commit 455a53ae115e1c92160dcc47a553bc1b65066624 Author: florentinakbq6 Date: Sat Apr 12 01:26:04 2025 -0700 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..993ca49 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are delighted 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 [release DeepSeek](https://www.longisland.com) [AI](https://git.gday.express)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://sangha.live) on AWS.
+
In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the models as well.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://unitenplay.ca) that uses reinforcement learning to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating function is its reinforcement learning (RL) action, which was utilized to improve the design's reactions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's [equipped](https://git.owlhosting.cloud) to break down complicated questions and reason through them in a detailed way. This guided reasoning procedure enables the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while focusing on interpretability and [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:VirgilLockhart2) user interaction. With its [wide-ranging capabilities](http://git.oksei.ru) DeepSeek-R1 has caught the industry's attention as a flexible [text-generation model](https://www.50seconds.com) that can be [incorporated](http://124.129.32.663000) into numerous workflows such as agents, rational thinking and data interpretation tasks.
+
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion [criteria](https://gitea.ws.adacts.com) in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient reasoning by routing questions to the most pertinent specialist "clusters." This [method enables](https://gitea.b54.co) the model to specialize in different problem domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for [inference](https://laviesound.com). In this post, we will use an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the reasoning abilities 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 refers to a process of training smaller sized, more efficient models to mimic the behavior and reasoning patterns of the larger 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 design, we recommend releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and examine designs against crucial security requirements. At the time of [writing](https://ravadasolutions.com) this blog site, for [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:EugeniaSebastian) DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://grailinsurance.co.ke) applications.
+
Prerequisites
+
To deploy the DeepSeek-R1 model, you [require access](http://sehwaapparel.co.kr) to an ml.p5e instance. To inspect 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 use. Make certain that you have at least one ml.P5e.48 [xlarge instance](https://paroldprime.com) in the AWS Region you are releasing. To request a limitation boost, produce a limit boost demand and connect to your account team.
+
Because you will be releasing this design with [Amazon Bedrock](https://meetpit.com) Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Set up consents to utilize guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails permits you to present safeguards, prevent damaging material, and evaluate designs against [key security](https://vlogloop.com) requirements. You can implement security steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
+
The general flow involves the following actions: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](http://jialcheerful.club3000) check, it's sent to the design for reasoning. After receiving the model's output, another guardrail check is used. If the [output passes](https://skilling-india.in) this final check, it's returned as the last result. However, if either the input or output is stepped in by the guardrail, a message is [returned](https://ruofei.vip) showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate [reasoning](https://hiphopmusique.com) using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace provides 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, choose Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.
+
The model detail page offers essential details about the model's abilities, rates structure, and implementation guidelines. You can find [detailed](http://gite.limi.ink) usage instructions, consisting of sample API calls and code snippets for combination. The model supports different text generation jobs, including material development, code generation, and question answering, utilizing its support learning optimization and CoT thinking capabilities. +The page likewise consists of implementation alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
+
You will be prompted to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, get in a variety of instances (in between 1-100). +6. For example type, select your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure innovative security and facilities settings, including virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For most use cases, the default settings will work well. However, for production releases, you might want to examine these [settings](http://dev.zenith.sh.cn) to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the model.
+
When the implementation is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can explore various prompts and adjust design parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For instance, material for inference.
+
This is an excellent method to explore the model's thinking and text generation capabilities before integrating it into your applications. The play ground provides immediate feedback, helping you comprehend how the design responds to different inputs and letting you fine-tune your triggers for ideal results.
+
You can rapidly test the model in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
+
Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
+
The following code example demonstrates how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up [reasoning](https://woodsrunners.com) parameters, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:NathanielRehfisc) and sends a demand to [produce text](http://www.s-golflex.kr) based upon a user prompt.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and release them into production utilizing either the UI or SDK.
+
Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical methods: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you pick the technique that finest 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 create a domain. +3. On the [SageMaker Studio](https://gitlab.dituhui.com) console, choose JumpStart in the [navigation](https://denis.usj.es) pane.
+
The design internet browser displays available models, with details like the service provider name and design abilities.
+
4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals essential details, consisting of:
+
- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
+
5. Choose the design card to see the model details page.
+
The design details page includes the following details:
+
- The model name and supplier details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
+
The About tab consists of crucial details, such as:
+
- Model description. +- License details. +[- Technical](https://www.jungmile.com) specifications. +[- Usage](https://git.xutils.co) standards
+
Before you deploy the model, it's recommended to evaluate the design details and license terms to validate compatibility with your usage case.
+
6. Choose Deploy to continue with deployment.
+
7. For Endpoint name, utilize the immediately generated name or [develop](http://47.93.192.134) a custom one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of instances (default: 1). +Selecting proper [instance types](http://git.gonstack.com) and counts is essential for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low [latency](https://recrutevite.com). +10. Review all setups for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the design.
+
The deployment procedure can take several minutes to finish.
+
When deployment is total, your endpoint status will alter to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is total, you can invoke the model using a SageMaker runtime client and incorporate it with your applications.
+
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
+
To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals 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 design is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
+
You can run [additional demands](https://ourehelp.com) against the predictor:
+
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your [SageMaker](http://www.xn--he5bi2aboq18a.com) JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as [displayed](http://185.5.54.226) in the following code:
+
Clean up
+
To avoid undesirable charges, finish the actions in this section to clean up your resources.
+
Delete the Amazon Bedrock Marketplace deployment
+
If you deployed the design utilizing Amazon Bedrock Marketplace, total the following steps:
+
1. On the Amazon Bedrock console, under Foundation designs in the [navigation](https://postyourworld.com) pane, choose Marketplace deployments. +2. In the [Managed implementations](https://easterntalent.eu) area, locate the [endpoint](https://www.89u89.com) you desire 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 release: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart model you deployed will sustain costs if you leave it running. Use the following code to delete 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 design using 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 models, SageMaker JumpStart pretrained models, [Amazon SageMaker](http://63.32.145.226) [JumpStart Foundation](http://47.95.216.250) Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
+
About the Authors
+
[Vivek Gangasani](https://denis.usj.es) is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://tapeway.com) companies develop innovative solutions utilizing AWS services and accelerated compute. Currently, he is focused on establishing methods for fine-tuning and optimizing the inference performance of big language models. In his downtime, Vivek delights in hiking, watching films, and attempting various foods.
+
Niithiyn Vijeaswaran is a Generative [AI](https://114jobs.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His [location](https://tottenhamhotspurfansclub.com) of focus is AWS [AI](https://philomati.com) [accelerators](http://116.62.145.604000) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://kaymack.careers) with the Third-Party Model Science group at AWS.
+
Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://globalhospitalitycareer.com) hub. She is enthusiastic about developing services that assist consumers accelerate their [AI](https://wiki.dulovic.tech) journey and unlock company value.
\ No newline at end of file