From 02de72207cc7ab7b4d659b00b137cb78aee0e84e Mon Sep 17 00:00:00 2001 From: susanneplumlee Date: Sun, 9 Feb 2025 11:52:34 +0000 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..119bb34 --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://pyra-handheld.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](http://git.storkhealthcare.cn) concepts on AWS.
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In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://4stour.com) that uses support finding out to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating feature is its reinforcement learning (RL) step, which was used to improve the design's responses beyond the basic pre-training and [tweak procedure](https://gitea.star-linear.com). By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately improving both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down [intricate inquiries](https://pakkjob.com) and [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/tawnyalamber) factor through them in a detailed way. This guided thinking process permits the design to produce more accurate, transparent, and detailed responses. This model combines RL-based [fine-tuning](http://1.94.27.2333000) with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation design that can be integrated into different workflows such as representatives, logical reasoning and data interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, [enabling effective](https://local.wuanwanghao.top3000) reasoning by routing queries to the most pertinent expert "clusters." This technique enables the model to specialize in various issue domains while maintaining overall performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to [release](http://gitlab.qu-in.com) the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to imitate the habits and [thinking patterns](http://www.shopmento.net) of the bigger DeepSeek-R1 design, using it as an [instructor design](https://youslade.com).
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and examine designs against crucial safety criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, [improving](https://kaamdekho.co.in) user experiences and standardizing security controls across your generative [AI](https://ipen.com.hk) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e [circumstances](https://careerportals.co.za). To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify 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 deploying. To ask for a limitation increase, create a limit increase demand and reach out to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Set up permissions to use guardrails for material filtering.
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Implementing [guardrails](https://repo.serlink.es) with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and evaluate designs against key security requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model reactions released 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 develop the guardrail, see the GitHub repo.
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The basic circulation includes the following steps: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for reasoning. After getting the model's output, another guardrail check is [applied](http://gitlab.iyunfish.com). 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 suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace 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, complete the following steps:
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1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.
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The model detail page offers essential details about the design's abilities, pricing structure, and application guidelines. You can find detailed usage instructions, including sample API calls and code bits for combination. The model supports various text generation jobs, consisting of content production, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking capabilities. +The page likewise includes implementation options and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be triggered to set up the [deployment details](https://youtoosocialnetwork.com) for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a variety of circumstances (in between 1-100). +6. For Instance type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may desire to review these settings to align with your company's security and compliance requirements. +7. [Choose Deploy](https://innovator24.com) to begin [utilizing](http://xn--vk1b975azoatf94e.com) the model.
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When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive interface where you can explore various triggers and change design parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, material for reasoning.
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This is an excellent way to explore the design's reasoning and text generation capabilities before incorporating it into your applications. The play area offers immediate feedback, assisting you understand how the model responds to various inputs and letting you fine-tune your triggers for ideal outcomes.
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You can rapidly check the model in the [play ground](http://47.244.232.783000) through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference using [guardrails](https://wiki.ragnaworld.net) with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have produced the guardrail, [utilize](http://www.xn--80agdtqbchdq6j.xn--p1ai) the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and [gratisafhalen.be](https://gratisafhalen.be/author/tamikalaf18/) sends out a demand to create text based on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML options 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 information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 convenient approaches: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the [SageMaker Python](https://dash.bss.nz) SDK. Let's check out both techniques to help you select the technique that best matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser displays available models, with details like the supplier name and [model capabilities](https://elit.press).
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card reveals essential details, consisting of:
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- Model name +- Provider name +- [Task classification](http://www.hanmacsamsung.com) (for example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the design
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5. Choose the [design card](https://hayhat.net) to see the model details page.
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The design details page includes the following details:
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- The model name and provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you release the design, it's recommended to examine the model details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the automatically generated name or produce a custom one. +8. For Instance type ΒΈ choose a [circumstances type](http://121.196.213.683000) (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of circumstances (default: 1). +Selecting suitable circumstances types and counts is vital for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the design.
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The deployment procedure can take a number of minutes to complete.
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When [deployment](https://code.linkown.com) is complete, your endpoint status will alter to InService. At this point, the model is prepared to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the [SageMaker Python](http://www.localpay.co.kr) SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional requests against the predictor:
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Implement guardrails and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, [gratisafhalen.be](https://gratisafhalen.be/author/dulcie01x5/) you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Clean up
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To avoid unwanted charges, complete the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. +2. In the Managed implementations section, find the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the correct release: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart model you released will sustain costs if you leave it [running](https://git.laser.di.unimi.it). Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, we explored how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and [SageMaker](https://gitea.ymyd.site) JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart [pretrained](https://www.jungmile.com) designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://snowboardwiki.net) business construct innovative options using AWS services and sped up compute. Currently, he is focused on developing methods for fine-tuning and optimizing the [inference efficiency](https://askcongress.org) of big language designs. In his downtime, [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/britney83x24) Vivek delights in treking, viewing motion pictures, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://66.85.76.122:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://150.158.183.74:10080) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and [Bioinformatics](https://git.teygaming.com).
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://1.15.150.90:3000) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://ezworkers.com) [AI](https://healthcarestaff.org) hub. She is passionate about building solutions that [assist clients](https://kaiftravels.com) accelerate their [AI](https://gogs.artapp.cn) journey and unlock company value.
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