how do i run a python script with tensorflow running in a docker on windows?

docker run - v / path / to / your / script: /path/to / script

$ docker run--rm - it - v~/.bash_history:/.bash_history ubuntu / bin / bash

docker pull tensorflow / tensorflow # latest stable release
docker pull tensorflow / tensorflow: devel - gpu # nightly dev release w / GPU support
docker pull tensorflow / tensorflow: latest - gpu - jupyter # latest release w / GPU support and Jupyter

docker run[-it][--rm][-p hostPort: containerPort] tensorflow / tensorflow[: tag][command]

docker run - it--rm tensorflow / tensorflow\
python - c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

docker run - it--rm - v $PWD: /tmp -w /tmp
tensorflow / tensorflow python. / script.py

docker run - it - p 8888: 8888 tensorflow / tensorflow: nightly - jupyter

 docker run hello - world

Unable to find image‘ hello - world: latest’ locally
latest: Pulling from library / hello - world
0e03 bdcc26d7: Pull complete
Digest: sha256: 4 cf9c47f86df71d48364001ede3a4fcd85ae80ce02ebad74156906caff5378bc
Status: Downloaded newer image
for hello - world: latest
Hello from Docker!
   This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
   1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the“ hello - world” image from the Docker Hub.(amd64)
3. The Docker daemon created a new container from that image which runs the executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it to your terminal.

docker pull tensorflow / serving

import matplotlib.pyplot as plt
import time
from numpy
import asarray
from numpy
import unique
from numpy
import argmax
from tensorflow.keras.datasets.mnist
import load_data
from tensorflow.keras
import Sequential
from tensorflow.keras.layers
import Dense
from tensorflow.keras.layers
import Conv2D
from tensorflow.keras.layers
import MaxPool2D
from tensorflow.keras.layers
import Flatten
from tensorflow.keras.layers
import Dropout

#load MNIST dataset
   (x_train, y_train), (x_test, y_test) = load_data()
print(f 'Train: X={x_train.shape}, y={y_train.shape}')
print(f 'Test: X={x_test.shape}, y={y_test.shape}')

# reshape data to have a single channel
x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], x_train.shape[2], 1))
x_test = x_test.reshape((x_test.shape[0], x_test.shape[1], x_test.shape[2], 1))

# normalize pixel values
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

# set input image shape
input_shape = x_train.shape[1: ]

# set number of classes
n_classes = len(unique(y_train))

# define model
model = Sequential()
model.add(Conv2D(64, (3, 3), activation = 'relu', input_shape = input_shape))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(32, (3, 3), activation = 'relu'))
model.add(MaxPool2D((2, 2)))
model.add(Flatten())
model.add(Dense(50, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation = 'softmax'))

# define loss and optimizer
model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])

# fit the model
model.fit(x_train, y_train, epochs = 10, batch_size = 128, verbose = 1)

# evaluate the model
loss, acc = model.evaluate(x_test, y_test, verbose = 0)
print('Accuracy: %.3f' % acc)

#save model
ts = int(time.time())
file_path = f "./img_classifier/{ts}/"
model.save(filepath = file_path, save_format = 'tf')

├──
img_classifier│├── 1600788643││├── assets││├── saved_model.pb││└── variables