Sample Running

This section uses the single-server 8-device networking and ranktable file as an example to describe how to run the sample code in Code Example.

1. Prepare the ranktable file.

   For details about the configuration examples and parameter description of the ranktable file for different product forms, see "Reference" > "Cluster Information Configuration" in the HCCL User Guide.

2. Construct the startup script.

   The following uses the script hccl_start_8p.sh as an example:

   1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

   # Configure environment variables of the CANN software. The default installation path of the root user is used as an example. source /usr/local/Ascend/cann/set_env.sh # TF Adapter Python library. ${TFPLUGIN_INSTALL_PATH} indicates the installation path of the TF Adapter package. export PYTHONPATH=${TFPLUGIN_INSTALL_PATH}:$PYTHONPATH export RANK_SIZE=8 export RANK_TABLE_FILE=/home/test/ranktable.json # Path of the ranktable resource configuration file. Replace it with the actual path. export JOB_ID=10087 # User-defined task ID, which can contain uppercase letters, lowercase letters, digits, hyphens (-), and underscores (_). for((RANK_ID=0;RANK_ID<$((RANK_SIZE));RANK_ID++)); do export RANK_ID=$RANK_ID export ASCEND_DEVICE_ID=$RANK_ID # Execute the script. Replace the script path and name as required. nohup python3 /home/test/hccl_test.py & done

3. Execute the startup script.

   1	bash hccl_start_8p.sh

   Result example:

   1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

   ... ... 'reduce_sum': array([[ 0, 0, 0, ..., 0, 0, 0], [ 0, 0, 0, ..., 0, 0, 0], [ 0, 0, 0, ..., 0, 0, 0], ..., [ 0, 0, 0, ..., 0, 0, 0], [ 0, 0, 0, ..., 0, 0, 0], [ 0, 0, 0, ..., 44, 44, 44]]), 'reduce_max': array([[4097, 4098, 4099, ..., 4222, 4223, 4224], [4225, 4226, 4227, ..., 4350, 4351, 4352], [4353, 4354, 4355, ..., 4478, 4479, 4480], ..., [4737, 4738, 4739, ..., 4862, 4863, 4864], [4865, 4866, 4867, ..., 4990, 4991, 4992], [4993, 4994, 4995, ..., 9, 9, 9]]), 'reduce_min': array([[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 2, 2, 2]]), 'reduce_prod': array([[ 0, 0, 0, ..., 0, 0, 0], [ 0, 0, 0, ..., 0, 0, 0], [ 0, 0, 0, ..., 0, 0, 0], ..., [ 0, 0, 0, ..., 0, 0, 0], [ 0, 0, 0, ..., 0, 0, 0], [ 0, 0, 0, ..., 362880, 362880, 362880]]), 'alltoallv_tensor': array([ 1, 2, 3, ..., 8246, 8247, 8248]), 'check_tensors': array([ 1, 2, 3, ..., 8246, 8247, 8248]) train success