alinephipps158
/
soundofrecovery
1
0
Code Issues Packages Projects Wiki

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

Aline Phipps 2025-04-10 04:24:44 +08:00
commit 96c8e56782
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 thrilled to announce that DeepSeek R1 [distilled Llama](http://www.umzumz.com) and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://lazerjobs.in)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://blablasell.com) ideas 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 comparable actions to release the distilled versions of the models also.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://git.spitkov.hu) that uses reinforcement learning to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating function is its reinforcement knowing (RL) step, which was used to improve the design's actions beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually boosting both [significance](https://bakery.muf-fin.tech) and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, [indicating](http://linyijiu.cn3000) it's geared up to break down complex queries and factor through them in a detailed manner. This directed reasoning process permits the design to produce more precise, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:JulissaFaust121) transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, [logical thinking](http://40.73.118.158) and data interpretation tasks.<br>
<br>DeepSeek-R1 [utilizes](https://gitea.ashcloud.com) a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, enabling effective reasoning by routing queries to the most relevant professional "clusters." This method allows the model to focus on different issue domains while maintaining total [effectiveness](http://120.48.141.823000). DeepSeek-R1 requires a minimum of 800 GB of [HBM memory](https://xn--939a42kg7dvqi7uo.com) in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of [training](https://www.sedatconsultlimited.com) smaller sized, more efficient models to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, using it as an instructor design.<br>
<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with [guardrails](https://git.goatwu.com) in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and assess designs against key security criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and [Bedrock](http://101.200.33.643000) Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://jollyday.club) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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 releasing. To ask for a limitation increase, create a limitation boost [request](http://47.93.234.49) and reach out to your account group.<br>
<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock [Guardrails](http://40th.jiuzhai.com). For directions, see Establish consents to utilize guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails allows you to introduce safeguards, prevent harmful content, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:FernandoSimmonds) and evaluate models against essential safety criteria. You can carry out safety procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model reactions released 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 produce the guardrail, see the [GitHub repo](https://pakkjob.com).<br>
<br>The general flow involves the following steps: First, the system [receives](https://trackrecord.id) an input for the design. This input is then processed through the [ApplyGuardrail API](https://gitlab.companywe.co.kr). If the input passes the guardrail check, it's sent to the model 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 last outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections show inference 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 foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:<br>
<br>1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.<br>
<br>The design detail page offers important details about the design's capabilities, pricing structure, and execution standards. You can discover detailed use instructions, consisting of [sample API](http://8.136.197.2303000) calls and code snippets for combination. The design supports different text generation jobs, including content development, code generation, and question answering, using its reinforcement finding out optimization and CoT thinking abilities.
The page likewise includes deployment alternatives and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, pick Deploy.<br>
<br>You will be triggered to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of instances, go into a variety of circumstances (between 1-100).
6. For example type, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ElizabethMcVeigh) choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may wish to review these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.<br>
<br>When the release is total, you can test DeepSeek-R1's capabilities 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 model specifications like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For instance, material for reasoning.<br>
<br>This is an excellent method to explore the model's thinking and text generation abilities before integrating it into your applications. The play area offers immediate feedback, helping you comprehend how the model reacts to different inputs and letting you fine-tune your triggers for ideal results.<br>
<br>You can quickly evaluate the model in the play area through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
<br>Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to perform reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 developed the guardrail, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends out a request to produce text based on a user prompt.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can models to your use case, with your information, and release them into production utilizing either the UI or SDK.<br>
<br>[Deploying](http://122.51.51.353000) DeepSeek-R1 model through SageMaker JumpStart provides two convenient techniques: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the method that best fits your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:<br>
<br>1. On the [SageMaker](https://git.micahmoore.io) console, select Studio in the navigation pane.
2. First-time users will be triggered to develop a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
<br>The model browser displays available models, with details like the service provider name and design abilities.<br>
<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card reveals essential details, including:<br>
<br>- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if suitable), suggesting that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design<br>
<br>5. Choose the [model card](https://premiergitea.online3000) to see the design details page.<br>
<br>The design details page [consists](http://185.87.111.463000) of the following details:<br>
<br>- The design name and [service provider](http://139.224.253.313000) details.
Deploy button to release the model.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of essential details, such as:<br>
<br>- Model description.
- License details.
- Technical requirements.
- Usage standards<br>
<br>Before you release the design, it's suggested to evaluate the design details and license terms to verify compatibility with your usage case.<br>
<br>6. [Choose Deploy](https://athleticbilbaofansclub.com) to continue with deployment.<br>
<br>7. For Endpoint name, utilize the instantly generated name or produce a customized one.
8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of instances (default: 1).
Selecting suitable instance types and counts is important for cost and efficiency optimization. [Monitor](http://81.71.148.578080) your deployment 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 setups for accuracy. For this design, we highly advise adhering to SageMaker JumpStart [default](https://marcosdumay.com) settings and making certain that [network](http://124.222.181.1503000) isolation remains in place.
11. Choose Deploy to deploy the model.<br>
<br>The release procedure can take several minutes to complete.<br>
<br>When deployment is complete, your endpoint status will change to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can keep an eye on the implementation development on the SageMaker [console Endpoints](http://47.97.6.98081) page, which will show relevant metrics and status details. When the [deployment](https://optimiserenergy.com) is complete, you can invoke the design utilizing a SageMaker runtime customer and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the [essential AWS](https://gitea.b54.co) permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for [reasoning programmatically](https://hyperwrk.com). The code for releasing the design is supplied 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 reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:<br>
<br>Clean up<br>
<br>To avoid [undesirable](http://194.87.97.823000) charges, finish the steps in this section to clean up your resources.<br>
<br>Delete the [Amazon Bedrock](https://git.fafadiatech.com) Marketplace release<br>
<br>If you deployed the model using Amazon Bedrock Marketplace, complete the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation [designs](https://www.ausfocus.net) in the navigation pane, pick Marketplace implementations.
2. In the Managed releases section, locate the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, [choose Delete](http://skupra-nat.uamt.feec.vutbr.cz30000).
4. Verify the endpoint details to make certain you're deleting the proper implementation: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart model you released 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.<br>
<br>Conclusion<br>
<br>In this post, we [checked](http://101.43.135.2349211) 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 start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:CelinaGilles88) 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://theindietube.com) companies develop innovative solutions utilizing AWS services and accelerated compute. Currently, he is focused on developing methods for fine-tuning and optimizing the inference performance of large language models. In his totally free time, Vivek enjoys treking, watching movies, and trying various cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://193.30.123.188:3500) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://www.sintramovextrema.com.br) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://rackons.com) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://dev.fleeped.com) center. She is enthusiastic about building solutions that [assist consumers](https://okk-shop.com) accelerate their [AI](http://47.107.80.236:3000) journey and unlock business worth.<br>