parent
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@ -134,7 +134,10 @@ firefox https://github.com/bazelbuild/bazel/releases
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# Install Tensorflow
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git clone tensorflow...
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cd tensorflow
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git checkout remotes/origin/r2.1
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git checkout v2.1.0
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bazel clean
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# Get flags to pass:
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grep flags -m1 /proc/cpuinfo | cut -d ":" -f 2 | tr '[:upper:]' '[:lower:]' | { read FLAGS; OPT="-march=native"; for flag in $FLAGS; do case "$flag" in "sse4_1" | "sse4_2" | "ssse3" | "fma" | "cx16" | "popcnt" | "avx" | "avx2") OPT+=" -m$flag";; esac; done; MODOPT=${OPT//_/\.}; echo "$MODOPT"; }
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./configure
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# Run Bazel to build pip package. Takes nearly 2 hours to build.
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bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
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@ -18,8 +18,8 @@
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HOSTNUM=`hostname | sed -e 's/ml//g'`
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#export TF_CONFIG='{"cluster": {"worker": [ "ml0-int:2222", "ml1-int:2222", "ml2-int:2222", "ml3-int:2222", "ml4-int:2222", "ml5-int:2222"]}, "task": {"index": '$HOSTNUM', "type": "worker"}}'
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#export TF_CONFIG='{"cluster": {"worker": [ "ml1-int:2222", "ml2-int:2222", "ml3-int:2222", "ml4-int:2222", "ml5-int:2222"]}}'
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export TF_CONFIG='{"cluster": {"chief": [ "ml0-int:2222" ], "worker": [ "ml1-int:2222", "ml2-int:2222", "ml3-int:2222", "ml4-int:2222", "ml5-int:2222"]}, "task": {"index": '$HOSTNUM', "type": "worker"}}'
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export TF_CONFIG='{"cluster": {"worker": [ "ml1-int:2222", "ml2-int:2222", "ml3-int:2222", "ml4-int:2222", "ml5-int:2222"]}}'
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#export TF_CONFIG='{"cluster": {"chief": [ "ml0-int:2222" ], "worker": [ "ml1-int:2222", "ml2-int:2222", "ml3-int:2222", "ml4-int:2222", "ml5-int:2222"]}, "task": {"index": '$HOSTNUM', "type": "worker"}}'
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echo $TF_CONFIG
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python3 wut-worker-mas.py
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@ -26,11 +26,9 @@ from tensorflow.python.keras.preprocessing import image
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from tensorflow.python.keras.preprocessing.image import img_to_array
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from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
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from tensorflow.python.keras.preprocessing.image import load_img
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#import tensorflow.python.distribute.cluster_resolver
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#from tensorflow.python.distribute.cluster_resolver import TFConfigClusterResolver
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#from tensorflow.python.distribute.cluster_resolver.TFConfigClusterResolver
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tf.keras.backend.clear_session()
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tf.config.optimizer.set_jit(True)
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options = tf.data.Options()
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os.environ["TF_CONFIG"] = json.dumps({
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"cluster": {
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@ -39,27 +37,16 @@ os.environ["TF_CONFIG"] = json.dumps({
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},
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"task": {"type": "chief", "index": 0 },
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})
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#os.environ["TF_CONFIG"] = json.dumps({
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# "cluster": {
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# "worker": [ "ml1-int:2222", "ml2-int:2222", "ml3-int:2222", "ml4-int:2222", "ml5-int:2222" ]
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# }#,
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# #"task": {"type": "worker", "index": 0 },
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#})
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print("Tensorflow Version: ", tf.__version__)
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print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
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print("Num CPUs Available: ", len(tf.config.experimental.list_physical_devices('CPU')))
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#with tf.device("GPU:0"):
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# tf.ones(()) # Make sure we can run on GPU
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# This ensures that XLA and ptxas work well together, and helps with scaling.
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print("XLA_FLAGS='{}'".format(os.getenv("XLA_FLAGS")))
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IMG_HEIGHT = 416
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IMG_WIDTH= 804
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batch_size = 32
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epochs = 4
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BUFFER_SIZE = 10000
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NUM_WORKERS = 6
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GLOBAL_BATCH_SIZE = 64 * NUM_WORKERS
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@ -68,12 +55,9 @@ GLOBAL_BATCH_SIZE = 64 * NUM_WORKERS
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POSITIVE_DIRECTORY = '/home/jebba/devel/spacecruft/satnogs-wut/data/pos'
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pos_dir = '/home/jebba/devel/spacecruft/satnogs-wut/data/posdir'
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from tensorflow.python.distribute.cluster_resolver import SimpleClusterResolver
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strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
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tf.distribute.experimental.CollectiveCommunication.RING)
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def get_bytes_and_label(filepath):
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raw_bytes = tf.io.read_file(filepath)
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label = tf.strings.regex_full_match(
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@ -117,13 +101,9 @@ dataset = dataset.map(process_image, num_parallel_calls=AUTOTUNE)
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dataset = dataset.batch(batch_size=32)
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dataset = dataset.prefetch(buffer_size=AUTOTUNE)
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os.makedirs(profile_dir, exist_ok=True)
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# tf.data.Dataset.from_generator
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tf.config.optimizer.set_jit(True)
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#tf.summary.trace_on(profiler=True)
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