commit 245bd763a63dd2ec9393d39754eda403e568214a Author: dieterosterman Date: Sun Feb 16 08:53:04 2025 +0800 Update '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..b37cdc8 --- /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 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 frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your [generative](https://git.i2edu.net) [AI](https://bug-bounty.firwal.com) ideas on AWS.
+
In this post, we demonstrate how to get going with DeepSeek-R1 on [Amazon Bedrock](https://wiki.monnaie-libre.fr) Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions of the designs too.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://newhopecareservices.com) that uses support learning to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying feature is its [support learning](https://git.xjtustei.nteren.net) (RL) step, which was used to fine-tune the design's actions beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, [ultimately improving](http://38.12.46.843333) both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, [implying](https://dinle.online) it's geared up to break down complex questions and reason through them in a detailed way. This assisted thinking procedure allows the model to [produce](https://git.boergmann.it) more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:DessieLundstrom) user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, logical thinking and data interpretation tasks.
+
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, enabling effective inference by routing questions to the most appropriate expert "clusters." This technique permits the model to specialize in different problem domains while maintaining total 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 deploy the model. ml.p5e.48 [xlarge features](http://git.hongtusihai.com) 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more efficient 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 smaller, more effective designs to simulate the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as a teacher model.
+
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and [evaluate](https://www.infiniteebusiness.com) models against [essential safety](http://101.200.127.153000) criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://scode.unisza.edu.my) applications.
+
Prerequisites
+
To deploy the DeepSeek-R1 design, 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 verify 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 deploying. To ask for a limitation increase, create a limitation boost request and connect to your account group.
+
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see [Establish permissions](http://207.180.250.1143000) to utilize guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous content, and examine models against crucial security criteria. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to [examine](https://gitea.ymyd.site) user inputs and design reactions deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://git.hmcl.net). You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
+
The basic circulation includes the following steps: First, the system gets an input for the design. This input is then [processed](https://www.jobseeker.my) through the [ApplyGuardrail API](http://xiaomaapp.top3000). 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 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 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 areas demonstrate inference using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
+
1. On the Amazon Bedrock console, choose Model catalog under [Foundation](http://git.picaiba.com) designs in the navigation 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 provider and select the DeepSeek-R1 design.
+
The design detail page provides vital details about the design's capabilities, prices structure, and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:WCTSteve75017) implementation guidelines. You can discover detailed usage guidelines, including sample API calls and code bits for integration. The model supports various [text generation](http://sp001g.dfix.co.kr) tasks, consisting of content creation, code generation, and concern answering, [utilizing](https://www.friend007.com) its reinforcement learning optimization and CoT reasoning capabilities. +The page likewise includes deployment choices and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, choose Deploy.
+
You will be triggered to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a variety of instances (between 1-100). +6. For Instance type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a [GPU-based](https://www.workinternational-df.com) instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role permissions, and file encryption settings. For many use cases, the [default](https://easterntalent.eu) settings will work well. However, for production deployments, you might wish to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
+
When the deployment is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive interface where you can experiment with various prompts and adjust model specifications like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For instance, material for reasoning.
+
This is an excellent method to check out the model's thinking and text generation abilities before integrating it into your applications. The play ground provides immediate feedback, helping you understand how the design responds to various inputs and letting you tweak your prompts for ideal outcomes.
+
You can rapidly check the design in the [play ground](https://git.profect.de) through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run [reasoning](https://gitea.lolumi.com) using guardrails with the deployed DeepSeek-R1 endpoint
+
The following code example demonstrates how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock [utilizing](https://fototik.com) the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock [console](http://gitlab.awcls.com) or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:HelenTennyson48) and sends a request to produce text based on a user timely.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production using either the UI or SDK.
+
[Deploying](https://git.xedus.ru) DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free methods: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the [SageMaker Python](http://dev.nextreal.cn) SDK. Let's check out both methods to help you select the approach that finest suits your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:
+
1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
+
The model browser shows available models, with details like the company name and model abilities.
+
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows essential details, consisting of:
+
- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
+
5. Choose the model card to view the design details page.
+
The design details page consists of the following details:
+
- The model name and company details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
+
The About tab includes crucial details, such as:
+
- Model description. +- License details. +- Technical specs. +- Usage guidelines
+
Before you release the model, it's advised to examine the model details and license terms to validate compatibility with your usage case.
+
6. Choose Deploy to continue with deployment.
+
7. For Endpoint name, utilize the instantly created name or create a custom one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of instances (default: 1). +Selecting proper circumstances types and counts is important for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the design.
+
The release procedure can take a number of minutes to finish.
+
When deployment is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept reasoning demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show 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.
+
Deploy DeepSeek-R1 using the SageMaker Python SDK
+
To get going 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 demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
+
You can run additional requests against the predictor:
+
[Implement guardrails](https://intgez.com) and run reasoning with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
+
Tidy up
+
To avoid unwanted charges, complete the actions in this section to tidy up your [resources](https://ruofei.vip).
+
Delete the [Amazon Bedrock](http://fcgit.scitech.co.kr) Marketplace deployment
+
If you released the design using Amazon Bedrock Marketplace, total the following steps:
+
1. On the Amazon Bedrock console, under [Foundation designs](http://www.sa1235.com) in the navigation pane, choose Marketplace implementations. +2. In the Managed releases section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the [correct](https://www.ndule.site) release: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The [SageMaker JumpStart](http://101.200.127.153000) design you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and [wiki-tb-service.com](http://wiki-tb-service.com/index.php?title=Benutzer:Milla01Z3855169) Resources.
+
Conclusion
+
In this post, we checked out how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
+
About the Authors
+
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://git.biosens.rs) business construct ingenious options utilizing AWS services and sped up calculate. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the reasoning performance of large [language](https://newvideos.com) models. In his spare time, Vivek delights in hiking, watching movies, and trying various foods.
+
Niithiyn Vijeaswaran is a Generative [AI](https://gruppl.com) Specialist Solutions Architect with the Third-Party Model [Science team](http://103.205.66.473000) at AWS. His area of focus is AWS [AI](http://1.92.128.200:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
+
Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://1.94.27.233:3000) with the Third-Party Model Science team at AWS.
+
Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://47.107.29.61:3000) center. She is enthusiastic about building services that assist customers accelerate their [AI](https://matchpet.es) journey and unlock organization value.
\ No newline at end of file