Keras/TF parallelization dependent on machine and data size

Question

I am calling TF model with following code:

def define_model_2D():
   model = Sequential()
   model.add( Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(inpLength, inpWidth, 1)))
   model.add(Dropout(0.2))
   model.add(MaxPooling2D((2, 2)))
   model.add( Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same') )
   model.add(Dropout(0.3))
   model.add(MaxPooling2D((2, 2)))
   model.add(Flatten())
   model.add(Dense(128, activation='relu', kernel_initializer='he_uniform', kernel_regularizer=l2(0.0001),))
   model.add(Dropout(0.4))
   model.add(Dense(1, activation='sigmoid'))
   opt = Adam(learning_rate=0.0005)
   model.compile(optimizer=opt, loss='mean_squared_error', metrics=['accuracy'])
   return model

model = define_model_2D()
history = model.fit(trainX, trainY, epochs=5, batch_size=512, validation_data=(testX, testY), verbose=1)

I noticed that on local laptop machine this runs on all cores available, while on remote Azure VM it uses only 1 core. The problem exists also for smaller model (conv1D). But when I switch to smaller data used in simulation (e.g 25% from initial 1000000 rows) it DO parallelize on the VM. I tried the solutions from thread: How to run Keras on multiple cores?.

Both laptop and VM are Windows with latest Anaconda. Also both have 32GB ram. Some meaningful versions listed below:

anaconda                  custom                   py38_1
anaconda-client           1.9.0            py38haa95532_0
anaconda-navigator        2.1.1                    py38_0
anaconda-project          0.10.2             pyhd3eb1b0_0
keras                     2.4.3              pyhd8ed1ab_0    conda-forge
keras-applications        1.0.8                      py_1    conda-forge
keras-preprocessing       1.1.2              pyhd8ed1ab_0    conda-forge
tensorboard               2.7.0              pyhd8ed1ab_0    conda-forge
tensorboard-data-server   0.6.0            py38haa244fe_1    conda-forge
tensorboard-plugin-wit    1.8.1              pyhd8ed1ab_0    conda-forge
tensorflow                2.3.0           mkl_py38h8c0d9a2_0
tensorflow-base           2.3.0           eigen_py38h75a453f_0
tensorflow-estimator      2.5.0              pyh81a9013_1    conda-forge

How to make it run in parallel on the VM with bigger data?