commit 3717b1a85c564cff81522aa7c36c8afd32104a17 Author: mathiassneddon Date: Tue Apr 8 21:27:43 2025 +0800 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..d3dfc16 --- /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 [AI](https://flexychat.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://basedwa.re) concepts on AWS.
+
In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the designs also.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a large [language design](https://gitlabdemo.zhongliangong.com) (LLM) developed by DeepSeek [AI](https://www.cbtfmytube.com) that utilizes support [discovering](http://gitpfg.pinfangw.com) to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its support learning (RL) action, which was used to refine the design's reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually improving both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's geared up to break down complex questions and factor through them in a detailed manner. This guided reasoning procedure allows the design to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the industry's attention as a [flexible text-generation](https://mxlinkin.mimeld.com) model that can be integrated into numerous workflows such as agents, logical reasoning and information interpretation tasks.
+
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, enabling effective reasoning by routing queries to the most appropriate professional "clusters." This approach permits the design to concentrate on different problem 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](https://gitea.malloc.hackerbots.net) to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of [GPU memory](https://corvestcorp.com).
+
DeepSeek-R1 [distilled](https://www.nas-store.com) models bring the reasoning capabilities of the main R1 design to more effective architectures based upon popular open [designs](https://157.56.180.169) like Qwen (1.5 B, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ClevelandFryar2) 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient models to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a [teacher design](https://www.belizetalent.com).
+
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and evaluate models against key security requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create [numerous guardrails](http://www5a.biglobe.ne.jp) tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://git.poggerer.xyz) applications.
+
Prerequisites
+
To release the DeepSeek-R1 design, you need 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 validate you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge [circumstances](http://www.aiki-evolution.jp) in the AWS Region you are deploying. To ask for a limitation increase, create a limitation increase demand and connect to your account group.
+
Because you will be this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to use [Amazon Bedrock](http://47.103.108.263000) Guardrails. For instructions, see Set up approvals to utilize guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging content, and examine models against crucial safety requirements. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
+
The general circulation includes the following steps: First, the system [receives](http://8.136.42.2418088) 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 model for inference. After getting the design's output, [it-viking.ch](http://it-viking.ch/index.php/User:JulianE7217234) another guardrail check is applied. 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 suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate inference utilizing this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock [Marketplace](https://gogs.artapp.cn) offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
+
1. On the Amazon Bedrock console, [links.gtanet.com.br](https://links.gtanet.com.br/vernon471078) select Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=12035368) other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 design.
+
The model detail page supplies vital details about the model's capabilities, pricing structure, and implementation guidelines. You can find detailed usage directions, including sample API calls and code bits for combination. The model supports different text generation jobs, consisting of material production, code generation, and question answering, using its reinforcement learning optimization and CoT reasoning capabilities. +The page also includes deployment options and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
+
You will be prompted to configure the deployment details for DeepSeek-R1. The model 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, get in a variety of circumstances (between 1-100). +6. For example type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for [production](https://albion-albd.online) releases, you may want to [evaluate](http://wrs.spdns.eu) these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
+
When the deployment is total, you can evaluate 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 experiment with different triggers and adjust design criteria like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, content for inference.
+
This is an exceptional way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play area provides immediate feedback, helping you understand how the design responds to numerous inputs and letting you tweak your triggers for optimal outcomes.
+
You can quickly test the model in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run reasoning using guardrails with the released DeepSeek-R1 endpoint
+
The following code example shows how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, [surgiteams.com](https://surgiteams.com/index.php/User:BennyWager47746) use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference parameters, and sends out a request to produce text based on a user prompt.
+
Deploy DeepSeek-R1 with [SageMaker](http://47.108.105.483000) JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML [services](http://123.206.9.273000) that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production using either the UI or SDK.
+
Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker [Python SDK](http://git.airtlab.com3000). Let's explore both approaches to help you select the [approach](https://git.bbh.org.in) that best fits your needs.
+
Deploy DeepSeek-R1 through [SageMaker JumpStart](http://tesma.co.kr) UI
+
Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
+
1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
+
The model browser shows available models, with details like the service provider name and design [capabilities](http://upleta.rackons.com).
+
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 category (for example, Text Generation). +Bedrock Ready badge (if relevant), indicating that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
+
5. Choose the model card to see the model details page.
+
The model details page includes the following details:
+
- The model name and company details. +Deploy button to deploy the model. +About and Notebooks tabs with [detailed](http://www.gbape.com) details
+
The About tab includes important details, such as:
+
- Model description. +- License details. +- Technical specs. +- Usage standards
+
Before you deploy the design, it's advised to evaluate the design details and license terms to verify compatibility with your use case.
+
6. Choose Deploy to continue with release.
+
7. For Endpoint name, utilize the instantly produced name or create a custom one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting suitable circumstances types and counts is essential for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we [highly advise](http://39.99.224.279022) adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the model.
+
The implementation process can take a number of minutes to finish.
+
When implementation is complete, your endpoint status will change to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is complete, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your [applications](https://bogazicitube.com.tr).
+
Deploy DeepSeek-R1 using the SageMaker Python SDK
+
To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a [detailed](http://51.222.156.2503000) code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is [supplied](http://linyijiu.cn3000) in the Github here. You can clone the note pad and range from SageMaker Studio.
+
You can run additional requests against the predictor:
+
Implement guardrails and run inference with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can [develop](http://git.permaviat.ru) a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
+
Clean up
+
To prevent unwanted charges, complete the steps in this section to clean up your resources.
+
Delete the Amazon Bedrock Marketplace release
+
If you released the design utilizing Amazon Bedrock Marketplace, complete the following steps:
+
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed releases 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 deleting the appropriate deployment: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart model you deployed 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.
+
Conclusion
+
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, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker [JumpStart](https://southwestjobs.so).
+
About the Authors
+
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://lovn1world.com) companies build innovative options using AWS services and accelerated calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference efficiency of large language models. In his spare time, Vivek delights in hiking, seeing films, and attempting different cuisines.
+
Niithiyn Vijeaswaran is a Generative [AI](http://kousokuwiki.org) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://git.bbh.org.in) [accelerators](https://taelimfwell.com) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://video.xaas.com.vn) with the Third-Party Model Science team at AWS.
+
Banu Nagasundaram leads product, engineering, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=12133864) and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://gitlab.adintl.cn) hub. She is passionate about building services that assist consumers accelerate their [AI](https://git.fanwikis.org) journey and unlock organization value.
\ No newline at end of file