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How GPU use in Jupyter works
A notebook runs code through a kernel. That kernel may be on your own computer, in a container, or on a remote server. The GPU must be accessible to that machine and kernel, and the framework—such as PyTorch or TensorFlow—must support the hardware and software stack. Jupyter launching successfully does not mean a GPU is available.
- Identify the kernel host. Determine whether the notebook kernel runs locally, in a container, or on a remote service. The computer displaying the browser page may not be the computer doing the computation.
- Check the GPU and driver on the host. For NVIDIA, confirm the host driver can see the GPU. If a container is involved, confirm that the GPU is passed through to it.
- Install a compatible framework build. Follow the framework’s current installation and compatibility instructions for the operating system, GPU, and driver/runtime.
- Attach Jupyter to that Python environment. The active notebook kernel must use the same environment where the GPU-capable framework was installed.
- Check the framework and place work on the GPU. Query device availability, then use the framework’s device APIs to run supported operations on that device.
Use a local NVIDIA GPU
For a local NVIDIA setup, the host needs a compatible GPU and driver, and the installed framework must be compatible with the system’s driver/runtime. Compare the combination against the NVIDIA Frameworks Support Matrix and the framework’s current installation guidance before choosing packages. Compatibility depends on the actual hardware and software versions; there is no single install command that fits every system.
Check and use CUDA in PyTorch
In the notebook, test CUDA availability through PyTorch:
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import torch
print(torch.cuda.is_available())If it prints True, PyTorch can access CUDA in this environment. Availability alone does not move your tensors or model to the GPU. Select a device and move both as needed:
device = torch.device("cuda")
model = model.to(device)
inputs = inputs.to(device)
outputs = model(inputs)PyTorch’s CUDA semantics documentation explains device selection and how CUDA operations apply to tensors on that device. Operations or data that remain on the CPU do not automatically become GPU work.
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Check TensorFlow GPU availability
Use TensorFlow’s device listing to see whether the installed build detects a GPU:
import tensorflow as tf
print(tf.config.list_physical_devices("GPU"))An empty list means TensorFlow did not detect a GPU in the current environment. Check the active kernel, driver/runtime, operating system, and the framework’s compatibility requirements. For installation, follow the current TensorFlow pip guide; it states that there is currently no official GPU support for macOS. Avoid old recipes that install tensorflow-gpu or pin dated version combinations without checking current official guidance.
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Run Jupyter in a GPU-enabled container
A container can package Jupyter and a framework, but it still relies on compatible host GPU hardware and driver/runtime access. In its April 16, 2024 announcement, Project Jupyter documented passing GPUs into containers with Docker’s --gpus all option or Podman’s --device 'nvidia.com/gpu=all', and showed CUDA-tagged pytorch-notebook and tensorflow-notebook images. Those examples are implementation details from that announcement, not a guarantee that the same image tags remain current. Check the Jupyter CUDA-enabled Docker Images announcement alongside current image tags before reproducing them.
The announcement specifies a Linux host with a compatible NVIDIA GPU and NVIDIA Linux driver, and notes its CUDA-enabled images are for x86_64. If the container starts but the framework reports no GPU, check both host driver visibility and container device pass-through, then confirm that the notebook kernel uses the framework environment inside the container.
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Use a hosted notebook or remote server
With a hosted or managed notebook, the accelerator belongs to the remote compute runtime, not necessarily the computer showing the notebook. Select or enable the provider’s GPU runtime, then verify GPU visibility from the notebook kernel using the framework check above. Provider labels and availability can change; this article does not establish any particular provider’s current free tier, pricing, or hardware availability.
For general information on Jupyter’s installation and deployment options, see the Project Jupyter installation guide. When choosing between local and remote compute, weigh where your data is handled, the operating system and framework compatibility, setup and reproducibility, GPU memory and compute requirements, local electricity or hosted usage costs, and any availability limits.
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Troubleshoot when Jupyter cannot see a GPU
- The notebook is on the wrong machine. Confirm where its kernel runs. A GPU installed in your laptop will not be available to a kernel running on another server unless that server has its own accessible GPU.
- The host cannot see the GPU. Check the GPU and driver on the host before troubleshooting the Python package. In a container, verify device pass-through as well.
- The framework does not support the setup. Check compatibility for the GPU, operating system, framework build, and driver/runtime in the current official documentation, including NVIDIA’s support matrix where applicable.
- Jupyter is using a different Python environment. Check the notebook’s active Python executable and install the framework in that environment, not merely in a separate terminal environment.
- The framework detects a GPU but work stays on CPU. In PyTorch, move the relevant tensors and model to the selected CUDA device. Framework detection does not automatically place all notebook code on the GPU.
- The GPU is available but the workload is not faster. Some operations remain on CPU, and small workloads may not benefit from GPU execution. Benchmark the workload you actually run rather than assuming a universal speedup.
Should you use a local or hosted GPU?
A local GPU gives you direct control over hardware and data handling, but requires compatible hardware, drivers, and environment setup. A hosted runtime avoids installing a local GPU stack, but depends on the provider’s current availability, data-handling terms, and usage costs. Choose based on your workload’s memory and compute needs, your operating system and framework support, and how much control or setup you want. A CUDA-capable NVIDIA card is only relevant if your workload and system need it; not every Jupyter user needs to buy one.
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