Getting Started with DeepSpeed

This guide provides simple steps for preparing a DeepSpeed model to run on Intel® Gaudi® AI accelerator. To set up the environment, refer to the Installation Guide and On-Premise System Update. Make sure to install the DeepSpeed package provided by Intel Gaudi as listed in the Support Matrix.

Note

Installing public DeepSpeed packages is not supported.

Start Training a DeepSpeed Model on Gaudi

This example uses the model defined in the cifar10_Deepspeed.py script . It is a simple CNN based model which loads the CIFAR-10 dataset automatically.

1. Run the Intel Gaudi Docker image:

   docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.16.2/ubuntu22.04/habanalabs/pytorch-installer-2.2.2:latest
   

2. Install the Intel Gaudi DeepSpeed fork:

   pip install git+https://github.com/HabanaAI/DeepSpeed.git@1.16.2
   

3. Clone the Model References repository inside the container that you have just started:

   git clone https://github.com/HabanaAI/Model-References.git
   

4. Move to the subdirectory containing the cifar_example:

   cd Model-References/PyTorch/examples/DeepSpeed/cifar_example/
   

5. Install the associated requirements:

   pip install -r requirements.txt
   

6. Update PYTHONPATH to include Model-References repository and set PYTHON to Python executable:

   export PYTHONPATH=$PYTHONPATH:Model-References
   export PYTHON=/usr/bin/python3.10
   

   Note

   The Python version depends on the operating system. Refer to the Support Matrix for a full list of supported operating systems and Python versions.

7. Execute the run_ds_habanax8.sh script. If you are running on a single Gaudi, modify the script to set --num_gpus=1.

   deepspeed --num_nodes=1 --num_gpus=8 cifar10_deepspeed.py --deepspeed --deepspeed_config ds_config.json --use_hpu
   

   [10,  2000] loss: 0.776
   [10,  2000] loss: 0.760
   [10,  2000] loss: 0.747
   [10,  2000] loss: 0.753
   [10,  2000] loss: 0.759
   [10,  2000] loss: 0.776
   [10,  2000] loss: 0.772
   [10,  2000] loss: 0.776
   Finished Training
   GroundTruth:    cat  ship   ship plane
   Predicted:      cat  ship   ship plane
   Accuracy of the network on the 10000 test images: 59 %
   Accuracy of  ship : 70 %
   Accuracy of truck : 57 %
   [2022-10-28 17:17:55,740] [INFO] [launch.py:212:main] Process 815 exits successfully.
   [2022-10-28 17:17:55,741] [INFO] [launch.py:212:main] Process 818 exits successfully.
   [2022-10-28 17:17:55,741] [INFO] [launch.py:212:main] Process 820 exits successfully.
   [2022-10-28 17:17:55,741] [INFO] [launch.py:212:main] Process 814 exits successfully.
   [2022-10-28 17:17:55,741] [INFO] [launch.py:212:main] Process 817 exits successfully.
   [2022-10-28 17:17:55,741] [INFO] [launch.py:212:main] Process 819 exits successfully.
   [2022-10-28 17:17:55,741] [INFO] [launch.py:212:main] Process 816 exits successfully.
   [2022-10-28 17:17:56,742] [INFO] [launch.py:212:main] Process 813 exits successfully.
   

To start training your own DeepSpeed models on Gaudi, refer to DeepSpeed User Guide for Training.