I cannot find any reference in the documentation, but I suspect the way you are updating Tensorflow is not totally supported by Sagemaker. Sagemaker usually uses prebuilt containers, that already contains tensorflow, but also CUDA. If you just update Tensorflow there is no guarantee that will be still compatible with the installed CUDA.
Answer from rok on Stack OverflowAWS
aws.amazon.com › amazon ec2 › instance types › g4 instances
Amazon EC2 G4 Instances — Amazon Web Services (AWS)
6 days ago - They provide up to 8 NVIDIA T4 GPUs, 96 vCPUs, 100 Gbps networking, and 1.8 TB local NVMe-based SSD storage and are also available as bare metal instances. G4dn instances are equipped with NVIDIA T4 GPUs which deliver up to 40X better low-latency throughput than CPUs, so more requests can be ...
Vantage
instances.vantage.sh › aws › ec2 › g4dn.xlarge
g4dn.xlarge pricing and specs - Vantage
The g4dn.xlarge instance is in the GPU instance family with 4 vCPUs, 16 GiB of memory and up to 25 Gibps of bandwidth starting at $0.526 per hour.
AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › machine learning environments offered by amazon sagemaker ai › amazon sagemaker studio › amazon sagemaker studio classic › use amazon sagemaker studio classic notebooks › available resources for amazon sagemaker studio classic notebooks › instance types available for use with amazon sagemaker studio classic notebooks
Instance Types Available for Use With Amazon SageMaker Studio Classic Notebooks - Amazon SageMaker AI
It also lists information about the specifications of each instance type. The default instance type for GPU-based images is ml.g4dn.xlarge.
Vantage
instances.vantage.sh › aws › ec2 › g4dn.4xlarge
g4dn.4xlarge pricing and specs - Vantage
The g4dn.4xlarge instance is in the GPU instance family with 16 vCPUs, 64 GiB of memory and up to 25 Gibps of bandwidth starting at $1.204 per hour.
Amazon Web Services
pages.awscloud.com › rs › 112-TZM-766 › images › AL-ML for Startups - Select the Right ML Instance.pdf pdf
Select the right ML instance for your training and inference ...
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Top answer 1 of 2
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An ml.t3.2xlarge is a CPU instance which has no GPU.
Instead, run your SageMaker notebook instance with one of the GPU instances listed here, like ml.g4dn.xlarge, and make sure to pick the PyTorch kernel for the notebook.
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ml.g4dn.xlarge, and make sure to pick the PyTorch kernel for the notebook.-- This worked for me
Cloudzero
advisor.cloudzero.com › aws › sagemaker › ml.g4dn.12xlarge
ml.g4dn.12xlarge SageMaker ML Instance Specs And Pricing
CloudZero's intelligent platform helps you optimize cloud costs and improve infrastructure efficiency.
EC2 Pricing Calculator
costcalc.cloudoptimo.com › aws-pricing-calculator › ec2 › g4dn.xlarge
g4dn.xlarge Pricing and Specs: AWS EC2
The g4dn.xlarge instance is part of the g4dn series, featuring 4 vCPUs and Up to 25 Gigabit of RAM, with Gpu Instances.
NVIDIA Developer
developer.nvidia.com › blog › getting-the-most-out-of-nvidia-t4-on-aws-g4-instances
Getting the Most Out of NVIDIA T4 on AWS G4 Instances | NVIDIA Technical Blog
August 21, 2022 - Select the Deep Learning AMI (Ubuntu 18.04) version 43.0 to run on a g4dn.xlarge instance with at least 150G of storage space. Log into your instance. Clone TensorRT repository into your local environment: git clone -b master https://github.com/nvidia/TensorRT TensorRT ... docker run --gpus all -it --rm -v $HOME/TensorRT:/workspace/TensorRT nvcr.io/nvidia/tensorflow:21.04-tf1-py3
Umbrella
anodot.com › home › learning center › aws cost management › amazon ec2 g4 instances
Using AWS EC2 G4dn and G4ad | Amazon EC2 G4 | Umbrella
April 10, 2025 - In December 2020, AWS released the Amazon EC2 G4ad instance subfamily — powered by AMD Radeon Pro V520 GPUs and second-generation AMD EPYC processors with up to 2.4 TB of local NVMe storage — that delivers up to 40% better price performance over comparable GPU-based instances for graphics intensive applications such as virtual workstations and game streaming. In July 2021, AWS expanded the G4ad subfamily with the g4ad.xlarge and g4ad.2xlarge sizes, which are designed to be cost-effective for workloads that don’t need the high vCPU and system memory that current larger G4ad instance sizes offer — rounding out their AMD offering and providing the lowest cost GPU instance in the AWS Cloud.
Cloudzero
advisor.cloudzero.com › aws › sagemaker › ml.g4dn.xlarge
ml.g4dn.xlarge SageMaker ML Instance Specs And Pricing
CloudZero's intelligent platform helps you optimize cloud costs and improve infrastructure efficiency.
Vantage
instances.vantage.sh › aws › ec2 › g4dn.16xlarge
g4dn.16xlarge pricing and specs - Vantage
The g4dn.16xlarge instance is in the GPU instance family with 64 vCPUs, 256 GiB of memory and 50 Gibps of bandwidth starting at $4.352 per hour.
Vantage
instances.vantage.sh › aws › ec2 › g4dn.8xlarge
g4dn.8xlarge pricing and specs - Vantage
The g4dn.8xlarge instance is in the GPU instance family with 32 vCPUs, 128 GiB of memory and 50 Gibps of bandwidth starting at $2.176 per hour.
Cloudzero
advisor.cloudzero.com › aws › sagemaker › ml.g4dn.8xlarge
ml.g4dn.8xlarge SageMaker ML Instance Specs And Pricing
CloudZero's intelligent platform helps you optimize cloud costs and improve infrastructure efficiency.
VPSBenchmarks
vpsbenchmarks.com › home › gpu plans › amazon aws › g4dn.xlarge gpu plan
g4dn.xlarge GPU Plan | VPSBenchmarks
This plan belongs to the Amazon AWS G4 server line (GPUs).
Vantage
instances.vantage.sh › aws › ec2 › g4dn.12xlarge
g4dn.12xlarge pricing and specs - Vantage
The g4dn.12xlarge instance is in the GPU instance family with 48 vCPUs, 192 GiB of memory and 50 Gibps of bandwidth starting at $3.912 per hour.
Top answer 1 of 3
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It looks like the issue is you have the cpu version of pytorch installed instead of the gpu version. If you go to the pytorch home page: https://pytorch.org/get-started/locally/ you can use the configuration table to install the cuda 11 or cuda 12 version of pytorch and you should be good to go.
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Use nvidia-smi to troubleshoot. Depending on what it says, you need to get and install the nvidia cuda driver. If you've upgraded your kernel (or had it automatically applied) you need to reinstall the cuda driver.
If nvidia-smi shows correct GPU data, then something is wrong with pytorch install.