Migrating to TAO 5.5

The major change in this release is the deprecation of command line parameters in place of experiment specfile fields. The only flag that will now be accepted is -e for the experiment specfile. All other arguments must correspond to a field in the experiment configuration.

The table below shows examples of these updates to the CLI. This is not an exhaustive list of all actions, but these changes can be generalized to all networks included as part of TAO 5.5.

Details on which fields are available to configure and more examples can be found in each respective network’s documentation.

Network	TAO 5.2.x	TAO 5.5.x
PyTorch	Copy Copied! `tao model <network> train -e $SPECS_DIR/train.yaml \ -r $RESULTS_DIR \ -k $KEY \ --gpus 2 \ train.num_epochs=2`	Copy Copied! `tao model <network> train -e $SPECS_DIR/train.yaml \ results_dir=$RESULTS_DIR \ encryption_key=$KEY \ train.num_gpus=2 \ train.num_epochs=2`
TF2	Copy Copied! `tao model <network> train -e $SPECS_DIR/train.yaml \ --gpus 1 \ --gpu_index 0 \ train.num_epochs=2`	Copy Copied! `tao model <network> train -e $SPECS_DIR/train.yaml \ num_gpus=1 \ gpu_ids=[0] \ train.num_epochs=2`
Data-Services	Copy Copied! `tao dataset augmentation generate -e $SPECS_DIR/augment.yaml \ -r $RESULTS_DIR \ --gpus 2 \ data.image_dir=$IMAGE_DIR`	Copy Copied! `tao dataset augmentation generate -e $SPECS_DIR/augment.yaml \ results_dir=$RESULTS_DIR \ num_gpus=2 \ data.image_dir=$IMAGE_DIR`
Deploy	Copy Copied! `tao deploy <network> gen_trt_engine -e $SPECS_DIR/gen_trt_engine.yaml \ -r $RESULTS_DIR \ -k $KEY \ --gpu_index 1 \ gen_trt_engine.onnx_file=$ONNX_FILE gen_trt_engine.trt_engine=$ENGINE_PATH`	Copy Copied! `tao deploy <network> gen_trt_engine -e $SPECS_DIR/gen_trt_engine.yaml \ results_dir=$RESULTS_DIR \ encryption_key=$KEY \ gen_trt_engine.gpu_id=1 \ gen_trt_engine.onnx_file=$ONNX_FILE gen_trt_engine.trt_engine=$ENGINE_PATH`

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