From aaa0091db8b230f65e3d32157c1bf5b430d7af57 Mon Sep 17 00:00:00 2001
From: ml server <ml@spacecruft.org>
Date: Mon, 20 Jan 2020 12:26:00 -0700
Subject: [PATCH] wtf scripts to check tensorflow setup

---
 wut-tf    | 25 +++++++++++++++++++++++
 wut-tf.py | 60 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 85 insertions(+)
 create mode 100755 wut-tf
 create mode 100644 wut-tf.py

diff --git a/wut-tf b/wut-tf
new file mode 100755
index 0000000..bf8c5f6
--- /dev/null
+++ b/wut-tf
@@ -0,0 +1,25 @@
+#!/bin/bash
+# wut-tf
+#
+# Starts worker client.
+#
+# Usage:
+# wut-tf
+# Example:
+# wut-tf
+#
+# Note:
+# Each node needs a unique index number.
+#
+# NOTE!
+# This generates the node number based off the hostname.
+# The hosts are ml0 through ml5.
+
+HOSTNUM=`hostname | sed -e 's/ml//g'`
+
+#export TF_CONFIG='{"cluster": {"worker": [ "ml0-int:2222", "ml1-int:2222", "ml2-int:2222", "ml3-int:2222", "ml4-int:2222", "ml5-int:2222"]}, "task": {"index": '$HOSTNUM', "type": "worker"}}'
+export TF_CONFIG='{"cluster": {"worker": [ "ml1-int:2222", "ml2-int:2222", "ml3-int:2222", "ml4-int:2222", "ml5-int:2222"]}}'
+
+echo $TF_CONFIG
+python3 wut-tf.py
+
diff --git a/wut-tf.py b/wut-tf.py
new file mode 100644
index 0000000..0e3b0de
--- /dev/null
+++ b/wut-tf.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+#
+# wut-tf.py
+#
+# https://spacecruft.org/spacecruft/satnogs-wut
+#
+# Distributed Learning
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+from __future__ import print_function
+import os
+import json
+import numpy as np
+import datetime
+import tensorflow as tf
+import tensorflow.python.keras
+from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
+from tensorflow.python.keras import optimizers
+from tensorflow.python.keras import Sequential
+from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
+from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
+from tensorflow.python.keras.layers import Input, concatenate
+from tensorflow.python.keras.models import load_model
+from tensorflow.python.keras.models import Model
+from tensorflow.python.keras.preprocessing import image
+from tensorflow.python.keras.preprocessing.image import img_to_array
+from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.python.keras.preprocessing.image import load_img
+os.environ["TF_CONFIG"] = json.dumps({
+    "cluster": {
+        "worker": [ "ml1-int:2222", "ml2-int:2222", "ml3-int:2222", "ml4-int:2222", "ml5-int:2222" ]
+    }#,
+   #"task": {"type": "worker", "index": 0 },
+})
+print("Tensorflow Version: ", tf.__version__)
+print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
+print("Num CPUs Available: ", len(tf.config.experimental.list_physical_devices('CPU')))
+print(tf.config.experimental.list_physical_devices())
+#with tf.device("GPU:0"):
+#  tf.ones(())  # Make sure we can run on GPU
+print("XLA_FLAGS='{}'".format(os.getenv("XLA_FLAGS")))
+print(os.getenv("XLA_FLAGS"))
+tf.keras.backend.clear_session()
+IMG_HEIGHT = 416
+IMG_WIDTH= 804
+batch_size = 32
+epochs = 4
+BUFFER_SIZE = 10000
+NUM_WORKERS = 6
+GLOBAL_BATCH_SIZE = 64 * NUM_WORKERS
+#strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
+#strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
+#    tf.distribute.experimental.CollectiveCommunication.RING)
+AUTOTUNE = tf.data.experimental.AUTOTUNE
+NUM_TOTAL_IMAGES=100
+tf.config.optimizer.set_jit(True)
+#tf.summary.trace_on(profiler=True)
+#tf.summary.trace_export(name=trace-export,profiler_outdir=logs)
+options = tf.data.Options()
+