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@ -48,7 +48,7 @@ Regardless of the installation choice, you can check that the installation was s
We use an image classification model, CIFAR10, and a language model, BERT, to demonstrate pretraining on Windows with DeepSpeed.
## Pretraining CIFAR10
The scripts and codes required for CIFAR10 pretraining example are available in the following path: DeepSpeedExamples\training\cifar. You can launch the CIFAR10 pretraining experiment using the following command: `deepspeed cifar10_deepspeed.py deepspeed`. The final output should look something like this:
The scripts and codes required for CIFAR10 pretraining example are available in the following path: DeepSpeedExamples\training\cifar. You can launch the CIFAR10 pretraining experiment using the following command: `deepspeed cifar10_deepspeed.py --deepspeed`. The final output should look something like this:
<div align="center">
<img src="./media/cifar10_training.png" style="width:6.5in;height:3.42153in" />
</div>