#1 - initial tensorflow docker run

environments/windows/Dockerfile

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+FROM tensorflow/tensorflow:latest-gpu
+WORKDIR /src
+COPY /src/tflow.py .
+CMD ["python", "tflow.py"]
+
+#RUN pip install -U jupyterlab pandas matplotlib
+#EXPOSE 8888
+#ENTRYPOINT ["jupyter", "lab","--ip=0.0.0.0","--allow-root","--no-browser"]

environments/windows/src/tflow.py

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+import tensorflow as tf
+#import keras
+#from keras.utils import multi_gpu_model
+#import keras.backend as k
+#https://github.com/microsoft/tensorflow-directml/issues/352
+
+# https://www.tensorflow.org/guide/distributed_training
+#
+# https://www.tensorflow.org/tutorials/distribute/keras
+# https://keras.io/guides/distributed_training/
+#strategy = tf.distribute.MirroredStrategy()
+#print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
+
+#NUM_GPUS = 2
+#strategy = tf.contrib.distribute.MirroredStrategy()#num_gpus=NUM_GPUS)
+# not working
+strategy = tf.distribute.MirroredStrategy(devices=["/gpu:0", "/gpu:1"])
+#WARNING:tensorflow:Some requested devices in `tf.distribute.Strategy` are not visible to TensorFlow: /replica:0/task:0/device:GPU:1,/replica:0/task:0/device:GPU:0
+#Number of devices: 2
+
+
+#central_storage_strategy = tf.distribute.experimental.CentralStorageStrategy()
+#strategy = tf.distribute.MultiWorkerMirroredStrategy() # not in tf 1.5
+#print("mirrored_strategy: ",mirrored_strategy)
+#strategy = tf.distribute.OneDeviceStrategy(device="/gpu:1")
+#mirrored_strategy = tf.distribute.MirroredStrategy(devices=["/gpu:0","/gpu:1"],cross_device_ops=tf.contrib.distribute.AllReduceCrossDeviceOps(all_reduce_alg="hierarchical_copy"))
+#mirrored_strategy = tf.distribute.MirroredStrategy(devices= ["/gpu:0","/gpu:1"],cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())
+
+#print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
+
+# https://learn.microsoft.com/en-us/windows/ai/directml/gpu-faq
+#a = tf.constant([1.])
+#b = tf.constant([2.])
+#c = tf.add(a, b)
+
+#gpu_config = tf.GPUOptions()
+#gpu_config.visible_device_list = "1"#"0,1"
+#gpu_config.visible_device_list = "0,1"
+#gpu_config.allow_growth=True
+
+#session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_config))
+#print(session.run(c))
+#tensorflow.python.framework.errors_impl.AlreadyExistsError: TensorFlow device (DML:0) is being mapped to multiple DML devices (0 now, and 1 previously), which is not supported. This may be the result of providing different GPU configurations (ConfigProto.gpu_options, for example different visible_device_list) when creating multiple Sessions in the same process. This is not  currently supported, see https://github.com/tensorflow/tensorflow/issues/19083
+#from keras import backend as K
+#K.set_session(session)
+
+cifar = tf.keras.datasets.cifar100
+(x_train, y_train), (x_test, y_test) = cifar.load_data()
+
+#with strategy.scope():
+
+# https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet50/ResNet50
+# https://keras.io/api/models/model/
+parallel_model = tf.keras.applications.ResNet50(
+    include_top=True,
+    weights=None,
+    input_shape=(32, 32, 3),
+    classes=100,)
+# https://saturncloud.io/blog/how-to-do-multigpu-training-with-keras/  
+#parallel_model = multi_gpu_model(model, gpus=2)
+#from tensorflow.python.keras import backend as K
+#tf.keras.set_session(session)
+
+
+loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)
+# https://keras.io/api/models/model_training_apis/
+parallel_model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
+
+parallel_model.fit(x_train, y_train, epochs=40, batch_size=5120)#7168)#7168)