yourpc$ ssh -L 10022:es:22 -l username as.abci.ai -i ~/.ssh/secret key filename

# Leave the terminal as it is while using ABCI.

yourpc$ ssh -p 10022 -l username localhost

yourpc$ scp -P 10022 local-filename username@localhost:/home/username/

yourpc$ scp -P 10022 username@localhost:/home/username/remote-filename ./

[username@es1 ~]$ qrsh -g groupname -l rt_F=1 -l h_rt=01:00:00

[username@es1 ~]$ qsub -g groupname -l rt_C.small=1 sample.sh

# First, execute qrsh

[username@es1 ~]$ qrsh -g groupname -l rt_F=1 -l h_rt=01:00:00

# Then, execute the followings

## Load modules

[username@g0001 ~]$ module load python/3.6/3.6.5 cuda/9.0/9.0.176.4 cudnn/7.4/7.4.2

## Get in the python virtual environment

[username@g0001 ~]$ python3 -m venv ~/Sample/v_tf_gpu

[username@g0001 ~]$ source ~/Sample/v_tf_gpu/bin/activate

## Install necessary libraries (only once)

(v_tf_gpu)[username@g0001 ~]$ pip3 uninstall tensorflow-gpu # when old tensorflow-gpu exists, unistall it

(v_tf_gpu)[username@g0001 ~]$ pip3 install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.12.0-cp36-cp36m-linux_x86_64.whl

## Execute TensorFlow by python

(v_tf_gpu)[username@g0001 ~]$ python

>>> import tensorflow as tf

>>> # use TensorFlow

## get out of python virtual environment（Ctrl-D (i.e. EOF) to exit）

(v_tf_gpu)[username@g0001 ~]$

#!/bin/bash

# Initialaze virtual environment

source /etc/profile.d/modules.sh

# Load modeules

module load python/3.6/3.6.5 cuda/9.0/9.0.176.4 cudnn/7.4/7.4.2

# Activate virtula environment

source ~/Sample/v_tf_gpu/bin/activate

# Execute program

cd ~/Sample/v_tf_gpu

python sample_mnist.py

import tensorflow as tf

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()

x_train, x_test = x_train / 255.0, x_test / 255.0

def create_model():

model = tf.keras.models.Sequential([

tf.keras.layers.Flatten(input_shape=(28, 28)),

tf.keras.layers.Dense(512, activation=tf.nn.relu),

tf.keras.layers.Dropout(0.2),

tf.keras.layers.Dense(10, activation=tf.nn.softmax)

])

return model

model = create_model()

model.compile(optimizer='adam',

loss='sparse_categorical_crossentropy',

metrics=['accuracy'])

model.fit(x_train, y_train, epochs=3)

model.save("./mnist_model.hdf5")

print(model.evaluate(x_test, y_test))

print("finished")

yourpc$ scp -P 10022 run_mnist.sh aaa12345xx@localhost:run_mnist.sh

yourpc$ scp -P 10022 sample_mnist.py aaa12345xx@localhost:sample_mnist.py

# Authorise access rights to the script and program

[username@es3 ~]$ chmod u+x run_mnist.sh

[username@es3 ~]$ chmod u+x sample_mnist.py

[username@es3 ~]$ ls –l

-rwxr----- 1 aaa12345xx aaa12345xx     417  3月 27 11:13 run_mnist.sh

-rwxr----- 1 aaa12345xx aaa12345xx     720  3月 27 10:05 sample_mnist.py

# Make sure that it has changed to "-rwxr–"

[username@es3 ~]$ qsub -g gaa12345 -l rt_G.small=1 run_mnist.sh

[username@es3 ~]$ module load python/3.6/3.6.5

[username@es3 ~]$ python3 -m venv ~/lib/pyenv/jupyter_test

[username@es3 ~]$ source ~/lib/pyenv/jupyter_test/bin/activate

(jupyter_test) es3 $ pip install --upgrade pip

(jupyter_test) es3 $ pip install jupyter

(jupyter_test) es3 $ deactivate

[username@es3 ~]$ qrsh -g gaa12345 -l rt_F=1 -l h_rt=01:00:00

[username@g0019 ~]$ module load python/3.6/3.6.5

# g0019: assigned computational resource

[username@g0019 ~]$ source ~/lib/pyenv/jupyter_test/bin/activate

(jupyter_test) [username@g0019 ~]$ jupyter notebook --no-browser --ip=`hostname` >> jupyter.log 2>&1 &

(jupyter_test) [username@g0019 ~]$ jupyter notebook list

Currently running servers:

http://g0004.abci.local:8888/?token=e7f0ba979d4ffd9eeb7e6debf5a326f853fc289583f92dc5 :: /fs3/home/username

yourpc$ ssh -L 10022:es:22 -l username as.abci.ai

yourpc$ ssh -L 18888:g0004:8888 -l username (-i ~/.ssh/id_rsa) -p 10022 localhost

# -i: secret key option (optional)

http://localhost:18888/?token=e7f0ba979d4ffd9eeb7e6debf5a326f853fc289583f92dc5