From 206058626c2911dde9cb0dc7132ebe7096f8bb0b Mon Sep 17 00:00:00 2001 From: Carmine Kitamura Date: Sun, 9 Feb 2025 08:52:46 +0800 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace 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..91069ee --- /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 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://pk.thehrlink.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative [AI](https://www.jobseeker.my) concepts on AWS.
+
In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models also.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](http://121.41.31.146:3000) that uses reinforcement finding out to [enhance reasoning](https://jobsingulf.com) capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying function is its reinforcement knowing (RL) action, which was utilized to refine the model's actions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually boosting both significance and [clearness](https://ofalltime.net). In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's geared up to break down complex queries and factor through them in a detailed way. This guided reasoning procedure enables the model to produce more accurate, transparent, and detailed answers. This model integrates [RL-based fine-tuning](http://dimarecruitment.co.uk) with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be integrated into different workflows such as agents, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:DannielleDixson) sensible thinking and data analysis tasks.
+
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, enabling efficient reasoning by routing questions to the most relevant professional "clusters." This method allows the model to specialize in different problem domains while maintaining overall effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the reasoning [capabilities](https://igit.heysq.com) of the main R1 design to more efficient architectures based on [popular](https://git.sitenevis.com) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient designs to simulate the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a teacher model.
+
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and examine designs against crucial safety requirements. At the time of composing 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 usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://ahlamhospitalityjobs.com) applications.
+
Prerequisites
+
To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas [console](https://kewesocial.site) and under AWS Services, select Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit increase, develop a limitation increase demand and connect to your account team.
+
Because you will be [releasing](http://xrkorea.kr) this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Set up approvals to utilize guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous content, and evaluate models against crucial safety requirements. You can carry out safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and design 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 develop the guardrail, see the GitHub repo.
+
The general [circulation](https://thebigme.cc3000) includes the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for inference. After receiving the design'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 stepped in by the guardrail, a message is returned indicating the nature of the [intervention](http://kandan.net) and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate inference using this API.
+
Deploy DeepSeek-R1 in [Amazon Bedrock](https://gitcq.cyberinner.com) Marketplace
+
Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and [specialized structure](https://thedatingpage.com) designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
+
1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and [raovatonline.org](https://raovatonline.org/author/namchism044/) other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.
+
The design detail page provides essential details about the design's abilities, prices structure, and application standards. You can find detailed usage directions, including sample API calls and code snippets for integration. The design supports numerous text generation jobs, including material creation, code generation, and concern answering, using its support finding out optimization and CoT reasoning [abilities](http://git.dashitech.com). +The page likewise includes release options and licensing details to help you start with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, select Deploy.
+
You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a variety of circumstances (in between 1-100). +6. For Instance type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a [GPU-based instance](https://vcanhire.com) type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For the [majority](https://www.jobspk.pro) of utilize cases, the default settings will work well. However, for production releases, you may want to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the model.
+
When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive interface where you can experiment with various prompts and change model specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For instance, material for reasoning.
+
This is an excellent way to check out the model's thinking and text generation capabilities before incorporating it into your applications. The play area provides instant feedback, assisting you understand how the model responds to different inputs and letting you fine-tune your triggers for optimum outcomes.
+
You can quickly check the design in the play ground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run inference using guardrails with the released DeepSeek-R1 endpoint
+
The following code example shows how to perform reasoning [utilizing](https://visorus.com.mx) a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, [utilize](http://git.liuhung.com) the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends a demand to produce text based on a user prompt.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production using either the UI or SDK.
+
Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you pick the technique that finest matches your needs.
+
Deploy DeepSeek-R1 through [SageMaker JumpStart](https://git.andert.me) UI
+
Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
+
1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be [prompted](http://git.szchuanxia.cn) to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
+
The design web browser displays available designs, with details like the service provider name and [model abilities](https://tiktack.socialkhaleel.com).
+
4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals key details, consisting of:
+
- Model name +- Provider name +- Task classification (for example, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073855) Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, allowing you to [utilize Amazon](https://vidhiveapp.com) Bedrock APIs to invoke the model
+
5. Choose the model card to view the design details page.
+
The model details page consists of the following details:
+
- The model name and provider details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
+
The About tab includes essential details, such as:
+
- Model description. +- License details. +- Technical requirements. +- Usage guidelines
+
Before you release the design, it's suggested to evaluate the design details and license terms to confirm compatibility with your use case.
+
6. Choose Deploy to proceed with implementation.
+
7. For Endpoint name, utilize the instantly produced name or create a custom-made one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the number of instances (default: 1). +Selecting proper circumstances types and counts is important for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
+
The implementation process can take several minutes to finish.
+
When implementation is complete, your endpoint status will alter 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 appropriate metrics and status details. When the release is total, you can conjure up the design using a SageMaker runtime client and integrate it with your [applications](https://www.jobseeker.my).
+
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
+
To get going with DeepSeek-R1 utilizing the [SageMaker Python](https://gitea.aambinnes.com) SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is 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, [fishtanklive.wiki](https://fishtanklive.wiki/User:GiuseppeXve) 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 displayed in the following code:
+
Tidy up
+
To avoid unwanted charges, finish the steps in this section to clean up your [resources](http://tv.houseslands.com).
+
Delete the Amazon Bedrock Marketplace deployment
+
If you released the model using Amazon Bedrock Marketplace, complete the following steps:
+
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. +2. In the Managed implementations section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [choose Delete](http://forum.ffmc59.fr). +4. Verify the endpoint details to make certain you're deleting the proper deployment: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart design you deployed will sustain costs 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun with Amazon SageMaker JumpStart.
+
About the Authors
+
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://git.runsimon.com) companies build innovative services utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the inference efficiency of large language designs. In his downtime, Vivek delights in hiking, seeing motion pictures, and attempting different cuisines.
+
Niithiyn Vijeaswaran is a Generative [AI](http://101.51.106.216) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://repo.magicbane.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
+
Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://gogs.dev.dazesoft.cn) with the Third-Party Model Science group at AWS.
+
Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://aircrew.co.kr) center. She is [passionate](http://anggrek.aplikasi.web.id3000) about constructing services that assist consumers accelerate their [AI](https://theneverendingstory.net) journey and unlock company value.
\ No newline at end of file