MLPclassifier from sklearn shows different accuracies when executed on other machine?

It looks like running the sklearn MLPclassifier with the same input on different devices will give different accuracy results, even if a global seed is set.

MWE:

import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split

np.random.seed(1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, stratify=y, random_state=np.random.RandomState(0))

nn = MLPClassifier(hidden_layer_sizes=(100,100),
                   activation='relu',
                   solver='adam',
                   alpha=0.001,
                   batch_size=50,
                   learning_rate_init=0.01,
                   max_iter=1000,
                   random_state=np.random.RandomState(0))

nn.fit(X_train, y_train)


y_train_pred = nn.predict(X_train)
acc_train = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]
y_test_pred = nn.predict(X_test)
acc_test = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]
results.append([acc_train,acc_test])

How can reproducibility be guaranteed (independent of the executing device)?