filemodel = 'xgboostModel.trail04.json'
bst = xgb.train(param, xgTrain, num_boost_round=numOfRounds, evals=watchList, early_stopping_rounds=2000, xgb_model=filemodel)

  File "/home/user/programs/pycharm/plugins/python-ce/helpers/pydev/pydevd.py", line 1438, in _exec
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "/home/user/programs/pycharm/plugins/python-ce/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "/home/user/projects/temp/pythontmp/lastPointClassifiers.py", line 270, in <module>
    trial04()
  File "/home/user/projects/temp/pythontmp/lastPointClassifiers.py", line 256, in trial04
    xgb_model=filemodel)
  File "/home/user/.local/lib/python3.7/site-packages/xgboost/training.py", line 212, in train
    xgb_model=xgb_model, callbacks=callbacks)
  File "/home/user/.local/lib/python3.7/site-packages/xgboost/training.py", line 37, in _train_internal
    model_file=xgb_model)
  File "/home/user/.local/lib/python3.7/site-packages/xgboost/core.py", line 1175, in __init__
    self.load_model(model_file)
  File "/home/user/.local/lib/python3.7/site-packages/xgboost/core.py", line 1804, in load_model
    self.handle, c_str(os_fspath(fname))))
  File "/home/user/.local/lib/python3.7/site-packages/xgboost/core.py", line 190, in _check_call
    raise XGBoostError(py_str(_LIB.XGBGetLastError()))
xgboost.core.XGBoostError: [19:57:42] /workspace/src/common/json.cc:413: Expecting: ",", got: "

def Example():
    X, Y = getData(randomize=True)
    X = np.array(X)
    Y = np.array(Y)
    l = int(len(X) * .5)
    xgTrain = xgb.DMatrix(X[:l], label=Y[:l])
    xgTest = xgb.DMatrix(X[l:], label=Y[l:])
    param = {
        'objective': 'multi:softmax',
        'eta': 0.0001,
        'max_depth': 6,
        'nthread': 16,
        'num_class': 2,
        'tree_method': 'gpu_hist',
    }
    watchList = [(xgTrain, 'train'), (xgTest, 'test')]
    numOfRounds = 1000000
    filemodel = 'xgboostModel.trail04.json'

    print("fit()")
    if path.exists(filemodel):
        bst = xgb.train(param, xgTrain, num_boost_round=numOfRounds, evals=watchList, early_stopping_rounds=2000,
                        xgb_model=filemodel)
    else:
        bst = xgb.train(param, xgTrain, num_boost_round=numOfRounds, evals=watchList, early_stopping_rounds=2000)

    bst.save_model(filemodel)