Fix examples windows support

examples/manual_reporting.py

@@ -1,5 +1,6 @@
 # TRAINS - Example of manual graphs and  statistics reporting
 #
+from PIL import Image
 import numpy as np
 import logging
 from trains import Task
@@ -49,11 +50,12 @@ logger.report_scatter3d("example_scatter_3d", "series_xyz", iteration=1, scatter
 
 # reporting images
 m = np.eye(256, 256, dtype=np.float)
-logger.report_image("test case", "image float", iteration=1, matrix=m)
+logger.report_image("test case", "image float", iteration=1, image=m)
 m = np.eye(256, 256, dtype=np.uint8)*255
-logger.report_image("test case", "image uint8", iteration=1, matrix=m)
+logger.report_image("test case", "image uint8", iteration=1, image=m)
 m = np.concatenate((np.atleast_3d(m), np.zeros((256, 256, 2), dtype=np.uint8)), axis=2)
-logger.report_image("test case", "image color red", iteration=1, matrix=m)
+logger.report_image("test case", "image color red", iteration=1, image=m)
-
+image_open = Image.open('./samples/picasso.jpg')
+logger.report_image("test case", "image PIL", iteration=1, image=image_open)
 # flush reports (otherwise it will be flushed in the background, every couple of seconds)
 logger.flush()

examples/pytorch_mnist.py

@@ -2,6 +2,9 @@
 #
 from __future__ import print_function
 import argparse
+import os
+from tempfile import gettempdir
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -117,7 +120,7 @@ def main():
         test(args, model, device, test_loader)
 
     if (args.save_model):
-        torch.save(model.state_dict(), "/tmp/mnist_cnn.pt")
+        torch.save(model.state_dict(), os.path.join(gettempdir(), "mnist_cnn.pt"))
 
 
 if __name__ == '__main__':

examples/pytorch_tensorboard.py

@@ -3,6 +3,9 @@
 from __future__ import print_function
 
 import argparse
+import os
+from tempfile import gettempdir
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -122,5 +125,5 @@ def test():
 
 for epoch in range(1, args.epochs + 1):
     train(epoch)
-    torch.save(model, '/tmp/model{}'.format(epoch))
+    torch.save(model, os.path.join(gettempdir(), 'model{}'.format(epoch)))
 test()

examples/pytorch_tensorboardX.py

@@ -3,6 +3,9 @@
 from __future__ import print_function
 
 import argparse
+import os
+from tempfile import gettempdir
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -122,5 +125,5 @@ def test():
 
 for epoch in range(1, args.epochs + 1):
     train(epoch)
-    torch.save(model, '/tmp/model{}'.format(epoch))
+    torch.save(model, os.path.join(gettempdir(), 'model{}'.format(epoch)))
 test()

examples/requirements.txt

@@ -2,7 +2,7 @@ absl-py>=0.7.1
 Keras>=2.2.4
 joblib>=0.13.2
 matplotlib>=3.1.1 ; python_version >= '3.6'
-matplotlib == 3.0.3 ; python_version < '3.6'
+matplotlib >= 2.2.4 ; python_version < '3.6'
 seaborn>=0.9.0
 sklearn>=0.0
 tensorboard>=1.14.0
@@ -10,7 +10,7 @@ tensorboardX>=1.8
 tensorflow>=1.14.0
 torch>=1.1.0
 torchvision>=0.3.0
-xgboost>=0.90
+xgboost>=0.90 ; python_version >= '3'
-
+xgboost >= 0.82 ; python_version < '3'
 # sudo apt-get install graphviz
 graphviz>=0.8

examples/tensorboard_pr_curve.py

@@ -30,6 +30,7 @@ from __future__ import division
 from __future__ import print_function
 
 import os.path
+from tempfile import gettempdir
 
 from absl import app
 from absl import flags
@@ -42,8 +43,8 @@ task = Task.init(project_name='examples', task_name='tensorboard pr_curve')
 
 tf.compat.v1.disable_v2_behavior()
 FLAGS = flags.FLAGS
-
+flags.DEFINE_string('logdir', os.path.join(gettempdir(), "pr_curve_demo"),
-flags.DEFINE_string('logdir', '/tmp/pr_curve_demo', 'Directory into which to write TensorBoard data.')
+                    "Directory into which to write TensorBoard data.")
 
 flags.DEFINE_integer('steps', 10,
                      'Number of steps to generate for each PR curve.')