[add]上传训练benchmark by z00560161

train/yaml/ResNet50.yaml

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+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
+