setup wut-worker-mas
ml server
parent
051346789f
commit
c6aa78fa0e
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@ -2,11 +2,30 @@
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#
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# wut-worker-mas.py
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#
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# https://spacecruft.org/spacecruft/satnogs-wut
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#
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# Distributed Learning
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import tensorflow as tf
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import json
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from __future__ import absolute_import, division, print_function, unicode_literals
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from __future__ import print_function
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import os
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import json
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import numpy as np
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import datetime
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import tensorflow as tf
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import tensorflow.python.keras
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from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
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from tensorflow.python.keras import optimizers
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from tensorflow.python.keras import Sequential
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from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
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from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
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from tensorflow.python.keras.layers import Input, concatenate
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from tensorflow.python.keras.models import load_model
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from tensorflow.python.keras.models import Model
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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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os.environ["TF_CONFIG"] = json.dumps({
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"cluster": {
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@ -15,12 +34,49 @@ os.environ["TF_CONFIG"] = json.dumps({
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"task": {"type": "worker", "index": 0 },
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})
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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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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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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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POSITIVE_DIRECTORY, pos_dir + ".+")
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return raw_bytes, label
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def uncompiled_model():
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model = Sequential([
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Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),
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MaxPooling2D(),
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Conv2D(32, 3, padding='same', activation='relu'),
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MaxPooling2D(),
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Conv2D(64, 3, padding='same', activation='relu'),
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MaxPooling2D(),
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Flatten(),
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Dense(512, activation='relu'),
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Dense(1, activation='sigmoid')
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])
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return model
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input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)
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def process_image(image_bytes, label):
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image = tf.io.decode_png(image_bytes)
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#image = tf.image.resize(image, resolution)
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image.set_shape(input_shape)
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#image = image / 255. - 0.5
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#image = tf.image.random_flip_left_right(image)
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#image = tf.image.random_flip_up_down(image)
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#image += tf.random.normal(
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# image.shape, mean=0, steddev=0.1)
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return image, tf.cast(label, tf.float32)
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AUTOTUNE = tf.data.experimental.AUTOTUNE
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NUM_TOTAL_IMAGES=100
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data_root = "/home/jebba/devel/spacecruft/satnogs-wut/data"
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@ -34,37 +90,53 @@ dataset = dataset.prefetch(buffer_size=AUTOTUNE)
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print(tf.__version__)
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print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
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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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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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os.makedirs(profile_dir, exist_ok=True)
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barf()
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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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strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
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strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
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tf.distribute.experimental.CollectiveCommunication.RING)
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def compiled_model():
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model = uncompiled_model()
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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return model
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with strategy.scope():
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# model = tf.keras.applications.mobilenet_v2.MobileNetV2(...)
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optimizer = tf.keras.optimzers.SGD(learning_rate=0.01)
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loss_fn = tf.nn.sigmoid_cross_entropy_with_logits
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model.compile(..., optimezer=optimizer)
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model.fit(train_dataset, epochs=10)
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#model = tf.keras.applications.mobilenet_v2.MobileNetV2(...)
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#optimizer = tf.keras.optimzers.SGD(learning_rate=0.01)
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#loss_fn = tf.nn.sigmoid_cross_entropy_with_logits
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#model.compile(..., optimizer=optimizer)
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model = uncompiled_model()
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model = compiled_model()
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#model.fit(train_dataset, epochs=10)
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model.fit(
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train_data_gen,
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steps_per_epoch=total_train // batch_size,
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epochs=epochs,
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validation_data=val_data_gen,
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validation_steps=total_val // batch_size,
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verbose=2
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)
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tf.summary.trace_export(name=trace-export,profiler_outdir=logs)
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strategy = tf.distribute.MirroredStrategy()
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with strategy.scope():
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model, loss_fn, optimzer = ...
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#model, loss_fn, optimzer = ...
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@tf.function
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def replicated_step(features, labels):
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return strategy.experimental_run_v2(step, (features, labels))
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@ -77,7 +149,7 @@ with strategy.scope():
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optimizer.apply_gradients(zip(grads, model.trainable_variables))
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return loss
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data = strategey.experimental_distribute_dataset(data)
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data = strategy.experimental_distribute_dataset(data)
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for features, labels in data:
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loss = replicated_step(features, labels)
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@ -89,7 +161,7 @@ def data_generator():
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# Load from disk
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image = imread(image_path)
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# Resize
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image = resize(image, resolution)
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# image = resize(image, resolution)
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# Horizontal and vertical flip
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#image = random_flip(image)
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# Normalize and add Gaussian noise
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@ -103,16 +175,54 @@ def handle_batching():
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yield concat(batch)
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batch.reset()
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def process_image(image_bytes, label):
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image = tf.io.decode_png(image_bytes)
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image = tf.image.resize(image, resolution)
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image.set_shape(input_shape)
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image = image / 255. - 0.5
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train_dir = os.path.join('data/', 'train')
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val_dir = os.path.join('data/', 'val')
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train_good_dir = os.path.join(train_dir, 'good')
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train_bad_dir = os.path.join(train_dir, 'bad')
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val_good_dir = os.path.join(val_dir, 'good')
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val_bad_dir = os.path.join(val_dir, 'bad')
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num_train_good = len(os.listdir(train_good_dir))
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num_train_bad = len(os.listdir(train_bad_dir))
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num_val_good = len(os.listdir(val_good_dir))
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num_val_bad = len(os.listdir(val_bad_dir))
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total_train = num_train_good + num_train_bad
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total_val = num_val_good + num_val_bad
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#image = tf.image.random_flip_left_right(image)
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#image = tf.image.random_flip_up_down(image)
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#image += tf.random.normal(
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# image.shape, mean=0, steddev=0.1)
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print('total training good images:', num_train_good)
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print('total training bad images:', num_train_bad)
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print("--")
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print("Total training images:", total_train)
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print('total validation good images:', num_val_good)
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print('total validation bad images:', num_val_bad)
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print("--")
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print("Total validation images:", total_val)
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print("--")
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print("Reduce training and validation set when testing")
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#total_train = 16
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#total_val = 16
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print("Reduced training images:", total_train)
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print("Reduced validation images:", total_val)
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return image, tf.cast(label, tf.float32)
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tf.keras.backend.clear_session()
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options = tf.data.Options()
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train_image_generator = ImageDataGenerator(
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rescale=1./255
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)
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val_image_generator = ImageDataGenerator(
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rescale=1./255
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)
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#train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
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# directory=train_dir,
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# shuffle=True,
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# target_size=(IMG_HEIGHT, IMG_WIDTH),
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# class_mode='binary')
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#val_data_gen = val_image_generator.flow_from_directory(batch_size=batch_size,
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# directory=val_dir,
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# target_size=(IMG_HEIGHT, IMG_WIDTH),
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# class_mode='binary')
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