Deep learning and AI frameworks for the Azure Data Science Virtual Machine
Deep learning frameworks on the DSVM are listed here:
CUDA, cuDNN, NVIDIA Driver
Category | Value |
---|---|
Supported versions | 11 |
Supported DSVM editions | Windows Server 2019 Linux |
How is it configured and installed on the DSVM? | nvidia-smi is available on the system path. |
How to run it | Open a command prompt (on Windows) or a terminal (on Linux), and then run nvidia-smi. |
Horovod
Category | Value |
---|---|
Supported versions | 0.21.3 |
Supported DSVM editions | Linux |
How is it configured and installed on the DSVM? | Horovod is installed in Python 3.5 |
How to run it | Activate the correct environment at the terminal, and then run Python. |
NVidia System Management Interface (nvidia-smi)
Category | Value |
---|---|
Supported versions | |
Supported DSVM editions | Windows Server 2019 Linux |
What is it used for? | As an NVIDIA tool to query GPU activity |
How is it configured and installed on the DSVM? | nvidia-smi is on the system path. |
How to run it | On a virtual machine with GPU's, open a command prompt (on Windows), or a terminal (on Linux), and then run nvidia-smi . |
PyTorch
Category | Value |
---|---|
Supported versions | 1.9.0 (Linux, Windows 2019) |
Supported DSVM editions | Windows Server 2019 Linux |
How is it configured and installed on the DSVM? | Installed in Python, conda environments 'py38_default', 'py38_pytorch' |
How to run it | At the terminal, activate the appropriate environment, and then run Python. * JupyterHub: Connect, and then open the PyTorch directory for samples. |
TensorFlow
Category | Value |
---|---|
Supported versions | 2.5 |
Supported DSVM editions | Windows Server 2019 Linux |
How is it configured and installed on the DSVM? | Installed in Python, conda environments 'py38_default', 'py38_tensorflow' |
How to run it | At the terminal, activate the correct environment, and then run Python. * Jupyter: Connect to Jupyter or JupyterHub, and then open the TensorFlow directory for samples. |
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