From 1f7412642ec43cf4699a531be5c7b96781aa59d5 Mon Sep 17 00:00:00 2001
From: sfloresk <sfkanter@amazon.com>
Date: Tue, 12 Dec 2023 07:51:39 -0700
Subject: [PATCH 01/17] Add distributed-ml-training blueprint

---
 .../distributed-ml-training/README.md         |  29 +
 .../distributed-ml-training/main.tf           | 501 ++++++++++++++++++
 .../distributed-ml-training/outputs.tf        |  42 ++
 .../distributed-ml-training/variables.tf      |   0
 .../distributed-ml-training/versions.tf       |  10 +
 5 files changed, 582 insertions(+)
 create mode 100644 terraform/ec2-examples/distributed-ml-training/README.md
 create mode 100644 terraform/ec2-examples/distributed-ml-training/main.tf
 create mode 100644 terraform/ec2-examples/distributed-ml-training/outputs.tf
 create mode 100644 terraform/ec2-examples/distributed-ml-training/variables.tf
 create mode 100644 terraform/ec2-examples/distributed-ml-training/versions.tf

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
new file mode 100644
index 00000000..a98de945
--- /dev/null
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -0,0 +1,29 @@
+# ECS machine learning distributed training
+
+This solution blueprint creates the infrastructure to run distributed training jobs using a Ray cluster and Pytorch. The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances. 
+
+## Cost warning!
+
+By default, this blueprint uses g5.12xlarge (with 4 GPUs) to showcase multi-GPU and multi-node distributed training, but **can increase costs considerably over time**. You can modify this blueprint to use g5.xlarge (with one GPU) instead from the local variable **instance_type_workers** - if you change the instance type, you need to also modify the worker task definition to use 1 GPU instead of 4 (see **resource_requirements** and container command parameter **--num-gpus**)
+
+## Deployment
+
+```shell
+terraform init
+terraform plan
+terraform apply 
+```
+
+## Components
+
+* Service discovery using AWS Cloud Map: The head node is registerer to a private DNS using loca zones via cloud map. This allow workers to discover the head service and join the cluster
+* 2 autoscaling groups: One for the head instance and other for the worker instances
+* ECS service definition:
+    * Task security group, task role and task execution role and 
+    * Service discovery ARN is used in the service definition. ECS will automatically manage the registration and deregistration of tasks to this service discovery registry.
+    * Tasks for this service will be deployed in private subnet
+    * Task definitions with GPU resource requirements
+
+## Support
+
+Please open an issue for questions or unexpected behaviour
\ No newline at end of file
diff --git a/terraform/ec2-examples/distributed-ml-training/main.tf b/terraform/ec2-examples/distributed-ml-training/main.tf
new file mode 100644
index 00000000..7fcfee34
--- /dev/null
+++ b/terraform/ec2-examples/distributed-ml-training/main.tf
@@ -0,0 +1,501 @@
+provider "aws" {
+  region = local.region
+}
+
+data "aws_availability_zones" "available" {}
+data "aws_caller_identity" "current" {}
+
+locals {
+  name   = "ecs-demo-distributed-ml-training"
+  region = "us-east-1"
+
+  vpc_cidr      = "10.0.0.0/16"
+  azs           = slice(data.aws_availability_zones.available.names, 0, 1)
+  instance_type_workers = "g5.12xlarge"
+  instance_type_head = "m5.xlarge"
+  ray_head_container_image = "docker.io/rayproject/ray-ml:2.7.1.artur.c9f4c6-py38"
+  ray_worker_container_image = "docker.io/rayproject/ray-ml:2.7.1.artur.c9f4c6-py38-gpu"
+
+  user_data_head = <<-EOT
+    #!/bin/bash
+    cat <<'EOF' >> /etc/ecs/ecs.config
+    ECS_CLUSTER=${local.name}
+    EOF
+  EOT
+
+  user_data_workers = <<-EOT
+    #!/bin/bash
+    cat <<'EOF' >> /etc/ecs/ecs.config
+    ECS_CLUSTER=${local.name}
+    EOF
+    echo "ip_resolve=4" >> /etc/yum.conf 
+  EOT
+
+  tags = {
+    Blueprint  = local.name
+    GithubRepo = "github.com/aws-ia/ecs-blueprints"
+  }
+}
+
+################################################################################
+# ECS Blueprint
+################################################################################
+
+module "ecs_cluster" {
+  source  = "terraform-aws-modules/ecs/aws//modules/cluster"
+  version = "~> 5.0"
+
+  cluster_name = local.name
+  # Capacity provider - autoscaling groups
+  default_capacity_provider_use_fargate = false
+  autoscaling_capacity_providers = {
+    distributed_ml_training_head = {
+      auto_scaling_group_arn         = module.autoscaling_head.autoscaling_group_arn
+
+      managed_scaling = {
+        maximum_scaling_step_size = 1
+        minimum_scaling_step_size = 1
+        status                    = "ENABLED"
+        target_capacity           = 60
+      }
+
+      default_capacity_provider_strategy = {
+        weight = 1
+        base   = 1
+      }
+    },
+    distributed_ml_training_workers = {
+      auto_scaling_group_arn         = module.autoscaling_workers.autoscaling_group_arn
+      managed_scaling = {
+        maximum_scaling_step_size = 1
+        minimum_scaling_step_size = 1
+        status                    = "ENABLED"
+        target_capacity           = 60
+      }
+    },
+  }
+
+  # Shared task execution role
+  create_task_exec_iam_role = false
+  tags = local.tags
+}
+
+resource "aws_service_discovery_private_dns_namespace" "this" {
+  name        = "default.${local.name}.local"
+  description = "Service discovery namespace.clustername.local"
+  vpc         = module.vpc.vpc_id
+  tags = local.tags
+}
+
+resource "aws_service_discovery_service" "this" {
+  name        = "head"
+  dns_config {
+    namespace_id = aws_service_discovery_private_dns_namespace.this.id
+    dns_records {
+      ttl  = 300
+      type = "A"
+    }
+    routing_policy = "MULTIVALUE"
+  }
+}
+
+################################################################################
+# Supporting Resources
+################################################################################
+
+module "vpc" {
+  source  = "terraform-aws-modules/vpc/aws"
+  version = "~> 5.2.0"
+
+  name = local.name
+  cidr = local.vpc_cidr
+
+  azs             = local.azs
+  private_subnets = [for k, v in local.azs : cidrsubnet(local.vpc_cidr, 4, k)]
+  public_subnets  = [for k, v in local.azs : cidrsubnet(local.vpc_cidr, 8, k + 48)]
+
+  enable_nat_gateway      = true
+  single_nat_gateway      = true
+  enable_dns_hostnames    = true
+  map_public_ip_on_launch = false
+
+  # Manage so we can name
+  manage_default_network_acl    = true
+  default_network_acl_tags      = { Name = "${local.name}-default" }
+  manage_default_route_table    = true
+  default_route_table_tags      = { Name = "${local.name}-default" }
+  manage_default_security_group = true
+  default_security_group_tags   = { Name = "${local.name}-default" }
+
+  tags = local.tags
+}
+
+# https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-optimized_AMI.html#ecs-optimized-ami-linux
+data "aws_ssm_parameter" "ecs_optimized_ami" {
+  name = "/aws/service/ecs/optimized-ami/amazon-linux-2/recommended"
+}
+
+data "aws_ssm_parameter" "ecs_gpu_optimized_ami" {
+  name = "/aws/service/ecs/optimized-ami/amazon-linux-2/gpu/recommended"
+}
+
+module "autoscaling_head" {
+  source  = "terraform-aws-modules/autoscaling/aws"
+  version = "~> 6.5"
+
+  name = "${local.name}-head"
+
+  image_id      = jsondecode(data.aws_ssm_parameter.ecs_optimized_ami.value)["image_id"]
+  instance_type = local.instance_type_head
+
+  security_groups                 = [module.autoscaling_sg.security_group_id]
+  user_data                       = base64encode(local.user_data_head)
+  ignore_desired_capacity_changes = true
+
+  create_iam_instance_profile = true
+  iam_role_name               = local.name
+  iam_role_description        = "ECS role for ${local.name}"
+  iam_role_policies = {
+    AmazonEC2ContainerServiceforEC2Role = "arn:aws:iam::aws:policy/service-role/AmazonEC2ContainerServiceforEC2Role"
+    AmazonSSMManagedEC2InstanceDefaultPolicy = "arn:aws:iam::aws:policy/AmazonSSMManagedEC2InstanceDefaultPolicy"
+    AmazonElasticFileSystemClientFullAccess = "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
+  }
+
+  vpc_zone_identifier = module.vpc.private_subnets
+  health_check_type   = "EC2"
+  min_size            = 1
+  max_size            = 1
+  desired_capacity    = 1
+
+  # https://github.com/hashicorp/terraform-provider-aws/issues/12582
+  autoscaling_group_tags = {
+    AmazonECSManaged = true
+  }
+
+  tags = local.tags
+}
+
+module "autoscaling_workers" {
+  source  = "terraform-aws-modules/autoscaling/aws"
+  version = "~> 6.5"
+
+  name = "${local.name}-workers"
+
+  #image_id      = data.aws_ssm_parameter.ecs_bottlerocket_gpu_optimized_ami.value
+  image_id      = jsondecode(data.aws_ssm_parameter.ecs_gpu_optimized_ami.value)["image_id"]
+  instance_type = local.instance_type_workers
+
+  security_groups                 = [module.autoscaling_sg.security_group_id]
+  user_data                       = base64encode(local.user_data_workers)
+  ignore_desired_capacity_changes = true
+
+  create_iam_instance_profile = true
+  iam_role_name               = local.name
+  iam_role_description        = "ECS role for ${local.name}"
+  iam_role_policies = {
+    AmazonEC2ContainerServiceforEC2Role = "arn:aws:iam::aws:policy/service-role/AmazonEC2ContainerServiceforEC2Role",
+    AmazonSSMManagedEC2InstanceDefaultPolicy = "arn:aws:iam::aws:policy/AmazonSSMManagedEC2InstanceDefaultPolicy"
+  }
+
+  vpc_zone_identifier = module.vpc.private_subnets
+  health_check_type   = "EC2"
+  min_size            = 2
+  max_size            = 2
+  desired_capacity    = 2
+
+  # https://github.com/hashicorp/terraform-provider-aws/issues/12582
+  autoscaling_group_tags = {
+    AmazonECSManaged = true
+  }
+  block_device_mappings = [
+    {
+      # Root volume
+      device_name = "/dev/xvda"
+      no_device   = 0
+      ebs = {
+        delete_on_termination = true
+        encrypted             = false
+        volume_size           = 50
+        volume_type           = "gp2"
+      }
+    }
+  ]
+
+  tags = local.tags
+}
+
+module "autoscaling_sg" {
+  source  = "terraform-aws-modules/security-group/aws"
+  version = "~> 4.0"
+
+  name                = local.name
+  description         = "Autoscaling group security group"
+  vpc_id              = module.vpc.vpc_id
+  ingress_cidr_blocks = [module.vpc.vpc_cidr_block]
+
+  ingress_rules = ["all-all"]
+
+  egress_rules = ["all-all"]
+
+  tags = local.tags
+}
+
+
+module "ecs_service_head" {
+  source  = "terraform-aws-modules/ecs/aws//modules/service"
+  version = "~> 5.0"
+
+  name               = "distributed_ml_training_head_service"
+  desired_count      = 1
+  cluster_arn        = module.ecs_cluster.arn
+  enable_autoscaling = false
+  memory = 10240
+  cpu = 3072
+  # Task Definition
+  
+  requires_compatibilities = ["EC2"]
+  capacity_provider_strategy = {
+    default = {
+      capacity_provider = "distributed_ml_training_head" # needs to match name of capacity provider
+      weight            = 1
+      base              = 1
+    }
+  }
+  
+  task_exec_iam_role_arn = aws_iam_role.task_execution_role.arn
+  tasks_iam_role_name        = "dt-role-tasks"
+  tasks_iam_role_description = "Tasks IAM role for ${local.name}"
+  tasks_iam_role_policies = {
+    AmazonElasticFileSystemClientFullAccess = "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
+  }
+  create_task_exec_iam_role = false
+  enable_execute_command = false
+  deployment_minimum_healthy_percent = 0
+  container_definitions = {
+    
+    ray_head = {
+      image                    = local.ray_head_container_image
+      user = 1000
+      readonly_root_filesystem = false
+      cpu = 3072
+      memory = 10240
+      memory_reservation = 10240
+      command = ["/bin/bash","-lc","--","ulimit -n 65536; ray start --head --dashboard-host=0.0.0.0 --metrics-export-port=8080 --num-cpus=0 --memory=10737418240 --block"]
+      linux_parameters = {
+        sharedMemorySize = 20480
+      }
+      mount_points = [{
+          sourceVolume = "ray_results"
+          containerPath = "/home/ray/ray_results"
+          readOnly = false
+      }]
+    }
+  }
+  volume = {
+    "ray_results" ={
+      efs_volume_configuration = {
+        file_system_id = module.efs.id,
+        root_directory = "/"
+        transit_encryption= "ENABLED",
+        authorization_config = {
+            access_point_id = module.efs.access_points.ray_results.id
+            iam="ENABLED"
+        }
+      }
+    }
+  }
+
+  service_registries = {
+    registry_arn = aws_service_discovery_service.this.arn
+  }
+
+  network_mode = "awsvpc"
+  subnet_ids = module.vpc.private_subnets
+  security_group_rules = {
+    ingress_private_ips = {
+      type                     = "ingress"
+      from_port   = 0
+      to_port     = 0
+      protocol    = "-1"
+      cidr_blocks = ["10.0.0.0/8"]
+    }
+    egress_all = {
+      type        = "egress"
+      from_port   = 0
+      to_port     = 0
+      protocol    = "-1"
+      cidr_blocks = ["0.0.0.0/0"]
+    }
+  }
+
+  tags = local.tags
+}
+
+
+module "ecs_service_workers" {
+  source  = "terraform-aws-modules/ecs/aws//modules/service"
+  version = "~> 5.0"
+  deployment_minimum_healthy_percent = 0
+  name               = "distributed_ml_training_worker_service"
+  desired_count      = 2
+  cluster_arn        = module.ecs_cluster.arn
+  enable_autoscaling = false
+  memory = 189440
+  cpu = 10240
+  # Task Definition
+  
+  requires_compatibilities = ["EC2"]
+  capacity_provider_strategy = {
+    default = {
+      capacity_provider = "distributed_ml_training_workers" # needs to match name of capacity provider
+      weight            = 1
+      base              = 1
+    }
+  }
+  
+  task_exec_iam_role_arn = aws_iam_role.task_execution_role.arn
+  tasks_iam_role_name        = "dt-role-tasks"
+  tasks_iam_role_description = "Tasks IAM role for ${local.name}"
+  tasks_iam_role_policies = {
+    AmazonElasticFileSystemClientFullAccess = "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
+  }
+  create_task_exec_iam_role = false
+  enable_execute_command = false
+
+  container_definitions = {
+    ray_work = {
+      image                    = local.ray_worker_container_image
+      user = 1000
+      readonly_root_filesystem = false
+      cpu = 10240
+      memory = 189440
+      memory_reservation = 189440
+      command = ["/bin/bash","-lc","--","ray start --block --num-cpus=10 --num-gpus=4 --address=head.default.ecs-demo-distributed-ml-training.local:6379 --metrics-export-port=8080 --memory=198642237440"]
+      linux_parameters = {
+        sharedMemorySize = 20480
+      }
+      resource_requirements = [{
+        type="GPU"
+        value=4
+      }]
+      mount_points = [{
+          sourceVolume = "ray_results"
+          containerPath = "/home/ray/ray_results"
+          readOnly = false
+      }]
+    }
+  }
+  # We are using network=host because there will be a single container in each host with GPUs. There is less overhead when using a single container with 
+  # access to all 4 GPUs available in g5.12xlarge than 4 containers with 1 GPU each. 
+  network_mode = "host"
+
+  volume = {
+    "ray_results" ={
+      efs_volume_configuration = {
+        file_system_id = module.efs.id,
+        root_directory = "/"
+        transit_encryption= "ENABLED",
+        authorization_config = {
+            access_point_id = module.efs.access_points.ray_results.id
+            iam="ENABLED"
+        }
+      }
+    }
+  }
+  tags = local.tags
+}
+
+
+################################################################################
+# Shared storage - EFS
+################################################################################
+
+
+module "efs" {
+  source = "terraform-aws-modules/efs/aws"
+
+  # File system
+  name           = "distributed-storage-shared"
+  creation_token = "distributed-storage-shared"
+  encrypted      = true
+  attach_policy = false
+
+  lifecycle_policy = {
+    transition_to_ia = "AFTER_30_DAYS"
+  }
+
+  # Mount targets / security group
+  mount_targets = {
+    (local.azs[0]) = {
+      subnet_id = module.vpc.private_subnets[0]
+    }
+  }
+
+  # Access point
+  access_points = {
+    ray_results = {
+      name = "ray_results"
+      posix_user = {
+        uid            = 1000
+        gid            = 100
+      }
+      root_directory = {
+          path = "/ray_results" 
+
+          creation_info ={
+              owner_uid   = 1000
+              owner_gid   = 100 
+              permissions = "755"
+            }
+        }
+
+      tags = local.tags
+    }
+  }
+  security_group_description = "EFS distributed training security group"
+  security_group_vpc_id      = module.vpc.vpc_id
+  security_group_rules = {
+    vpc = {
+      # relying on the defaults provdied for EFS/NFS (2049/TCP + ingress)
+      description = "NFS ingress from VPC private subnets"
+      cidr_blocks = ["10.0.0.0/8"]
+    }
+  }
+
+  tags = local.tags
+}
+
+
+resource "aws_iam_role" "task_execution_role" {
+  name = "distributed_training_task_execution_role"
+
+  # Terraform's "jsonencode" function converts a
+  # Terraform expression result to valid JSON syntax.
+  assume_role_policy = jsonencode({
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Sid": "ECSTasksAssumeRole",
+            "Effect": "Allow",
+            "Principal": {
+                "Service": "ecs-tasks.amazonaws.com"
+            },
+            "Action": "sts:AssumeRole",
+            "Condition": {
+                "StringEquals": {
+                    "aws:SourceAccount": data.aws_caller_identity.current.account_id
+                },
+                "ArnLike": {
+                    "aws:SourceArn": "arn:aws:ecs:us-east-1:${data.aws_caller_identity.current.account_id}:*"
+                }
+            }
+        }
+    ]
+})
+  managed_policy_arns = [
+  "arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy",
+  "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
+  ]
+
+  tags = local.tags
+}
diff --git a/terraform/ec2-examples/distributed-ml-training/outputs.tf b/terraform/ec2-examples/distributed-ml-training/outputs.tf
new file mode 100644
index 00000000..2c221ceb
--- /dev/null
+++ b/terraform/ec2-examples/distributed-ml-training/outputs.tf
@@ -0,0 +1,42 @@
+################################################################################
+# VPC
+################################################################################
+
+output "vpc_id" {
+  description = "The ID of the VPC"
+  value       = module.vpc.vpc_id
+}
+
+output "private_subnets" {
+  description = "A list of private subnets for the client app"
+  value       = module.vpc.private_subnets
+}
+
+output "private_subnets_cidr_blocks" {
+  description = "A list of private subnets CIDRs"
+  value       = module.vpc.private_subnets_cidr_blocks
+}
+
+################################################################################
+# Cluster
+################################################################################
+
+output "cluster_arn" {
+  description = "ARN that identifies the cluster"
+  value       = module.ecs_cluster.arn
+}
+
+output "cluster_id" {
+  description = "ID that identifies the cluster"
+  value       = module.ecs_cluster.id
+}
+
+output "cluster_name" {
+  description = "Name that identifies the cluster"
+  value       = module.ecs_cluster.name
+}
+
+output "cluster_capacity_providers" {
+  description = "Map of cluster capacity providers attributes"
+  value       = module.ecs_cluster.cluster_capacity_providers
+}
diff --git a/terraform/ec2-examples/distributed-ml-training/variables.tf b/terraform/ec2-examples/distributed-ml-training/variables.tf
new file mode 100644
index 00000000..e69de29b
diff --git a/terraform/ec2-examples/distributed-ml-training/versions.tf b/terraform/ec2-examples/distributed-ml-training/versions.tf
new file mode 100644
index 00000000..35402be4
--- /dev/null
+++ b/terraform/ec2-examples/distributed-ml-training/versions.tf
@@ -0,0 +1,10 @@
+terraform {
+  required_version = ">= 1.0"
+
+  required_providers {
+    aws = {
+      source  = "hashicorp/aws"
+      version = ">= 4.6"
+    }
+  }
+}

From 3ebc805cbeba90197c0506b42e0c4afd8c1d7796 Mon Sep 17 00:00:00 2001
From: sfloresk <sfkanter@amazon.com>
Date: Tue, 12 Dec 2023 08:40:47 -0700
Subject: [PATCH 02/17] Fix docs

---
 .../distributed-ml-training/README.md         | 128 ++++++++++++++++--
 1 file changed, 119 insertions(+), 9 deletions(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index a98de945..35f7d291 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -4,7 +4,17 @@ This solution blueprint creates the infrastructure to run distributed training j
 
 ## Cost warning!
 
-By default, this blueprint uses g5.12xlarge (with 4 GPUs) to showcase multi-GPU and multi-node distributed training, but **can increase costs considerably over time**. You can modify this blueprint to use g5.xlarge (with one GPU) instead from the local variable **instance_type_workers** - if you change the instance type, you need to also modify the worker task definition to use 1 GPU instead of 4 (see **resource_requirements** and container command parameter **--num-gpus**)
+By default, this blueprint uses g5.12xlarge (with 4 GPUs) to showcase multi-GPU and multi-node distributed training, but **can increase costs considerably over time**. You can modify this blueprint to use g5.xlarge (with one GPU) instead from the local variable **instance_type_workers** - if you change the instance type, you need to also modify the worker task definition to use 1 GPU instead of 4 (see **resource_requirements** and container command parameter **--num-gpus**) and the example training script outline below (see **num_workers** parameter)
+
+## Components
+
+* Service discovery using AWS Cloud Map: The head node is registerer to a private DNS using loca zones via cloud map. This allow workers to discover the head service and join the cluster
+* 2 autoscaling groups: One for the head instance and other for the worker instances
+* ECS service definition:
+    * Task security group, task role and task execution role and 
+    * Service discovery ARN is used in the service definition. ECS will automatically manage the registration and deregistration of tasks to this service discovery registry.
+    * Tasks for this service will be deployed in single private subnet to avoid AZ data transfer costs
+    * Task definitions with GPU resource requirements
 
 ## Deployment
 
@@ -14,15 +24,115 @@ terraform plan
 terraform apply 
 ```
 
-## Components
+## Example training script: training the resnet18 model with the FashionMNIST dataset
+
+Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - sagemaker notebooks provide a better user experience to run training jobs in python than using the bash shell
+
+```bash
+HEAD_INSTANCE_ID=$(aws ec2 describe-instances \
+  --filters 'Name=tag:Name,Values=ecs-demo-distributed-ml-training-head' \
+  --query 'Reservations[*].Instances[*].InstanceId' --output text)
+
+aws ssm start-session --target $HEAD_INSTANCE_ID
+CONTAINER_ID=$(sudo docker ps -qf "name=.*rayhead.*")
+sudo docker exec -it $CONTAINER_ID bash
+```
+
+```python
+export RAY_DEDUP_LOGS=0 # Makes the logs verbose per each process in the training
+python
+
+# import required torch and ray libraries
+import tempfile
+import torch
+from torchvision.models import resnet18
+from torchvision.datasets import FashionMNIST
+from torchvision.transforms import ToTensor, Normalize, Compose
+from torch.utils.data import DataLoader
+from torch.optim import Adam
+from torch.nn import CrossEntropyLoss
+from ray.train.torch import TorchTrainer
+from ray.train import ScalingConfig, Checkpoint
+import ray
+from pprint import pprint
+import time
+from pprint import pprint
+
+# Connect to the Ray cluster
+ray.init()
+
+# Download the data in the shared storage
+transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
+train_data = FashionMNIST(root='/home/ray/ray_results/data', 
+                          train=True, download=True, 
+                          transform=transform)
+
+# Define the training function that the distributed processes will run
+def train_func(config):
+    import os
+    # The NVIDIA Collective Communications Library (NCCL) implements multi-GPU 
+    # and multi-node communication primitives optimized for NVIDIA GPUs. 
+    # Since containers can have multiple interfaces, we explicitly set which one
+    # NCCL should use.
+    os.environ['NCCL_SOCKET_IFNAME']='eth0' 
+    #os.environ['NCCL_DEBUG']='INFO' Uncomment this line if you want to debug NCCL
+    # Set up the model
+    model = resnet18(num_classes=10)
+    model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), 
+                                  stride=(2, 2), 
+                                  padding=(3, 3), 
+                                  bias=False)
+    # Prepare model for distributed training
+    model = ray.train.torch.prepare_model(model)
+    # Setup loss and optimizer
+    criterion = CrossEntropyLoss()
+    optimizer = Adam(model.parameters(), lr=0.001)
+    # Retrieve the data from the shared storage.
+    transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
+    train_data = FashionMNIST(root='/home/ray/ray_results/data', train=True, download=False, transform=transform)
+    train_loader = DataLoader(train_data, batch_size=128, shuffle=True)
+    # Prepare dataloader for distributed training
+    train_loader = ray.train.torch.prepare_data_loader(train_loader)
+    # Define training loop
+    for epoch in range(10):
+        start = time.time()
+        for images, labels in train_loader:
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+        print(f"[GPU{torch.cuda.current_device()}: Process rank {torch.distributed.get_rank()}] | [Epoch {epoch} | Batchsize: {128} | Steps: {len(train_loader)} | Total epoch time: {time.time()-start}]")
+        # Only save checkpoint after the last epoch
+        if epoch == 9:
+            checkpoint_dir = tempfile.gettempdir()    
+            checkpoint_path = checkpoint_dir + "/model.checkpoint"
+            torch.save(model.state_dict(), checkpoint_path)
+            # Report metrics and checkpoint to Ray.
+            ray.train.report({"loss": loss.item()},checkpoint=Checkpoint.from_directory(checkpoint_dir))
+
+# The scaling config defines how many workers 
+# In this case is equal to the total GPU count            
+scaling_config = ScalingConfig(num_workers=8, use_gpu=True)
+
+# Create the trainer instance
+trainer = TorchTrainer(train_func, 
+                       scaling_config=scaling_config)
+                       
+# Run the training
+result = trainer.fit()
+
+# Print the results of the training
+print(result)
+
+```
+
+## Clean up
+
+```shell
+terraform destroy
+```
 
-* Service discovery using AWS Cloud Map: The head node is registerer to a private DNS using loca zones via cloud map. This allow workers to discover the head service and join the cluster
-* 2 autoscaling groups: One for the head instance and other for the worker instances
-* ECS service definition:
-    * Task security group, task role and task execution role and 
-    * Service discovery ARN is used in the service definition. ECS will automatically manage the registration and deregistration of tasks to this service discovery registry.
-    * Tasks for this service will be deployed in private subnet
-    * Task definitions with GPU resource requirements
 
 ## Support
 

From d15e67450efd4f677cb2c56f3e741cd3d730d388 Mon Sep 17 00:00:00 2001
From: sfloresk <sfkanter@amazon.com>
Date: Tue, 12 Dec 2023 08:45:07 -0700
Subject: [PATCH 03/17] Fix docs

---
 terraform/ec2-examples/distributed-ml-training/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 35f7d291..7ca6bc37 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -1,6 +1,6 @@
 # ECS machine learning distributed training
 
-This solution blueprint creates the infrastructure to run distributed training jobs using a Ray cluster and Pytorch. The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances. 
+This solution blueprint creates the infrastructure to run distributed training jobs using a Ray cluster and PyTorch. The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances. 
 
 ## Cost warning!
 
@@ -24,9 +24,9 @@ terraform plan
 terraform apply 
 ```
 
-## Example training script: training the resnet18 model with the FashionMNIST dataset
+## Example: training the resnet18 model with the FashionMNIST dataset
 
-Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - sagemaker notebooks provide a better user experience to run training jobs in python than using the bash shell
+Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - [https://aws.amazon.com/sagemaker/notebooks/](SageMaker notebooks) provide a better user experience to run training jobs in python than using the bash shell
 
 ```bash
 HEAD_INSTANCE_ID=$(aws ec2 describe-instances \

From 20f758785d016a1e78dc9018d74436b3aac6b294 Mon Sep 17 00:00:00 2001
From: sfloresk <sfkanter@amazon.com>
Date: Tue, 12 Dec 2023 09:19:37 -0700
Subject: [PATCH 04/17] Fix format

---
 .../distributed-ml-training/README.md         |  34 +--
 .../distributed-ml-training/main.tf           | 195 +++++++++---------
 2 files changed, 115 insertions(+), 114 deletions(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 7ca6bc37..16502f15 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -1,6 +1,6 @@
 # ECS machine learning distributed training
 
-This solution blueprint creates the infrastructure to run distributed training jobs using a Ray cluster and PyTorch. The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances. 
+This solution blueprint creates the infrastructure to run distributed training jobs using a Ray cluster and PyTorch. The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances.
 
 ## Cost warning!
 
@@ -11,7 +11,7 @@ By default, this blueprint uses g5.12xlarge (with 4 GPUs) to showcase multi-GPU
 * Service discovery using AWS Cloud Map: The head node is registerer to a private DNS using loca zones via cloud map. This allow workers to discover the head service and join the cluster
 * 2 autoscaling groups: One for the head instance and other for the worker instances
 * ECS service definition:
-    * Task security group, task role and task execution role and 
+    * Task security group, task role and task execution role and
     * Service discovery ARN is used in the service definition. ECS will automatically manage the registration and deregistration of tasks to this service discovery registry.
     * Tasks for this service will be deployed in single private subnet to avoid AZ data transfer costs
     * Task definitions with GPU resource requirements
@@ -21,7 +21,7 @@ By default, this blueprint uses g5.12xlarge (with 4 GPUs) to showcase multi-GPU
 ```shell
 terraform init
 terraform plan
-terraform apply 
+terraform apply
 ```
 
 ## Example: training the resnet18 model with the FashionMNIST dataset
@@ -63,24 +63,24 @@ ray.init()
 
 # Download the data in the shared storage
 transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
-train_data = FashionMNIST(root='/home/ray/ray_results/data', 
-                          train=True, download=True, 
+train_data = FashionMNIST(root='/home/ray/ray_results/data',
+                          train=True, download=True,
                           transform=transform)
 
 # Define the training function that the distributed processes will run
 def train_func(config):
     import os
-    # The NVIDIA Collective Communications Library (NCCL) implements multi-GPU 
-    # and multi-node communication primitives optimized for NVIDIA GPUs. 
+    # The NVIDIA Collective Communications Library (NCCL) implements multi-GPU
+    # and multi-node communication primitives optimized for NVIDIA GPUs.
     # Since containers can have multiple interfaces, we explicitly set which one
     # NCCL should use.
-    os.environ['NCCL_SOCKET_IFNAME']='eth0' 
+    os.environ['NCCL_SOCKET_IFNAME']='eth0'
     #os.environ['NCCL_DEBUG']='INFO' Uncomment this line if you want to debug NCCL
     # Set up the model
     model = resnet18(num_classes=10)
-    model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), 
-                                  stride=(2, 2), 
-                                  padding=(3, 3), 
+    model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=(7, 7),
+                                  stride=(2, 2),
+                                  padding=(3, 3),
                                   bias=False)
     # Prepare model for distributed training
     model = ray.train.torch.prepare_model(model)
@@ -105,20 +105,20 @@ def train_func(config):
         print(f"[GPU{torch.cuda.current_device()}: Process rank {torch.distributed.get_rank()}] | [Epoch {epoch} | Batchsize: {128} | Steps: {len(train_loader)} | Total epoch time: {time.time()-start}]")
         # Only save checkpoint after the last epoch
         if epoch == 9:
-            checkpoint_dir = tempfile.gettempdir()    
+            checkpoint_dir = tempfile.gettempdir()  
             checkpoint_path = checkpoint_dir + "/model.checkpoint"
             torch.save(model.state_dict(), checkpoint_path)
             # Report metrics and checkpoint to Ray.
             ray.train.report({"loss": loss.item()},checkpoint=Checkpoint.from_directory(checkpoint_dir))
 
-# The scaling config defines how many workers 
-# In this case is equal to the total GPU count            
+# The scaling config defines how many workers
+# In this case is equal to the total GPU count  
 scaling_config = ScalingConfig(num_workers=8, use_gpu=True)
 
 # Create the trainer instance
-trainer = TorchTrainer(train_func, 
+trainer = TorchTrainer(train_func,
                        scaling_config=scaling_config)
-                       
+
 # Run the training
 result = trainer.fit()
 
@@ -136,4 +136,4 @@ terraform destroy
 
 ## Support
 
-Please open an issue for questions or unexpected behaviour
\ No newline at end of file
+Please open an issue for questions or unexpected behaviour
diff --git a/terraform/ec2-examples/distributed-ml-training/main.tf b/terraform/ec2-examples/distributed-ml-training/main.tf
index 7fcfee34..3a32454c 100644
--- a/terraform/ec2-examples/distributed-ml-training/main.tf
+++ b/terraform/ec2-examples/distributed-ml-training/main.tf
@@ -9,11 +9,11 @@ locals {
   name   = "ecs-demo-distributed-ml-training"
   region = "us-east-1"
 
-  vpc_cidr      = "10.0.0.0/16"
-  azs           = slice(data.aws_availability_zones.available.names, 0, 1)
-  instance_type_workers = "g5.12xlarge"
-  instance_type_head = "m5.xlarge"
-  ray_head_container_image = "docker.io/rayproject/ray-ml:2.7.1.artur.c9f4c6-py38"
+  vpc_cidr                   = "10.0.0.0/16"
+  azs                        = slice(data.aws_availability_zones.available.names, 0, 1)
+  instance_type_workers      = "g5.12xlarge"
+  instance_type_head         = "m5.xlarge"
+  ray_head_container_image   = "docker.io/rayproject/ray-ml:2.7.1.artur.c9f4c6-py38"
   ray_worker_container_image = "docker.io/rayproject/ray-ml:2.7.1.artur.c9f4c6-py38-gpu"
 
   user_data_head = <<-EOT
@@ -28,7 +28,7 @@ locals {
     cat <<'EOF' >> /etc/ecs/ecs.config
     ECS_CLUSTER=${local.name}
     EOF
-    echo "ip_resolve=4" >> /etc/yum.conf 
+    echo "ip_resolve=4" >> /etc/yum.conf
   EOT
 
   tags = {
@@ -50,7 +50,7 @@ module "ecs_cluster" {
   default_capacity_provider_use_fargate = false
   autoscaling_capacity_providers = {
     distributed_ml_training_head = {
-      auto_scaling_group_arn         = module.autoscaling_head.autoscaling_group_arn
+      auto_scaling_group_arn = module.autoscaling_head.autoscaling_group_arn
 
       managed_scaling = {
         maximum_scaling_step_size = 1
@@ -65,10 +65,11 @@ module "ecs_cluster" {
       }
     },
     distributed_ml_training_workers = {
-      auto_scaling_group_arn         = module.autoscaling_workers.autoscaling_group_arn
+      auto_scaling_group_arn = module.autoscaling_workers.autoscaling_group_arn
       managed_scaling = {
         maximum_scaling_step_size = 1
         minimum_scaling_step_size = 1
+        _scaling_step_size        = 1
         status                    = "ENABLED"
         target_capacity           = 60
       }
@@ -77,18 +78,18 @@ module "ecs_cluster" {
 
   # Shared task execution role
   create_task_exec_iam_role = false
-  tags = local.tags
+  tags                      = local.tags
 }
 
 resource "aws_service_discovery_private_dns_namespace" "this" {
   name        = "default.${local.name}.local"
   description = "Service discovery namespace.clustername.local"
   vpc         = module.vpc.vpc_id
-  tags = local.tags
+  tags        = local.tags
 }
 
 resource "aws_service_discovery_service" "this" {
-  name        = "head"
+  name = "head"
   dns_config {
     namespace_id = aws_service_discovery_private_dns_namespace.this.id
     dns_records {
@@ -156,9 +157,9 @@ module "autoscaling_head" {
   iam_role_name               = local.name
   iam_role_description        = "ECS role for ${local.name}"
   iam_role_policies = {
-    AmazonEC2ContainerServiceforEC2Role = "arn:aws:iam::aws:policy/service-role/AmazonEC2ContainerServiceforEC2Role"
+    AmazonEC2ContainerServiceforEC2Role      = "arn:aws:iam::aws:policy/service-role/AmazonEC2ContainerServiceforEC2Role"
     AmazonSSMManagedEC2InstanceDefaultPolicy = "arn:aws:iam::aws:policy/AmazonSSMManagedEC2InstanceDefaultPolicy"
-    AmazonElasticFileSystemClientFullAccess = "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
+    AmazonElasticFileSystemClientFullAccess  = "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
   }
 
   vpc_zone_identifier = module.vpc.private_subnets
@@ -193,7 +194,7 @@ module "autoscaling_workers" {
   iam_role_name               = local.name
   iam_role_description        = "ECS role for ${local.name}"
   iam_role_policies = {
-    AmazonEC2ContainerServiceforEC2Role = "arn:aws:iam::aws:policy/service-role/AmazonEC2ContainerServiceforEC2Role",
+    AmazonEC2ContainerServiceforEC2Role      = "arn:aws:iam::aws:policy/service-role/AmazonEC2ContainerServiceforEC2Role",
     AmazonSSMManagedEC2InstanceDefaultPolicy = "arn:aws:iam::aws:policy/AmazonSSMManagedEC2InstanceDefaultPolicy"
   }
 
@@ -249,10 +250,10 @@ module "ecs_service_head" {
   desired_count      = 1
   cluster_arn        = module.ecs_cluster.arn
   enable_autoscaling = false
-  memory = 10240
-  cpu = 3072
+  memory             = 10240
+  cpu                = 3072
   # Task Definition
-  
+
   requires_compatibilities = ["EC2"]
   capacity_provider_strategy = {
     default = {
@@ -261,45 +262,45 @@ module "ecs_service_head" {
       base              = 1
     }
   }
-  
-  task_exec_iam_role_arn = aws_iam_role.task_execution_role.arn
+
+  task_exec_iam_role_arn     = aws_iam_role.task_execution_role.arn
   tasks_iam_role_name        = "dt-role-tasks"
   tasks_iam_role_description = "Tasks IAM role for ${local.name}"
   tasks_iam_role_policies = {
     AmazonElasticFileSystemClientFullAccess = "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
   }
-  create_task_exec_iam_role = false
-  enable_execute_command = false
+  create_task_exec_iam_role          = false
+  enable_execute_command             = false
   deployment_minimum_healthy_percent = 0
   container_definitions = {
-    
+
     ray_head = {
       image                    = local.ray_head_container_image
-      user = 1000
+      user                     = 1000
       readonly_root_filesystem = false
-      cpu = 3072
-      memory = 10240
-      memory_reservation = 10240
-      command = ["/bin/bash","-lc","--","ulimit -n 65536; ray start --head --dashboard-host=0.0.0.0 --metrics-export-port=8080 --num-cpus=0 --memory=10737418240 --block"]
+      cpu                      = 3072
+      memory                   = 10240
+      memory_reservation       = 10240
+      command                  = ["/bin/bash", "-lc", "--", "ulimit -n 65536; ray start --head --dashboard-host=0.0.0.0 --metrics-export-port=8080 --num-cpus=0 --memory=10737418240 --block"]
       linux_parameters = {
         sharedMemorySize = 20480
       }
       mount_points = [{
-          sourceVolume = "ray_results"
-          containerPath = "/home/ray/ray_results"
-          readOnly = false
+        sourceVolume  = "ray_results"
+        containerPath = "/home/ray/ray_results"
+        readOnly      = false
       }]
     }
   }
   volume = {
-    "ray_results" ={
+    "ray_results" = {
       efs_volume_configuration = {
-        file_system_id = module.efs.id,
-        root_directory = "/"
-        transit_encryption= "ENABLED",
+        file_system_id     = module.efs.id,
+        root_directory     = "/"
+        transit_encryption = "ENABLED",
         authorization_config = {
-            access_point_id = module.efs.access_points.ray_results.id
-            iam="ENABLED"
+          access_point_id = module.efs.access_points.ray_results.id
+          iam             = "ENABLED"
         }
       }
     }
@@ -310,10 +311,10 @@ module "ecs_service_head" {
   }
 
   network_mode = "awsvpc"
-  subnet_ids = module.vpc.private_subnets
+  subnet_ids   = module.vpc.private_subnets
   security_group_rules = {
     ingress_private_ips = {
-      type                     = "ingress"
+      type        = "ingress"
       from_port   = 0
       to_port     = 0
       protocol    = "-1"
@@ -333,17 +334,17 @@ module "ecs_service_head" {
 
 
 module "ecs_service_workers" {
-  source  = "terraform-aws-modules/ecs/aws//modules/service"
-  version = "~> 5.0"
+  source                             = "terraform-aws-modules/ecs/aws//modules/service"
+  version                            = "~> 5.0"
   deployment_minimum_healthy_percent = 0
-  name               = "distributed_ml_training_worker_service"
-  desired_count      = 2
-  cluster_arn        = module.ecs_cluster.arn
-  enable_autoscaling = false
-  memory = 189440
-  cpu = 10240
+  name                               = "distributed_ml_training_worker_service"
+  desired_count                      = 2
+  cluster_arn                        = module.ecs_cluster.arn
+  enable_autoscaling                 = false
+  memory                             = 189440
+  cpu                                = 10240
   # Task Definition
-  
+
   requires_compatibilities = ["EC2"]
   capacity_provider_strategy = {
     default = {
@@ -352,52 +353,52 @@ module "ecs_service_workers" {
       base              = 1
     }
   }
-  
-  task_exec_iam_role_arn = aws_iam_role.task_execution_role.arn
+
+  task_exec_iam_role_arn     = aws_iam_role.task_execution_role.arn
   tasks_iam_role_name        = "dt-role-tasks"
   tasks_iam_role_description = "Tasks IAM role for ${local.name}"
   tasks_iam_role_policies = {
     AmazonElasticFileSystemClientFullAccess = "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
   }
   create_task_exec_iam_role = false
-  enable_execute_command = false
+  enable_execute_command    = false
 
   container_definitions = {
     ray_work = {
       image                    = local.ray_worker_container_image
-      user = 1000
+      user                     = 1000
       readonly_root_filesystem = false
-      cpu = 10240
-      memory = 189440
-      memory_reservation = 189440
-      command = ["/bin/bash","-lc","--","ray start --block --num-cpus=10 --num-gpus=4 --address=head.default.ecs-demo-distributed-ml-training.local:6379 --metrics-export-port=8080 --memory=198642237440"]
+      cpu                      = 10240
+      memory                   = 189440
+      memory_reservation       = 189440
+      command                  = ["/bin/bash", "-lc", "--", "ray start --block --num-cpus=10 --num-gpus=4 --address=head.default.ecs-demo-distributed-ml-training.local:6379 --metrics-export-port=8080 --memory=198642237440"]
       linux_parameters = {
         sharedMemorySize = 20480
       }
       resource_requirements = [{
-        type="GPU"
-        value=4
+        type  = "GPU"
+        value = 4
       }]
       mount_points = [{
-          sourceVolume = "ray_results"
-          containerPath = "/home/ray/ray_results"
-          readOnly = false
+        sourceVolume  = "ray_results"
+        containerPath = "/home/ray/ray_results"
+        readOnly      = false
       }]
     }
   }
-  # We are using network=host because there will be a single container in each host with GPUs. There is less overhead when using a single container with 
-  # access to all 4 GPUs available in g5.12xlarge than 4 containers with 1 GPU each. 
+  # We are using network=host because there will be a single container in each host with GPUs. There is less overhead when using a single container with
+  # access to all 4 GPUs available in g5.12xlarge than 4 containers with 1 GPU each.
   network_mode = "host"
 
   volume = {
-    "ray_results" ={
+    "ray_results" = {
       efs_volume_configuration = {
-        file_system_id = module.efs.id,
-        root_directory = "/"
-        transit_encryption= "ENABLED",
+        file_system_id     = module.efs.id,
+        root_directory     = "/"
+        transit_encryption = "ENABLED",
         authorization_config = {
-            access_point_id = module.efs.access_points.ray_results.id
-            iam="ENABLED"
+          access_point_id = module.efs.access_points.ray_results.id
+          iam             = "ENABLED"
         }
       }
     }
@@ -418,7 +419,7 @@ module "efs" {
   name           = "distributed-storage-shared"
   creation_token = "distributed-storage-shared"
   encrypted      = true
-  attach_policy = false
+  attach_policy  = false
 
   lifecycle_policy = {
     transition_to_ia = "AFTER_30_DAYS"
@@ -436,18 +437,18 @@ module "efs" {
     ray_results = {
       name = "ray_results"
       posix_user = {
-        uid            = 1000
-        gid            = 100
+        uid = 1000
+        gid = 100
       }
       root_directory = {
-          path = "/ray_results" 
+        path = "/ray_results"
 
-          creation_info ={
-              owner_uid   = 1000
-              owner_gid   = 100 
-              permissions = "755"
-            }
+        creation_info = {
+          owner_uid   = 1000
+          owner_gid   = 100
+          permissions = "755"
         }
+      }
 
       tags = local.tags
     }
@@ -472,29 +473,29 @@ resource "aws_iam_role" "task_execution_role" {
   # Terraform's "jsonencode" function converts a
   # Terraform expression result to valid JSON syntax.
   assume_role_policy = jsonencode({
-    "Version": "2012-10-17",
-    "Statement": [
-        {
-            "Sid": "ECSTasksAssumeRole",
-            "Effect": "Allow",
-            "Principal": {
-                "Service": "ecs-tasks.amazonaws.com"
-            },
-            "Action": "sts:AssumeRole",
-            "Condition": {
-                "StringEquals": {
-                    "aws:SourceAccount": data.aws_caller_identity.current.account_id
-                },
-                "ArnLike": {
-                    "aws:SourceArn": "arn:aws:ecs:us-east-1:${data.aws_caller_identity.current.account_id}:*"
-                }
-            }
+    "Version" : "2012-10-17",
+    "Statement" : [
+      {
+        "Sid" : "ECSTasksAssumeRole",
+        "Effect" : "Allow",
+        "Principal" : {
+          "Service" : "ecs-tasks.amazonaws.com"
+        },
+        "Action" : "sts:AssumeRole",
+        "Condition" : {
+          "StringEquals" : {
+            "aws:SourceAccount" : data.aws_caller_identity.current.account_id
+          },
+          "ArnLike" : {
+            "aws:SourceArn" : "arn:aws:ecs:us-east-1:${data.aws_caller_identity.current.account_id}:*"
+          }
         }
+      }
     ]
-})
+  })
   managed_policy_arns = [
-  "arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy",
-  "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
+    "arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy",
+    "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
   ]
 
   tags = local.tags

From 31484192c7a5931a00afc89ff818d6537b240819 Mon Sep 17 00:00:00 2001
From: sfloresk <sfkanter@amazon.com>
Date: Tue, 12 Dec 2023 10:42:13 -0700
Subject: [PATCH 05/17] Add solution diagram and reference links

---
 .../distributed-ml-training/README.md           |   7 +++++--
 .../docs/architecture.jpg                       | Bin 0 -> 92284 bytes
 2 files changed, 5 insertions(+), 2 deletions(-)
 create mode 100644 terraform/ec2-examples/distributed-ml-training/docs/architecture.jpg

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 16502f15..252d95e3 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -1,6 +1,8 @@
 # ECS machine learning distributed training
 
-This solution blueprint creates the infrastructure to run distributed training jobs using a Ray cluster and PyTorch. The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances.
+This solution blueprint creates the infrastructure to run distributed training jobs using a [Ray cluster](https://docs.ray.io/en/latest/cluster/getting-started.html) and (https://pytorch.org/)[PyTorch]. The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances.
+
+![Solution architecture](docs/architecture.jpg)
 
 ## Cost warning!
 
@@ -15,6 +17,7 @@ By default, this blueprint uses g5.12xlarge (with 4 GPUs) to showcase multi-GPU
     * Service discovery ARN is used in the service definition. ECS will automatically manage the registration and deregistration of tasks to this service discovery registry.
     * Tasks for this service will be deployed in single private subnet to avoid AZ data transfer costs
     * Task definitions with GPU resource requirements
+* EFS file system for shared storage between the ECS cluster tasks
 
 ## Deployment
 
@@ -26,7 +29,7 @@ terraform apply
 
 ## Example: training the resnet18 model with the FashionMNIST dataset
 
-Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - [https://aws.amazon.com/sagemaker/notebooks/](SageMaker notebooks) provide a better user experience to run training jobs in python than using the bash shell
+Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - [SageMaker notebooks](https://aws.amazon.com/sagemaker/notebooks/) provide a better user experience to run training jobs in python than using the bash shell
 
 ```bash
 HEAD_INSTANCE_ID=$(aws ec2 describe-instances \
diff --git a/terraform/ec2-examples/distributed-ml-training/docs/architecture.jpg b/terraform/ec2-examples/distributed-ml-training/docs/architecture.jpg
new file mode 100644
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From 2d7bdbe4d5d42eae5f17ccecae3225d5388411b9 Mon Sep 17 00:00:00 2001
From: sfloresk <sfkanter@amazon.com>
Date: Tue, 12 Dec 2023 10:43:18 -0700
Subject: [PATCH 06/17] Add solution diagram and reference links

---
 terraform/ec2-examples/distributed-ml-training/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 252d95e3..5bbcdbd8 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -1,6 +1,6 @@
 # ECS machine learning distributed training
 
-This solution blueprint creates the infrastructure to run distributed training jobs using a [Ray cluster](https://docs.ray.io/en/latest/cluster/getting-started.html) and (https://pytorch.org/)[PyTorch]. The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances.
+This solution blueprint creates the infrastructure to run distributed training jobs using a [Ray cluster](https://docs.ray.io/en/latest/cluster/getting-started.html) and [PyTorch](https://pytorch.org/). The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances.
 
 ![Solution architecture](docs/architecture.jpg)
 

From 29a20e6dece86d02eff877ed33c7c222a6e01c1a Mon Sep 17 00:00:00 2001
From: sfloresk <sfkanter@amazon.com>
Date: Wed, 13 Dec 2023 08:35:21 -0700
Subject: [PATCH 07/17] Fix typo in docs

---
 terraform/ec2-examples/distributed-ml-training/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 5bbcdbd8..5c3521cd 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -10,7 +10,7 @@ By default, this blueprint uses g5.12xlarge (with 4 GPUs) to showcase multi-GPU
 
 ## Components
 
-* Service discovery using AWS Cloud Map: The head node is registerer to a private DNS using loca zones via cloud map. This allow workers to discover the head service and join the cluster
+* Service discovery using AWS Cloud Map: The head node is registered to a private DNS using loca zones via cloud map. This allow workers to discover the head service and join the cluster
 * 2 autoscaling groups: One for the head instance and other for the worker instances
 * ECS service definition:
     * Task security group, task role and task execution role and

From e372d83625a5ff1a8db9ace6e4c55a06145a8a7b Mon Sep 17 00:00:00 2001
From: sfloresk <sfkanter@amazon.com>
Date: Thu, 14 Dec 2023 08:36:15 -0700
Subject: [PATCH 08/17] Add training example file and simplify docs

---
 .../distributed-ml-training/README.md         | 102 +++---------------
 .../training_example.py                       |  83 ++++++++++++++
 2 files changed, 97 insertions(+), 88 deletions(-)
 create mode 100644 terraform/ec2-examples/distributed-ml-training/training_example.py

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 5c3521cd..15cce9c6 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -27,9 +27,11 @@ terraform plan
 terraform apply
 ```
 
+Due to the size of the container images, it might take several minutes until the cluster is ready
+
 ## Example: training the resnet18 model with the FashionMNIST dataset
 
-Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - [SageMaker notebooks](https://aws.amazon.com/sagemaker/notebooks/) provide a better user experience to run training jobs in python than using the bash shell
+Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - Using [SageMaker](https://aws.amazon.com/sagemaker/) or [Cloud 9](https://aws.amazon.com/cloud9/) provide a better user experience to run training jobs in python than using the bash shell
 
 ```bash
 HEAD_INSTANCE_ID=$(aws ec2 describe-instances \
@@ -37,97 +39,21 @@ HEAD_INSTANCE_ID=$(aws ec2 describe-instances \
   --query 'Reservations[*].Instances[*].InstanceId' --output text)
 
 aws ssm start-session --target $HEAD_INSTANCE_ID
-CONTAINER_ID=$(sudo docker ps -qf "name=.*rayhead.*")
+CONTAINER_ID=$(sudo docker ps -qf "name=.*-rayhead-.*")
 sudo docker exec -it $CONTAINER_ID bash
 ```
 
-```python
-export RAY_DEDUP_LOGS=0 # Makes the logs verbose per each process in the training
-python
-
-# import required torch and ray libraries
-import tempfile
-import torch
-from torchvision.models import resnet18
-from torchvision.datasets import FashionMNIST
-from torchvision.transforms import ToTensor, Normalize, Compose
-from torch.utils.data import DataLoader
-from torch.optim import Adam
-from torch.nn import CrossEntropyLoss
-from ray.train.torch import TorchTrainer
-from ray.train import ScalingConfig, Checkpoint
-import ray
-from pprint import pprint
-import time
-from pprint import pprint
-
-# Connect to the Ray cluster
-ray.init()
-
-# Download the data in the shared storage
-transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
-train_data = FashionMNIST(root='/home/ray/ray_results/data',
-                          train=True, download=True,
-                          transform=transform)
-
-# Define the training function that the distributed processes will run
-def train_func(config):
-    import os
-    # The NVIDIA Collective Communications Library (NCCL) implements multi-GPU
-    # and multi-node communication primitives optimized for NVIDIA GPUs.
-    # Since containers can have multiple interfaces, we explicitly set which one
-    # NCCL should use.
-    os.environ['NCCL_SOCKET_IFNAME']='eth0'
-    #os.environ['NCCL_DEBUG']='INFO' Uncomment this line if you want to debug NCCL
-    # Set up the model
-    model = resnet18(num_classes=10)
-    model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=(7, 7),
-                                  stride=(2, 2),
-                                  padding=(3, 3),
-                                  bias=False)
-    # Prepare model for distributed training
-    model = ray.train.torch.prepare_model(model)
-    # Setup loss and optimizer
-    criterion = CrossEntropyLoss()
-    optimizer = Adam(model.parameters(), lr=0.001)
-    # Retrieve the data from the shared storage.
-    transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
-    train_data = FashionMNIST(root='/home/ray/ray_results/data', train=True, download=False, transform=transform)
-    train_loader = DataLoader(train_data, batch_size=128, shuffle=True)
-    # Prepare dataloader for distributed training
-    train_loader = ray.train.torch.prepare_data_loader(train_loader)
-    # Define training loop
-    for epoch in range(10):
-        start = time.time()
-        for images, labels in train_loader:
-            outputs = model(images)
-            loss = criterion(outputs, labels)
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-        print(f"[GPU{torch.cuda.current_device()}: Process rank {torch.distributed.get_rank()}] | [Epoch {epoch} | Batchsize: {128} | Steps: {len(train_loader)} | Total epoch time: {time.time()-start}]")
-        # Only save checkpoint after the last epoch
-        if epoch == 9:
-            checkpoint_dir = tempfile.gettempdir()  
-            checkpoint_path = checkpoint_dir + "/model.checkpoint"
-            torch.save(model.state_dict(), checkpoint_path)
-            # Report metrics and checkpoint to Ray.
-            ray.train.report({"loss": loss.item()},checkpoint=Checkpoint.from_directory(checkpoint_dir))
-
-# The scaling config defines how many workers
-# In this case is equal to the total GPU count  
-scaling_config = ScalingConfig(num_workers=8, use_gpu=True)
-
-# Create the trainer instance
-trainer = TorchTrainer(train_func,
-                       scaling_config=scaling_config)
-
-# Run the training
-result = trainer.fit()
-
-# Print the results of the training
-print(result)
+Inside the container shell, check the cluster status 
+```bash
+ray status
+```
 
+Run the [training script example](./training_example.py) - you can look at the comments inside the python script to learn more.
+
+```bash
+export RAY_DEDUP_LOGS=0 # Makes the logs verbose per each process in the training
+wget https://raw.githubusercontent.com/aws-ia/ecs-blueprints/main/terraform/ec2-examples/distributed-ml-training/training_example.py
+python training_example.py
 ```
 
 ## Clean up
diff --git a/terraform/ec2-examples/distributed-ml-training/training_example.py b/terraform/ec2-examples/distributed-ml-training/training_example.py
new file mode 100644
index 00000000..fd3b8abe
--- /dev/null
+++ b/terraform/ec2-examples/distributed-ml-training/training_example.py
@@ -0,0 +1,83 @@
+
+# import required torch and ray libraries
+import tempfile
+import torch
+from torchvision.models import resnet18
+from torchvision.datasets import FashionMNIST
+from torchvision.transforms import ToTensor, Normalize, Compose
+from torch.utils.data import DataLoader
+from torch.optim import Adam
+from torch.nn import CrossEntropyLoss
+from ray.train.torch import TorchTrainer
+from ray.train import ScalingConfig, Checkpoint
+import ray
+from pprint import pprint
+import time
+from pprint import pprint
+
+# Connect to the Ray cluster
+ray.init()
+
+# Download the data in the shared storage
+transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
+train_data = FashionMNIST(root='/home/ray/ray_results/data',
+                          train=True, download=True,
+                          transform=transform)
+
+# Define the training function that the distributed processes will run
+def train_func(config):
+    import os
+    # The NVIDIA Collective Communications Library (NCCL) implements multi-GPU
+    # and multi-node communication primitives optimized for NVIDIA GPUs.
+    # Since containers can have multiple interfaces, we explicitly set which one
+    # NCCL should use.
+    os.environ['NCCL_SOCKET_IFNAME']='eth0'
+    #os.environ['NCCL_DEBUG']='INFO' Uncomment this line if you want to debug NCCL
+    # Set up the model
+    model = resnet18(num_classes=10)
+    model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=(7, 7),
+                                  stride=(2, 2),
+                                  padding=(3, 3),
+                                  bias=False)
+    # Prepare model for distributed training
+    model = ray.train.torch.prepare_model(model)
+    # Setup loss and optimizer
+    criterion = CrossEntropyLoss()
+    optimizer = Adam(model.parameters(), lr=0.001)
+    # Retrieve the data from the shared storage.
+    transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
+    train_data = FashionMNIST(root='/home/ray/ray_results/data', train=True, download=False, transform=transform)
+    train_loader = DataLoader(train_data, batch_size=128, shuffle=True)
+    # Prepare dataloader for distributed training
+    train_loader = ray.train.torch.prepare_data_loader(train_loader)
+    # Define training loop
+    for epoch in range(10):
+        start = time.time()
+        for images, labels in train_loader:
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+        print(f"[GPU{torch.cuda.current_device()}: Process rank {torch.distributed.get_rank()}] | [Epoch {epoch} | Batchsize: {128} | Steps: {len(train_loader)} | Total epoch time: {time.time()-start}]")
+        # Only save checkpoint after the last epoch
+        if epoch == 9:
+            checkpoint_dir = tempfile.gettempdir()  
+            checkpoint_path = checkpoint_dir + "/model.checkpoint"
+            torch.save(model.state_dict(), checkpoint_path)
+            # Report metrics and checkpoint to Ray.
+            ray.train.report({"loss": loss.item()},checkpoint=Checkpoint.from_directory(checkpoint_dir))
+
+# The scaling config defines how many workers
+# In this case is equal to the total GPU count  
+scaling_config = ScalingConfig(num_workers=8, use_gpu=True)
+
+# Create the trainer instance
+trainer = TorchTrainer(train_func,
+                       scaling_config=scaling_config)
+
+# Run the training
+result = trainer.fit()
+
+# Print the results of the training
+print(result)
\ No newline at end of file

From 6ccd034d818779f538df7929539df53b90cddc81 Mon Sep 17 00:00:00 2001
From: sfloresk <sfkanter@amazon.com>
Date: Thu, 14 Dec 2023 08:52:58 -0700
Subject: [PATCH 09/17] Add training example script file

---
 .../distributed-ml-training/README.md         | 102 +++---------------
 .../training_example.py                       |  83 ++++++++++++++
 2 files changed, 97 insertions(+), 88 deletions(-)
 create mode 100644 terraform/ec2-examples/distributed-ml-training/training_example.py

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 5c3521cd..b50c4c15 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -27,9 +27,11 @@ terraform plan
 terraform apply
 ```
 
+Due to the size of the container images, it might take several minutes until the cluster is ready
+
 ## Example: training the resnet18 model with the FashionMNIST dataset
 
-Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - [SageMaker notebooks](https://aws.amazon.com/sagemaker/notebooks/) provide a better user experience to run training jobs in python than using the bash shell
+Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - Using notebooks with [SageMaker](https://aws.amazon.com/sagemaker/) or [Cloud 9](https://aws.amazon.com/cloud9/) provide a better user experience to run training jobs in python than using the bash shell
 
 ```bash
 HEAD_INSTANCE_ID=$(aws ec2 describe-instances \
@@ -37,97 +39,21 @@ HEAD_INSTANCE_ID=$(aws ec2 describe-instances \
   --query 'Reservations[*].Instances[*].InstanceId' --output text)
 
 aws ssm start-session --target $HEAD_INSTANCE_ID
-CONTAINER_ID=$(sudo docker ps -qf "name=.*rayhead.*")
+CONTAINER_ID=$(sudo docker ps -qf "name=.*-rayhead-.*")
 sudo docker exec -it $CONTAINER_ID bash
 ```
 
-```python
-export RAY_DEDUP_LOGS=0 # Makes the logs verbose per each process in the training
-python
-
-# import required torch and ray libraries
-import tempfile
-import torch
-from torchvision.models import resnet18
-from torchvision.datasets import FashionMNIST
-from torchvision.transforms import ToTensor, Normalize, Compose
-from torch.utils.data import DataLoader
-from torch.optim import Adam
-from torch.nn import CrossEntropyLoss
-from ray.train.torch import TorchTrainer
-from ray.train import ScalingConfig, Checkpoint
-import ray
-from pprint import pprint
-import time
-from pprint import pprint
-
-# Connect to the Ray cluster
-ray.init()
-
-# Download the data in the shared storage
-transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
-train_data = FashionMNIST(root='/home/ray/ray_results/data',
-                          train=True, download=True,
-                          transform=transform)
-
-# Define the training function that the distributed processes will run
-def train_func(config):
-    import os
-    # The NVIDIA Collective Communications Library (NCCL) implements multi-GPU
-    # and multi-node communication primitives optimized for NVIDIA GPUs.
-    # Since containers can have multiple interfaces, we explicitly set which one
-    # NCCL should use.
-    os.environ['NCCL_SOCKET_IFNAME']='eth0'
-    #os.environ['NCCL_DEBUG']='INFO' Uncomment this line if you want to debug NCCL
-    # Set up the model
-    model = resnet18(num_classes=10)
-    model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=(7, 7),
-                                  stride=(2, 2),
-                                  padding=(3, 3),
-                                  bias=False)
-    # Prepare model for distributed training
-    model = ray.train.torch.prepare_model(model)
-    # Setup loss and optimizer
-    criterion = CrossEntropyLoss()
-    optimizer = Adam(model.parameters(), lr=0.001)
-    # Retrieve the data from the shared storage.
-    transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
-    train_data = FashionMNIST(root='/home/ray/ray_results/data', train=True, download=False, transform=transform)
-    train_loader = DataLoader(train_data, batch_size=128, shuffle=True)
-    # Prepare dataloader for distributed training
-    train_loader = ray.train.torch.prepare_data_loader(train_loader)
-    # Define training loop
-    for epoch in range(10):
-        start = time.time()
-        for images, labels in train_loader:
-            outputs = model(images)
-            loss = criterion(outputs, labels)
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-        print(f"[GPU{torch.cuda.current_device()}: Process rank {torch.distributed.get_rank()}] | [Epoch {epoch} | Batchsize: {128} | Steps: {len(train_loader)} | Total epoch time: {time.time()-start}]")
-        # Only save checkpoint after the last epoch
-        if epoch == 9:
-            checkpoint_dir = tempfile.gettempdir()  
-            checkpoint_path = checkpoint_dir + "/model.checkpoint"
-            torch.save(model.state_dict(), checkpoint_path)
-            # Report metrics and checkpoint to Ray.
-            ray.train.report({"loss": loss.item()},checkpoint=Checkpoint.from_directory(checkpoint_dir))
-
-# The scaling config defines how many workers
-# In this case is equal to the total GPU count  
-scaling_config = ScalingConfig(num_workers=8, use_gpu=True)
-
-# Create the trainer instance
-trainer = TorchTrainer(train_func,
-                       scaling_config=scaling_config)
-
-# Run the training
-result = trainer.fit()
-
-# Print the results of the training
-print(result)
+Inside the container shell, check the cluster status 
+```bash
+ray status
+```
 
+Run the [training script example](./training_example.py) - you can look at the comments inside the python script to learn more.
+
+```bash
+export RAY_DEDUP_LOGS=0 # Makes the logs verbose per each process in the training
+wget https://raw.githubusercontent.com/aws-ia/ecs-blueprints/main/terraform/ec2-examples/distributed-ml-training/training_example.py
+python training_example.py
 ```
 
 ## Clean up
diff --git a/terraform/ec2-examples/distributed-ml-training/training_example.py b/terraform/ec2-examples/distributed-ml-training/training_example.py
new file mode 100644
index 00000000..fd3b8abe
--- /dev/null
+++ b/terraform/ec2-examples/distributed-ml-training/training_example.py
@@ -0,0 +1,83 @@
+
+# import required torch and ray libraries
+import tempfile
+import torch
+from torchvision.models import resnet18
+from torchvision.datasets import FashionMNIST
+from torchvision.transforms import ToTensor, Normalize, Compose
+from torch.utils.data import DataLoader
+from torch.optim import Adam
+from torch.nn import CrossEntropyLoss
+from ray.train.torch import TorchTrainer
+from ray.train import ScalingConfig, Checkpoint
+import ray
+from pprint import pprint
+import time
+from pprint import pprint
+
+# Connect to the Ray cluster
+ray.init()
+
+# Download the data in the shared storage
+transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
+train_data = FashionMNIST(root='/home/ray/ray_results/data',
+                          train=True, download=True,
+                          transform=transform)
+
+# Define the training function that the distributed processes will run
+def train_func(config):
+    import os
+    # The NVIDIA Collective Communications Library (NCCL) implements multi-GPU
+    # and multi-node communication primitives optimized for NVIDIA GPUs.
+    # Since containers can have multiple interfaces, we explicitly set which one
+    # NCCL should use.
+    os.environ['NCCL_SOCKET_IFNAME']='eth0'
+    #os.environ['NCCL_DEBUG']='INFO' Uncomment this line if you want to debug NCCL
+    # Set up the model
+    model = resnet18(num_classes=10)
+    model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=(7, 7),
+                                  stride=(2, 2),
+                                  padding=(3, 3),
+                                  bias=False)
+    # Prepare model for distributed training
+    model = ray.train.torch.prepare_model(model)
+    # Setup loss and optimizer
+    criterion = CrossEntropyLoss()
+    optimizer = Adam(model.parameters(), lr=0.001)
+    # Retrieve the data from the shared storage.
+    transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
+    train_data = FashionMNIST(root='/home/ray/ray_results/data', train=True, download=False, transform=transform)
+    train_loader = DataLoader(train_data, batch_size=128, shuffle=True)
+    # Prepare dataloader for distributed training
+    train_loader = ray.train.torch.prepare_data_loader(train_loader)
+    # Define training loop
+    for epoch in range(10):
+        start = time.time()
+        for images, labels in train_loader:
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+        print(f"[GPU{torch.cuda.current_device()}: Process rank {torch.distributed.get_rank()}] | [Epoch {epoch} | Batchsize: {128} | Steps: {len(train_loader)} | Total epoch time: {time.time()-start}]")
+        # Only save checkpoint after the last epoch
+        if epoch == 9:
+            checkpoint_dir = tempfile.gettempdir()  
+            checkpoint_path = checkpoint_dir + "/model.checkpoint"
+            torch.save(model.state_dict(), checkpoint_path)
+            # Report metrics and checkpoint to Ray.
+            ray.train.report({"loss": loss.item()},checkpoint=Checkpoint.from_directory(checkpoint_dir))
+
+# The scaling config defines how many workers
+# In this case is equal to the total GPU count  
+scaling_config = ScalingConfig(num_workers=8, use_gpu=True)
+
+# Create the trainer instance
+trainer = TorchTrainer(train_func,
+                       scaling_config=scaling_config)
+
+# Run the training
+result = trainer.fit()
+
+# Print the results of the training
+print(result)
\ No newline at end of file

From 35cf69665d627ae76b8a6c8a3bde0a60dd8e54ad Mon Sep 17 00:00:00 2001
From: sfloresk <sfkanter@amazon.com>
Date: Thu, 14 Dec 2023 12:10:17 -0700
Subject: [PATCH 10/17] Update task to use read only root fs

---
 .../distributed-ml-training/README.md         | 10 +++++--
 .../distributed-ml-training/main.tf           | 26 +++++++++++++++++--
 2 files changed, 32 insertions(+), 4 deletions(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index b50c4c15..9a00e7f3 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -29,7 +29,7 @@ terraform apply
 
 Due to the size of the container images, it might take several minutes until the cluster is ready
 
-## Example: training the resnet18 model with the FashionMNIST dataset
+## Example: training the resnet18 model with the [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) dataset
 
 Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - Using notebooks with [SageMaker](https://aws.amazon.com/sagemaker/) or [Cloud 9](https://aws.amazon.com/cloud9/) provide a better user experience to run training jobs in python than using the bash shell
 
@@ -39,11 +39,16 @@ HEAD_INSTANCE_ID=$(aws ec2 describe-instances \
   --query 'Reservations[*].Instances[*].InstanceId' --output text)
 
 aws ssm start-session --target $HEAD_INSTANCE_ID
+```
+
+Connect to the bash shell of the conntainer
+
+```bash
 CONTAINER_ID=$(sudo docker ps -qf "name=.*-rayhead-.*")
 sudo docker exec -it $CONTAINER_ID bash
 ```
 
-Inside the container shell, check the cluster status 
+Inside the container shell, check the cluster status - you should see three healthy nodes and 8 GPUs available
 ```bash
 ray status
 ```
@@ -52,6 +57,7 @@ Run the [training script example](./training_example.py) - you can look at the c
 
 ```bash
 export RAY_DEDUP_LOGS=0 # Makes the logs verbose per each process in the training
+cd /tmp
 wget https://raw.githubusercontent.com/aws-ia/ecs-blueprints/main/terraform/ec2-examples/distributed-ml-training/training_example.py
 python training_example.py
 ```
diff --git a/terraform/ec2-examples/distributed-ml-training/main.tf b/terraform/ec2-examples/distributed-ml-training/main.tf
index 3a32454c..3fcda75b 100644
--- a/terraform/ec2-examples/distributed-ml-training/main.tf
+++ b/terraform/ec2-examples/distributed-ml-training/main.tf
@@ -275,9 +275,9 @@ module "ecs_service_head" {
   container_definitions = {
 
     ray_head = {
+      readonly_root_filesystem = true
       image                    = local.ray_head_container_image
       user                     = 1000
-      readonly_root_filesystem = false
       cpu                      = 3072
       memory                   = 10240
       memory_reservation       = 10240
@@ -289,10 +289,21 @@ module "ecs_service_head" {
         sourceVolume  = "ray_results"
         containerPath = "/home/ray/ray_results"
         readOnly      = false
+      },
+      {
+        sourceVolume  = "tmp"
+        containerPath = "/tmp"
+        readOnly      = false
       }]
     }
   }
   volume = {
+    "tmp" = {
+      docker_volume_configuration = {
+        scope = "task"
+        driver = "local"
+      }
+    }
     "ray_results" = {
       efs_volume_configuration = {
         file_system_id     = module.efs.id,
@@ -365,9 +376,9 @@ module "ecs_service_workers" {
 
   container_definitions = {
     ray_work = {
+      readonly_root_filesystem = true
       image                    = local.ray_worker_container_image
       user                     = 1000
-      readonly_root_filesystem = false
       cpu                      = 10240
       memory                   = 189440
       memory_reservation       = 189440
@@ -383,6 +394,11 @@ module "ecs_service_workers" {
         sourceVolume  = "ray_results"
         containerPath = "/home/ray/ray_results"
         readOnly      = false
+      },
+      {
+        sourceVolume  = "tmp"
+        containerPath = "/tmp"
+        readOnly      = false
       }]
     }
   }
@@ -391,6 +407,12 @@ module "ecs_service_workers" {
   network_mode = "host"
 
   volume = {
+     "tmp" = {
+      docker_volume_configuration = {
+        scope = "task"
+        driver = "local"
+      }
+    }
     "ray_results" = {
       efs_volume_configuration = {
         file_system_id     = module.efs.id,

From ecc44f327c8688d3316d65b3272e6c8d0e53caeb Mon Sep 17 00:00:00 2001
From: sfloresk <santiagofloreskanter@gmail.com>
Date: Tue, 9 Jan 2024 13:43:16 -0700
Subject: [PATCH 11/17] Remove EFS - Add S3

---
 .../distributed-ml-training/README.md         |  20 ++-
 .../docs/architecture.jpg                     | Bin 92284 -> 0 bytes
 .../docs/architecture.png                     | Bin 0 -> 76095 bytes
 .../distributed-ml-training/main.tf           | 163 ++++++++----------
 .../distributed-ml-training/outputs.tf        |  11 +-
 .../training_example.py                       |  60 +++++--
 .../distributed-ml-training/versions.tf       |   4 +
 7 files changed, 139 insertions(+), 119 deletions(-)
 delete mode 100644 terraform/ec2-examples/distributed-ml-training/docs/architecture.jpg
 create mode 100644 terraform/ec2-examples/distributed-ml-training/docs/architecture.png

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 15cce9c6..99b669ef 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -2,7 +2,7 @@
 
 This solution blueprint creates the infrastructure to run distributed training jobs using a [Ray cluster](https://docs.ray.io/en/latest/cluster/getting-started.html) and [PyTorch](https://pytorch.org/). The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances.
 
-![Solution architecture](docs/architecture.jpg)
+![Solution architecture](docs/architecture.png)
 
 ## Cost warning!
 
@@ -17,7 +17,7 @@ By default, this blueprint uses g5.12xlarge (with 4 GPUs) to showcase multi-GPU
     * Service discovery ARN is used in the service definition. ECS will automatically manage the registration and deregistration of tasks to this service discovery registry.
     * Tasks for this service will be deployed in single private subnet to avoid AZ data transfer costs
     * Task definitions with GPU resource requirements
-* EFS file system for shared storage between the ECS cluster tasks
+* S3 bucket to store the results
 
 ## Deployment
 
@@ -27,33 +27,39 @@ terraform plan
 terraform apply
 ```
 
-Due to the size of the container images, it might take several minutes until the cluster is ready
+Due to the size of the container images, it might take several minutes until the containers reach a running state
 
 ## Example: training the resnet18 model with the FashionMNIST dataset
 
-Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - Using [SageMaker](https://aws.amazon.com/sagemaker/) or [Cloud 9](https://aws.amazon.com/cloud9/) provide a better user experience to run training jobs in python than using the bash shell
+Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - Using notebooks with [SageMaker](https://aws.amazon.com/sagemaker/) or [Cloud 9](https://aws.amazon.com/cloud9/) provide a better user experience to run training jobs in python than using the bash shell
 
+1. Connect to the instance
 ```bash
 HEAD_INSTANCE_ID=$(aws ec2 describe-instances \
   --filters 'Name=tag:Name,Values=ecs-demo-distributed-ml-training-head' \
   --query 'Reservations[*].Instances[*].InstanceId' --output text)
 
 aws ssm start-session --target $HEAD_INSTANCE_ID
+```
+
+2. Connect to the container
+```
 CONTAINER_ID=$(sudo docker ps -qf "name=.*-rayhead-.*")
 sudo docker exec -it $CONTAINER_ID bash
 ```
 
-Inside the container shell, check the cluster status 
+3. Inside the container shell, check the cluster status
 ```bash
 ray status
 ```
 
-Run the [training script example](./training_example.py) - you can look at the comments inside the python script to learn more.
+4. Run the [training script example](./training_example.py) - you can look at the comments inside the python script to learn more about each step.
+A bucket was created as part of the terraform plan, make sure to add the name of that bucket (starts with "dt-results-") as argument of the training_example.py script
 
 ```bash
 export RAY_DEDUP_LOGS=0 # Makes the logs verbose per each process in the training
 wget https://raw.githubusercontent.com/aws-ia/ecs-blueprints/main/terraform/ec2-examples/distributed-ml-training/training_example.py
-python training_example.py
+python training_example.py YOUR_BUCKET_NAME
 ```
 
 ## Clean up
diff --git a/terraform/ec2-examples/distributed-ml-training/docs/architecture.jpg b/terraform/ec2-examples/distributed-ml-training/docs/architecture.jpg
deleted file mode 100644
index 493c3d4f250f4812f0c7cbf62e6e1eb109c02ce7..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/terraform/ec2-examples/distributed-ml-training/main.tf b/terraform/ec2-examples/distributed-ml-training/main.tf
index 3a32454c..cd090f56 100644
--- a/terraform/ec2-examples/distributed-ml-training/main.tf
+++ b/terraform/ec2-examples/distributed-ml-training/main.tf
@@ -140,6 +140,11 @@ data "aws_ssm_parameter" "ecs_gpu_optimized_ami" {
   name = "/aws/service/ecs/optimized-ami/amazon-linux-2/gpu/recommended"
 }
 
+resource "aws_placement_group" "workers" {
+  name     = "ml-training"
+  strategy = "cluster"
+}
+
 module "autoscaling_head" {
   source  = "terraform-aws-modules/autoscaling/aws"
   version = "~> 6.5"
@@ -159,7 +164,6 @@ module "autoscaling_head" {
   iam_role_policies = {
     AmazonEC2ContainerServiceforEC2Role      = "arn:aws:iam::aws:policy/service-role/AmazonEC2ContainerServiceforEC2Role"
     AmazonSSMManagedEC2InstanceDefaultPolicy = "arn:aws:iam::aws:policy/AmazonSSMManagedEC2InstanceDefaultPolicy"
-    AmazonElasticFileSystemClientFullAccess  = "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
   }
 
   vpc_zone_identifier = module.vpc.private_subnets
@@ -182,9 +186,9 @@ module "autoscaling_workers" {
 
   name = "${local.name}-workers"
 
-  #image_id      = data.aws_ssm_parameter.ecs_bottlerocket_gpu_optimized_ami.value
-  image_id      = jsondecode(data.aws_ssm_parameter.ecs_gpu_optimized_ami.value)["image_id"]
-  instance_type = local.instance_type_workers
+  placement_group = aws_placement_group.workers.name
+  image_id        = jsondecode(data.aws_ssm_parameter.ecs_gpu_optimized_ami.value)["image_id"]
+  instance_type   = local.instance_type_workers
 
   security_groups                 = [module.autoscaling_sg.security_group_id]
   user_data                       = base64encode(local.user_data_workers)
@@ -264,11 +268,20 @@ module "ecs_service_head" {
   }
 
   task_exec_iam_role_arn     = aws_iam_role.task_execution_role.arn
-  tasks_iam_role_name        = "dt-role-tasks"
-  tasks_iam_role_description = "Tasks IAM role for ${local.name}"
+  tasks_iam_role_name        = "taskRole"
+  tasks_iam_role_description = "Task role for ${local.name}"
   tasks_iam_role_policies = {
-    AmazonElasticFileSystemClientFullAccess = "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
+    ReadOnlyAccess = "arn:aws:iam::aws:policy/ReadOnlyAccess"
   }
+  tasks_iam_role_statements = [
+    {
+      actions = ["s3:*"]
+      resources = [
+        "arn:aws:s3:::${aws_s3_bucket.results.bucket}",
+        "arn:aws:s3:::${aws_s3_bucket.results.bucket}/*"
+      ]
+    }
+  ]
   create_task_exec_iam_role          = false
   enable_execute_command             = false
   deployment_minimum_healthy_percent = 0
@@ -286,22 +299,18 @@ module "ecs_service_head" {
         sharedMemorySize = 20480
       }
       mount_points = [{
-        sourceVolume  = "ray_results"
-        containerPath = "/home/ray/ray_results"
+        sourceVolume  = "tmp"
+        containerPath = "/tmp"
         readOnly      = false
       }]
     }
   }
   volume = {
-    "ray_results" = {
-      efs_volume_configuration = {
-        file_system_id     = module.efs.id,
-        root_directory     = "/"
-        transit_encryption = "ENABLED",
-        authorization_config = {
-          access_point_id = module.efs.access_points.ray_results.id
-          iam             = "ENABLED"
-        }
+    "tmp" = {
+      dockerVolumeConfiguration = {
+        scope         = "shared",
+        driver        = "local",
+        autoprovision = true
       }
     }
   }
@@ -355,11 +364,21 @@ module "ecs_service_workers" {
   }
 
   task_exec_iam_role_arn     = aws_iam_role.task_execution_role.arn
-  tasks_iam_role_name        = "dt-role-tasks"
-  tasks_iam_role_description = "Tasks IAM role for ${local.name}"
+  tasks_iam_role_name        = "taskRole"
+  tasks_iam_role_description = "Task role for ${local.name}"
   tasks_iam_role_policies = {
-    AmazonElasticFileSystemClientFullAccess = "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
+    ReadOnlyAccess = "arn:aws:iam::aws:policy/ReadOnlyAccess"
   }
+  tasks_iam_role_statements = [
+    {
+      actions = ["s3:*"]
+      resources = [
+        "arn:aws:s3:::${aws_s3_bucket.results.bucket}",
+        "arn:aws:s3:::${aws_s3_bucket.results.bucket}/*"
+      ]
+    }
+  ]
+
   create_task_exec_iam_role = false
   enable_execute_command    = false
 
@@ -380,92 +399,52 @@ module "ecs_service_workers" {
         value = 4
       }]
       mount_points = [{
-        sourceVolume  = "ray_results"
-        containerPath = "/home/ray/ray_results"
+        sourceVolume  = "tmp"
+        containerPath = "/tmp"
         readOnly      = false
       }]
     }
   }
-  # We are using network=host because there will be a single container in each host with GPUs. There is less overhead when using a single container with
-  # access to all 4 GPUs available in g5.12xlarge than 4 containers with 1 GPU each.
-  network_mode = "host"
-
   volume = {
-    "ray_results" = {
-      efs_volume_configuration = {
-        file_system_id     = module.efs.id,
-        root_directory     = "/"
-        transit_encryption = "ENABLED",
-        authorization_config = {
-          access_point_id = module.efs.access_points.ray_results.id
-          iam             = "ENABLED"
-        }
+    "tmp" = {
+      dockerVolumeConfiguration = {
+        scope         = "shared",
+        driver        = "local",
+        autoprovision = true
       }
     }
   }
-  tags = local.tags
-}
-
-
-################################################################################
-# Shared storage - EFS
-################################################################################
-
-
-module "efs" {
-  source = "terraform-aws-modules/efs/aws"
-
-  # File system
-  name           = "distributed-storage-shared"
-  creation_token = "distributed-storage-shared"
-  encrypted      = true
-  attach_policy  = false
-
-  lifecycle_policy = {
-    transition_to_ia = "AFTER_30_DAYS"
-  }
-
-  # Mount targets / security group
-  mount_targets = {
-    (local.azs[0]) = {
-      subnet_id = module.vpc.private_subnets[0]
-    }
-  }
 
-  # Access point
-  access_points = {
-    ray_results = {
-      name = "ray_results"
-      posix_user = {
-        uid = 1000
-        gid = 100
-      }
-      root_directory = {
-        path = "/ray_results"
-
-        creation_info = {
-          owner_uid   = 1000
-          owner_gid   = 100
-          permissions = "755"
-        }
-      }
-
-      tags = local.tags
-    }
-  }
-  security_group_description = "EFS distributed training security group"
-  security_group_vpc_id      = module.vpc.vpc_id
+  network_mode = "awsvpc"
+  subnet_ids   = module.vpc.private_subnets
   security_group_rules = {
-    vpc = {
-      # relying on the defaults provdied for EFS/NFS (2049/TCP + ingress)
-      description = "NFS ingress from VPC private subnets"
+    ingress_private_ips = {
+      type        = "ingress"
+      from_port   = 0
+      to_port     = 0
+      protocol    = "-1"
       cidr_blocks = ["10.0.0.0/8"]
     }
+    egress_all = {
+      type        = "egress"
+      from_port   = 0
+      to_port     = 0
+      protocol    = "-1"
+      cidr_blocks = ["0.0.0.0/0"]
+    }
   }
-
   tags = local.tags
 }
 
+resource "random_id" "bucket_name" {
+  byte_length = 8
+}
+
+resource "aws_s3_bucket" "results" {
+  bucket        = "dt-results-${random_id.bucket_name.hex}"
+  tags          = local.tags
+  force_destroy = true
+}
 
 resource "aws_iam_role" "task_execution_role" {
   name = "distributed_training_task_execution_role"
@@ -495,8 +474,6 @@ resource "aws_iam_role" "task_execution_role" {
   })
   managed_policy_arns = [
     "arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy",
-    "arn:aws:iam::aws:policy/AmazonElasticFileSystemClientFullAccess"
   ]
-
   tags = local.tags
 }
diff --git a/terraform/ec2-examples/distributed-ml-training/outputs.tf b/terraform/ec2-examples/distributed-ml-training/outputs.tf
index 2c221ceb..b40e18ef 100644
--- a/terraform/ec2-examples/distributed-ml-training/outputs.tf
+++ b/terraform/ec2-examples/distributed-ml-training/outputs.tf
@@ -36,7 +36,12 @@ output "cluster_name" {
   value       = module.ecs_cluster.name
 }
 
-output "cluster_capacity_providers" {
-  description = "Map of cluster capacity providers attributes"
-  value       = module.ecs_cluster.cluster_capacity_providers
+
+################################################################################
+# S3
+################################################################################
+
+output "s3_bucket" {
+  description = "ARN that identifies the bucket for results"
+  value       = aws_s3_bucket.results.arn
 }
diff --git a/terraform/ec2-examples/distributed-ml-training/training_example.py b/terraform/ec2-examples/distributed-ml-training/training_example.py
index fd3b8abe..b49ffa53 100644
--- a/terraform/ec2-examples/distributed-ml-training/training_example.py
+++ b/terraform/ec2-examples/distributed-ml-training/training_example.py
@@ -1,6 +1,5 @@
 
 # import required torch and ray libraries
-import tempfile
 import torch
 from torchvision.models import resnet18
 from torchvision.datasets import FashionMNIST
@@ -9,18 +8,25 @@
 from torch.optim import Adam
 from torch.nn import CrossEntropyLoss
 from ray.train.torch import TorchTrainer
-from ray.train import ScalingConfig, Checkpoint
+from ray.train import ScalingConfig, RunConfig
+from filelock import FileLock
 import ray
-from pprint import pprint
 import time
-from pprint import pprint
+import argparse
+
+# Get arguments
+
+parser = argparse.ArgumentParser()
+parser.add_argument("bucket_name", help="Bucket to publish results.", type=str)
+args = parser.parse_args()
+
 
 # Connect to the Ray cluster
 ray.init()
 
 # Download the data in the shared storage
 transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
-train_data = FashionMNIST(root='/home/ray/ray_results/data',
+train_data = FashionMNIST(root='./data',
                           train=True, download=True,
                           transform=transform)
 
@@ -46,38 +52,60 @@ def train_func(config):
     optimizer = Adam(model.parameters(), lr=0.001)
     # Retrieve the data from the shared storage.
     transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
-    train_data = FashionMNIST(root='/home/ray/ray_results/data', train=True, download=False, transform=transform)
-    train_loader = DataLoader(train_data, batch_size=128, shuffle=True)
+    with FileLock(os.path.expanduser("./data.lock")):
+        train_data = FashionMNIST(root='./data', train=True, download=True, transform=transform)
+        # Download test data from open datasets
+        test_data = FashionMNIST(root="./data",train=False,download=True,transform=transform)
+    batch_size=128
+    train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
+    test_loader = DataLoader(test_data, batch_size=batch_size)
     # Prepare dataloader for distributed training
     train_loader = ray.train.torch.prepare_data_loader(train_loader)
+    test_loader = ray.train.torch.prepare_data_loader(test_loader)
     # Define training loop
     for epoch in range(10):
         start = time.time()
+        model.train()
         for images, labels in train_loader:
             outputs = model(images)
             loss = criterion(outputs, labels)
             optimizer.zero_grad()
             loss.backward()
             optimizer.step()
-        print(f"[GPU{torch.cuda.current_device()}: Process rank {torch.distributed.get_rank()}] | [Epoch {epoch} | Batchsize: {128} | Steps: {len(train_loader)} | Total epoch time: {time.time()-start}]")
-        # Only save checkpoint after the last epoch
+        print(f"[Epoch {epoch} | GPU{torch.cuda.current_device()}: Process rank {torch.distributed.get_rank()} | Batchsize: {128} | Steps: {len(train_loader)} | Total epoch time: {time.time()-start}]")
+        model.eval()
+        test_loss, num_correct, num_total = 0, 0, 0
+
+        # Calculate loss and accuaricy in the 10th epoch
+        # you might want to do this in each epoch to detect overfitting as early as possible
         if epoch == 9:
-            checkpoint_dir = tempfile.gettempdir()  
-            checkpoint_path = checkpoint_dir + "/model.checkpoint"
-            torch.save(model.state_dict(), checkpoint_path)
+            with torch.no_grad():
+                for images, labels in test_loader:
+                    prediction = model(images)
+                    loss = criterion(prediction, labels)
+                    test_loss += loss.item()
+                    num_total += labels.shape[0]
+                    num_correct += (prediction.argmax(1) == labels).sum().item()
+
+                test_loss /= len(test_loader)
+                accuracy = num_correct / num_total
             # Report metrics and checkpoint to Ray.
-            ray.train.report({"loss": loss.item()},checkpoint=Checkpoint.from_directory(checkpoint_dir))
+            ray.train.report(metrics={"loss": test_loss, "accuracy": accuracy})
 
 # The scaling config defines how many workers
-# In this case is equal to the total GPU count  
+# In this case is equal to the total GPU count
 scaling_config = ScalingConfig(num_workers=8, use_gpu=True)
 
 # Create the trainer instance
 trainer = TorchTrainer(train_func,
-                       scaling_config=scaling_config)
+                       scaling_config=scaling_config,
+                       run_config=RunConfig(
+                           storage_path=f"s3://{args.bucket_name}/",
+                            name="ecs_dt_results")
+                    )
 
 # Run the training
 result = trainer.fit()
 
 # Print the results of the training
-print(result)
\ No newline at end of file
+print(result)
diff --git a/terraform/ec2-examples/distributed-ml-training/versions.tf b/terraform/ec2-examples/distributed-ml-training/versions.tf
index 35402be4..c32bfca9 100644
--- a/terraform/ec2-examples/distributed-ml-training/versions.tf
+++ b/terraform/ec2-examples/distributed-ml-training/versions.tf
@@ -6,5 +6,9 @@ terraform {
       source  = "hashicorp/aws"
       version = ">= 4.6"
     }
+    random = {
+      source  = "hashicorp/random"
+      version = ">= 3.6.0"
+    }
   }
 }

From a9b4436671699a87bffe89eb391d6d8c3c50fd1c Mon Sep 17 00:00:00 2001
From: sfloresk <santiagofloreskanter@gmail.com>
Date: Thu, 11 Jan 2024 08:41:12 -0700
Subject: [PATCH 12/17] Fix bugs and remove deployment of supporting resources

---
 .../distributed-ml-training-architecture.png  | Bin
 .../distributed-ml-training/README.md         |  83 +++++++---
 .../distributed-ml-training/main.tf           | 147 ++++++++----------
 .../distributed-ml-training/outputs.tf        |  30 ----
 .../training_example.py                       |   5 +-
 .../distributed-ml-training/variables.tf      |   0
 6 files changed, 130 insertions(+), 135 deletions(-)
 rename terraform/ec2-examples/distributed-ml-training/docs/architecture.png => docs/distributed-ml-training-architecture.png (100%)
 delete mode 100644 terraform/ec2-examples/distributed-ml-training/variables.tf

diff --git a/terraform/ec2-examples/distributed-ml-training/docs/architecture.png b/docs/distributed-ml-training-architecture.png
similarity index 100%
rename from terraform/ec2-examples/distributed-ml-training/docs/architecture.png
rename to docs/distributed-ml-training-architecture.png
diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 99b669ef..42318a3d 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -1,35 +1,40 @@
 # ECS machine learning distributed training
 
-This solution blueprint creates the infrastructure to run distributed training jobs using a [Ray cluster](https://docs.ray.io/en/latest/cluster/getting-started.html) and [PyTorch](https://pytorch.org/). The Ray head node runs on a m5.xlarge instance, while the 2 workers run on g5.12xlarge instances.
+This solution blueprint creates the infrastructure to run distributed training jobs using a [Ray cluster](https://docs.ray.io/en/latest/cluster/getting-started.html) and [PyTorch](https://pytorch.org/).
 
-![Solution architecture](docs/architecture.png)
+![Distributed ML architecture](../../../docs/distributed-ml-training-architecture.png)
 
-## Cost warning!
-
-By default, this blueprint uses g5.12xlarge (with 4 GPUs) to showcase multi-GPU and multi-node distributed training, but **can increase costs considerably over time**. You can modify this blueprint to use g5.xlarge (with one GPU) instead from the local variable **instance_type_workers** - if you change the instance type, you need to also modify the worker task definition to use 1 GPU instead of 4 (see **resource_requirements** and container command parameter **--num-gpus**) and the example training script outline below (see **num_workers** parameter)
+By default, this blueprint uses g5.xlarge (with 1 GPU) instances to showcase a multi-node, data parallel distributed training. You can modify this blueprint to use larger instances from the local variable **instance_type_workers** if you need more GPUs. - if you change the instance type, you need to also modify the worker task and service definition memory, CPU and GPUs and the container command parameters. The [training script example](./training_example.py) assumes 2 machines with a single GPU each, but can be changed via the **num_workers** variable.
 
 ## Components
 
-* Service discovery using AWS Cloud Map: The head node is registered to a private DNS using loca zones via cloud map. This allow workers to discover the head service and join the cluster
-* 2 autoscaling groups: One for the head instance and other for the worker instances
+* Service discovery: The head node is registered to a private DNS using local zones via cloud map. This allows worker tasks to discover the head task and join the cluster on start up.
+* 2 autoscaling groups: One for the head instance and another one for the worker instances
 * ECS service definition:
-    * Task security group, task role and task execution role and
-    * Service discovery ARN is used in the service definition. ECS will automatically manage the registration and deregistration of tasks to this service discovery registry.
-    * Tasks for this service will be deployed in single private subnet to avoid AZ data transfer costs
-    * Task definitions with GPU resource requirements
+    * Head service: runs singleton processes responsible for cluster management
+    * Worker service: runs training jobs
 * S3 bucket to store the results
 
 ## Deployment
 
+1. Deploy core-infra resources
+
 ```shell
+cd ./terraform/ec2-examples/core-infra
 terraform init
-terraform plan
-terraform apply
+terraform apply -target=module.vpc -target=aws_service_discovery_private_dns_namespace.this
+
 ```
 
-Due to the size of the container images, it might take several minutes until the containers reach a running state
+2. Deploy this blueprint
 
-## Example: training the resnet18 model with the FashionMNIST dataset
+```shell
+cd ../distributed-ml-training
+terraform init
+terraform apply
+```
+
+## Example: training the resnet model with the FashionMNIST dataset
 
 Once the cluster is deployed, you can connect to the EC2 instance running the head container using SSM, and open a bash shell in the container from there. This is only for demonstration purposes - Using notebooks with [SageMaker](https://aws.amazon.com/sagemaker/) or [Cloud 9](https://aws.amazon.com/cloud9/) provide a better user experience to run training jobs in python than using the bash shell
 
@@ -43,18 +48,51 @@ aws ssm start-session --target $HEAD_INSTANCE_ID
 ```
 
 2. Connect to the container
+
+Due to the size of the container images, it might take several minutes until the containers reach a running state. The following command will fail if the contains is not running.
+
 ```
 CONTAINER_ID=$(sudo docker ps -qf "name=.*-rayhead-.*")
 sudo docker exec -it $CONTAINER_ID bash
 ```
 
-3. Inside the container shell, check the cluster status
+3. Inside the container shell, check the cluster status. 3 nodes should be listed as healthy with 2.0 GPUs available
 ```bash
 ray status
 ```
 
+Example output:
+
+```
+======== Autoscaler status: 2024-01-11 07:19:06.991162 ========
+Node status
+---------------------------------------------------------------
+Healthy:
+ 1 node_a3d74b6d5089c52f9848c1529349ba5c4966edaa633374b0566c7d69
+ 1 node_a5a1aa596068c73e17e029ca221bfad7a7b0085a0273da3c7ad86096
+ 1 node_3ae0c0cabb682158fef418bbabdf2ea63820e8b68e4ae2f4b24c8e66
+Pending:
+ (no pending nodes)
+Recent failures:
+ (no failures)
+
+(...)
+
+Resources
+---------------------------------------------------------------
+Usage:
+ 0.0/6.0 CPU
+ 0.0/2.0 GPU
+ 0B/38.00GiB memory
+ 0B/11.87GiB object_store_memory
+
+Demands:
+ (no resource demands)
+
+```
+
 4. Run the [training script example](./training_example.py) - you can look at the comments inside the python script to learn more about each step.
-A bucket was created as part of the terraform plan, make sure to add the name of that bucket (starts with "dt-results-") as argument of the training_example.py script
+A bucket is created as part of the terraform plan (Bucket ARN is printed as output). Make sure to add the name of that bucket (starts with "dt-results-") as argument of the training_example.py script
 
 ```bash
 export RAY_DEDUP_LOGS=0 # Makes the logs verbose per each process in the training
@@ -64,11 +102,20 @@ python training_example.py YOUR_BUCKET_NAME
 
 ## Clean up
 
+1. Destroy this blueprint
+
 ```shell
 terraform destroy
 ```
 
+1. Destroy core-infra resources
+
+```shell
+cd ../core-infra
+terraform destroy
+
+```
 
 ## Support
 
-Please open an issue for questions or unexpected behaviour
+Please open an issue for questions or unexpected behavior
diff --git a/terraform/ec2-examples/distributed-ml-training/main.tf b/terraform/ec2-examples/distributed-ml-training/main.tf
index 05cda63d..db34935f 100644
--- a/terraform/ec2-examples/distributed-ml-training/main.tf
+++ b/terraform/ec2-examples/distributed-ml-training/main.tf
@@ -2,16 +2,12 @@ provider "aws" {
   region = local.region
 }
 
-data "aws_availability_zones" "available" {}
 data "aws_caller_identity" "current" {}
 
 locals {
-  name   = "ecs-demo-distributed-ml-training"
-  region = "us-east-1"
-
-  vpc_cidr                   = "10.0.0.0/16"
-  azs                        = slice(data.aws_availability_zones.available.names, 0, 1)
-  instance_type_workers      = "g5.12xlarge"
+  name                       = "ecs-demo-distributed-ml-training"
+  region                     = "us-west-2"
+  instance_type_workers      = "g5.xlarge"
   instance_type_head         = "m5.xlarge"
   ray_head_container_image   = "docker.io/rayproject/ray-ml:2.7.1.artur.c9f4c6-py38"
   ray_worker_container_image = "docker.io/rayproject/ray-ml:2.7.1.artur.c9f4c6-py38-gpu"
@@ -81,17 +77,10 @@ module "ecs_cluster" {
   tags                      = local.tags
 }
 
-resource "aws_service_discovery_private_dns_namespace" "this" {
-  name        = "default.${local.name}.local"
-  description = "Service discovery namespace.clustername.local"
-  vpc         = module.vpc.vpc_id
-  tags        = local.tags
-}
-
 resource "aws_service_discovery_service" "this" {
   name = "head"
   dns_config {
-    namespace_id = aws_service_discovery_private_dns_namespace.this.id
+    namespace_id = data.aws_service_discovery_dns_namespace.core_infra.id
     dns_records {
       ttl  = 300
       type = "A"
@@ -100,45 +89,6 @@ resource "aws_service_discovery_service" "this" {
   }
 }
 
-################################################################################
-# Supporting Resources
-################################################################################
-
-module "vpc" {
-  source  = "terraform-aws-modules/vpc/aws"
-  version = "~> 5.2.0"
-
-  name = local.name
-  cidr = local.vpc_cidr
-
-  azs             = local.azs
-  private_subnets = [for k, v in local.azs : cidrsubnet(local.vpc_cidr, 4, k)]
-  public_subnets  = [for k, v in local.azs : cidrsubnet(local.vpc_cidr, 8, k + 48)]
-
-  enable_nat_gateway      = true
-  single_nat_gateway      = true
-  enable_dns_hostnames    = true
-  map_public_ip_on_launch = false
-
-  # Manage so we can name
-  manage_default_network_acl    = true
-  default_network_acl_tags      = { Name = "${local.name}-default" }
-  manage_default_route_table    = true
-  default_route_table_tags      = { Name = "${local.name}-default" }
-  manage_default_security_group = true
-  default_security_group_tags   = { Name = "${local.name}-default" }
-
-  tags = local.tags
-}
-
-# https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-optimized_AMI.html#ecs-optimized-ami-linux
-data "aws_ssm_parameter" "ecs_optimized_ami" {
-  name = "/aws/service/ecs/optimized-ami/amazon-linux-2/recommended"
-}
-
-data "aws_ssm_parameter" "ecs_gpu_optimized_ami" {
-  name = "/aws/service/ecs/optimized-ami/amazon-linux-2/gpu/recommended"
-}
 
 resource "aws_placement_group" "workers" {
   name     = "ml-training"
@@ -166,7 +116,7 @@ module "autoscaling_head" {
     AmazonSSMManagedEC2InstanceDefaultPolicy = "arn:aws:iam::aws:policy/AmazonSSMManagedEC2InstanceDefaultPolicy"
   }
 
-  vpc_zone_identifier = module.vpc.private_subnets
+  vpc_zone_identifier = data.aws_subnets.private.ids
   health_check_type   = "EC2"
   min_size            = 1
   max_size            = 1
@@ -202,7 +152,7 @@ module "autoscaling_workers" {
     AmazonSSMManagedEC2InstanceDefaultPolicy = "arn:aws:iam::aws:policy/AmazonSSMManagedEC2InstanceDefaultPolicy"
   }
 
-  vpc_zone_identifier = module.vpc.private_subnets
+  vpc_zone_identifier = data.aws_subnets.private.ids
   health_check_type   = "EC2"
   min_size            = 2
   max_size            = 2
@@ -233,12 +183,19 @@ module "autoscaling_sg" {
   source  = "terraform-aws-modules/security-group/aws"
   version = "~> 4.0"
 
-  name                = local.name
-  description         = "Autoscaling group security group"
-  vpc_id              = module.vpc.vpc_id
-  ingress_cidr_blocks = [module.vpc.vpc_cidr_block]
+  name        = local.name
+  description = "Autoscaling group security group"
+  vpc_id      = data.aws_vpc.core_infra.id
 
-  ingress_rules = ["all-all"]
+  ingress_with_cidr_blocks = [
+    {
+      from_port   = -1
+      to_port     = -1
+      protocol    = -1
+      description = "Allow all from VPC CIDR block"
+      cidr_blocks = data.aws_vpc.core_infra.cidr_block
+    },
+  ]
 
   egress_rules = ["all-all"]
 
@@ -288,7 +245,7 @@ module "ecs_service_head" {
   container_definitions = {
 
     ray_head = {
-      readonly_root_filesystem = true
+      readonly_root_filesystem = false
       image                    = local.ray_head_container_image
       user                     = 1000
       cpu                      = 3072
@@ -302,11 +259,6 @@ module "ecs_service_head" {
         sourceVolume  = "tmp"
         containerPath = "/tmp"
         readOnly      = false
-      },
-      {
-        sourceVolume  = "tmp"
-        containerPath = "/tmp"
-        readOnly      = false
       }]
     }
   }
@@ -325,7 +277,7 @@ module "ecs_service_head" {
   }
 
   network_mode = "awsvpc"
-  subnet_ids   = module.vpc.private_subnets
+  subnet_ids   = data.aws_subnets.private.ids
   security_group_rules = {
     ingress_private_ips = {
       type        = "ingress"
@@ -355,8 +307,8 @@ module "ecs_service_workers" {
   desired_count                      = 2
   cluster_arn                        = module.ecs_cluster.arn
   enable_autoscaling                 = false
-  memory                             = 189440
-  cpu                                = 10240
+  memory                             = 15360
+  cpu                                = 3072
   # Task Definition
 
   requires_compatibilities = ["EC2"]
@@ -389,29 +341,24 @@ module "ecs_service_workers" {
 
   container_definitions = {
     ray_work = {
-      readonly_root_filesystem = true
+      readonly_root_filesystem = false
       image                    = local.ray_worker_container_image
       user                     = 1000
-      cpu                      = 10240
-      memory                   = 189440
-      memory_reservation       = 189440
-      command                  = ["/bin/bash", "-lc", "--", "ray start --block --num-cpus=10 --num-gpus=4 --address=head.default.ecs-demo-distributed-ml-training.local:6379 --metrics-export-port=8080 --memory=198642237440"]
+      cpu                      = 3072
+      memory                   = 15360
+      memory_reservation       = 15360
+      command                  = ["/bin/bash", "-lc", "--", "ulimit -n 65536; ray start --block --num-cpus=3 --num-gpus=1 --address=head.default.core-infra.local:6379 --metrics-export-port=8080 --memory=15032385536"]
       linux_parameters = {
-        sharedMemorySize = 20480
+        sharedMemorySize = 10240
       }
       resource_requirements = [{
         type  = "GPU"
-        value = 4
+        value = 1
       }]
       mount_points = [{
         sourceVolume  = "tmp"
         containerPath = "/tmp"
         readOnly      = false
-      },
-      {
-        sourceVolume  = "tmp"
-        containerPath = "/tmp"
-        readOnly      = false
       }]
     }
   }
@@ -426,7 +373,7 @@ module "ecs_service_workers" {
   }
 
   network_mode = "awsvpc"
-  subnet_ids   = module.vpc.private_subnets
+  subnet_ids   = data.aws_subnets.private.ids
   security_group_rules = {
     ingress_private_ips = {
       type        = "ingress"
@@ -476,7 +423,7 @@ resource "aws_iam_role" "task_execution_role" {
             "aws:SourceAccount" : data.aws_caller_identity.current.account_id
           },
           "ArnLike" : {
-            "aws:SourceArn" : "arn:aws:ecs:us-east-1:${data.aws_caller_identity.current.account_id}:*"
+            "aws:SourceArn" : "arn:aws:ecs:${local.region}:${data.aws_caller_identity.current.account_id}:*"
           }
         }
       }
@@ -487,3 +434,35 @@ resource "aws_iam_role" "task_execution_role" {
   ]
   tags = local.tags
 }
+
+################################################################################
+# Supporting Resources
+################################################################################
+
+# https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-optimized_AMI.html#ecs-optimized-ami-linux
+data "aws_ssm_parameter" "ecs_optimized_ami" {
+  name = "/aws/service/ecs/optimized-ami/amazon-linux-2/recommended"
+}
+
+data "aws_ssm_parameter" "ecs_gpu_optimized_ami" {
+  name = "/aws/service/ecs/optimized-ami/amazon-linux-2/gpu/recommended"
+}
+
+data "aws_subnets" "private" {
+  filter {
+    name   = "tag:Name"
+    values = ["core-infra-private-${local.region}a"]
+  }
+}
+
+data "aws_vpc" "core_infra" {
+  filter {
+    name   = "tag:Name"
+    values = ["core-infra"]
+  }
+}
+
+data "aws_service_discovery_dns_namespace" "core_infra" {
+  name = "default.core-infra.local"
+  type = "DNS_PRIVATE"
+}
diff --git a/terraform/ec2-examples/distributed-ml-training/outputs.tf b/terraform/ec2-examples/distributed-ml-training/outputs.tf
index b40e18ef..a686db64 100644
--- a/terraform/ec2-examples/distributed-ml-training/outputs.tf
+++ b/terraform/ec2-examples/distributed-ml-training/outputs.tf
@@ -1,22 +1,3 @@
-################################################################################
-# VPC
-################################################################################
-
-output "vpc_id" {
-  description = "The ID of the VPC"
-  value       = module.vpc.vpc_id
-}
-
-output "private_subnets" {
-  description = "A list of private subnets for the client app"
-  value       = module.vpc.private_subnets
-}
-
-output "private_subnets_cidr_blocks" {
-  description = "A list of private subnets CIDRs"
-  value       = module.vpc.private_subnets_cidr_blocks
-}
-
 ################################################################################
 # Cluster
 ################################################################################
@@ -26,17 +7,6 @@ output "cluster_arn" {
   value       = module.ecs_cluster.arn
 }
 
-output "cluster_id" {
-  description = "ID that identifies the cluster"
-  value       = module.ecs_cluster.id
-}
-
-output "cluster_name" {
-  description = "Name that identifies the cluster"
-  value       = module.ecs_cluster.name
-}
-
-
 ################################################################################
 # S3
 ################################################################################
diff --git a/terraform/ec2-examples/distributed-ml-training/training_example.py b/terraform/ec2-examples/distributed-ml-training/training_example.py
index b49ffa53..c9f435bd 100644
--- a/terraform/ec2-examples/distributed-ml-training/training_example.py
+++ b/terraform/ec2-examples/distributed-ml-training/training_example.py
@@ -92,9 +92,8 @@ def train_func(config):
             # Report metrics and checkpoint to Ray.
             ray.train.report(metrics={"loss": test_loss, "accuracy": accuracy})
 
-# The scaling config defines how many workers
-# In this case is equal to the total GPU count
-scaling_config = ScalingConfig(num_workers=8, use_gpu=True)
+# The scaling config defines how many worker processes to use for the training. Usually equals to the number of GPUs
+scaling_config = ScalingConfig(num_workers=2, use_gpu=True)
 
 # Create the trainer instance
 trainer = TorchTrainer(train_func,
diff --git a/terraform/ec2-examples/distributed-ml-training/variables.tf b/terraform/ec2-examples/distributed-ml-training/variables.tf
deleted file mode 100644
index e69de29b..00000000

From 40013059fd885440002730de4fd89ca323a045fa Mon Sep 17 00:00:00 2001
From: sfloresk <santiagofloreskanter@gmail.com>
Date: Thu, 11 Jan 2024 08:43:26 -0700
Subject: [PATCH 13/17] Change aws provider version to >= 5.0

---
 terraform/ec2-examples/distributed-ml-training/README.md   | 2 +-
 terraform/ec2-examples/distributed-ml-training/versions.tf | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 42318a3d..a50b62ba 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -64,7 +64,7 @@ ray status
 Example output:
 
 ```
-======== Autoscaler status: 2024-01-11 07:19:06.991162 ========
+(...)
 Node status
 ---------------------------------------------------------------
 Healthy:
diff --git a/terraform/ec2-examples/distributed-ml-training/versions.tf b/terraform/ec2-examples/distributed-ml-training/versions.tf
index c32bfca9..c60e8f58 100644
--- a/terraform/ec2-examples/distributed-ml-training/versions.tf
+++ b/terraform/ec2-examples/distributed-ml-training/versions.tf
@@ -4,7 +4,7 @@ terraform {
   required_providers {
     aws = {
       source  = "hashicorp/aws"
-      version = ">= 4.6"
+      version = ">= 5.0"
     }
     random = {
       source  = "hashicorp/random"

From 49f5c07c7c31ed7b762dfeeac024708d696cff0f Mon Sep 17 00:00:00 2001
From: sfloresk <santiagofloreskanter@gmail.com>
Date: Thu, 11 Jan 2024 08:47:48 -0700
Subject: [PATCH 14/17] Improve docs format

---
 terraform/ec2-examples/distributed-ml-training/README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index a50b62ba..5ac350f0 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -23,7 +23,6 @@ By default, this blueprint uses g5.xlarge (with 1 GPU) instances to showcase a m
 cd ./terraform/ec2-examples/core-infra
 terraform init
 terraform apply -target=module.vpc -target=aws_service_discovery_private_dns_namespace.this
-
 ```
 
 2. Deploy this blueprint

From 486dd47ba5c12c6aeb108e497666b686b1a6de51 Mon Sep 17 00:00:00 2001
From: sfloresk <santiagofloreskanter@gmail.com>
Date: Thu, 11 Jan 2024 08:59:39 -0700
Subject: [PATCH 15/17] Include region in test commands, change output bucket
 arn to id

---
 .../ec2-examples/distributed-ml-training/README.md     | 10 ++++++----
 .../ec2-examples/distributed-ml-training/outputs.tf    |  4 ++--
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 5ac350f0..220c3718 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -40,10 +40,11 @@ Once the cluster is deployed, you can connect to the EC2 instance running the he
 1. Connect to the instance
 ```bash
 HEAD_INSTANCE_ID=$(aws ec2 describe-instances \
-  --filters 'Name=tag:Name,Values=ecs-demo-distributed-ml-training-head' \
-  --query 'Reservations[*].Instances[*].InstanceId' --output text)
+  --filters 'Name=tag:Name,Values=ecs-demo-distributed-ml-training-head' 'Name=instance-state-name,Values=running' \
+  --query 'Reservations[*].Instances[0].InstanceId' --output text --region us-west-2
+)
 
-aws ssm start-session --target $HEAD_INSTANCE_ID
+aws ssm start-session --target $HEAD_INSTANCE_ID --region us-west-2
 ```
 
 2. Connect to the container
@@ -55,7 +56,8 @@ CONTAINER_ID=$(sudo docker ps -qf "name=.*-rayhead-.*")
 sudo docker exec -it $CONTAINER_ID bash
 ```
 
-3. Inside the container shell, check the cluster status. 3 nodes should be listed as healthy with 2.0 GPUs available
+3. Inside the container shell, check the cluster status. 3 nodes should be listed as healthy with 2.0 GPUs available - If you do not see 2.0 GPUs, the workers have not started yet.
+
 ```bash
 ray status
 ```
diff --git a/terraform/ec2-examples/distributed-ml-training/outputs.tf b/terraform/ec2-examples/distributed-ml-training/outputs.tf
index a686db64..b0889634 100644
--- a/terraform/ec2-examples/distributed-ml-training/outputs.tf
+++ b/terraform/ec2-examples/distributed-ml-training/outputs.tf
@@ -12,6 +12,6 @@ output "cluster_arn" {
 ################################################################################
 
 output "s3_bucket" {
-  description = "ARN that identifies the bucket for results"
-  value       = aws_s3_bucket.results.arn
+  description = "Bucket name for results"
+  value       = aws_s3_bucket.results.id
 }

From a32c91dadff3caff4e866adf4f12bdb266ff4d3a Mon Sep 17 00:00:00 2001
From: sfloresk <santiagofloreskanter@gmail.com>
Date: Thu, 11 Jan 2024 09:55:06 -0700
Subject: [PATCH 16/17] Change bucket ARN for name in the docs

---
 terraform/ec2-examples/distributed-ml-training/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 220c3718..99233af7 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -93,7 +93,7 @@ Demands:
 ```
 
 4. Run the [training script example](./training_example.py) - you can look at the comments inside the python script to learn more about each step.
-A bucket is created as part of the terraform plan (Bucket ARN is printed as output). Make sure to add the name of that bucket (starts with "dt-results-") as argument of the training_example.py script
+A bucket is created as part of the terraform plan (Bucket name is printed as output). Make sure to add the name of that bucket (starts with "dt-results-") as argument of the training_example.py script
 
 ```bash
 export RAY_DEDUP_LOGS=0 # Makes the logs verbose per each process in the training

From da408a81689d9f10a964de899d8ed9f43b91ce08 Mon Sep 17 00:00:00 2001
From: sfloresk <santiagofloreskanter@gmail.com>
Date: Thu, 11 Jan 2024 12:43:53 -0700
Subject: [PATCH 17/17] Add result of training script

---
 .../distributed-ml-training/README.md         | 53 ++++++++++++++++++-
 1 file changed, 51 insertions(+), 2 deletions(-)

diff --git a/terraform/ec2-examples/distributed-ml-training/README.md b/terraform/ec2-examples/distributed-ml-training/README.md
index 99233af7..e84fc22d 100644
--- a/terraform/ec2-examples/distributed-ml-training/README.md
+++ b/terraform/ec2-examples/distributed-ml-training/README.md
@@ -92,8 +92,7 @@ Demands:
 
 ```
 
-4. Run the [training script example](./training_example.py) - you can look at the comments inside the python script to learn more about each step.
-A bucket is created as part of the terraform plan (Bucket name is printed as output). Make sure to add the name of that bucket (starts with "dt-results-") as argument of the training_example.py script
+4. Run the [training script example](./training_example.py) - It uses distributed data parallel to split the data between GPUs. You can look at the comments inside the python script to learn more about each step. A bucket is created as part of the terraform plan (Bucket name is printed as output). Make sure to add the name of that bucket (starts with "dt-results-") as argument of the training_example.py script
 
 ```bash
 export RAY_DEDUP_LOGS=0 # Makes the logs verbose per each process in the training
@@ -101,6 +100,56 @@ wget https://raw.githubusercontent.com/aws-ia/ecs-blueprints/main/terraform/ec2-
 python training_example.py YOUR_BUCKET_NAME
 ```
 
+Example output:
+
+```
+(...)
+Wrapping provided model in DistributedDataParallel.
+(...)
+
+(RayTrainWorker pid=227, ip=10.0.2.255) [Epoch 0 | GPU0: Process rank 0 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.660568237304688]
+(RayTrainWorker pid=234, ip=10.0.15.42) [Epoch 0 | GPU0: Process rank 1 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.65453052520752]
+(RayTrainWorker pid=227, ip=10.0.2.255) [Epoch 1 | GPU0: Process rank 0 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.172431230545044]
+(RayTrainWorker pid=234, ip=10.0.15.42) [Epoch 1 | GPU0: Process rank 1 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.17476797103882]
+(RayTrainWorker pid=227, ip=10.0.2.255) [Epoch 2 | GPU0: Process rank 0 | Batchsize: 128 | Steps: 235 | Total epoch time: 16.807305574417114]
+(RayTrainWorker pid=234, ip=10.0.15.42) [Epoch 2 | GPU0: Process rank 1 | Batchsize: 128 | Steps: 235 | Total epoch time: 16.807661056518555]
+(RayTrainWorker pid=227, ip=10.0.2.255) [Epoch 3 | GPU0: Process rank 0 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.16184115409851]
+(RayTrainWorker pid=234, ip=10.0.15.42) [Epoch 3 | GPU0: Process rank 1 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.164414882659912]
+(RayTrainWorker pid=227, ip=10.0.2.255) [Epoch 4 | GPU0: Process rank 0 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.43423628807068]
+(RayTrainWorker pid=234, ip=10.0.15.42) [Epoch 4 | GPU0: Process rank 1 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.430140495300293]
+(RayTrainWorker pid=234, ip=10.0.15.42) [Epoch 5 | GPU0: Process rank 1 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.319995880126953]
+(RayTrainWorker pid=227, ip=10.0.2.255) [Epoch 5 | GPU0: Process rank 0 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.331279277801514]
+(RayTrainWorker pid=234, ip=10.0.15.42) [Epoch 6 | GPU0: Process rank 1 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.402108669281006]
+(RayTrainWorker pid=227, ip=10.0.2.255) [Epoch 6 | GPU0: Process rank 0 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.385886192321777]
+(RayTrainWorker pid=234, ip=10.0.15.42) [Epoch 7 | GPU0: Process rank 1 | Batchsize: 128 | Steps: 235 | Total epoch time: 16.865890741348267]
+(RayTrainWorker pid=227, ip=10.0.2.255) [Epoch 7 | GPU0: Process rank 0 | Batchsize: 128 | Steps: 235 | Total epoch time: 16.86034846305847]
+(RayTrainWorker pid=234, ip=10.0.15.42) [Epoch 8 | GPU0: Process rank 1 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.0880389213562]
+(RayTrainWorker pid=227, ip=10.0.2.255) [Epoch 8 | GPU0: Process rank 0 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.094018697738647]
+(RayTrainWorker pid=234, ip=10.0.15.42) [Epoch 9 | GPU0: Process rank 1 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.191094160079956]
+(RayTrainWorker pid=227, ip=10.0.2.255) [Epoch 9 | GPU0: Process rank 0 | Batchsize: 128 | Steps: 235 | Total epoch time: 17.189364910125732]
+
+(..)
+╭───────────────────────────────╮
+│ Training result               │
+├───────────────────────────────┤
+│ checkpoint_dir_name           │
+│ time_this_iter_s      182.976 │
+│ time_total_s          182.976 │
+│ training_iteration          1 │
+│ accuracy               0.8852 │
+│ loss                  0.41928 │
+╰───────────────────────────────╯
+
+(...) Total running time: 3min 7s
+
+Result(
+  metrics={'loss': 0.4192830347106792, 'accuracy': 0.8852},
+  path='dt-results-EXAMPLE/ecs_dt_results/TorchTrainer_d1824_00000_0_(...)',
+  filesystem='s3',
+  checkpoint=None
+)
+```
+
 ## Clean up
 
 1. Destroy this blueprint