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Prossima revisione		Revisione precedente
calcoloscientifico:cluster:softwareapplicativo:conda [23/10/2021 17:32] – [Table] fabio.spatarocalcoloscientifico:cluster:softwareapplicativo:conda [05/07/2024 10:04] (versione attuale) – federico.prost
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 Available versions of the "statistical-genetics" environment: Available versions of the "statistical-genetics" environment:
  
-^ condaenv                         | **build date**  |+^ condaenv                         ^ **build date**  ^
 | statistical-genetics             | 2020.06.29      | | statistical-genetics             | 2020.06.29      |
 | statistical-genetics-2020.03.14  | 2020.03.14      | | statistical-genetics-2020.03.14  | 2020.03.14      |
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 </code> </code>
  
-Now we will see how to run the scripts that you find on the [[calcoloscientifico:cluster:software_tools:r|R]] page.+Now we will see how to run the scripts that you find on the [[calcoloscientifico:userguide:r|R]] page.
  
 Script ''slurm-statistical-genetics-vrt.sh'': Script ''slurm-statistical-genetics-vrt.sh'':
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 #SBATCH --mem=512M #SBATCH --mem=512M
 #SBATCH --partition=vrt #SBATCH --partition=vrt
 +#SBATCH --qos=vrt
 #SBATCH --time=0-00:05:00 #SBATCH --time=0-00:05:00
 ##SBATCH --account=<account> ##SBATCH --account=<account>
Linea 102: Linea 103:
 </code> </code>
  
-Script ''slurm-statistical-genetics-bdw.sh'':+Script ''slurm-statistical-genetics-cpu.sh'':
  
-<code bash slurm-statistical-genetics-bdw.sh>+<code bash slurm-statistical-genetics-cpu.sh>
 #!/bin/bash #!/bin/bash
 #SBATCH --job-name=loop #SBATCH --job-name=loop
Linea 112: Linea 113:
 #SBATCH --ntasks-per-node=4 #SBATCH --ntasks-per-node=4
 #SBATCH --mem=512M #SBATCH --mem=512M
-#SBATCH --partition=bdw+#SBATCH --partition=cpu 
 +#SBATCH --qos=cpu
 #SBATCH --time=0-00:05:00 #SBATCH --time=0-00:05:00
 ##SBATCH --account=<account> ##SBATCH --account=<account>
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 ==== Virtualenv "machine-learning-cuda" ==== ==== Virtualenv "machine-learning-cuda" ====
  
-Main packages included:+Main packages included in 2022 condaenvs:
  
 <code> <code>
 conda install: conda install:
-cudatoolkit pip mpi4py h5py keras opencv opencv-python-headless tensorflow torchvision easydict pytorch + - cudatoolkit 
 + - pip 
 + - mpi4py 
 + - h5py 
 + - keras 
 + - opencv 
 + - opencv-python-headless 
 + - tensorflow 
 + - torchvision 
 + - easydict 
 + - pytorch  
 + - statsmodels 
 + - flax 
 + - transformers
  
 pip install: pip install:
-keras.datasets sklearn+ - keras.datasets 
 + - sklearn
 </code> </code>
  
 Available versions of the "machine-learning-cuda" environment: Available versions of the "machine-learning-cuda" environment:
  
-^ machine-learning-cuda                   ^ build date | +^ condaenv                                 ^ build date  ^ 
-| machine-learning-cuda-11.2.2            | 2021.10.23 | +| machine-learning-cuda-11.6.0             | 2022.05.20  | 
-| machine-learning-cuda-11.2.2-2021.10.23 | 2021.10.23 | +| machine-learning-cuda-11.6.0-2022.05.20  | 2022.05.20  | 
-| machine-learning-cuda-11.2.1            | 2021.10.23 | +| machine-learning-cuda-11.6.0-2022.03.09  | 2022.03.09  | 
-| machine-learning-cuda-11.2.1-2021.10.23 | 2021.10.23 | +| machine-learning-cuda-11.4.2             | 2022.05.20  | 
-| machine-learning-cuda-11.2.0            | 2021.10.23 | +| machine-learning-cuda-11.4.2-2022.05.20  | 2022.05.20  | 
-| machine-learning-cuda-11.2.0-2021.10.23 | 2021.10.23 | +| machine-learning-cuda-11.4.2-2022.03.09  | 2022.03.09  | 
-| machine-learning-cuda-11.1              | 2021.10.22 | + 
-| machine-learning-cuda-11.1-2021.10.22   | 2021.10.22 | +Main packages included in 2021 condaenvs: 
-| machine-learning-cuda-11.0              | 2021.10.23 | + 
-| machine-learning-cuda-11.0-2021.10.23   | 2021.10.23 | +<code> 
-| machine-learning-cuda-10.2              | 2021.10.22 | +conda install: 
-| machine-learning-cuda-10.2-2021.10.22   | 2021.10.22 | + - cudatoolkit 
-| machine-learning-cuda-10.1              | 2021.10.23 | + - pip 
-| machine-learning-cuda-10.1-2021.10.23   | 2021.10.23 | + - mpi4py 
-| machine-learning-cuda-10.0              | 2021.10.23 | + - h5py 
-| machine-learning-cuda-10.0-2021.10.23   | 2021.10.23 | + - keras 
-| machine-learning-cuda-9.0               | 2019.10.22 | + - opencv 
-| machine-learning-cuda-9.0-2019.10.22    | 2019.10.22 | + - opencv-python-headless 
-| machine-learning-cuda-8.0               | 2019.10.22 | + - tensorflow 
-| machine-learning-cuda-8.0-2019.10.22    | 2019.10.22 |+ - torchvision 
 + - easydict 
 + - pytorch  
 + 
 +pip install: 
 + - keras.datasets 
 + - sklearn 
 +</code> 
 + 
 +Available versions of the "machine-learning-cuda" environment: 
 + 
 +^ condaenv                                 ^ build date  ^ 
 +| machine-learning-cuda-11.2.2             | 2021.10.23  | 
 +| machine-learning-cuda-11.2.2-2021.10.23  | 2021.10.23  | 
 +| machine-learning-cuda-11.2.1             | 2021.10.23  | 
 +| machine-learning-cuda-11.2.1-2021.10.23  | 2021.10.23  | 
 +| machine-learning-cuda-11.2.0             | 2021.10.23  | 
 +| machine-learning-cuda-11.2.0-2021.10.23  | 2021.10.23  | 
 +| machine-learning-cuda-11.1               | 2021.10.22  | 
 +| machine-learning-cuda-11.1-2021.10.22    | 2021.10.22  | 
 +| machine-learning-cuda-11.0               | 2021.10.23  | 
 +| machine-learning-cuda-11.0-2021.10.23    | 2021.10.23  | 
 +| machine-learning-cuda-10.2               | 2021.10.22  | 
 +| machine-learning-cuda-10.2-2021.10.22    | 2021.10.22  | 
 +| machine-learning-cuda-10.1               | 2021.10.23  | 
 +| machine-learning-cuda-10.1-2021.10.23    | 2021.10.23  | 
 +| machine-learning-cuda-10.0               | 2021.10.23  | 
 +| machine-learning-cuda-10.0-2021.10.23    | 2021.10.23  | 
 +| machine-learning-cuda-9.0                | 2019.10.22  | 
 +| machine-learning-cuda-9.0-2019.10.22     | 2019.10.22  | 
 +| machine-learning-cuda-8.0                | 2019.10.22  | 
 +| machine-learning-cuda-8.0-2019.10.22     | 2019.10.22  |
  
 Creation scripts: Creation scripts:
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 #SBATCH --nodes=1 #SBATCH --nodes=1
 #SBATCH --ntasks-per-node=1 #SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:tesla:1+#SBATCH --gres=gpu:p100:1
 #SBATCH --partition=gpu #SBATCH --partition=gpu
 +#SBATCH --qos=gpu
 #SBATCH --mem=16G #SBATCH --mem=16G
 #SBATCH --time=0-00:30:00 #SBATCH --time=0-00:30:00
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 Available versions of the "cryolo-cpu" environment: Available versions of the "cryolo-cpu" environment:
  
-^ cryolo-cpu ^ +| **condaenv**           ^ build date  ^ 
-| 2020.09.18 |+| cryolo-cpu             | 2020.09.18  | 
 +| cryolo-cpu-2020.09.18  | 2020.09.18  |
  
 Enable the default version of the "cryolo-cpu" environment: Enable the default version of the "cryolo-cpu" environment:
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 #SBATCH --mem=1G #SBATCH --mem=1G
 #SBATCH --partition=vrt #SBATCH --partition=vrt
 +#SBATCH --qos=vrt
 #SBATCH --time=0-01:00:00 #SBATCH --time=0-01:00:00
 ##SBATCH --account=<account> ##SBATCH --account=<account>
Linea 285: Linea 335:
 </code> </code>
  
-Script ''slurm-cryolo-bdw.sh'':+Script ''slurm-cryolo-cpu.sh'':
  
-<code bash slurm-cryolo-bdw.sh>+<code bash slurm-cryolo-cpu.sh>
 #!/bin/bash #!/bin/bash
 #SBATCH --job-name=cryolo #SBATCH --job-name=cryolo
Linea 295: Linea 345:
 #SBATCH --ntasks-per-node=4 #SBATCH --ntasks-per-node=4
 #SBATCH --mem=4G #SBATCH --mem=4G
-#SBATCH --partition=bdw+#SBATCH --partition=cpu 
 +#SBATCH --qos=cpu
 #SBATCH --time=0-01:00:00 #SBATCH --time=0-01:00:00
 ##SBATCH --account=<account> ##SBATCH --account=<account>
Linea 317: Linea 368:
 Available versions of the "cryolo-gpu" environment: Available versions of the "cryolo-gpu" environment:
  
-^ cryolo-gpu ^ +^ condaenv               ^ build date  ^ 
-| 2020.09.18 |+| cryolo-gpu             | 2020.09.18  | 
 +| cryolo-gpu-2020.09.18  | 2020.09.18  |
  
 Enable the default version of the "cryolo-gpu" environment: Enable the default version of the "cryolo-gpu" environment:
Linea 377: Linea 429:
 </code> </code>
  
 +==== Virtualenv "mdanalysis" ====
  
 +Available versions of the "mdanalysis" environment:
 +
 +^ condaenv          ^ **build date**  ^
 +| mdanalysis        | 2023.03.20      |
 +| mdanalysis-2.3.0  | 2023.03.20      |
 +
 +Enable the default version of the "mdanalysis" environment:
 +
 +<code>
 +module load miniconda3
 +source "$CONDA_PREFIX/etc/profile.d/conda.sh"
 +conda activate mdanalysis
 +</code>
 +
 +Enable the 2.3.0 version of the "mdanalysis" environment:
 +
 +<code>
 +module load miniconda3
 +source "$CONDA_PREFIX/etc/profile.d/conda.sh"
 +conda activate mdanalysis-2.3.0
 +</code>
 +
 +Packages in the "mdanalysis" environment (any version):
 +
 +<code>
 +conda list
 +</code>
 +
 +Disable the "mdanalysis" environment (any version):
 +
 +<code>
 +conda deactivate
 +</code>
 +
 +Script ''slurm-mdanalysis-cpu.sh'':
 +
 +<code bash slurm-mdanalysis-cpu.sh>
 +#!/bin/bash
 +#SBATCH --job-name=mdanalysis
 +#SBATCH --output=%x.o%j
 +#SBATCH --error=%x.e%j
 +#SBATCH --nodes=1
 +#SBATCH --ntasks-per-node=4
 +#SBATCH --mem=20G
 +#SBATCH --qos=cpu
 +#SBATCH --partition=cpu
 +#SBATCH --time=0-01:00:00
 +##SBATCH --account=<account>
 +
 +module load gnu7/7.3.0
 +module load python/3.7.2
 +
 +module load miniconda3
 +source "$CONDA_PREFIX/etc/profile.d/conda.sh"
 +conda activate mdanalysis-2.3.0
 +
 +python --version
 +python3 Dihedral_analysis-MDA.py
 +
 +conda deactivate
 +</code>
 +
 +
 +Script ''Dihedral_analysis-MDA.py'':
 +
 +<code pyton Dihedral_analysis-MDA.py>
 +#!/usr/bin/env python3
 +
 +
 +import numpy as np
 +import MDAnalysis as MDA
 +from matplotlib import pyplot as plt
 +#from MDAnalysis import analysis as MDAa
 +from MDAnalysis.analysis.dihedrals import Dihedral as MDAaD
 +import MDAnalysis.lib.distances as MDAld
 +
 +
 +## EDIT HERE the resname and traj-file-name(no dcd)
 +resn='CBP'  # MCB # MCN # PCZ
 +trajfn='1atm_300k_CBP_512_mainrun_2'   #'pCBP_shorttraj' ## change for others
 +# number of frames to skip 
 +nskip=500
 +
 +# loading trajectory
 +u=MDA.Universe(resn+'-512-new.psf',trajfn+'.dcd')
 +#u=MDA.Universe('MCB-512-new.psf','mCBP_shorttraj.dcd')
 +
 +nmols=512
 +nframes=len(u.trajectory)
 +
 +#print('u.trajectory')
 +#for i, ttr in enumerate(u.trajectory[::2]):
 +#    print(i,ttr)
 +
 +if resn=='CBP':
 +    dh_atoms=[["C39", "N32", "C36", "C35"], ["C8", "N1", "C5", "C4"], ["C34", "C33", "C2", "C3"]]
 +elif resn=='MCB':
 +    dh_atoms=[["C43", "N42", "C35", "C33"], ["C7", "N8", "C22", "C23"], ["C23", "C25", "C32", "C33"]]
 +elif resn=='MCN':
 +    dh_atoms=[["C7", "N8", "C22", "C23"], ["C43", "N42", "C35", "C33"], ["C23", "C25", "C32", "C33"] ]
 +# add PCZ case here
 +    
 +ndhs=len(dh_atoms)
 +print(ndhs)
 +
 +fig, ax = plt.subplots(ndhs,1)
 +
 +print('qua')
 +print(len(u.trajectory[::nskip]))
 +
 +dhs=np.zeros( (len(u.trajectory[::nskip]),nmols,ndhs) )
 +
 +bindh=np.linspace(-180, 180, 361)
 +
 +
 +for kkd, a4dh in enumerate(dh_atoms):
 +
 +    dA1=[res.atoms.select_atoms("name "+a4dh[0]) for res in u.residues[:nmols]]
 +    dA2=[res.atoms.select_atoms("name "+a4dh[1]) for res in u.residues[:nmols]]
 +    dA3=[res.atoms.select_atoms("name "+a4dh[2]) for res in u.residues[:nmols]]
 +    dA4=[res.atoms.select_atoms("name "+a4dh[3]) for res in u.residues[:nmols]]
 +
 +    for i1, ts in enumerate(u.trajectory[::nskip]):
 +        #for d11 in d1:
 +            #xa1,xa2,xa3,xa4=np.split(d11.positions,4)
 +            #print(xa1[0])
 +            #print(dC39)
 +            #x=Dihedrals(xa4[0],xa1[0],xa3[0],xa2[0])
 +        for jj in range(nmols):
 +            x1=MDAld.calc_dihedrals(dA1[jj],dA2[jj],dA3[jj],dA4[jj])*180./np.pi
 +            dhs[i1,jj,kkd]=x1
 +        #MDAdh(d11.positions[:3],d11.positions[3:6], d11.positions[6:9], d11.positions[9:])
 +
 +    #print(dhs[:,:,kkd])
 +    
 +    ax[kkd].hist(dhs[:,:,kkd].flatten(), label=" ".join(a4dh), bins=bindh, alpha=0.6)
 +    ax[kkd].legend()
 +
 +
 +
 +
 +np.savetxt('dhs_'+resn+'_long_3.dat',dhs.reshape(-1,ndhs))
 +
 +print('fffffff')
 +print(dhs)
 +print()
 +print('qqqqqq')
 +print(dhs.reshape(-1,ndhs))
 +dhsnew = []
 +dhsresh = dhs.reshape(-1,ndhs)
 +print(len(dhsresh[:,0]))
 +for i in range(0,len(dhsresh[:,0])):
 +    if dhsresh[i,0] != 0.0:
 +        dhsnew = np.append(dhsnew,dhsresh[i,:])
 +
 +dhsnew = np.reshape(dhsnew,(int(len(dhsnew)/ndhs),ndhs))
 +#print(len(dhsnew))
 +
 +fig.suptitle(resn, fontsize=16)
 +fig.savefig('dhs_'+resn+'_long_3.png')
 +
 +</code>
 +
 +==== Virtualenv "bioconda-ngs" ====
 +
 +Available versions of the "bioconda-ngs" environment:
 +
 +^ condaenv          ^ **build date**  ^
 +| bioconda-ngs         | 2023.05.31      |
 +| bioconda-ngs-py3.11  | 2023.05.31      |
 +
 +Enable the default version of the "bioconda-ngs" environment:
 +
 +<code>
 +module load miniconda3
 +source "$CONDA_PREFIX/etc/profile.d/conda.sh"
 +conda activate bioconda-ngs
 +</code>
 +
 +Enable the py3.11 version of the "bioconda-ngs" environment:
 +
 +<code>
 +module load miniconda3
 +source "$CONDA_PREFIX/etc/profile.d/conda.sh"
 +conda activate bioconda-ngs-py3.11
 +</code>
 +
 +Packages in the "bioconda-ngs" environment (any version):
 +
 +<code>
 +conda list
 +</code>
 +
 +Disable the "bioconda-ngs" environment (any version):
 +
 +<code>
 +conda deactivate
 +</code>
 +
 +Script ''slurm-bioconda-ngs-cpu.sh'':
 +
 +<code bash slurm-bioconda-ngs-cpu.sh>
 +#!/bin/bash
 +#SBATCH --job-name=mdanalysis
 +#SBATCH --output=%x.o%j
 +#SBATCH --error=%x.e%j
 +#SBATCH --nodes=1
 +#SBATCH --ntasks-per-node=4
 +#SBATCH --mem=20G
 +#SBATCH --qos=cpu
 +#SBATCH --partition=cpu
 +#SBATCH --time=0-01:00:00
 +##SBATCH --account=<account>
 +
 +module load miniconda3
 +source "$CONDA_PREFIX/etc/profile.d/conda.sh"
 +conda activate bioconda-ngs
 +
 +conda list
 +
 +conda deactivate
 +</code>
 +
 +==== Virtualenv "pennylane" ====
 +
 +Available versions of the "pennylane" environment:
 +
 +^ condaenv        ^ **build date**  ^
 +| pennylane       | 2023.11.03      |
 +| pennylane-gpu   | 2023.11.03      |
 +
 +Enable the default version of the "pennylane" environment:
 +
 +<code>
 +module load miniconda3
 +source "$CONDA_PREFIX/etc/profile.d/conda.sh"
 +conda activate pennylane
 +</code>
 +
 +Enable the gpu version of the "pennylane" environment:
 +
 +<code>
 +module load miniconda3
 +source "$CONDA_PREFIX/etc/profile.d/conda.sh"
 +conda activate pennylane-gpu
 +</code>
 +
 +Packages in the "pennylane" environment (any version):
 +
 +<code>
 +conda list
 +</code>
 +
 +Disable the "pennylane" environment (any version):
 +
 +<code>
 +conda deactivate
 +</code>
 +
 +Script ''slurm-pennylane-gpu.sh'':
 +
 +<code bash slurm-pennylane-gpu.sh>
 +#!/bin/bash
 +#SBATCH --job-name=plExample
 +#SBATCH --output=%x.o%j
 +#SBATCH --error=%x.e%j
 +#SBATCH --nodes=1
 +#SBATCH --partition=gpu
 +#SBATCH --qos=gpu
 +#SBATCH --gres=gpu:a100_40g:1
 +#SBATCH --mem=8G
 +#SBATCH --time=0-10:00:00
 +#SBATCH --ntasks-per-node 4
 +#SBATCH --account=<account>
 +
 +module load miniconda3
 +source "$CONDA_PREFIX/etc/profile.d/conda.sh"
 +conda activate pennylane-gpu
 +
 +PYTHON_VERSION=$(python -V | awk '{ print $2 }')
 +echo $PYTHON_VERSION
 +
 +python -u ./plExample.py
 +
 +conda deactivate
 +</code>
 +
 +
 +Script ''plExample.py'':
 +
 +<code pyton plExample.py>
 +#!/usr/bin/env python3
 +
 +# Simple example where we train a parameterized quantum circuit
 +# to minimize the expectation value of a Pauli-Z operator.
 +import pennylane as qml
 +from pennylane import numpy as np
 +
 +# Define a quantum device with one qubit
 +dev = qml.device("default.qubit", wires=1)
 +
 +# Define a variational quantum circuit
 +@qml.qnode(dev)
 +def circuit(params):
 +    qml.RX(params[0], wires=0)  # Apply a rotation around the X-axis
 +    qml.RY(params[1], wires=0)  # Apply a rotation around the Y-axis
 +    return qml.expval(qml.PauliZ(0))
 +
 +# Define the cost function to minimize
 +def cost(params):
 +    return circuit(params)
 +
 +# Initialize parameters
 +init_params = np.array([0.01, 0.01], requires_grad=True)
 +
 +# Set up the optimizer
 +opt = qml.GradientDescentOptimizer(stepsize=0.1)
 +
 +# Number of optimization steps
 +steps = 100
 +
 +# Optimize the circuit parameters
 +params = init_params
 +for i in range(steps):
 +    params = opt.step(cost, params)
 +    if (i + 1) % 10 == 0:
 +        print(f"Step {i + 1}: Cost = {cost(params):.6f}, Params = {params}")
 +
 +print(f"Optimized Parameters: {params}")
 +print(f"Final Cost: {cost(params)}")
 +
 +</code>
calcoloscientifico/cluster/softwareapplicativo/conda.1635003172.txt.gz · Ultima modifica: da fabio.spataro
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