The system version of all platform images is Ubuntu, with the majority being Ubuntu 18.04 and a few being Ubuntu 20.04.
Additionally, the initial startup of the Community Images may take a considerable amount of time (potentially over one hour). Please wait patiently for the system to complete initialization.
Pre-installed images
Pre-installed images
| Frameworks | Framework Version | Python Version | CUDA Version |
|---|---|---|---|
| PyTorch | 1.1.0 | 3.7 | 10.0 |
| PyTorch | 1.5.1 | 3.8 | 10.1 |
| PyTorch | 1.6.0 | 3.8 | 10.1 |
| PyTorch | 1.7.0 | 3.8 | 11.0 |
| PyTorch | 1.8.1 | 3.8 | 11.1 |
| PyTorch | 1.9.0 | 3.8 | 11.1 |
| PyTorch | 1.10.0 | 3.8 | 11.3 |
| PyTorch | 1.11.0 | 3.8 | 11.3 |
| PyTorch | 2.0.0 | 3.8 | 11.8 |
| PyTorch | 2.1.0 | 3.10 | 12.1 |
| PyTorch | 2.1.2 | 3.10 | 11.8 |
| PyTorch | 2.3.0 | 3.12 | 12.1 |
| PyTorch | 2.5.1 | 3.12 | 12.4 |
| PyTorch | 2.7.0 | 3.12 | 12.8 |
| PyTorch | 2.8.0 | 3.12 | 12.8 |
| TensorFlow | 1.15.5 | 3.8 | 11.4 |
| TensorFlow | 2.5.0 | 3.8 | 11.2 |
| TensorFlow | 2.9.0 | 3.8 | 11.2 |
| Miniconda | conda3 | 3.7 | 9.0 |
| Miniconda | conda3 | 3.8 | 10.1 |
| Miniconda | conda3 | 3.8 | 10.2 |
| Miniconda | conda3 | 3.8 | 11.1 |
| Miniconda | conda3 | 3.8 | 11.3 |
| Miniconda | conda3 | 3.8 | 11.3(cudagl) |
| Miniconda | conda3 | 3.8 | 11.6 |
| Miniconda | conda3 | 3.8 | 11.8 |
| Miniconda | conda3 | 3.10 | 11.8 |
| tritonserver | 24.12 | 3.12 | 12.6 |
| JAX | 0.3.10 | 3.8 | 11.1 |
| PaddlePaddle | 2.2.0 | 3.8 | 11.2 |
| PaddlePaddle | 2.4.0 | 3.8 | 11.2 |
| TensorRT | 8.5.1 | 3.8 | 11.8 |
| TensorRT | 8.6.1 | 3.8 | 11.8 |
| Gromacs | 2022.2 | 3.8 | 11.4 |
| Gromacs | 2023.2 | 3.10 | 11.8 |
Installation of Other Python Versions
Refer to the documentation
Installation of Other CUDA Versions
Refer to the documentation
Installation of PyTorch: Refer to the documentation
Refer to the documentation
Installation of TensorFlow
Refer to the documentation
Recommended Usage
- First, check if the platform’s pre-installed images include the required versions of PyTorch, TensorFlow, or other frameworks. If available, prioritize using the platform’s built-in images.
- If the platform does not have the desired framework versions, determine the required CUDA version for your framework. For example, PyTorch 1.9.0 requires CUDA 11.1. You can then select a platform image with Miniconda and CUDA 11.1 pre-installed. This allows you to install the required framework without the hassle of setting up cudatoolkit. (The pre-installed CUDA on the platform includes .h header files, which is more convenient if you need to compile code.)
- If neither of the above conditions is met, you can choose any Miniconda image and install the required frameworks, CUDA, or even other Python versions after the instance is started.