How to configure NFS on CentOS 7

Step 1: Do a Yum Install

# yum install nfs-utils rpcbind

Step 2: Enable the Service at Boot Time

# systemctl enable nfs-server
# systemctl enable rpcbind
# systemctl enable nfs-lock     (it does not need to be enabled since rpc-statd.service  is static.)
# systemctl enable nfs-idmap    (it does not need to be enabled since nfs-idmapd.service is static.)

Step 3: Start the Services

# systemctl start rpcbind
# systemctl start nfs-server
# systemctl start nfs-lock
# systemctl start nfs-idmap

Step 4: Confirm the status of NFS

# systemctl status nfs

Step 5: Create a mount point

# mkdir /shared-data

Step 6: Exports the Share

# vim /etc/exports
/shared-data 192.168.0.0/16(rw,no_root_squash)

Step 7: Export the Share

# exportfs -rv

Step 8: Restart the NFS Services

# systemctl restart nfs-server

Step 9: Configure the Firewall

# firewall-cmd --add-service=nfs --zone=internal --permanent
# firewall-cmd --add-service=mountd --zone=internal --permanent
# firewall-cmd --add-service=rpc-bind --zone=internal --permanent

References:

  1. How to configure NFS in RHEL 7
  2. What firewalld services should be active on an NFS server in RHEL 7?

Performance Required for Deep Learning

There is this question that I wanted to find out about deep learning. What are essential System, Network, Protocol that will speed up the Training and/or Inferencing. There may not be necessary to employ the same level of requirements from Training to Inferencing and Vice Versa. I have received this information during a Nvidia Presentation

Training:

  1. Scalability requires ultra-fast networking
  2. Same hardware needs as HPC
  3. Extreme network bandwidth
  4. RDMA
  5. SHARP (Mellanox Scalable Hierarchical Aggregation and Reduction Protocol)
  6. GPUDirect (https://developer.nvidia.com/gpudirect)
  7. Fast Access Storage

Influencing

  1. Highly Transactional
  2. Ultra-low Latency
  3. Instant Network Response
  4. RDMA
  5. PeerDirect, GPUDirect