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BNL NSLS-II in-house ptychography software

Introduction

Installation

While one can pip install this pacakge directly, most likely the non-Python dependencies will not be available. For the time being, therefore, we recommend using Conda.

On NSLS-II beamline machines

The instruction below is for admins who have the root priviledge to install the software at the system level so that all users logging in the machine can run the software directly without any setup.

Fully automatic way (recommended)

  1. Create a new conda environment named ptycho_production: sudo /opt/conda/bin/conda create -p /opt/conda_envs/ptycho_production python=3.6 nsls2ptycho (If you need beamline-specific packages, such as hxntools for HXN, append the package names in the conda create command. This helps resolve possible conflict/downgrade issues.) The conda environment ptycho_production is activated under the hood using the run-ptycho script to be installed in the last step.
  2. fix_conda_privileges.sh
  3. sudo -i (switch to root)
  4. /opt/conda_envs/ptycho_production/bin/pip install 'cupy-cudaXX>=6.0.0b3', where XX is your CUDA toolkit version, available from nvcc --version
  5. If needed, copy the script run-ptycho in the root of this repo to /usr/local/bin/: sudo cp ./run-ptycho /usr/local/bin/

To update the software, simple do sudo conda update -n ptycho_production nsls2ptycho

Manual installation

  1. Create a new conda environment named ptycho_production: sudo conda create -n ptycho_production python=3.6 pyfftw pyqt=5 numpy scipy matplotlib pillow h5py posix_ipc databroker openmpi mpi4py (If you need beamline-specific packages, such as hxntools for HXN, append the package names in the conda create command. This helps resolve possible conflict/downgrade issues.) The conda environment ptycho_production is activated under the hood using the run-ptycho script to be installed in Step 7.
  2. Make sure you are granted access to the backend, currently hosted in this private GitHub repo.
  3. Create a temporary workspace: mkdir /tmp/build_ptycho; cd /tmp/build_ptycho
  4. Clone from the mirror of this repo: git clone --recursive https://github.com/NSLS-II/ptycho_gui.git (During the process git may prompt you to enter your GitHub login and password for cloning the backend.)
  5. Move this repo to /usr/local/: sudo mv ./ptycho_gui /usr/local/; cd /usr/local/ptycho_gui; rmdir /tmp/build_ptycho
  6. Install the GUI in "develop" mode: sudo /opt/conda_envs/ptycho_production/bin/pip install -e .
  7. Copy the script run-ptycho to /usr/local/bin/: sudo cp ./run-ptycho /usr/local/bin/

To update the software, simple go to the code location and do git pull there. Since we installed in the develop mode (with -e flag) the files are symlinked to the conda env, so any updates we do to the code will be immediately up online. This can also work as a way to do "hot fixes".

cd /usr/local/ptycho_gui/
sudo git pull origin master    # update frontend
cd ./nsls2ptycho/core/ptycho/
sudo git pull origin master    # update backend

On personal machines

Basically the procedure is similar to those outlined above, except that we don't need sudo:

  1. Make sure you are granted access to the backend, currently hosted in this private GitHub repo
  2. git clone --recursive https://github.com/NSLS-II/ptycho_gui.git (during the process git will prompt you to enther your GitHub id and password for cloning the backend)
  3. Either use the current Conda environment, or create a new one, and then do conda install python=3.6 pyfftw pyqt=5 numpy scipy matplotlib pillow h5py posix_ipc databroker
  4. If you need beamline-specific packages, install it now. Ex: conda install hxntools
  5. conda install -c conda-forge openmpi mpi4py
  6. Enter the cloned directory: cd ./ptycho_gui
  7. pip install .

Execution

  1. Start the GUI: run-ptycho
  2. Spawn two MPI processes without GUI: mpirun -n 2 run-ptycho-backend input_file

Conventions

  1. The GUI writes a config file to ~/.ptycho_gui/
  2. Once the working directory is specified in the GUI, it assumes that all HDF5 files are stored there, and the outputs are written to working_dir/recon_results/SXXXXX/, where XXXXX is the scan-number string.
  3. A few compiled .cubin files are stored with the Python code

References

  • High-Performance Multi-Mode Ptychography Reconstruction on Distributed GPUs, Z. Dong, Y.-L. L. Fang et al., 2018 NYSDS, DOI:10.1109/NYSDS.2018.8538964

For users using the new solvers mADMM, PM, and APG, you are advised to cite additionlly the following paper:

License

MIT (subject to change)

Users are encouraged to cite the references above.

Maintainer