From 006756c23db3d4457cfe33fc377e553e24384e96 Mon Sep 17 00:00:00 2001 From: lashayespinal Date: Thu, 6 Feb 2025 23:09:33 +0000 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-And-Amazon-SageMaker-JumpStart.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md 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..6bf28f1 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:FranklinBreillat) we are excited to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://drapia.org)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](https://www.jccer.com:2223) ideas on AWS.
+
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock [Marketplace](https://vacaturebank.vrijwilligerspuntvlissingen.nl) and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the designs as well.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](http://112.112.149.146:13000) that utilizes reinforcement discovering to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing feature is its support knowing (RL) step, which was used to fine-tune the model's actions beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately enhancing both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it's [equipped](http://163.228.224.1053000) to break down complex questions and reason through them in a detailed manner. This directed thinking procedure permits the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured responses while [concentrating](https://gitea.daysofourlives.cn11443) on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually recorded the market's attention as a [versatile text-generation](http://83.151.205.893000) design that can be [incorporated](https://git.chirag.cc) into different workflows such as agents, sensible thinking and data interpretation jobs.
+
DeepSeek-R1 utilizes a Mixture of [Experts](https://sajano.com) (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient reasoning by routing inquiries to the most [relevant expert](http://47.122.66.12910300) "clusters." This approach allows the design to concentrate on different problem domains while maintaining total performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will [utilize](https://sajano.com) an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
+
DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more [efficient architectures](http://gungang.kr) based upon [popular](http://111.230.115.1083000) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient models to mimic the habits and thinking patterns of the bigger DeepSeek-R1 design, utilizing 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 releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and evaluate designs against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](http://101.200.127.15:3000) applications.
+
Prerequisites
+
To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas [console](https://dimension-gaming.nl) and under AWS Services, pick Amazon SageMaker, and verify 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 deploying. To ask for a limitation increase, create a limit boost request and connect to your account group.
+
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For instructions, see Establish permissions to utilize guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful content, and assess models against [essential](http://gitlab.rainh.top) safety requirements. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model actions deployed 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 create the guardrail, see the GitHub repo.
+
The basic circulation involves the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the check, it's sent to the design for reasoning. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show inference using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
+
1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the [InvokeModel API](https://medifore.co.jp) to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.
+
The model detail page offers necessary details about the model's capabilities, pricing structure, and execution guidelines. You can discover detailed use directions, including sample API calls and code snippets for integration. The design supports different text generation tasks, consisting of content creation, code generation, and question answering, using its reinforcement discovering optimization and CoT reasoning abilities. +The page also consists of release alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
+
You will be triggered to set up the deployment 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 Variety of instances, enter a variety of [circumstances](https://pelangideco.com) (between 1-100). +6. For example type, select your circumstances type. For [ideal performance](https://www.sportfansunite.com) with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [recommended](https://www.indianhighcaste.com). +Optionally, you can set up sophisticated security and [facilities](http://wiki.pokemonspeedruns.com) settings, including virtual personal cloud (VPC) networking, service function approvals, and encryption settings. For many use cases, the default settings will work well. However, for production releases, you may wish to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the design.
+
When the deployment is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can try out different triggers and adjust model parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For instance, material for reasoning.
+
This is an outstanding way 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 numerous inputs and letting you fine-tune your prompts for ideal outcomes.
+
You can [rapidly test](https://bcstaffing.co) the design in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
+
The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a [guardrail utilizing](https://24frameshub.com) the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, 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 solutions that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production utilizing either the UI or SDK.
+
Deploying DeepSeek-R1 model through [SageMaker JumpStart](https://gitlab.tenkai.pl) provides two [practical](https://mensaceuta.com) techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you select the approach that best suits your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
+
1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to [develop](https://lovelynarratives.com) a domain. +3. On the SageMaker Studio console, pick JumpStart in the [navigation](http://www.machinekorea.net) pane.
+
The design internet browser shows available designs, with details like the provider name and model abilities.
+
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals key details, including:
+
- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if relevant), showing that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model
+
5. Choose the design card to view the design details page.
+
The model details page includes the following details:
+
- The model name and company details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
+
The About tab includes crucial details, such as:
+
- Model [description](https://www.keeperexchange.org). +- License details. +- Technical specifications. +- Usage standards
+
Before you release the model, it's suggested to review the design details and license terms to validate compatibility with your use case.
+
6. Choose Deploy to continue with release.
+
7. For Endpoint name, use the immediately produced name or create a custom one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is important for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
+
The release process can take numerous minutes to complete.
+
When deployment is complete, your endpoint status will change to InService. At this moment, the model is prepared to accept inference requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is complete, you can invoke the design utilizing a SageMaker runtime client and [incorporate](http://dibodating.com) 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 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 use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
+
You can run extra demands against the predictor:
+
Implement guardrails and run inference with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
+
Tidy up
+
To avoid unwanted charges, finish the actions in this section to clean up your resources.
+
Delete the Amazon Bedrock Marketplace release
+
If you released the design using Amazon Bedrock Marketplace, complete the following actions:
+
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. +2. In the [Managed implementations](https://yes.youkandoit.com) area, locate the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the correct implementation: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart design you released will sustain costs if you leave it [running](https://nexthub.live). 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 get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker [JumpStart Foundation](http://112.126.100.1343000) Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
+
About the Authors
+
[Vivek Gangasani](https://39.129.90.14629923) is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://hot-chip.com) companies construct innovative solutions using [AWS services](https://accc.rcec.sinica.edu.tw) and sped up compute. Currently, he is concentrated on developing methods for fine-tuning and enhancing the reasoning efficiency of large language models. In his complimentary time, Vivek takes pleasure in hiking, seeing films, and trying various foods.
+
Niithiyn Vijeaswaran is a Generative [AI](https://www.waitumusic.com) Specialist Solutions Architect with the Third-Party Model [Science](http://182.92.163.1983000) team at AWS. His area of focus is AWS [AI](http://git.nextopen.cn) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://applykar.com) with the Third-Party Model Science group at AWS.
+
Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://andyfreund.de) hub. She is enthusiastic about building options that [assist consumers](https://skillsvault.co.za) accelerate their [AI](https://owangee.com) journey and unlock business worth.
\ No newline at end of file