agustinvirtue8
/
sommerschein
1
0
Code Issues Packages Projects Wiki

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

Agustin Virtue 2025-03-14 23:30:36 +08:00
commit 6ea55ec066
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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://www.grainfather.global)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](https://doum.cn) concepts on AWS.<br>
<br>In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs too.<br>
<br>[Overview](https://wiki.roboco.co) of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://career.agricodeexpo.org) that uses reinforcement discovering to improve [reasoning capabilities](http://huaang6688.gnway.cc3000) through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing function is its reinforcement knowing (RL) action, which was utilized to fine-tune the design's actions beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually improving both relevance and clearness. In addition, DeepSeek-R1 utilizes a [chain-of-thought](https://jobsekerz.com) (CoT) technique, indicating it's geared up to break down complex inquiries and reason through them in a detailed way. This guided thinking procedure enables the design to produce more accurate, transparent, and detailed answers. This model combines RL-based [fine-tuning](http://git.setech.ltd8300) with CoT capabilities, aiming to produce structured responses while [concentrating](https://owow.chat) on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be incorporated into various workflows such as representatives, rational reasoning and data interpretation tasks.<br>
<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion parameters, making it possible for efficient inference by routing inquiries to the most appropriate specialist "clusters." This [method enables](https://git.pandaminer.com) the model to focus on various issue domains while maintaining general efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to simulate the [behavior](http://hanbitoffice.com) and thinking patterns of the larger DeepSeek-R1 model, using it as a teacher design.<br>
<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and assess models against essential safety requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://www.chinami.com) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 design, [pediascape.science](https://pediascape.science/wiki/User:PearlNqi45856054) you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limit increase, create a limit increase request and connect to your account group.<br>
<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for content filtering.<br>
<br>Implementing guardrails with the [ApplyGuardrail](http://xiaomu-student.xuetangx.com) API<br>
<br>Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous material, and evaluate designs against essential security requirements. You can implement safety measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and design reactions 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 produce the guardrail, see the [GitHub repo](http://121.41.31.1463000).<br>
<br>The general flow includes the following actions: First, the system gets an input for the model. 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 receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a [message](http://1688dome.com) is returned indicating the nature of the intervention and whether it happened at the input or output phase. The [examples showcased](https://git.goatwu.com) in the following areas demonstrate reasoning utilizing this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure (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, select Model catalog under Foundation designs in the [navigation](https://pittsburghpenguinsclub.com) pane.
At the time of composing 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](http://www.hxgc-tech.com3000) and pick the DeepSeek-R1 model.<br>
<br>The design detail page provides necessary [details](http://git.cnibsp.com) about the model's capabilities, rates structure, and implementation guidelines. You can find detailed usage guidelines, including sample API calls and code snippets for integration. The model supports various text generation tasks, including content production, code generation, and question answering, using its support finding out optimization and CoT reasoning abilities.
The page also includes deployment alternatives and licensing details to help you get going with DeepSeek-R1 in your [applications](http://dndplacement.com).
3. To begin utilizing DeepSeek-R1, choose Deploy.<br>
<br>You will be prompted to configure the release 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 Number of instances, enter a number of circumstances (in between 1-100).
6. For example type, choose your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can configure sophisticated [security](http://124.222.7.1803000) and facilities settings, [consisting](https://b52cum.com) of virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you may wish to evaluate these settings to align with your company's security and compliance requirements.
7. Choose Deploy to start using the design.<br>
<br>When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive user interface where you can try out different prompts and adjust model criteria like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For instance, material for reasoning.<br>
<br>This is an excellent way to explore the model's reasoning and text generation capabilities before integrating it into your applications. The play area offers instant feedback, assisting you [understand](https://youtubegratis.com) how the design reacts to various inputs and letting you tweak your triggers for ideal outcomes.<br>
<br>You can [rapidly test](http://gungang.kr) the model in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run inference using guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to perform inference utilizing a deployed 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 produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a demand to produce text based upon a user prompt.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, [raovatonline.org](https://raovatonline.org/author/dustinz7422/) built-in algorithms, and prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you choose the technique that best matches your [requirements](https://twittx.live).<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:<br>
<br>1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be triggered to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.<br>
<br>The design internet browser shows available models, with details like the supplier name and design abilities.<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card shows essential details, consisting of:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
[Bedrock Ready](http://kousokuwiki.org) badge (if appropriate), showing that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design<br>
<br>5. Choose the model card to view the model details page.<br>
<br>The model details page consists of the following details:<br>
<br>- The model name and [service provider](https://peoplesmedia.co) details.
Deploy button to release the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab includes essential details, such as:<br>
<br>- Model description.
- License details.
- Technical requirements.
- Usage guidelines<br>
<br>Before you release the design, it's advised to examine the model details and license terms to confirm compatibility with your use case.<br>
<br>6. Choose Deploy to continue with deployment.<br>
<br>7. For Endpoint name, use the instantly generated name or create a custom-made one.
8. For example type ¸ pick an instance type (default: [wiki.eqoarevival.com](https://wiki.eqoarevival.com/index.php/User:GeorginaBackhous) ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of circumstances (default: 1).
Selecting suitable instance types and counts is essential for expense and performance optimization. Monitor your release 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](https://sso-ingos.ru). For this design, we strongly [recommend adhering](https://4kwavemedia.com) to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. [Choose Deploy](https://git.thetoc.net) to deploy the design.<br>
<br>The deployment process can take several minutes to finish.<br>
<br>When release is complete, your endpoint status will alter to InService. At this point, the design is ready to accept inference [demands](https://www.bolsadetrabajotafer.com) through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the [SageMaker Python](https://gitea.neoaria.io) SDK<br>
<br>To get begun 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](https://gitlab-heg.sh1.hidora.com) and environment setup. The following is a detailed code example that demonstrates how to release and [utilize](https://git.learnzone.com.cn) DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.<br>
<br>You can run additional requests against the predictor:<br>
<br>[Implement guardrails](https://git.l1.media) and run reasoning 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 utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br>
<br>Tidy up<br>
<br>To [prevent undesirable](http://xn---atd-9u7qh18ebmihlipsd.com) charges, finish the steps in this area to tidy up your [resources](https://hebrewconnect.tv).<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you released the model using Amazon Bedrock Marketplace, complete the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations.
2. In the Managed deployments 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 erasing the proper release: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart design you deployed will [sustain costs](http://47.92.149.1533000) 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.<br>
<br>Conclusion<br>
<br>In this post, we explored how you can access and [release](https://library.kemu.ac.ke) the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation 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 assists emerging [generative](https://fromkorea.kr) [AI](https://municipalitybank.com) business construct ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the [inference performance](https://kahps.org) of large language designs. In his leisure time, Vivek delights in treking, watching films, and trying different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://103.235.16.81:3000) Specialist Solutions Architect with the Third-Party Model [Science](http://47.112.200.2063000) group at AWS. His location of focus is AWS [AI](https://code.in-planet.net) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://wiki.vifm.info) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Yolanda31R) and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://jobs.cntertech.com) center. She is passionate about constructing options that assist clients accelerate their [AI](https://thankguard.com) journey and unlock organization worth.<br>