# Path: studio/fs_tracker.py
# STUDIOML_EXPERIMENT = 'STUDIOML_EXPERIMENT'
# STUDIOML_HOME = 'STUDIOML_HOME'
# STUDIOML_ARTIFACT_MAPPING = 'STUDIOML_ARTIFACT_MAPPING'
# def get_experiment_key():
# def get_studio_home():
# def setup_experiment(env, experiment, clean=True):
# def get_artifact(tag):
# def get_artifacts():
# def get_model_directory(experiment_name=None):
# def get_artifact_cache(tag, experiment_name=None):
# def get_blob_cache(blobkey):
# def get_model_directory(experiment_name=None):
# def _get_artifact_mapping_path(experiment_name=None):
# def _get_experiment_key(experiment):
# def _setup_model_directory(experiment_name, clean=False):
# def get_queue_directory():
# def get_tensorboard_dir(experiment_name=None):


import tensorflow as tf
import sys
import os
import time
import logging
from keras.objectives import categorical_crossentropy
from keras.layers import Dense
from tensorflow.examples.tutorials.mnist import input_data
from keras import backend as K
from studio import fs_tracker

labels = tf.placeholder(tf.float32, shape=(None, 10))

loss = tf.reduce_mean(categorical_crossentropy(labels, preds))
# Let's train the model with a TensorFlow optimizer:

mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)

global_step = tf.Variable(0, name='global_step', trainable=False)
train_step = tf.train.GradientDescentOptimizer(
    0.5).minimize(loss, global_step=global_step)
# Initialize all variables
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
sess.run(init_op)


logger = logging.get_logger('train_mnist')
logger.setLevel(10)
# Run training loop
with sess.as_default():
    while True:
        batch = mnist_data.train.next_batch(50)
        train_step.run(feed_dict={img: batch[0],
                                  labels: batch[1]})

        sys.stdout.flush()
        saver.save(
            sess,
            os.path.join(

# Path: studio/fs_tracker.py
# STUDIOML_EXPERIMENT = 'STUDIOML_EXPERIMENT'
# STUDIOML_HOME = 'STUDIOML_HOME'
# STUDIOML_ARTIFACT_MAPPING = 'STUDIOML_ARTIFACT_MAPPING'
# def get_experiment_key():
# def get_studio_home():
# def setup_experiment(env, experiment, clean=True):
# def get_artifact(tag):
# def get_artifacts():
# def get_model_directory(experiment_name=None):
# def get_artifact_cache(tag, experiment_name=None):
# def get_blob_cache(blobkey):
# def get_model_directory(experiment_name=None):
# def _get_artifact_mapping_path(experiment_name=None):
# def _get_experiment_key(experiment):
# def _setup_model_directory(experiment_name, clean=False):
# def get_queue_directory():
# def get_tensorboard_dir(experiment_name=None):


# Path: examples/tensorflow/train_mnist.py
import tensorflow as tf
import sys
import os
import time
import logging
from keras.objectives import categorical_crossentropy
from keras.layers import Dense
from tensorflow.examples.tutorials.mnist import input_data
from keras import backend as K
from studio import fs_tracker


labels = tf.placeholder(tf.float32, shape=(None, 10))

loss = tf.reduce_mean(categorical_crossentropy(labels, preds))
# Let's train the model with a TensorFlow optimizer:

mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)

global_step = tf.Variable(0, name='global_step', trainable=False)
train_step = tf.train.GradientDescentOptimizer(
    0.5).minimize(loss, global_step=global_step)
# Initialize all variables
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
sess.run(init_op)


logger = logging.get_logger('train_mnist')
logger.setLevel(10)
# Run training loop
with sess.as_default():
    while True:
        batch = mnist_data.train.next_batch(50)
        train_step.run(feed_dict={img: batch[0],
                                  labels: batch[1]})

        sys.stdout.flush()
        saver.save(
            sess,
            os.path.join(

                fs_tracker.get_model_directory(),