/23SIGCOMM-raysort

Primary LanguagePython

RaySort

Development Setup

If you created your VM using the raysort AMI, you should already have a Conda environment ready. Otherwise, install Miniconda3 and run conda create -n raysort python=3.9.13. Then run:

conda activate raysort
pip install -Ur requirements/dev.txt
pip install -Ur requirements/worker.txt
pip install -e .
pushd raysort/sortlib && python setup.py build_ext --inplace && popd
scripts/installers/install_binaries.sh

Create an S3 Bucket (Optional)

You can use an existing S3 bucket or create one using the provided terraform script as follows.

  1. Install Terraform: scripts/installers/install_terraform.sh
  2. Navigate to scripts/config/terraform/aws-s3-template
  3. Run terraform init
  4. Run terraform apply -var="bucket_count=1" -var="bucket_prefix=YOUR_PREFIX_HERE". Replace YOUR_PREFIX_HERE with a bucket name prefix.
  5. Your bucket should be created with name similar to YOUR_PREFIX_HERE-000.

Edit .envrc and change USERNAME and S3_BUCKET to your own. Set up direnv so that the .envrc files are sourced automatically when you cd into a directory. Otherwise, manually source .envrc.

Running Locally

A good first step to sanity check your setup is to run raysort on a single node with:

CONFIG=LocalNative python raysort/main.py

It should complete without errors with an All OK! message. The detailed configuration is in config.py.

Starting up a Cluster

  1. Install Terraform: scripts/installers/install_terraform.sh
  2. Run export CONFIG=1tb-1gb-s3-native-s3 && python scripts/cls.py up --ray to launch a Ray cluster, or --yarn to launch a YARN cluster for Spark
  3. Run a test run on the cluster: python raysort/main.py 2>&1 | tee main.log

The 1tb-1gb-s3-native-s3 config launches 10 r6i.2xlarge nodes, and runs a 1TB sort with 1GB partitions using S3 for I/O and for shuffle spilling.

Cluster Management

scripts/cls.py is the centralized place for cluster management code.

  • python scripts/cls.py up launches a cluster via Terraform and configures it via Ansible. Add --ray or --yarn to start a Ray or a YARN cluster.
  • python scripts/cls.py setup skips Terraform and only runs Ansible for software setup. Add --ray or --yarn to start a Ray or a YARN cluster.
  • python scripts/cls.py down terminates the cluster via Terraform. Tip: when you're done for the day, run python scripts/cls.py down && sudo shutdown -h now to terminate the cluster and stop your head node.
  • python scripts/cls.py start/stop/reboot calls the AWS CLI tool to start/stop/reboot all your machines in the cluster. Useful when you want to stop the cluster but not terminate the machines.

Autoscaler Management

While scripts/cls.py uses Terraform to manage the cluster, scripts/autoscaler.py uses the Ray autoscaler to manage the cluster.

  • python scripts/autoscaler.py up -y launches a cluster via the Ray autoscaler.
  • python scripts/autoscaler.py submit scripts/main.py submits a job to be executed by the Ray autoscaler that has been launched.
  • python scripts/autoscaler.py down -y terminates the cluster.

Misc

Configuring Ray

  • All of Ray's system configuration parameters can be found in ray_config_defs.h.
  • You only need to specify the config on the head node. All worker nodes will use the same config.
  • There are two ways to specify a configuration value. Suppose you want to set min_spilling_size to 0, then:
    1. You can set it in Python, where you do ray.init(..., _system_config={"min_spilling_size": 0, ...})
    2. You can set it in the environment variable by running export RAY_min_spilling_size=0 before running your ray start command or your Python program that calls ray.init(). This is preferred as our experiment tracker will automatically pick up these environment variables and log them in the W&B trials. Again, it suffices to only set this environment variable on the head node.

Useful Ray environment variables:

# Enable debug logging for all raylets and workers
export RAY_BACKEND_LOG_LEVEL=debug

Mounting a new volume

  • Create a new volume in the same region as your machine on the AWS Dashboard.
  • Attach it to your machine and format it as follows:
    • Create a partition by running sudo parted /dev/nvme1n1, where nvme1n1 is the device name which you can find with lsblk.
    • If a partition table does not exist, create it with mklabel gpt.
    • Run mkpart part0 ext4 0% 100%. Make sure no warnings appear.
    • Exit parted and run sudo mkfs.ext4 /dev/nvme1n1p1. Note the extra p1.
    • Run sudo mount -o sync path_to_volume /mnt/data0. Only use -o sync if you are running microbenchmarks.
  • Verify that the mounting worked with lsblk.
    • If the desired volume is not mounted, edit /etc/fstab to remove any conflicting lines. Then, restart your machine and remount.

Setting up Grafana

Troubleshooting

Package is missing

Make sure the Conda environment is running. Install missing packages with:

pip install package_name

Missing AWS credentials

Install AWS's CLI and set credentials with:

pip install awscli
aws configure

Cluster missing packages/version mismatch

Verify that the image the nodes are being created from matches expectations. This image raysort-worker-20230111 is currently being used.

Cannot connect to worker node

First, try manually connect: ssh -i ~/.aws/login-us-west-2.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null <worker_ip>. If this doesn't work, it's likely because your current VM is not in the same security group as the worker nodes (which are in the default security group). The easiest solution is to find your instance on the AWS EC2 UI, right click "Security -> Change Security Groups", and add your instance to the default security group. TODO: this might be possible to automate in Terraform.

FIO test

Code for testing disk bandwidth.

sudo fio --directory=. --ioengine=psync --name fio_test_file --direct=1 --rw=write --bs=1m --size=1g --numjobs=8
sudo fio --directory=. --ioengine=psync --name fio_test_file --direct=1 --rw=read --bs=1m --size=1g --numjobs=8