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prepare for multi node cluster

This commit is contained in:
Kerem Kayabay 2024-01-05 16:08:04 +01:00
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74
README.md
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@ -7,7 +7,7 @@ This guide shows you how to launch a Ray cluster on HLRS' Hawk system.
- [Table of Contents](#table-of-contents)
- [Prerequisites](#prerequisites)
- [Getting Started](#getting-started)
- [Launch a Ray Cluster in Interactive Mode](#launch-a-ray-cluster-in-interactive-mode)
- [Launch a local Ray Cluster in Interactive Mode](#launch-a-local-ray-cluster-in-interactive-mode)
- [Launch a Ray Cluster in Batch Mode](#launch-a-ray-cluster-in-batch-mode)
## Prerequisites
@ -23,13 +23,13 @@ For more information, look at the documentation for [Conda on HLRS HPC systems](
Only the main and r channels are available using the conda module on the clusters. To use custom packages, we need to move the local conda environment to Hawk.
1. Clone this repository to your local machine:
**Step 1.** Clone this repository to your local machine:
```bash
git clone <repository_url>
```
2. Go into the directory and create an environment using Conda and environment.yaml.
**Step 2.** Go into the directory and create an environment using Conda and environment.yaml.
Note: Be sure to add the necessary packages in `deployment_scripts/environment.yaml`:
@ -38,7 +38,7 @@ cd deployment_scripts
./create-env.sh <your-env>
```
3. Package the environment and transfer the archive to the target system:
**Step 3.** Package the environment and transfer the archive to the target system:
```bash
(base) $ conda pack -n <your-env> -o ray_env.tar.gz # conda-pack must be installed in the base environment
@ -58,49 +58,76 @@ scp ray_env.tar.gz <username>@hawk.hww.hlrs.de:<workspace_directory>
rm ray_env.tar.gz # We don't need the archive locally anymore.
```
4. Clone the repository on Hawk to use the deployment scripts and project structure:
**Step 4.** Clone the repository on Hawk to use the deployment scripts and project structure:
```bash
cd <workspace_directory>
git clone <repository_url>
```
## Launch a Ray Cluster in Interactive Mode
## Launch a local Ray Cluster in Interactive Mode
Using a single node interactively provides opportunities for faster code debugging.
1. On the Hawk login node, start an interactive job using:
**Step 1.** On the Hawk login node, start an interactive job using:
```bash
qsub -I -l select=1:node_type=rome -l walltime=01:00:00
```
2. Go into the directory with all code:
**Step 2.** Go into the project directory:
```bash
cd <project_directory>/deployment_scripts
```
3. Deploy the conda environment to the ram disk:
**Step 3.** Deploy the conda environment to the ram disk:
Change the following line by editing `deploy-env.sh`:
```bash
export WS_DIR=<workspace_dir>
```
Then, use the following command to deploy and activate the environment:
```bash
source deploy-env.sh
```
Note: Make sure all permissions are set using `chmod +x`.
4. Initialize the Ray cluster.
**Step 4.** Initialize the Ray cluster.
You can use a Python interpreter to start a Ray cluster:
You can use a Python interpreter to start a local Ray cluster:
```python
import ray
ray.init(dashboard_host='127.0.0.1')
ray.init()
```
1. Connect to the dashboard.
**Step 5.** Connect to the dashboard.
Warning: Always use `127.0.0.1` as the dashboard host to make the Ray cluster reachable by only you.
Warning: Do not change the default dashboard host `127.0.0.1` to keep Ray cluster reachable by only you.
Note: We recommend using a dedicated Firefox profile for accessing web-based services on HLRS Compute Platforms. If you haven't created a profile, check out our [guide](https://kb.hlrs.de/platforms/index.php/How_to_use_Web_Based_Services_on_HLRS_Compute_Platforms).
You need the job id and the hostname for your current job. You can obtain this information on the login node using:
```bash
qstat -anw # get the job id and the hostname
```
Then, on your local computer,
```bash
export PBS_JOBID=<job-id> # e.g., 2316419.hawk-pbs5
ssh <compute-host> # e.g., r38c3t8n3
```
Check your SSH config in the first step if this doesn't work.
Then, launch Firefox web browser using the configured profile. Open `localhost:8265` to access the Ray dashboard.
## Launch a Ray Cluster in Batch Mode
@ -108,11 +135,24 @@ Warning: Always use `127.0.0.1` as the dashboard host to make the Ray cluster re
```bash
cd deployment_scripts
chmod +x ray-start-worker.sh
chmod +x start-ray-worker.sh
```
2. Submit a job to launch the head and worker nodes.
You must modify the following variables in `submit-ray-job.sh`:
You must modify the following lines in `submit-ray-job.sh`:
- Line 3 changes the cluster size. The default configuration launches a 3 node cluster.
- `$PROJECT_DIR`
- `export WS_DIR=<workspace_dir>` - set the correct workspace directory.
- `export PROJECT_DIR=$WS_DIR/<project_name>` - set the correct project directory.
Note: The job script `src/monte-carlo-pi.py` waits for all nodes in the Ray cluster to become available. Preserve this pattern in your Python code while using a multiple node Ray cluster.
Launch the job and monitor the progress. As the job starts, its status (S) shifts from Q (Queued) to R (Running). Upon completion, the job will no longer appear in the `qstat -a` display.
```bash
qsub submit-ray-job.pbs
qstat -anw # Q: Queued, R: Running, E: Ending
ls -l # list files after the job finishes
cat ray-job.o... # inspect the output file
cat ray-job.e... # inspect the error file
```

8
deployment_scripts/start-ray-worker.sh
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@ -11,11 +11,7 @@ export RAY_ADDRESS=$3
export REDIS_PASSWORD=$4
export OBJECT_STORE_MEMORY=$5
# printenv | grep 'RAY_ADDRESS\|REDIS_PASSWORD'
# module load system/nvidia/ALL.ALL.525.125.06
export ENV_PATH=/run/user/$PBS_JOBID/ray_env
export ENV_PATH=/run/user/$PBS_JOBID/ray_env # We use the ram disk to extract the environment packages since a large number of files decreases the performance of the parallel file system.
mkdir -p $ENV_PATH
tar -xzf $WS_DIR/$ENV_ARCHIVE -C $ENV_PATH
@ -27,4 +23,4 @@ ray start --address=$RAY_ADDRESS \
--object-store-memory=$OBJECT_STORE_MEMORY \
--block
rm -rf $ENV_PATH
rm -rf $ENV_PATH # It's nice to clean up before you terminate the job

50
deployment_scripts/submit-ray-job.pbs Normal file
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@ -0,0 +1,50 @@
#!/bin/bash
#PBS -N ray-job
#PBS -l select=2:node_type=rome-ai
#PBS -l walltime=1:00:00
export WS_DIR=<workspace_dir>
export PROJECT_DIR=$WS_DIR/<project_name>
export JOB_SCRIPT=monte-carlo-pi.py
export ENV_ARCHIVE=ray_env.tar.gz
export OBJECT_STORE_MEMORY=128000000000
# Environment variables after this line should not change
export SRC_DIR=$PROJECT_DIR/src
export PYTHON_FILE=$SRC_DIR/$JOB_SCRIPT
export DEPLOYMENT_SCRIPTS=$PROJECT_DIR/deployment_scripts
export ENV_PATH=/run/user/$PBS_JOBID/ray_env # We use the ram disk to extract the environment packages since a large number of files decreases the performance of the parallel file system.
mkdir -p $ENV_PATH
tar -xzf $WS_DIR/$ENV_ARCHIVE -C $ENV_PATH # This line extracts the packages to ram disk.
source $ENV_PATH/bin/activate
export IP_ADDRESS=`ip addr show ib0 | grep -oP '(?<=inet\s)\d+(\.\d+){3}' | awk '{print $1}'`
export RAY_ADDRESS=$IP_ADDRESS:6379
export REDIS_PASSWORD=$(openssl rand -base64 32)
export NCCL_DEBUG=INFO
ray start --disable-usage-stats \
--head \
--node-ip-address=$IP_ADDRESS \
--port=6379 \
--dashboard-host=127.0.0.1 \
--redis-password=$REDIS_PASSWORD \
--object-store-memory=$OBJECT_STORE_MEMORY
export NUM_NODES=$(sort $PBS_NODEFILE |uniq | wc -l)
for ((i=1;i<$NUM_NODES;i++)); do
pbsdsh -n $i -- bash -l -c "'$DEPLOYMENT_SCRIPTS/ray-start-worker.sh' '$WS_DIR' '$ENV_ARCHIVE' '$RAY_ADDRESS' '$REDIS_PASSWORD' '$OBJECT_STORE_MEMORY'" &
done
python3 $PYTHON_FILE
ray stop --grace-period 30
rm -rf $ENV_PATH # It's nice to clean up before you terminate the job.

59
deployment_scripts/submit-ray-job.sh
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@ -1,59 +0,0 @@
#!/bin/bash
#PBS -N output-ray-job
#PBS -l select=2:node_type=rome-ai
#PBS -l walltime=1:00:00
export JOB_SCRIPT=modeling_evaluation.py
export WS_DIR=/lustre/hpe/ws10/ws10.3/ws/hpckkaya-ifu
export ENV_ARCHIVE=ray-environment-v0.3.tar.gz
export SRC_DIR=$WS_DIR/ifu/src/ray-workflow
export DATA_DIR=/lustre/hpe/ws10/ws10.3/ws/hpckkaya-ifu-data/hpclzhon-ifu_data-1668830707
export RESULTS_DIR=$WS_DIR/ray_results
export NCCL_DEBUG=INFO
# Environment variables after this line should not change
export PYTHON_FILE=$SRC_DIR/$JOB_SCRIPT
export ENV_PATH=/run/user/$PBS_JOBID/ray_env
mkdir -p $ENV_PATH
tar -xzf $WS_DIR/$ENV_ARCHIVE -C $ENV_PATH
source $ENV_PATH/bin/activate
conda-unpack
export IP_ADDRESS=`ip addr show ib0 | grep -oP '(?<=inet\s)\d+(\.\d+){3}' | awk '{print $1}'`
export RAY_ADDRESS=$IP_ADDRESS:6379
export REDIS_PASSWORD=$(uuidgen)
# export RAY_scheduler_spread_threshold=0.0
export OBJECT_STORE_MEMORY=128000000000
ray start --disable-usage-stats \
--head \
--node-ip-address=$IP_ADDRESS \
--port=6379 \
--dashboard-host=127.0.0.1 \
--redis-password=$REDIS_PASSWORD \
--object-store-memory=$OBJECT_STORE_MEMORY
export NUM_NODES=$(sort $PBS_NODEFILE |uniq | wc -l)
for ((i=1;i<$NUM_NODES;i++)); do
pbsdsh -n $i -- bash -l -c "'$SRC_DIR/ray-start-worker.sh' '$WS_DIR' '$ENV_ARCHIVE' '$RAY_ADDRESS' '$REDIS_PASSWORD' '$OBJECT_STORE_MEMORY'" &
done
# uncomment if you don't already control inside the code
# if [[ $NUM_NODES -gt 1 ]]
# then
# sleep 90
#fi
python3 $PYTHON_FILE
ray stop
rm -rf $ENV_PATH

71
src/monte-carlo-pi.py
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@ -6,12 +6,11 @@ import time
import random
import os
ray.init(address="auto", _node_ip_address=os.environ["IP_ADDRESS"], _redis_password=os.environ["REDIS_PASSWORD"])
cluster_resources = ray.available_resources()
available_cpu_cores = cluster_resources.get('CPU', 0)
print(cluster_resources)
# Change this to match your cluster scale.
NUM_SAMPLING_TASKS = 100
NUM_SAMPLES_PER_TASK = 10_000_000
TOTAL_NUM_SAMPLES = NUM_SAMPLING_TASKS * NUM_SAMPLES_PER_TASK
@ray.remote
class ProgressActor:
def __init__(self, total_num_samples: int):
@ -44,31 +43,47 @@ def sampling_task(num_samples: int, task_id: int,
progress_actor.report_progress.remote(task_id, num_samples)
return num_inside
# Change this to match your cluster scale.
NUM_SAMPLING_TASKS = 100
NUM_SAMPLES_PER_TASK = 10_000_000
TOTAL_NUM_SAMPLES = NUM_SAMPLING_TASKS * NUM_SAMPLES_PER_TASK
def wait_for_nodes(expected_num_nodes: int):
while True:
num_nodes = len(ray.nodes())
if num_nodes >= expected_num_nodes:
break
print(f'Currently {num_nodes} nodes connected. Waiting for more...')
time.sleep(5) # wait for 5 seconds before checking again
if __name__ == "__main__":
# Create the progress actor.
progress_actor = ProgressActor.remote(TOTAL_NUM_SAMPLES)
num_nodes = int(os.environ["NUM_NODES"])
assert num_nodes > 1, "If the environment variable NUM_NODES is set, it should be greater than 1."
# Create and execute all sampling tasks in parallel.
results = [
sampling_task.remote(NUM_SAMPLES_PER_TASK, i, progress_actor)
for i in range(NUM_SAMPLING_TASKS)
]
redis_password = os.environ["REDIS_PASSWORD"]
ray.init(address="auto", _redis_password=redis_password)
# Query progress periodically.
while True:
progress = ray.get(progress_actor.get_progress.remote())
print(f"Progress: {int(progress * 100)}%")
wait_for_nodes(num_nodes)
if progress == 1:
break
cluster_resources = ray.available_resources()
print(cluster_resources)
time.sleep(1)
# Create the progress actor.
progress_actor = ProgressActor.remote(TOTAL_NUM_SAMPLES)
# Get all the sampling tasks results.
total_num_inside = sum(ray.get(results))
pi = (total_num_inside * 4) / TOTAL_NUM_SAMPLES
print(f"Estimated value of π is: {pi}")
# Create and execute all sampling tasks in parallel.
results = [
sampling_task.remote(NUM_SAMPLES_PER_TASK, i, progress_actor)
for i in range(NUM_SAMPLING_TASKS)
]
# Query progress periodically.
while True:
progress = ray.get(progress_actor.get_progress.remote())
print(f"Progress: {int(progress * 100)}%")
if progress == 1:
break
time.sleep(1)
# Get all the sampling tasks results.
total_num_inside = sum(ray.get(results))
pi = (total_num_inside * 4) / TOTAL_NUM_SAMPLES
print(f"Estimated value of π is: {pi}")