tensorflow_config:
    # 基本参数
    data_url: /home/imagenet_TF/
    batch_size: 32
    # 1p/8p, epoches设为90
    epoches: 1
    # 跑精度时max_train_steps设为None
    max_train_steps: 1000
    epochs_between_evals: 1
    iterations_per_loop: 100
    save_checkpoints_steps: 115200

    # 仅多机执行需要配置: ip1:卡数量1,ip2:卡数量2
    mpirun_ip: 90.90.176.152:8,90.90.176.154:8

    # docker 镜像名称:版本号
    docker_image: c73:b02

    # 指定 device id, 多个 id 使用空格分隔, 数量需与 rank_size 相同
    device_group_1p: 0
    device_group_2p: 0 1
    device_group_4p: 0 1 2 3

pytorch_config:
    # 基本参数
    data_url: /home/imagenet/
    #跑1p时batch_size为512；2p时为1024;4p时为2048;跑8p时batch_size为4096
    batch_size: 512
    epoches: 90
    # train_and_evaluate、evaluate两种模式
    mode: train_and_evaluate
    ckpt_path: /home/train/result/pt_resnet50/training_job_20200916042624/7/checkpoint_npu7model_best.pth.tar
    # docker 镜像名称:版本号
    docker_image: c73:b02
    # 默认参数1p时为0.2，2p/4p/8p时为2.048
    lr: 0.2
    # 指定 device id, 数量需与 rank_size 相同
    device_group_1p: 0
    device_group_2p: 0 1
    device_group_4p: 0 1 2 3

mindspore_config:
    # 基本参数
    # 训练时数据集/home/data/imagenet/train, 评测是数据集是/home/data/imagenet/val
    data_url: /home/data/imagenet/train
    #跑1p/2p/4p/8p时batch_size均为256
    batch_size: 256
    epoches: 5
    pre_trained: None
    save_checkpoint_epochs: 5
    loss_scale: 1024
    # mode：train or evaluate
    mode: train
    # 将训练后生成的ckpt的路径配置在此处
    checkpoint_path: /home/wx933135/benchmark_20200924-benchmark_Alpha/benchmark_20200924-benchmark_Alpha/train/result/ms_resnet50/training_job_20200928154504/2/ckpt_2/resnet-5_625.ckpt

    # eval_device_id,评测是指定的device id
    eval_device_id: 4
    # docker 镜像名称:版本号
    docker_image: c73:b02

    # 指定 device id, 数量需与 rank_size 相同
    device_group_1p: 0
    device_group_1p: 0 1
    device_group_1p: 0 1 2 3