python

We strongly suggest all users use intelpython for better speed and builtin conda enviroment. Just spack load intel-parallel-studio %intel.

Pip

We don't recommand to use pip in the bare enviroment. instead, you should use conda to manage your python workflow as below. When using pip, always check by which pip, make sure the pip is provided by some conda env (/home/<user>/.conda/envs/<env-name>/bin/pip) instead of local pip /opt/intel/intelpython3/bin/pip or /usr/bin/pip.

Conda

You can actually manage your own python environment and install python packages as you like as follows which conda enviroment is utilized.

$ spack load intel-parallel-studio % intel
$ conda create -n <env_name> <packages_list>
$ conda init bash  # after this, intel python is by default when log in
# you may need a relogin to proceed correctly
(base)$ conda activate test 
(test)$ conda deactivate
(base)$

The python3 intepreter in this conda env is still provided by Intel.

It is recommended that pip is included in the default <package_list> for conda env create.

One can install packages in conda env by conda install --file requirements.txt.

For packages only available to pip, please refer to this article on possible conflict issues and solution workflows.

To use conda env in sbatch, one only need call python binary as /home/<user>/.conda/envs/<conda-env>/bin/python, this is enough for activate the conda env.

Advanced usage on conda

• Conda channels explained: so. Better use default channel, where intel builtin packages are installed by default.

• Fix package to certain version in conda enviroment: so. Add a requirement style like file as ~/.conda/envs/test/conda-meta/pinned.

• remove unnessary envs: conda remove --name myenv --all

• Using tuna mirror to speed up conda install: see help. conda install blah -c https://tuna-channel-url The anaconda main repo url is https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main

Jupyter notebooks

$ spack load intel-parallel-studio %intel`
$ tmux
$ jupyter notebook --ip=`hostname` --no-browser
### ctrl-b d to leave
### after using
$ tmux attach
### ctrl-c to kill jupyter

General way to set up tunnels to compute nodes in your local desktop (MacOS or Linux)

$ ssh -N -L <local_port>:<remote_hostname>:<remote_port> <ssh_user>@<ssh_server_ip>

Such an ssh tunnel can make us visit both jupyter server in master node and in compute nodes.

A more specific step by step example for using jupyter on compute nodes:

Say you want to run your jupyter notebook on c9 and access it by your laptop.

• SSH log into hpc2 master, and salloc -p gpu -w c9 to allocate c9. Note: salloc can be relinquished under exit or ctrl-D or logout of the session, so it is unstable for long run work. And it is interesting to see how the job can continue to run in compute node tmux session while the job is officially ended in slurm...

• ssh c9 from master.

• In c9, tmux and prepare the conda env as well as jupyter notebook --ip="0.0.0.0", say the allocated port is 8888.

• ctrl-B d to detach tmux and ctrl-d to log out c9 (remember cancel the allocate job after finishing using jupyter).

• In your own laptop terminal, use ssh -N -L 9000:c9:8888 ias, then you can visit jupyter in your own laptop broswer as http://127.0.0.1:9000. Note ias here is register in your ~/.ssh/config on your laptop, something as

  # this file is in ~/.ssh/config on your laptop
  Host ias
      Hostname <hpc2 ip addr>
      User <user name @ hpc2>
      Port <hpc2 server port>

See this post for a simpilar workflow to use jupyter on HPC.

sbatch

The use of sbatch script for python is similar for usual scripts, just use python some.py is enough (and remember source /etc/spack-load and prepare the environment).

Python Packages

Tensorflow

See this section