commit 95aa78d2e8436e9f639ef3af11f663aad24966f0 Author: dominiquemosel Date: Sun Apr 6 13:11:21 2025 +0000 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..25dd70a --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock [Marketplace](http://kanghexin.work3000) and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://wiki.atlantia.sca.org)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your [generative](https://legatobooks.com) [AI](https://forum.webmark.com.tr) ideas on AWS.
+
In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the models as well.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://contractoe.com) that utilizes support discovering to enhance thinking abilities through a [multi-stage training](https://hotjobsng.com) procedure from a DeepSeek-V3-Base structure. An essential identifying function is its support learning (RL) action, which was used to fine-tune the model's reactions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually improving both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, meaning it's equipped to break down intricate questions and reason through them in a detailed manner. This guided reasoning procedure enables the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation model that can be integrated into numerous workflows such as agents, logical reasoning and information tasks.
+
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, allowing effective inference by routing questions to the most pertinent specialist "clusters." This technique allows the model to specialize in various problem domains while maintaining general efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
+
DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more efficient architectures based upon 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 efficient models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.
+
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://git.bluestoneapps.com) model, we advise releasing this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and evaluate designs against essential security requirements. At the time of composing this blog, 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 usage cases and use them to the DeepSeek-R1 model, improving user experiences and [standardizing security](http://dev.onstyler.net30300) controls across your generative [AI](https://titikaka.unap.edu.pe) applications.
+
Prerequisites
+
To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) and confirm you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limit boost, create a limit increase demand and connect to your account group.
+
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) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Set up approvals to use guardrails for material filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful content, and assess designs against key security criteria. You can execute security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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 involves the following steps: First, the system [receives](https://git.devinmajor.com) 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 getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the final 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 stage. The examples showcased in the following sections demonstrate reasoning using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace gives 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 actions:
+
1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [provider](https://aaalabourhire.com) and choose the DeepSeek-R1 design.
+
The model detail page provides important details about the model's capabilities, rates structure, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:GarrettMcConnel) and [wiki.whenparked.com](https://wiki.whenparked.com/User:ECYDessie34967) execution guidelines. You can find detailed usage directions, including sample API calls and code bits for combination. The model supports numerous text generation tasks, consisting of content creation, code generation, and question answering, using its support discovering optimization and CoT reasoning capabilities. +The page also includes release choices and licensing details to assist you start with DeepSeek-R1 in your [applications](http://121.36.37.7015501). +3. To begin using DeepSeek-R1, select Deploy.
+
You will be triggered to set up the release 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 Number of circumstances, enter a number of instances (between 1-100). +6. For Instance type, select your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you may desire to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin using the model.
+
When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive user interface where you can try out various triggers and adjust design specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For example, content for reasoning.
+
This is an exceptional method to explore the model's thinking and text generation abilities before incorporating it into your applications. The play ground provides immediate feedback, helping you comprehend how the design reacts to different inputs and letting you tweak your triggers for optimum outcomes.
+
You can rapidly check the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run [reasoning](https://praca.e-logistyka.pl) using guardrails with the deployed DeepSeek-R1 endpoint
+
The following code example shows how to perform reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends out a demand to [generate text](http://101.42.21.1163000) based upon a user timely.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, [integrated](https://edurich.lk) algorithms, and prebuilt ML options that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.
+
Deploying DeepSeek-R1 design through [SageMaker JumpStart](https://nepalijob.com) offers two hassle-free approaches: using the [instinctive SageMaker](https://git.viorsan.com) JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the technique that best fits your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following [actions](https://intermilanfansclub.com) to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
+
1. On the SageMaker console, [wiki.whenparked.com](https://wiki.whenparked.com/User:AlejandrinaVanno) pick Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
+
The design internet browser shows available designs, with details like the service provider name and model abilities.
+
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows crucial details, including:
+
- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), showing that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the design
+
5. Choose the model card to see the model details page.
+
The [model details](https://careers.mycareconcierge.com) page includes the following details:
+
- The design name and service provider details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
+
The About tab consists of important details, such as:
+
- Model description. +- License details. +- Technical specs. +[- Usage](http://101.132.73.143000) standards
+
Before you deploy the model, it's recommended to review the model details and license terms to verify compatibility with your use case.
+
6. Choose Deploy to [continue](https://gitstud.cunbm.utcluj.ro) with release.
+
7. For Endpoint name, utilize the automatically created name or develop a custom-made one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of instances (default: 1). +Selecting appropriate circumstances types and counts is important for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low [latency](https://fumbitv.com). +10. Review all setups for precision. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
+
The implementation procedure can take numerous minutes to complete.
+
When deployment is complete, your endpoint status will change to [InService](https://git.corp.xiangcms.net). At this moment, the model is all set to accept reasoning requests through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is complete, you can invoke the design using a SageMaker runtime client and incorporate it with your applications.
+
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
+
To begin with DeepSeek-R1 utilizing the [SageMaker Python](http://jobteck.com) SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:EssieHalliday) utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
+
You can run extra requests against the predictor:
+
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can likewise use 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 revealed in the following code:
+
Clean up
+
To avoid unwanted charges, finish the steps in this area to clean up your resources.
+
Delete the Amazon Bedrock Marketplace deployment
+
If you released the design using Amazon Bedrock Marketplace, complete the following steps:
+
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. +2. In the Managed implementations area, find the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate implementation: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart design you deployed will [sustain costs](https://ourehelp.com) 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.
+
Conclusion
+
In this post, we explored how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. [Visit SageMaker](https://wellandfitnessgn.co.kr) JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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 Getting going with Amazon SageMaker JumpStart.
+
About the Authors
+
Vivek Gangasani is a Lead Specialist Solutions Architect for [Inference](http://mpowerstaffing.com) at AWS. He assists emerging generative [AI](https://wiki.snooze-hotelsoftware.de) business develop ingenious services using AWS services and sped up compute. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language designs. In his free time, Vivek enjoys treking, [enjoying](http://47.98.226.2403000) movies, and attempting different foods.
+
Niithiyn Vijeaswaran is a Generative [AI](https://jobs.web4y.online) Specialist Solutions Architect with the Third-Party Model [Science](https://git.bluestoneapps.com) team at AWS. His area of focus is AWS [AI](https://chefandcookjobs.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
+
Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://asicwiki.org) with the [Third-Party Model](https://gold8899.online) Science team at AWS.
+
Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://forum.kirmizigulyazilim.com) hub. She is passionate about building options that help clients accelerate their [AI](https://peopleworknow.com) journey and unlock service value.
\ No newline at end of file