Distributed Neural Networks for Spark. Details are available in the paper. Ask questions on the sparknet-users mailing list!
Start a Spark cluster using our AMI
-
Create an AWS secret key and access key. Instructions here.
-
Run
export AWS_SECRET_ACCESS_KEY=andexport AWS_ACCESS_KEY_ID=with the relevant values. -
Clone our repository locally.
-
Start a 5-worker Spark cluster on EC2 by running
SparkNet/ec2/spark-ec2 --key-pair=key \ --identity-file=key.pem \ --region=eu-west-1 \ --zone=eu-west-1c \ --instance-type=g2.8xlarge \ --ami=ami-d0833da3 \ --copy-aws-credentials \ --spark-version=1.5.0 \ --spot-price=1.5 \ --no-ganglia \ --user-data SparkNet/ec2/cloud-config.txt \ --slaves=5 \ launch sparknet
You will probably have to change several fields in this command.
For example, the flags --key-pair and --identity-file specify the key pair you will use to connect to the cluster.
The flag --slaves specifies the number of Spark workers.
Train Cifar using SparkNet
-
SSH to the Spark master as
root. -
Run
bash /root/SparkNet/data/cifar10/get_cifar10.shto get the Cifar data -
Train Cifar on 5 workers using
/root/spark/bin/spark-submit --class apps.CifarApp /root/SparkNet/target/scala-2.10/sparknet-assembly-0.1-SNAPSHOT.jar 5 -
That's all! Information is logged on the master in
/root/SparkNet/training_log*.txt.
Train ImageNet using SparkNet
-
Obtain the ImageNet data by following the instructions here with
wget http://.../ILSVRC2012_img_train.tar wget http://.../ILSVRC2012_img_val.tarThis involves creating an account and submitting a request.
-
On the Spark master, create
~/.aws/credentialswith the following content:[default] aws_access_key_id= aws_secret_access_key=and fill in the two fields.
-
Copy this to the workers with
~/spark-ec2/copy-dir ~/.aws(copy this command exactly because it is somewhat sensitive to the trailing backslashes and that kind of thing). -
Create an Amazon S3 bucket with name
S3_BUCKET. -
Upload the ImageNet data in the appropriate format to S3 with the command
python $SPARKNET_HOME/scripts/put_imagenet_on_s3.py $S3_BUCKET \ --train_tar_file=/path/to/ILSVRC2012_img_train.tar \ --val_tar_file=/path/to/ILSVRC2012_img_val.tar \ --new_width=256 \ --new_height=256This command resizes the images to 256x256, shuffles the training data, and tars the validation files into chunks.
-
Train ImageNet on 5 workers using
/root/spark/bin/spark-submit --class apps.ImageNetApp /root/SparkNet/target/scala-2.10/sparknet-assembly-0.1-SNAPSHOT.jar 5 $S3_BUCKET
The specific instructions might depend on your cluster configurations, if you run into problems, make sure to share your experience on the mailing list.
-
If you are going to use GPUs, make sure that CUDA-7.0 is installed on all the nodes.
-
Depending on your configuration, you might have to add the following to your
~/.bashrc, and runsource ~/.bashrc.export LD_LIBRARY_PATH=/usr/local/cuda-7.0/targets/x86_64-linux/lib/ export _JAVA_OPTIONS=-Xmx8g export SPARKNET_HOME=/root/SparkNet/Keep in mind to substitute in the right directories (the first one should contain the file
libcudart.so.7.0). -
Clone the SparkNet repository
git clone https://github.com/amplab/SparkNet.gitin your home directory. -
Copy the SparkNet directory on all the nodes using
~/spark-ec2/copy-dir ~/SparkNet -
Build SparkNet with
cd ~/SparkNet git pull sbt assembly -
Now you can for example run the CIFAR App as shown above.
-
Start an EC2 instance with Ubuntu 14.04 and a GPU instance type (e.g., g2.8xlarge). Suppose it has IP address xxx.xx.xx.xxx.
-
Connect to the node as
ubuntu:ssh -i ~/.ssh/key.pem [email protected] -
Install an editor
sudo apt-get update sudo apt-get install emacs -
Open the file
sudo emacs /root/.ssh/authorized_keysand delete everything before
ssh-rsa ...so that you can connect to the node asroot. -
Close the connection with
exit. -
Connect to the node as
root:ssh -i ~/.ssh/key.pem [email protected] -
Install CUDA-7.0.
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_7.0-28_amd64.deb dpkg -i cuda-repo-ubuntu1404_7.0-28_amd64.deb apt-get update apt-get upgrade -y apt-get install -y linux-image-extra-`uname -r` linux-headers-`uname -r` linux-image-`uname -r` apt-get install cuda-7-0 -y -
Install sbt. Instructions here.
-
apt-get update -
apt-get install awscli s3cmd -
Install Java
apt-get install openjdk-7-jdk. -
Clone the SparkNet repository
git clone https://github.com/amplab/SparkNet.gitin your home directory. -
Add the following to your
~/.bashrc, and runsource ~/.bashrc.export LD_LIBRARY_PATH=/usr/local/cuda-7.0/targets/x86_64-linux/lib/ export _JAVA_OPTIONS=-Xmx8g export SPARKNET_HOME=/root/SparkNet/Some of these paths may need to be adapted, but the
LD_LIBRARY_PATHdirectory should containlibcudart.so.7.0(this file can be found withlocate libcudart.so.7.0after runningupdatedb). -
Build SparkNet with
cd ~/SparkNet git pull sbt assembly -
Create the file
~/.bash_profileand add the following:if [ "$BASH" ]; then if [ -f ~/.bashrc ]; then . ~/.bashrc fi fi export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64Spark expects
JAVA_HOMEto be set in your~/.bash_profileand the launch scriptSparkNet/ec2/spark-ec2will give an error if it isn't there. -
Clear your bash history
cat /dev/null > ~/.bash_history && history -c && exit. -
Now you can create an image of your instance, and you're all set! This is the procedure that we used to create our AMI.
We have built the JavaCPP binaries for a couple platforms. They are stored at the following locations:
- Ubuntu with GPUs: http://www.eecs.berkeley.edu/~rkn/snapshot-2016-03-05/
- Ubuntu with CPUs: http://www.eecs.berkeley.edu/~rkn/snapshot-2016-03-16-CPU/
- CentOS 6 with CPUs: http://www.eecs.berkeley.edu/~rkn/snapshot-2016-03-23-CENTOS6-CPU/
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent