pip install -r ./bin/requirements.txt
activate-global-python-argcomplete
echo "... ..." >> touch user_cluster_config.json 

# artificial software development
alias asd=~/Desktop/pytorch_parallel_training/bin/index.py

# setup default region on to ~/.aws/config
[default]
region = eu-central-1

# 0. copy proper .aws/credentials into /secrets/credentials_head_node_aws
# 1. init VPC + key_pair
./infra__cluster_vpc_init.py
# 2. create a new pcluster into the created VPC 
./infra__cluster_create.py
# 3. spawn slurm sbatch /app with basic ML script
./exec__run.py
# 4.check progress via tensorboard
./infra__tensorboard_open_to_internet.py
./exec__tensor_board_start.py
# 5. connect to the cluster via ssh to debug scripts
./exec__ssh_connect.py
# 6. destroy cluster head node to not to spend money
./infra__cluster_delete.py

----------------------------------------
AWS GPU options summary:
https://docs.aws.amazon.com/dlami/latest/devguide/gpu.html

### P2 = NVIDIA K80 GPU
https://aws.amazon.com/ec2/instance-types/p2/
p2.xlarge     - 1 GPU ($0.900)
p2.8xlarge    - 8 GPU ($7.200)

### P3 = NVIDIA Tesla V100-SXM2-16GB
https://aws.amazon.com/ec2/instance-types/p3/
p3.2xlarge - 1 GPU ($3.06/h)
p3.2xlarge - 4 GPU ($12.24/h)

### P4 = NVIDIA A100 => too expensive
https://aws.amazon.com/ec2/instance-types/p4/

### G4 = AMD + NVIDIA 
NVIDIA Tesla T4 => THE BEST
g4dn.xlarge   - 1 NVIDIA GPU ($0.526/h)
g4dn.12xlarge - 4 NVIDIA GPU ($3.912/h)

### G3 = NVIDIA Tesla M60 => cheapest
https://aws.amazon.com/ec2/instance-types/g3/
g3.8xlarge    - 2 NVIDIA GPU ($2.28/h)