annmariebracke/begild
1
0
Code Issues Packages Projects Wiki

Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Annmarie Bracker 2025-02-16 15:30:04 +08:00
parent 4bde0900c7
commit f71d9f2923
1 changed files with 93 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

93
DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md Normal file

@ -0,0 +1,93 @@
<br>Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and [Amazon SageMaker](http://sdongha.com) JumpStart. With this launch, you can now release DeepSeek [AI](https://git.christophhagen.de)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative [AI](http://163.228.224.105:3000) concepts on AWS.<br>
<br>In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the designs as well.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://3.223.126.156) that uses reinforcement finding out to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3[-Base structure](https://www.klaverjob.com). An essential differentiating feature is its reinforcement learning (RL) action, which was used to improve the model's responses beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, [eventually improving](https://vibestream.tv) both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's geared up to break down complex questions and reason through them in a detailed manner. This assisted thinking procedure [enables](http://59.56.92.3413000) the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured [responses](https://geetgram.com) while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation model that can be integrated into different workflows such as agents, rational thinking and information analysis tasks.<br>
<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, making it possible for efficient inference by routing inquiries to the most relevant professional "clusters." This approach allows the model to concentrate on various [issue domains](https://4kwavemedia.com) while maintaining general efficiency. 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 deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, [gratisafhalen.be](https://gratisafhalen.be/author/dulcie01x5/) and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to mimic the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor model.<br>
<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and examine models against key security requirements. At the time of [writing](https://nerm.club) this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can [produce numerous](https://git.lab.evangoo.de) guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://git.pt.byspectra.com) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 model, you need access to an ml.p5e [circumstances](http://gite.limi.ink). To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 ask for a limit boost, [develop](http://106.14.125.169) a limitation boost request and connect to your account team.<br>
<br>Because you will be releasing this model with Amazon Bedrock Guardrails, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:GeraldDonnithorn) make certain you have the correct AWS Identity and Gain Access To Management (IAM) [permissions](https://gallery.wideworldvideo.com) to use Amazon Bedrock Guardrails. For instructions, see Set up approvals to use guardrails for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>[Amazon Bedrock](https://gitlab.grupolambda.info.bo) Guardrails allows you to present safeguards, prevent hazardous material, and assess designs against crucial safety requirements. You can carry out safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.<br>
<br>The general [flow involves](http://88.198.122.2553001) the following actions: 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 out to the design for reasoning. After getting the [design's](https://convia.gt) output, another guardrail check is used. If the output passes this last check, it's returned as the last result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas demonstrate [inference](https://gitea.ws.adacts.com) using this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:<br>
<br>1. On the Amazon Bedrock console, choose [Model catalog](https://code.estradiol.cloud) under Foundation designs in the [navigation](https://nurseportal.io) pane.
At the time of composing 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 [service provider](http://185.87.111.463000) and pick the DeepSeek-R1 model.<br>
<br>The design detail page supplies necessary details about the design's capabilities, rates structure, and execution standards. You can discover detailed use guidelines, consisting of sample API calls and code bits for integration. The [design supports](https://git.highp.ing) numerous text generation tasks, consisting of material development, code generation, and concern answering, utilizing its support discovering optimization and CoT reasoning abilities.
The page also consists of implementation choices and licensing details to assist you get started with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, choose Deploy.<br>
<br>You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, enter a number of instances (between 1-100).
6. For Instance type, pick your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is [suggested](http://202.90.141.173000).
Optionally, you can configure advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role consents, and encryption settings. For a lot of [utilize](https://findgovtsjob.com) cases, the default settings will work well. However, for production implementations, you may want to review these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.<br>
<br>When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive interface where you can explore various triggers and change design criteria like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For instance, content for inference.<br>
<br>This is an excellent way to check out the model's reasoning and [wavedream.wiki](https://wavedream.wiki/index.php/User:MorrisVerge81) text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, helping you understand how the design reacts to numerous inputs and letting you fine-tune your triggers for optimal results.<br>
<br>You can rapidly evaluate the design in the playground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
<br>Run reasoning using guardrails with the [deployed](https://mp3talpykla.com) DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to perform inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock [utilizing](http://207.180.250.1143000) the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends out a demand to based on a user timely.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1078514) with your data, and deploy them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 [practical](https://git.kicker.dev) techniques: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the technique that finest suits your requirements.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be triggered to create a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br>
<br>The design web browser shows available models, [classificados.diariodovale.com.br](https://classificados.diariodovale.com.br/author/bonniekings/) with details like the provider name and model abilities.<br>
<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 [model card](http://123.56.247.1933000).
Each design card shows key details, consisting of:<br>
<br>- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to [conjure](http://park8.wakwak.com) up the design<br>
<br>5. Choose the design card to see the design details page.<br>
<br>The model details page consists of the following details:<br>
<br>- The design name and supplier details.
Deploy button to deploy the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of crucial details, such as:<br>
<br>- Model description.
- License details.
- Technical specs.
- Usage guidelines<br>
<br>Before you release the design, it's suggested to examine the model details and license terms to validate compatibility with your use case.<br>
<br>6. Choose Deploy to continue with implementation.<br>
<br>7. For [Endpoint](http://88.198.122.2553001) name, utilize the automatically generated name or develop a custom-made one.
8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the number of circumstances (default: 1).
Selecting appropriate instance types and counts is important for cost and efficiency optimization. [Monitor](https://job.bzconsultant.in) your deployment to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this design, we strongly recommend sticking to SageMaker JumpStart default [settings](https://vybz.live) and making certain that network isolation remains in place.
11. Choose Deploy to deploy the design.<br>
<br>The [release procedure](https://gertsyhr.com) can take several minutes to complete.<br>
<br>When implementation is complete, your endpoint status will change to InService. At this point, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) the design is all set to accept reasoning [demands](http://114.34.163.1743333) through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is total, you can invoke the model using a SageMaker runtime client and integrate it with your [applications](https://www.jobsires.com).<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>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 essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.<br>
<br>You can run additional requests against the predictor:<br>
<br>Implement guardrails and run inference with your SageMaker JumpStart predictor<br>
<br>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 shown in the following code:<br>
<br>Clean up<br>
<br>To prevent unwanted charges, complete the steps in this area to tidy up your resources.<br>
<br>Delete the [Amazon Bedrock](https://repo.gusdya.net) Marketplace deployment<br>
<br>If you released the design utilizing Amazon Bedrock Marketplace, complete the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments.
2. In the Managed releases section, find the endpoint you wish to delete.
3. Select the endpoint, and on the [Actions](https://18plus.fun) menu, select Delete.
4. Verify the endpoint details to make certain you're erasing the right deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
<br>Conclusion<br>
<br>In this post, we checked out 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 start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart [Foundation](https://skytube.skyinfo.in) Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://www.pakalljobz.com) companies build innovative options using AWS services and accelerated calculate. Currently, he is focused on developing techniques for fine-tuning and optimizing the inference efficiency of large language designs. In his complimentary time, Vivek enjoys hiking, seeing films, and trying various foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://govtpakjobz.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://newnormalnetwork.me) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://mp3talpykla.com) with the Third-Party Model [Science](http://1024kt.com3000) team at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.bubbleioa.top) center. She is enthusiastic about building services that assist customers accelerate their [AI](http://192.241.211.111) journey and [unlock company](http://git.z-lucky.com90) value.<br>