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spot-instance.yaml
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spot-instance.yaml
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AWSTemplateFormatVersion: "2010-09-09"
Description: Setup a spot EC2 Autoscaling Group for deep racer
Parameters:
InstanceType:
Type: String
Default: g4dn.2xlarge
ResourcesStackName:
Type: String
TimeToLiveInMinutes:
Type: Number
Description: timeout in minutes after which training is stopped and this stack is deleted
Default: 60
MinValue: 10
MaxValue: 1440 # 24 hours
AmiId:
Type: String
Description: the AMI we want to launch an ec2 instance against. By default this is the image created by central account 747447086422 owned by Tyler Wooten
BUCKET:
Type: String
CUSTOMFILELOCATION:
Type: String
Outputs:
Instance:
Value: !Ref ASG
Resources:
LaunchTemplate:
Type: AWS::EC2::LaunchTemplate
Properties:
LaunchTemplateName: !Sub ${AWS::StackName}-launch-template
LaunchTemplateData:
IamInstanceProfile:
Name:
!ImportValue
'Fn::Sub': '${ResourcesStackName}-InstanceProfile'
ImageId: !Ref AmiId
InstanceType: !Ref InstanceType
InstanceMarketOptions:
MarketType: spot
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
VolumeType: gp3
VolumeSize: 60
DeleteOnTermination: 'true'
UserData:
Fn::Base64: !Sub |
#!/bin/bash -xe
/usr/local/bin/cfn-init --stack ${AWS::StackName} --resource ASG --region ${AWS::Region}
bash
sudo su ubuntu
cd /home/ubuntu/deepracer-for-cloud/
source bin/activate.sh
export DEEPRACER_S3_URI=${BUCKET}
export CUSTOM_FILE_LOCATION="${CUSTOMFILELOCATION}"
SecurityGroupIds:
- !ImportValue
'Fn::Sub': '${ResourcesStackName}-SecurityGroup'
TagSpecifications:
- ResourceType: instance
Tags:
- Key: Name
Value: !Sub '${AWS::StackName}'
- ResourceType: volume
Tags:
- Key: Name
Value: !Sub '${AWS::StackName}'
ASG:
Type: AWS::AutoScaling::AutoScalingGroup
CreationPolicy:
ResourceSignal:
Count: '1'
Timeout: PT30M
Metadata:
AWS::CloudFormation::Init:
config:
commands:
1-append-fstab:
command: "bash -c 'cat /tmp/fstabcontent.txt >> /etc/fstab'"
2-mount-fstab:
command: "bash -c 'mount /home/ubuntu/efs'"
3-signal-cfn:
command:
!Sub "bash -c '/usr/local/bin/cfn-signal -s true -e 0 --stack ${AWS::StackName} --resource ASG --region ${AWS::Region}'"
4-start-train:
command: "su -l ubuntu bash -c '/home/ubuntu/bin/start_training.sh'"
files:
/tmp/fstabcontent.txt:
content:
Fn::Sub:
- "${EFS} /home/ubuntu/efs efs _netdev,tls,accesspoint=${EFSAP} 0 0"
- EFS:
Fn::ImportValue:
!Sub "${ResourcesStackName}-EFS"
EFSAP:
Fn::ImportValue:
!Sub "${ResourcesStackName}-EFSAccessPoint"
mode : "000755"
owner: root
/etc/profile.d/my_efs.sh:
content:
Fn::Sub:
- "export MY_EFS=${EFS}"
- EFS:
Fn::ImportValue:
!Sub "${ResourcesStackName}-EFS"
mode : "000755"
owner: root
group: root
/etc/profile.d/my_sns_topic.sh:
content:
Fn::Sub:
- "export MY_SNS_TOPIC=${SNS}"
- SNS:
Fn::ImportValue:
!Sub "${ResourcesStackName}-InterruptionNotification"
/etc/profile.d/my_public_ip.sh:
content: "export PUBLIC_IP=$(curl http://169.254.169.254/latest/meta-data/public-ipv4)"
mode : "000755"
owner: root
group: root
/etc/profile.d/my_bucket.sh:
content:
Fn::Sub:
- "export MY_BUCKET=${BUCKET};export DR_S3_URI=${BUCKET};export DEEPRACER_S3_URI=${BUCKET}"
- BUCKET:
Fn::ImportValue:
!Sub "${ResourcesStackName}-Bucket"
mode : "000755"
owner: root
group: root
/etc/profile.d/my_custom_files.sh:
content:
Fn::Sub: |
export CUSTOM_FILE_LOCATION="${CUSTOMFILELOCATION}"
mode : "000755"
owner: root
group: root
/etc/profile.d/my_region.sh:
content: "export DEEPRACER_REGION=$(curl http://169.254.169.254/latest/meta-data/placement/region)"
mode : "000755"
owner: root
group: root
/home/ubuntu/deepracer-for-cloud/interrupt_spot.sh:
content: |
date >> /tmp/interrupt.log
sudo su ubuntu
INTERRUPT_TIME=$(date '+%Y%m%d-%H%M')
cd ~/deepracer-for-cloud
source bin/activate.sh
source /etc/profile.d/my_bucket.sh
dr-reload
dr-upload-model -bfw
dr-stop-training
sed -i '/DR_LOCAL_S3_PRETRAINED/d' ~/deepracer-for-cloud/run.env
sed -i -e '$aDR_LOCAL_S3_PRETRAINED=True' ~/deepracer-for-cloud/run.env
PRE_TRAINED_LOCATION=$(cat run.env | grep DR_LOCAL_S3_MODEL_PREFIX= | awk -F'=' '{print $2}')
sed -i 's|-continued-.*||' ~/deepracer-for-cloud/run.env
TRAINING_LOCATION=$(cat run.env | grep DR_LOCAL_S3_MODEL_PREFIX= | awk -F'=' '{print $2}')
UPLOAD_LOCATION=$(cat run.env | grep DR_UPLOAD_S3_PREFIX= | awk -F'=' '{print $2}')
sed -i -e '$aDR_LOCAL_S3_PRETRAINED_PREFIX=PLACEHOLDER' ~/deepracer-for-cloud/run.env
sed -i "s|DR_LOCAL_S3_PRETRAINED_PREFIX=PLACEHOLDER|DR_LOCAL_S3_PRETRAINED_PREFIX=$PRE_TRAINED_LOCATION|" ~/deepracer-for-cloud/run.env
sed -i "s|DR_LOCAL_S3_MODEL_PREFIX=$TRAINING_LOCATION|DR_LOCAL_S3_MODEL_PREFIX=$TRAINING_LOCATION-continued-$INTERRUPT_TIME|" ~/deepracer-for-cloud/run.env
sed -i "s|DR_UPLOAD_S3_PREFIX=$UPLOAD_LOCATION|DR_UPLOAD_S3_PREFIX=$UPLOAD_LOCATION-continued-$INTERRUPT_TIME|" ~/deepracer-for-cloud/run.env
sed -i -e '$aDR_LOCAL_S3_PRETRAINED_CHECKPOINT=last' ~/deepracer-for-cloud/run.env
aws s3 cp ~/deepracer-for-cloud/run.env s3://$DEEPRACER_S3_URI/$DR_LOCAL_S3_CUSTOM_FILES_PREFIX/
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/bin/menu.html:
content: |
<!DOCTYPE html>
<html>
<body>
<h2>All logs summary in one view</h2>
<p><a href="output.txt">Output</a></p>
<h2>Docker logs (last 1000 lines)</h2>
<p><a href="sagemaker.txt">Sagemaker</a></p>
<p><a href="robomaker.txt">Robomaker (Main worker)</a></p>
<p><a href="dockerstatus.txt">docker ps -a (command output)</a></p>
<h2>Nvidia GPU status</h2>
<p><a href="nvidia-smi.txt">nvidia-smi (command output)</a></p>
<h2>Custom logs (last 1000 lines)</h2>
<p><a href="OutputLog.txt">OutputLog</a></p>
<p><a href="completedlaps.txt">Completed Laps - last step from Robomaker output (all Workers)</a></p>
<h2>Configuration files</h2>
<p><a href="run.env.txt">run.env</a></p>
<p><a href="system.env.txt">system.env</a></p>
<p><a href="hyperparameters.json">hyperparameters.json</a></p>
<p><a href="model_metadata.json">model_metadata.json</a></p>
<p><a href="reward_function.py.txt">reward_function.py</a></p>
<h2>Training metrics</h2>
<p><a href="TrainingMetrics.json">TrainingMetrics.json</a></p>
<p><a href="deepracer_checkpoints.json">deepracer_checkpoints.json</a></p>
<p><a href="robomaker1.log" download>robomaker1.log</a></p>
<h2>Training/Evaluation monitoring graphs</h2>
<p><a href="Training_and_Evaluation_Overview.html">Training_and_Evaluation_Overview</a></p>
<p><a href="Training_progress.html">Training_progress</a></p>
<p><a href="Quintiles.html">Quintiles</a></p>
<p><a href="Heatmap.html">Reward Heatmap</a></p>
<p><a href="Data_tables.html">Data in tables</a></p>
<p><a href="Path_for_complete_laps.html">Path_for_complete_laps</a></p>
<p><a href="update_to_jupyter_url">Access Jupyter Notebook</a></p>
</body>
</html>
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Training_and_Evaluation_Overview.html:
content: |
<!DOCTYPE html>
<html>
<body>
<h2>Training analysis is typically available 20-30 minutes into training. Please refresh in a few minutes. </h2>
</body>
</html>
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Training_progress.html:
content: |
<!DOCTYPE html>
<html>
<body>
<h2>Training analysis is typically available 20-30 minutes into training. Please refresh in a few minutes. </h2>
</body>
</html>
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Quintiles.html:
content: |
<!DOCTYPE html>
<html>
<body>
<h2>Training analysis is typically available 20-30 minutes into training. Please refresh in a few minutes. </h2>
</body>
</html>
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Heatmap.html:
content: |
<!DOCTYPE html>
<html>
<body>
<h2>Training analysis is typically available 20-30 minutes into training. Please refresh in a few minutes. </h2>
</body>
</html>
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Data_tables.html:
content: |
<!DOCTYPE html>
<html>
<body>
<h2>Training analysis is typically available 20-30 minutes into training. Please refresh in a few minutes. </h2>
</body>
</html>
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Path_for_complete_laps.html:
content: |
<!DOCTYPE html>
<html>
<body>
<h2>Training analysis is typically available 20-30 minutes into training. Please refresh in a few minutes. </h2>
</body>
</html>
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/bin/web_monitoring.sh:
content: |
#!/bin/bash
/home/ubuntu/bin/start_analysis.sh
USAGE_OUTPUT=output.txt
cd ~/deepracer-for-cloud
while [ true ]
do
# This loop collects training data available and publishes it on the nginx docker. accessible through Public_IP:8100/menu.html
# Update variable references before every iteration in case of any change on the config files, this is similar to dr-reload
source ~/deepracer-for-cloud/bin/activate.sh > /dev/null 2>&1
echo "-----------------------------------" > $USAGE_OUTPUT
# Get model name being trained
cat ~/deepracer-for-cloud/run.env | egrep "^DR_LOCAL_S3_MODEL_PREFIX" >> $USAGE_OUTPUT
# get timestamp to know if the data published is current
date --utc +%F_%T_UTC >> $USAGE_OUTPUT
# known training issues # 1 - GPU ran out of memory
outofmemoryerrors=$(docker logs $(dr-find-sagemaker) 2>&1 | grep "ran out of memory"|wc -l)
if [[ $outofmemoryerrors -ge 1 ]];then
echo " ########### ERROR ------> GPU RAN OUT OF MEMORY !!!!!! ###########" >> $USAGE_OUTPUT
fi
# get Checkpoint status (best checkpoint, last checkpoint, current checkpoint)
docker logs $(dr-find-sagemaker) 2>&1 | grep "Best checkpoint" | tail -n 1 >> $USAGE_OUTPUT
docker logs $(dr-find-sagemaker) 2>&1 | grep Checkpoint | tail -n 1 >> $USAGE_OUTPUT
echo "=====Robomaker (main Worker)=====" >> $USAGE_OUTPUT
docker logs $(dr-find-robomaker) 2>&1 | egrep '^(SIM_TRACE_LOG.*(omplete|off_)|^reward_output)' | tail -n 10 | grep "omplete\|off_\|reward_output\|checkpoint" >> $USAGE_OUTPUT
echo "=====Sagemaker policy training=====" >> $USAGE_OUTPUT
docker logs $(dr-find-sagemaker) 2>&1 | egrep '^Policy training' | tail -n 1 >> $USAGE_OUTPUT
echo "=====GPU performance=====" >> $USAGE_OUTPUT
nvidia-smi > nvidia-smi.txt 2>&1
grep Default nvidia-smi.txt >> $USAGE_OUTPUT 2>&1
echo "=====Docker containers status=====" >> $USAGE_OUTPUT
docker ps -a > dockerstatus.txt 2>&1
cat dockerstatus.txt >> $USAGE_OUTPUT 2>&1
# known training issues # 2 - At least one required DOCKER CONTAINER EXITED
dockererrors=$(grep "exited" dockerstatus.txt | egrep 'deepracer-(sagemaker|rlcoach|robomaker)' | wc -l)
if [[ $dockererrors -ge 1 ]];then
echo " ########### ERROR ------> At least one required DOCKER CONTAINER EXITED !!!!!! ###########" >> $USAGE_OUTPUT
fi
echo "=====CPU average load (1min / 5min / 15min avg)=====" >> $USAGE_OUTPUT
cat /proc/loadavg >> $USAGE_OUTPUT 2>&1
echo "=====Memory usage=====" >> $USAGE_OUTPUT
cat /proc/meminfo | egrep '(^MemTotal|^MemFree|^SwapTotal|^SwapFree)' >> $USAGE_OUTPUT 2>&1
echo "=====Robomaker Testing result logs (all Workers)=====" >> $USAGE_OUTPUT
for name in `docker ps --format "{{.Names}}" | grep obomaker`
do
docker logs ${name} 2>&1 | egrep '^Testing>' | tail -n 10 >> $USAGE_OUTPUT
docker logs ${name} >& ${name}.log
done
mv deepracer-0_robomaker.1.*.log robomaker1.log
echo "=====Sagemaker training logs=====" >> $USAGE_OUTPUT
docker logs $(dr-find-sagemaker) 2>&1 | egrep '^Training>' | tail -n 10 >> $USAGE_OUTPUT
echo "=====Robomaker Top 10 completed laps (all Workers)=====" >> $USAGE_OUTPUT
if [ -f $USAGE_OUTPUT.tmp ] ;then
rm "$USAGE_OUTPUT.tmp" > /dev/null 2>&1
fi
for name in `docker ps --format "{{.Names}}"`
do
docker logs ${name} 2>&1 | egrep '^SIM_TRACE_LOG.*(omplete)' | sort --field-separator=',' --key=2 | head -n 10000 >> $USAGE_OUTPUT.tmp
done
echo "Number of completed laps: $(cat $USAGE_OUTPUT.tmp | wc -l)" >> $USAGE_OUTPUT 2>&1
cat $USAGE_OUTPUT.tmp | sort --field-separator=',' --key=2 | head -n 1000 > completedlaps.txt 2>&1
head completedlaps.txt -n 10 >> $USAGE_OUTPUT 2>&1
echo "=====Robomaker (main Worker) - OutputLog: =====" >> $USAGE_OUTPUT
docker logs $(dr-find-robomaker) 2>&1 | egrep '^OutputLog' | tail -n 1000 > OutputLog.txt
tail OutputLog.txt -n 10 >> $USAGE_OUTPUT
rm $USAGE_OUTPUT.tmp > /dev/null 2>&1
echo "###################" >> $USAGE_OUTPUT
# Collecting remaining common output files, metrics and uploading them to website
docker logs $(dr-find-sagemaker) 2>&1 | tail -n 1000 > sagemaker.txt
docker logs $(dr-find-robomaker) 2>&1 | tail -n 1000 > robomaker.txt
aws s3 cp s3://$DR_LOCAL_S3_BUCKET/$DR_LOCAL_S3_MODEL_PREFIX/metrics/TrainingMetrics.json . > /dev/null 2>&1
aws s3 cp s3://$DR_LOCAL_S3_BUCKET/$DR_LOCAL_S3_MODEL_PREFIX/model/deepracer_checkpoints.json . > /dev/null 2>&1
for ID in `docker ps --filter name=viewer_proxy --format "{{.ID}}"`
do
docker cp $USAGE_OUTPUT $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp nvidia-smi.txt $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp dockerstatus.txt $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp completedlaps.txt $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp OutputLog.txt $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp sagemaker.txt $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp robomaker.txt $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp robomaker1.log $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp TrainingMetrics.json $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp deepracer_checkpoints.json $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp ~/deepracer-for-cloud/run.env $ID:/usr/share/nginx/html/run.env.txt > /dev/null 2>&1
docker cp ~/deepracer-for-cloud/system.env $ID:/usr/share/nginx/html/system.env.txt > /dev/null 2>&1
docker cp ~/deepracer-for-cloud/custom_files/hyperparameters.json $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp ~/deepracer-for-cloud/custom_files/model_metadata.json $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker cp ~/deepracer-for-cloud/custom_files/reward_function.py $ID:/usr/share/nginx/html/reward_function.py.txt > /dev/null 2>&1
docker cp /home/ubuntu/bin/menu.html $ID:/usr/share/nginx/html/ > /dev/null 2>&1
#Update training analysis
ANALYSIS_ID=$(docker ps --filter name=deepracer-analysis --format "{{.ID}}")
docker exec $ANALYSIS_ID jupyter nbconvert --no-input --to html --execute import_from_s3.ipynb
docker exec $ANALYSIS_ID jupyter nbconvert --no-input --to html --execute Training_progress.ipynb
docker cp $ANALYSIS_ID:/workspace/Training_progress.html .
docker cp Training_progress.html $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker exec $ANALYSIS_ID jupyter nbconvert --no-input --to html --execute Heatmap.ipynb
docker cp $ANALYSIS_ID:/workspace/Heatmap.html .
docker cp Heatmap.html $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker exec $ANALYSIS_ID jupyter nbconvert --no-input --to html --execute Quintiles.ipynb
docker cp $ANALYSIS_ID:/workspace/Quintiles.html .
docker cp Quintiles.html $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker exec $ANALYSIS_ID jupyter nbconvert --no-input --to html --execute Data_tables.ipynb
docker cp $ANALYSIS_ID:/workspace/Data_tables.html .
docker cp Data_tables.html $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker exec $ANALYSIS_ID jupyter nbconvert --no-input --to html --execute Path_for_complete_laps.ipynb
docker cp $ANALYSIS_ID:/workspace/Path_for_complete_laps.html .
docker cp Path_for_complete_laps.html $ID:/usr/share/nginx/html/ > /dev/null 2>&1
docker exec $ANALYSIS_ID jupyter nbconvert --no-input --to html --execute Training_and_Evaluation_Overview.ipynb
docker cp $ANALYSIS_ID:/workspace/Training_and_Evaluation_Overview.html .
docker cp Training_and_Evaluation_Overview.html $ID:/usr/share/nginx/html/ > /dev/null 2>&1
done
# if the EC2 has started the termination process we do not want to upload $USAGE_OUTPUT to S3
if [[ ! -f /home/ubuntu/bin/termination.started ]];then
cp $USAGE_OUTPUT /tmp/logs/ > /dev/null 2>&1
fi
sleep 60
done
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/bin/start_training.sh:
content: |
#!/bin/bash
source /etc/profile.d/my_custom_files.sh
aws sns publish --topic-arn $MY_SNS_TOPIC --message "Training has initiated on a new instance. The new url to monitor progress is http://$PUBLIC_IP:8100/menu.html" --region $DEEPRACER_REGION
cd ~/deepracer-for-cloud
sed -i "s/DR_UPLOAD_S3_BUCKET=not-defined/DR_UPLOAD_S3_BUCKET=$DEEPRACER_S3_URI/" ~/deepracer-for-cloud/system.env
sed -i "s/DR_LOCAL_S3_BUCKET=bucket/DR_LOCAL_S3_BUCKET=$DEEPRACER_S3_URI/" ~/deepracer-for-cloud/system.env
sed -i "s/DR_UPLOAD_S3_PREFIX=upload/DR_UPLOAD_S3_PREFIX=$DR_LOCAL_S3_MODEL_PREFIX-upload/" ~/deepracer-for-cloud/run.env
sed -i "s|DR_LOCAL_S3_CUSTOM_FILES_PREFIX=custom_files|DR_LOCAL_S3_CUSTOM_FILES_PREFIX=$CUSTOM_FILE_LOCATION|" ~/deepracer-for-cloud/run.env
source bin/activate.sh
dr-download-custom-files
cp custom_files/*.env .
dr-reload
# There is a bug where at some times the training fails to start, so we start, stop and start it again to reduce the occurrences of this issue.
nohup /bin/bash -lc 'cd ~/deepracer-for-cloud/; dr-start-training -qw; sleep 120; dr-stop-training; sleep 60; echo y | docker container prune; dr-reload; dr-start-training -qwv' &
mkdir -p /tmp/logs/
# We want to be able to monitor our EC2 training without needing to connect to console, so we upload all needed info to Public_IP:8100/menu.html using this script
nohup /bin/bash -lc 'source /home/ubuntu/bin/web_monitoring.sh >/dev/null 2>&1' &
sleep 180 > /dev/null
while [ True ]; do
# if the EC2 started termination process upon interruption notification, this file should exist, hence we leave termination process to manage final uploads without conflict
if [[ -f /home/ubuntu/bin/termination.started ]];then
break
fi
# Update variable references before every iteration in case of any change on the config files
source ~/deepracer-for-cloud/bin/activate.sh
for name in `docker ps -a --format "{{.Names}}"`; do
docker logs ${name} > /tmp/logs/${name}.log 2>&1
done
# Only upload best Checkpoint if best Checkpoint has changed
bestcheckpoint=$(echo n | dr-upload-model -b 2>&1 | grep "checkpoint:")
aws s3 cp /tmp/logs/ s3://$DEEPRACER_S3_URI/$DR_LOCAL_S3_MODEL_PREFIX/logs/ --recursive
rm -rf /tmp/logs/*.* > /dev/null 2>&1
if [ [ "$bestcheckpoint" != "$lastbestcheckpoint" ] && [ "$bestcheckpoint" != "" ] ];then
# update file timestamp just to avoid conflict with termination process
touch /home/ubuntu/bin/uploading_best_model.timestamp 2>&1
dr-upload-model -bfw > /dev/null 2>&1
lastbestcheckpoint=$bestcheckpoint
fi
sleep 120
done
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/bin/start_analysis.sh:
content: |
#!/bin/bash
cd ~/deepracer-for-cloud
source bin/activate.sh
docker run -d -p 8888:8888 --name deepracer-analysis awsdeepracercommunity/deepracer-analysis:cpu
while [ "$TOKEN" == "" ]
do
TOKEN=$(docker logs deepracer-analysis 2>&1 | grep -o -E "token=[0-9a-f]+" | head -n 1)
done
PUBLIC_IP=$(curl http://169.254.169.254/latest/meta-data/public-ipv4)
JUPYTER_URL=http://$PUBLIC_IP:8888/?$TOKEN
sudo sed -i "s|update_to_jupyter_url|${JUPYTER_URL}|" /home/ubuntu/bin/menu.html
S3_PREFIX_FOR_ANALYSIS=$(cat run.env | grep DR_LOCAL_S3_MODEL_PREFIX= | awk -F'=' '{print $2}')
DEEPRACER_TRACK=$(cat run.env | grep DR_WORLD_NAME= | awk -F'=' '{print $2}')
DEEPRACER_WORKERS=$(cat system.env | grep DR_WORKERS= | awk -F'=' '{print $2}')
sed -i "s|DR_LOCAL_S3_MODEL_PREFIX|${S3_PREFIX_FOR_ANALYSIS}|" import_from_s3.py
sed -i "s|DEEPRACER_S3_URI|${DEEPRACER_S3_URI}|" import_from_s3.py
sed -i "s|DEEPRACER_TRACK|${DEEPRACER_TRACK}|" import_from_s3.py
sed -i "s|DR_LOCAL_S3_MODEL_PREFIX|${S3_PREFIX_FOR_ANALYSIS}|" Training_and_Evaluation_Overview.py
sed -i "s|DEEPRACER_S3_URI|${DEEPRACER_S3_URI}|" Training_and_Evaluation_Overview.py
sed -i "s|DEEPRACER_WORKERS|${DEEPRACER_WORKERS}|" Training_and_Evaluation_Overview.py
ANALYSIS_ID=$(docker ps --filter name=deepracer-analysis --format "{{.ID}}")
docker cp import_from_s3.py $ANALYSIS_ID:/workspace/
docker exec $ANALYSIS_ID jupytext --to notebook import_from_s3.py
docker cp Training_progress.py $ANALYSIS_ID:/workspace/
docker exec $ANALYSIS_ID jupytext --to notebook Training_progress.py
docker cp Quintiles.py $ANALYSIS_ID:/workspace/
docker exec $ANALYSIS_ID jupytext --to notebook Quintiles.py
docker cp Heatmap.py $ANALYSIS_ID:/workspace/
docker exec $ANALYSIS_ID jupytext --to notebook Heatmap.py
docker cp Data_tables.py $ANALYSIS_ID:/workspace/
docker exec $ANALYSIS_ID jupytext --to notebook Data_tables.py
docker cp Path_for_complete_laps.py $ANALYSIS_ID:/workspace/
docker exec $ANALYSIS_ID jupytext --to notebook Path_for_complete_laps.py
docker cp Training_and_Evaluation_Overview.py $ANALYSIS_ID:/workspace/
docker exec $ANALYSIS_ID jupytext --to notebook Training_and_Evaluation_Overview.py
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/import_from_s3.py:
content: |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.14.1
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# +
#Import block to bring in dependencies
import pandas as pd
import matplotlib.pyplot as plt
from pprint import pprint
from deepracer.tracks import TrackIO, Track
from deepracer.tracks.track_utils import track_breakdown, track_meta
from deepracer.logs import \
SimulationLogsIO as slio, \
NewRewardUtils as nr, \
AnalysisUtils as au, \
PlottingUtils as pu, \
ActionBreakdownUtils as abu, \
DeepRacerLog, \
S3FileHandler
# Ignore deprecation warnings we have no power over
import warnings
warnings.filterwarnings('ignore')
# -
# +
#Get logs from S3
fh = S3FileHandler(bucket="DEEPRACER_S3_URI",
prefix="DR_LOCAL_S3_MODEL_PREFIX")
#Attempt to load logs but catch error if training not yet far enough advanced
try:
log = DeepRacerLog(filehandler=fh)
log.load_training_trace()
df = log.dataframe()
simulation_agg = au.simulation_agg(df, secondgroup="unique_episode")
complete_ones = simulation_agg[simulation_agg['progress']==100]
%store df
%store simulation_agg
%store complete_ones
except Exception:
print("Logs are not yet available. It typically takes 25 minutes from the start of training for them to be available.")
tu = TrackIO()
try:
track: Track = tu.load_track("DEEPRACER_TRACK")
%store track
except Exception:
print("Track not currently included in the solution. Copy track into the tracks folder or check you're using the latest deepracer-analysis image.")
# -
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Training_progress.py:
content: |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.14.1
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# +
#Import block to bring in dependencies
import pandas as pd
import matplotlib.pyplot as plt
from pprint import pprint
from deepracer.tracks import TrackIO, Track
from deepracer.tracks.track_utils import track_breakdown, track_meta
from deepracer.logs import \
SimulationLogsIO as slio, \
NewRewardUtils as nr, \
AnalysisUtils as au, \
PlottingUtils as pu, \
ActionBreakdownUtils as abu, \
DeepRacerLog, \
S3FileHandler
%store -r simulation_agg
%store -r df
%store -r track
%store -r complete_ones
# Ignore deprecation warnings we have no power over
import warnings
warnings.filterwarnings('ignore')
# -
# ## Training Progress Graphs
# +
try:
au.analyze_training_progress(simulation_agg, title='Training progress')
au.scatter_aggregates(simulation_agg, 'Stats for all laps')
complete_ones = simulation_agg[simulation_agg['progress']==100]
if complete_ones.shape[0] > 0:
au.scatter_aggregates(complete_ones, 'Stats for complete laps')
else:
print('Stats for complete laps - No complete laps yet.')
try:
au.analyze_training_progress(complete_ones, title='Complete lap training progress')
except Exception:
print('Complete lap training progress - No complete laps yet.')
except Exception:
print("Logs are not yet available. It typically takes 25 minutes from the start of training for them to be available.")
# -
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Quintiles.py:
content: |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.14.1
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# +
#Import block to bring in dependencies
import pandas as pd
import matplotlib.pyplot as plt
from pprint import pprint
from deepracer.tracks import TrackIO, Track
from deepracer.tracks.track_utils import track_breakdown, track_meta
from deepracer.logs import \
SimulationLogsIO as slio, \
NewRewardUtils as nr, \
AnalysisUtils as au, \
PlottingUtils as pu, \
ActionBreakdownUtils as abu, \
DeepRacerLog, \
S3FileHandler
%store -r simulation_agg
%store -r df
%store -r track
%store -r complete_ones
# Ignore deprecation warnings we have no power over
import warnings
warnings.filterwarnings('ignore')
# -
# ## Training Progress Graphs
# +
try:
complete_ones = simulation_agg[simulation_agg['progress']==100]
au.scatter_by_groups(simulation_agg, title='Quintiles')
au.scatter_by_groups(complete_ones, title='Complete Lap Quintiles')
except Exception:
print("Logs are not yet available. It typically takes 25 minutes from the start of training for them to be available.")
# -
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Heatmap.py:
content: |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.14.1
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# +
#Import block to bring in dependencies
import pandas as pd
import matplotlib.pyplot as plt
from pprint import pprint
from deepracer.tracks import TrackIO, Track
from deepracer.tracks.track_utils import track_breakdown, track_meta
from deepracer.logs import \
SimulationLogsIO as slio, \
NewRewardUtils as nr, \
AnalysisUtils as au, \
PlottingUtils as pu, \
ActionBreakdownUtils as abu, \
DeepRacerLog, \
S3FileHandler
%store -r simulation_agg
%store -r df
%store -r track
%store -r complete_ones
# Ignore deprecation warnings we have no power over
import warnings
warnings.filterwarnings('ignore')
# -
# ## Training Heatmap
# +
try:
pu.plot_track(df, track)
except Exception:
print("Logs are not yet available. It typically takes 25 minutes from the start of training for them to be available.")
# -
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Data_tables.py:
content: |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.14.1
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# +
#Import block to bring in dependencies
import pandas as pd
import matplotlib.pyplot as plt
from pprint import pprint
from deepracer.tracks import TrackIO, Track
from deepracer.tracks.track_utils import track_breakdown, track_meta
from deepracer.logs import \
SimulationLogsIO as slio, \
NewRewardUtils as nr, \
AnalysisUtils as au, \
PlottingUtils as pu, \
ActionBreakdownUtils as abu, \
DeepRacerLog, \
S3FileHandler
%store -r simulation_agg
%store -r df
%store -r track
%store -r complete_ones
# Ignore deprecation warnings we have no power over
import warnings
warnings.filterwarnings('ignore')
# -
# ## Data in tables
# ## Ten best rewarded episodes in the training
# +
simulation_agg.nlargest(10, 'reward')
# -
# ## Ten fastest complete laps in the training
# +
complete_ones.nsmallest(10, 'time')
# -
# ## Ten fastest incomplete laps in the training
# +
simulation_agg.nsmallest(10, 'time_if_complete')
# -
# ## Ten best rewarded complete laps in the training
# +
complete_ones.nlargest(10, 'reward')
# -
# ## Ten most progressed episodes in the training
# +
simulation_agg.nlargest(10, 'progress')
# -
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Path_for_complete_laps.py:
content: |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.14.1
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# +
#Import block to bring in dependencies
import pandas as pd
import matplotlib.pyplot as plt
from pprint import pprint
from deepracer.tracks import TrackIO, Track
from deepracer.tracks.track_utils import track_breakdown, track_meta
from deepracer.logs import \
SimulationLogsIO as slio, \
NewRewardUtils as nr, \
AnalysisUtils as au, \
PlottingUtils as pu, \
ActionBreakdownUtils as abu, \
DeepRacerLog, \
S3FileHandler
%store -r simulation_agg
%store -r df
%store -r track
%store -r complete_ones
# Ignore deprecation warnings we have no power over
import warnings
warnings.filterwarnings('ignore')
print("Number of completed laps so far:", len(complete_ones))
# -
# ## Path taken for quickest complete laps
# +
episodes_to_plot = complete_ones.nsmallest(5, 'time')
pu.plot_selected_laps(episodes_to_plot, df, track, section_to_plot="unique_episode")
# -
# ## Path taken for highest rewarded complete laps
# +
episodes_to_plot = complete_ones.nlargest(5,'reward')
pu.plot_selected_laps(episodes_to_plot, df, track, section_to_plot="unique_episode")
# -
mode : "000755"
owner: ubuntu
group: ubuntu
/home/ubuntu/deepracer-for-cloud/Training_and_Evaluation_Overview.py:
content: |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.14.1
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# +
#Import block to bring in dependencies
from deepracer.logs import metrics
import matplotlib.pyplot as plt
import numpy as np
# Ignore deprecation warnings we have no power over
import warnings
warnings.filterwarnings('ignore')
# -
# ## Completion of Training and Evaluation per Iteration
# +
try:
tm = metrics.TrainingMetrics("DEEPRACER_S3_URI")
tm.addRound("DR_LOCAL_S3_MODEL_PREFIX", training_round=1, workers=DEEPRACER_WORKERS)
results = tm.plotProgress(method=['median','mean'], rolling_average=1, figsize=(20,5))
except Exception:
print("Logs are not yet available. It typically takes 25 minutes from the start of training for them to be available.")
# -
# ## Best Lap Progression - is your model getting faster?
# +
try:
train=tm.getTraining()
ev=tm.getEvaluation()
train_complete_lr = train[(train['round']>(0)) & (train['complete']==1)]
eval_complete_lr = ev[(ev['round']>(0)) & (ev['complete']==1)]
plt.figure(figsize=(15,5))
plt.title('Best lap progression')
plt.scatter(train_complete_lr['master_iteration'],train_complete_lr['time'],alpha=0.4)
plt.scatter(eval_complete_lr['master_iteration'],eval_complete_lr['time'],alpha=0.4)
plt.show()
except Exception:
print("Logs are not yet available. It typically takes 25 minutes from the start of training for them to be available.")
# -
mode : "000755"
owner: ubuntu
group: ubuntu
Properties:
AvailabilityZones: !GetAZs
LaunchTemplate:
LaunchTemplateId: !Ref LaunchTemplate
Version: !GetAtt LaunchTemplate.LatestVersionNumber
MaxSize: '1'
MinSize: '0'
DesiredCapacity: '1'
SpotInterruptionHandlerFunction:
Type: AWS::Lambda::Function
DependsOn:
- LambdaFunctionRole
Properties:
Handler: index.handler
Role: !GetAtt LambdaFunctionRole.Arn
Code:
ZipFile: !Sub |
import boto3
from botocore.exceptions import ClientError
import os
def handler(event, context):
asg_created_by_this_stack = '${ASG}'
asg_client = boto3.client('autoscaling')
asg_info = asg_client.describe_auto_scaling_groups(
AutoScalingGroupNames=[
'${ASG}',
],
)
instance_in_the_asg = asg_info['AutoScalingGroups'][0]['Instances'][0]['InstanceId']
instance_id = event['detail']['instance-id']