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First impl for satanas car

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This commit is contained in:
Cyrille Nofficial 2019-11-05 19:45:46 +01:00
parent b81cb57230
commit 9ec80414c9
7 changed files with 495 additions and 43 deletions
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1
.envrc Normal file
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@ -0,0 +1 @@
layout_pipenv

3
Dockerfile.gpu
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@ -6,6 +6,7 @@ WORKDIR /usr/src
RUN python3 setup.py sdist
#FROM tensorflow/tensorflow:1.8.0-py3
FROM tensorflow/tensorflow:1.8.0-gpu-py3
#tensorflow-serving-api-python3==1.7.0
@ -32,5 +33,5 @@ RUN pip3 install robocars_sagemaker_container-1.0.0.tar.gz
RUN rm robocars_sagemaker_container-1.0.0.tar.gz
ENTRYPOINT ["entry.py"]
ENTRYPOINT ["train"]

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Pipfile Normal file
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[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[packages]
robocars_sagemaker_container= {editable = true, path = ".", extras = []}
importlib = "*"
[dev-packages]
robocars_sagemaker_container = {editable = true, path = ".", extras = ["tests"]}
[requires]
python_version = "3.7"

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Pipfile.lock generated Normal file
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@ -0,0 +1,425 @@
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}

18
create_job.sh
View File

@ -1,22 +1,24 @@
#!/bin/bash
job_name=$1
if [ -z $job_name ]
if [[ -z ${job_name} ]]
then
echo 'Provide model name'
exit 0
fi
echo 'Creating training job '$1
training_image="<replace_me>.dkr.ecr.eu-west-1.amazonaws.com/robocars:1.8.0-gpu-py3"
iam_role_arn="arn:aws:iam::<replace_me>:role/service-role/<replace_me>"
training_image="117617958416.dkr.ecr.eu-west-1.amazonaws.com/robocars:latest"
iam_role_arn="arn:aws:iam::117617958416:role/robocar-training"
DATA_BUCKET="s3://robocars-cyrilix-learning/input"
DATA_OUTPUT="s3://robocars-cyrilix-learning/output"
aws sagemaker create-training-job \
--training-job-name $job_name \
--training-job-name ${job_name} \
--hyper-parameters '{ "sagemaker_region": "\"eu-west-1\"", "with_slide": "true" }' \
--algorithm-specification TrainingImage=$training_image,TrainingInputMode=File \
--role-arn $iam_role_arn \
--input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<replace_me>", "S3DataDistributionType": "FullyReplicated" }} }]' \
--output-data-config S3OutputPath=s3://<replace_me> \
--algorithm-specification TrainingImage="${training_image}",TrainingInputMode=File \
--role-arn ${iam_role_arn} \
--input-data-config "[{ \"ChannelName\": \"train\", \"DataSource\": { \"S3DataSource\": { \"S3DataType\": \"S3Prefix\", \"S3Uri\": \"${DATA_BUCKET}\", \"S3DataDistributionType\": \"FullyReplicated\" }} }]" \
--output-data-config S3OutputPath=${DATA_OUTPUT} \
--resource-config InstanceType=ml.p2.xlarge,InstanceCount=1,VolumeSizeInGB=1 \
--stopping-condition MaxRuntimeInSeconds=1800

8
setup.py
View File

@ -1,8 +1,8 @@
import os
from glob import glob
from os.path import basename
from os.path import splitext
from glob import glob
from setuptools import setup, find_packages
@ -21,7 +21,11 @@ setup(
classifiers=[
'Programming Language :: Python :: 3.5',
],
entry_points={
'console_scripts': [
'train=tf_container.train_entry_point:train',
]
},
install_requires=['sagemaker-container-support'],
extras_require={},
)

53
src/tf_container/train_entry_point.py
View File

@ -16,6 +16,25 @@ from keras.layers import Activation, Dropout, Flatten, Dense
from keras import callbacks
from tensorflow.python.client import device_lib
def get_data(root_dir, filename):
print('load data from file '+ filename)
d = json.load(open(os.path.join(root_dir, filename)))
if 'pilot/throttle' in d:
return [d['user/mode'], d['user/throttle'], d['user/angle'], root_dir, d['cam/image_array'], d['pilot/throttle'], d['pilot/angle']]
else:
return [d['user/mode'], d['user/throttle'], d['user/angle'], root_dir, d['cam/image_array']]
numbers = re.compile(r'(\d+)')
def unzip_file(root,f):
zip_ref = zipfile.ZipFile(os.path.join(root,f), 'r')
zip_ref.extractall(root)
zip_ref.close()
def train():
env = cs.TrainingEnvironment()
@ -23,24 +42,10 @@ def train():
os.system('mkdir -p logs')
# ### Loading the files ###
# ** You need to copy all your files to the directory where you are runing this notebook into a folder named "data" **
# ** You need to copy all your files to the directory where you are runing this notebook **
# ** into a folder named "data" **
numbers = re.compile(r'(\d+)')
data = []
def get_data(root,f):
d = json.load(open(os.path.join(root,f)))
if ('pilot/throttle' in d):
return [d['user/mode'],d['user/throttle'],d['user/angle'],root,d['cam/image_array'],d['pilot/throttle'],d['pilot/angle']]
else:
return [d['user/mode'],d['user/throttle'],d['user/angle'],root,d['cam/image_array']]
def numericalSort(value):
parts = numbers.split(value)
parts[1::2] = map(int, parts[1::2])
return parts
def unzip_file(root,f):
zip_ref = zipfile.ZipFile(os.path.join(root,f), 'r')
zip_ref.extractall(root)
zip_ref.close()
for root, dirs, files in os.walk('/opt/ml/input/data/train'):
for f in files:
@ -48,13 +53,13 @@ def train():
unzip_file(root, f)
for root, dirs, files in os.walk('/opt/ml/input/data/train'):
data.extend([get_data(root,f) for f in sorted(files, key=numericalSort) if f.startswith('record') and f.endswith('.json')])
data.extend([get_data(root,f) for f in sorted(files, key=str.lower) if f.startswith('record') and f.endswith('.json')])
# Normalize / correct data
data = [d for d in data if d[1] > 0.1]
for d in data:
if d[1] < 0.2:
d[1] = 0.2
#data = [d for d in data if d[1] > 0.1]
#for d in data:
# if d[1] < 0.2:
# d[1] = 0.2
# ### Loading throttle and angle ###
@ -62,7 +67,8 @@ def train():
throttle = [d[1] for d in data]
angle_array = np.array(angle)
throttle_array = np.array(throttle)
if (len(data[0]) > 5):
if len(data[0]) > 5:
pilot_angle = [d[6] for d in data]
pilot_throttle = [d[5] for d in data]
pilot_angle_array = np.array(pilot_angle)
@ -71,7 +77,6 @@ def train():
pilot_angle = []
pilot_throttle = []
# ### Loading images ###
images = np.array([img_to_array(load_img(os.path.join(d[3],d[4]))) for d in data],'f')
@ -96,7 +101,7 @@ def train():
patience=10,
verbose=1,
mode='auto')
img_in = Input(shape=(120, 160, 3), name='img_in') # First layer, input layer, Shape comes from camera.py resolution, RGB
img_in = Input(shape=(128, 160, 3), name='img_in') # First layer, input layer, Shape comes from camera.py resolution, RGB
x = img_in
x = Convolution2D(24, (5,5), strides=(2,2), activation='relu')(x) # 24 features, 5 pixel x 5 pixel kernel (convolution, feauture) window, 2wx2h stride, relu activation
x = Convolution2D(32, (5,5), strides=(2,2), activation='relu')(x) # 32 features, 5px5p kernel window, 2wx2h stride, relu activatiion