Using the Jupyter image
For convenience, Leafcloud offers a pre-built image with Nvidia CUDA and CuDNN, TensorFlow and PyTorch and Jupyter Notebook pre-installed.
Launching the instance
To start an instance using this image, go to the instance view of the dashboard.
- Press Launch instance
- in the Source tab select "Image" and the boot source
- select the
ubuntu-pytorch-tensorflow-jupyter
image - set the volume size to 40GB
- as the flavor select any gpu flavor, starting with
eg1
. - Select a network, to give the instance a public IP address use the
external
network - Select a key pair, this should will be used to
SSH
into the machine - Select a security group that exposes the
8888
port - Press "Launch Instance"
Open Jupyter Notebook
In the instances view, copy the IP address of your new instance.
Obtaining the Jupyter Notebook token
From a terminal SSH
into the instance:
ssh ubuntu@<ip-address>
sudo systemctl status jupyter
Near the bottom of the output of the above command will be a line simila to:
http://45.135.56.154:8888/?token=<your-token>
Copy this url and open it in a web browser.
This is your Jupyter Notebook server!
Validating the installation
To validate that the machine is using a GPU you can create a new notebook and run the following
For TensorFlow
import tensorflow as tf
if tf.test.gpu_device_name():
print('Default GPU Device:{}'.format(tf.test.gpu_device_name()))
else:
print("Please install GPU version of TF")
This should output something like:
Default GPU Device:/device:GPU:0
For PyTorch
import torch
torch.cuda.is_available()
torch.cuda.device_count()
torch.cuda.current_device()
torch.cuda.device(0)
torch.cuda.get_device_name(0)
This should output something like:
'NVIDIA A30'