DP-GEN simplify

本文介绍如何在Bohrium平台进行DP-GEN simplify。

步骤1 开启节点

如何开启节点参照"DPGEN教程"。

步骤2 准备输入文件

使用DP-GEN simplify需要准备初始数据、参数文件与计算文件。以使用钙钛矿为例：

(base) ➜ example tree
.
├── data
│   ├── init.000
│   │   ├── set.000
│   │   │   ├── box.npy
│   │   │   ├── coord.npy
│   │   │   ├── energy.npy
│   │   │   ├── force.npy
│   │   │   └── virial.npy
│   │   ├── type_map.raw
│   │   └── type.raw
│   ├── sys.000
│   │   ├── set.000
│   │   │   ├── box.npy
│   │   │   ├── coord.npy
│   │   │   ├── energy.npy
│   │   │   ├── force.npy
│   │   │   └── virial.npy
│   │   ├── type_map.raw
│   │   └── type.raw
│   └── sys.001
│       ├── set.000
│       │   ├── box.npy
│       │   ├── coord.npy
│       │   ├── energy.npy
│       │   ├── force.npy
│       │   └── virial.npy
│       ├── type_map.raw
│       └── type.raw
├── simplify_example
│   ├── INCAR
│   ├── machine_test_simplify.json
│   └── simplify.json
└── vasp_inputs
    ├── POTCAR_C
    ├── POTCAR_H
    ├── POTCAR_I
    ├── POTCAR_N
    └── POTCAR_Pb_d

示例simplify.json文件

{
  "type_map": ["I","Pb","C","N","H"],
  "mass_map": [126.90447, 207.2, 12.0108, 14.0067, 1.00795],

  "pick_data":  "../data", 
  "init_data_prefix": "",
  "init_data_sys":   [],
  "sys_configs": [null],
  "sys_batch_size":  [1,1,1], 

  "_comment": " 00.train ",
  "numb_models": 4,
  "model_devi_activation_func":[["tanh","tanh"],["tanh","gelu"],["gelu","gelu"],["gelu","tanh"]],

  "train_param": "input.json",
 
  "default_training_param": {
    "model": {
      "type_map":           ["I","Pb","C","N","H"],
      "descriptor": {        
        "type":             "se_e2_a",
        "sel": 
        [
          20,
          8,
          8,
          8,
          48
        ],
        "rcut_smth":        2.00,
        "rcut":             6.00,
        "neuron":           [25, 50, 100],
        "resnet_dt":        false,
        "type_one_side":     true,
        "trainable":      true,
          "axis_neuron":      12,
        "seed":             0
      },
      "fitting_net": {
        "neuron":           [240, 240, 240],
        "resnet_dt":        true,
        "trainable":      [true, true, true, true],
        "seed": 0
      }
    },
      "loss": {
        "start_pref_e": 0.02,
        "limit_pref_e": 2,
        "start_pref_f": 1000,
        "limit_pref_f": 2,
        "start_pref_v": 0.01,
        "limit_pref_v": 1
    },
      "learning_rate": {
        "type":              "exp",
        "start_lr":          0.001,
        "decay_steps":       100000,
        "decay_rate":        0.95
    },
      "training": {
        "set_prefix":        "set",
        "stop_batch":        1000000,
        "batch_size":        "auto",
        "seed": 1,
        "_comment":          "frequencies counted in batch",
        "disp_file":         "lcurve.out",
        "disp_freq":         100000,
        "numb_test":         4,
        "save_freq":         100000,
        "save_ckpt":         "model.ckpt", 
        "disp_training":     true,
        "time_training":     true,
        "profiling":         false,
        "profiling_file":    "timeline.json"
    }
  },

  "_comment":                   "02.fp",
  "fp_style":                   "vasp",
  "fp_skip_bad_box":  "length_ratio:5;height_ratio:5",
  "fp_accurate_threshold":       0.95,
  "fp_accurate_soft_threshold":  0.90,
  "shuffle_poscar":              false,
  "fp_task_max":                 20,
  "fp_task_min":                 5,
  "ratio_failed":                0.30,
  "fp_pp_path": "../vasp_inputs/",
  "fp_pp_files": ["POTCAR_I","POTCAR_Pb_d","POTCAR_C","POTCAR_N","POTCAR_H"],
  "fp_incar": "INCAR",

  "use_clusters": false,
  "labeled": false,
  "init_pick_number":20,
  "iter_pick_number":20,
  "model_devi_f_trust_lo":0.30,
  "model_devi_f_trust_hi":100.00, 
  "cvasp": false
}

示例machine.json文件

{
    "api_version": "1.0",
    "train" :[
      {
        "command": "dp",
        "machine": {
        "batch_type": "Lebesgue",
        "context_type": "LebesgueContext",
        "local_root" : "./",
          "remote_profile":{
          "email": "",
          "password": "",
          "project_id": 0000,
              "input_data":{
                  "api_version":2,
                  "job_type": "indicate",
                  "log_file": "00*/lcurve.out",
                  "grouped": true,
                  "job_name": "simplify_MAPbI3-scan_train_job",
                  "disk_size": 100,
                  "scass_type":"c8_m32_1 * NVIDIA V100",
                  "platform": "ali",
                  "checkpoint_files": ["00*/model.ckpt*", "00*/checkpoint"],
                  "checkpoint_time": 15,
                  "job_type": "container",
                  "image_name": "registry.dp.tech/dptech/deepmd-kit:2.1.5-cuda11.6" ,
                  "on_demand":0
              }
          }
        },
        "resources": {
          "number_node": 15,
          "cpu_per_node": 4,
          "gpu_per_node": 1,
          "queue_name": "V100_8_32",
          "group_size": 1
        }
      }],
    "model_devi":
      [{
        "command": "dp",
        "machine": {
        "batch_type": "Lebesgue",
        "context_type": "LebesgueContext",
        "local_root" : "./",
          "remote_profile":{
          "email": "",
          "password": "",
          "project_id": 0000,
              "input_data":{
                "api_version":2,
                "job_type": "indicate",
                "log_file": "cubic-*/*/md.log",
                "grouped": true,
                "job_name": "MAPbI3-scan_md_job",
                "disk_size": 100,
                "scass_type":"c8_m32_1 * NVIDIA V100",
                "platform": "ali",
                "checkpoint_files": "sync_files",
                "checkpoint_time": 15,
                "job_type": "container",
                "image_name": "registry.dp.tech/dptech/deepmd-kit:2.1.5-cuda11.6" ,
                "on_demand":0
              }
          }
        },
        "resources": {
          "number_node": 15,
          "cpu_per_node": 4,
          "gpu_per_node": 1,
          "queue_name": "V100_8_32",
          "group_size": 1
        }
      }],
    "fp":
      [{
        "command": "ulimit -m unlimited; ulimit -s unlimited; mpirun -n 32 vasp_std",
        "machine": {
        "batch_type": "Lebesgue",
        "context_type": "LebesgueContext",
        "local_root" : "./",
          "remote_profile":{
          "email": "",
          "password": "",
          "project_id": 0000,
              "input_data":{
                "api_version":2,
                "job_type": "indicate",
                "log_file": "**/fp.log",
                "grouped": true,
                "job_name": "simplify_MAPbI3-scan_fp_job",
                "disk_size": 100,
                "scass_type":"c32_m256_cpu",
                "platform": "ali",
                "checkpoint_files": "sync_files",
                "checkpoint_time": 15,
                "job_type": "container",
                "image_name":"您需提供授权凭证到 bohrium@dp.tech 邮箱获取VASP镜像名称",
                "on_demand":0
              }
          }
        },
        "resources": {
          "number_node": 15,
          "cpu_per_node": 32,
          "gpu_per_node": 0,
          "queue_name": "CPU",
          "group_size": 1,
      "source_list": ["/opt/intel/oneapi/setvars.sh"]
        }
      }
    ]
  }

注意：所有 "project_id": 后的 0000 均需替换为您自己的项目ID，可在“项目管理”页查看。

步骤3 提交任务

在/simplify_example目录下执行：

监测任务状态与运行结果获取参考 DP-GEN 文档。