mindcv/scripts

• Utility Scripts
• • gen_benchmark.py
  • package.sh(Deprecated)
  • launch_dist.sh or launch_dist.py

Utility Scripts

This folder is a collection of utility scripts, listed and explained below.

All scripts need to be run in the root path of project, unless otherwise noted.

gen_benchmark.py

Generating benchmark by collecting results from configs folder. Usage:

python ./scripts/gen_benchmark.py

It will generate a markdown file, named as benchmark_results.md.

package.sh(Deprecated)

Making wheel package of mindcv and sha256sum of the wheel files. Usage:

./scripts/package.sh

New! Just simply run the following command to make the wheel:

python -m build

launch_dist.sh or launch_dist.py

A simple clean launcher for distributed training on Ascend.
Following instruction from Mindspore,
except launching distributed training with mpirun, we can also use multiprocess
with multi-card networking configuration rank_table.json to manually start a process on each card.
To get rank_table.json on your machine, try the hccl tools from here.

After you get the rank_table.json, replace the "/path/to/rank_table.json" in launch_dist.sh with the actual path.

Now, you can replace your standalone launching with distributed launching:

- python script.py --arg1=value1 --arg2=value2
+ ./scripts/launch_dist.sh script.py --arg1=value1 --arg2=value2

where --arg* are arguments of script.py.

For example:

./scripts/launch_dist.sh train.py --config=configs/resnet/resnet_50_ascend.yaml --data_dir=/my/awesome/dataset

Note: Don't forget to check the argument --distribute if you are using train.py or train_with_func.py!

For anyone who hates shell scripts, we offer python scripts launch_dist.py as well. Both are used in the same way!