning procedure enables the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually recorded the industry's attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, logical reasoning 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 enables activation of 37 billion criteria, making it possible for effective inference by routing inquiries to the most relevant specialist "clusters." This technique permits the model to [specialize](http://40.73.118.158) in various problem domains while maintaining overall efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 [xlarge circumstances](https://corvestcorp.com) to deploy 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 reasoning abilities 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 refers to a process of training smaller, more efficient models to mimic the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as an instructor model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in [location](https://wiki.airlinemogul.com). In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and [evaluate models](http://gitlab.ileadgame.net) against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](https://git.fracturedcode.net) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e [instance](https://gitea.urkob.com). To inspect 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 [instance](https://git.markscala.org) in the AWS Region you are releasing. To ask for a limitation boost, produce a limit boost request and [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:ShermanBorelli4) reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Set up permissions to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous content, and evaluate models against essential safety criteria. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and design reactions deployed 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 create the guardrail, see the GitHub repo.
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The basic flow involves the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After getting the design's output, another [guardrail check](http://41.111.206.1753000) is applied. If the output passes this last check, it's returned as the outcome. 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](http://1cameroon.com) showcased in the following areas demonstrate inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides 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:
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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 design. It does not 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 design detail page offers necessary details about the design's capabilities, rates structure, and application guidelines. You can discover detailed usage directions, including sample API calls and code bits for combination. The model supports various text generation tasks, [consisting](https://nukestuff.co.uk) of content development, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1347207) code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking capabilities. +The page also consists of deployment options and licensing details to assist you get begun 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 deployment 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, enter a number of circumstances (in between 1-100). +6. For Instance type, choose your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the release is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can explore different triggers and change model criteria like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For example, material for inference.
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This is an outstanding method to explore the model's reasoning and text generation capabilities before integrating it into your applications. The play ground supplies immediate feedback, helping you comprehend how the design reacts to different inputs and letting you fine-tune your prompts for ideal outcomes.
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You can rapidly check the model in the play ground 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 demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock [console](https://tricityfriends.com) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends a demand to generate 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) hub with FMs, built-in algorithms, and prebuilt ML [options](https://reckoningz.com) that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://code.thintz.com) models to your use case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free methods: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the technique that best matches 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, 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.
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The model internet browser displays available models, with details like the supplier name and design abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if suitable), showing that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model 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 design name and supplier details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the design, it's advised to examine the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, utilize the immediately generated name or produce a customized one. +8. For Instance type ΒΈ choose an [instance type](http://repo.bpo.technology) (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of circumstances (default: [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1105855) 1). +Selecting suitable circumstances types and counts is crucial for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that [isolation](https://onsanmo.co.kr) remains in place. +11. Choose Deploy to deploy the design.
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The release procedure can take several minutes to complete.
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When deployment is total, your endpoint status will alter to InService. At this point, the design is ready to accept reasoning requests through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can invoke the design utilizing a SageMaker runtime customer 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 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run extra 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 produce a guardrail using the Amazon Bedrock console or the API, and implement it as [displayed](https://gitlab.syncad.com) in the following code:
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Clean up
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To avoid unwanted charges, complete the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the [navigation](https://moztube.com) pane, choose Marketplace implementations. +2. In the Managed implementations section, locate the endpoint you want to erase. +3. Select the endpoint, and on the Actions menu, [it-viking.ch](http://it-viking.ch/index.php/User:MargotBrough0) choose Delete. +4. Verify the endpoint details to make certain you're erasing the right implementation: 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 design you deployed will sustain costs 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](https://www.nepaliworker.com).
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Conclusion
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In this post, we checked out how you can access and [release](https://freelyhelp.com) the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:BernadinePowell) SageMaker JumpStart pretrained designs, 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 is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://idemnaposao.rs) companies develop ingenious options using AWS services and [accelerated compute](http://175.178.199.623000). Currently, he is concentrated on developing strategies for fine-tuning and optimizing the inference efficiency of large language designs. In his [totally free](http://lty.co.kr) time, Vivek enjoys hiking, enjoying films, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.chir.rs) [Specialist Solutions](http://git.jaxc.cn) Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://wdz.imix7.com:13131) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with [generative](https://git.chirag.cc) [AI](https://ibs3457.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and [tactical collaborations](https://www.hireprow.com) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](http://47.103.112.133) [AI](https://repos.ubtob.net) hub. She is passionate about constructing services that help customers accelerate their [AI](https://gogs.eldarsoft.com) journey and [unlock business](https://soundfy.ebamix.com.br) worth.
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