Differenze

Queste sono le differenze tra la revisione selezionata e la versione attuale della pagina.

--- calcoloscientifico:cluster:softwareapplicativo:conda [23/10/2021 17:36] – [Virtualenv "cryolo-cpu"] fabio.spataro
+++ calcoloscientifico:cluster:softwareapplicativo:conda [05/07/2024 10:04] (versione attuale) – federico.prost
@@ Linea 42: / Linea 42: @@
 Available versions of the "statistical-genetics" environment:
-^ condaenv                         | **build date**  |
+^ condaenv                         ^ **build date**  ^
 | statistical-genetics             | 2020.06.29      |
 | statistical-genetics-2020.03.14  | 2020.03.14      |
@@ Linea 75: / Linea 75: @@
 </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'':
@@ Linea 88: / Linea 88: @@
 #SBATCH --mem=512M
 #SBATCH --partition=vrt
+#SBATCH --qos=vrt
 #SBATCH --time=0-00:05:00
 ##SBATCH --account=<account>
@@ Linea 102: / Linea 103: @@
 </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
 #SBATCH --job-name=loop
@@ Linea 112: / Linea 113: @@
 #SBATCH --ntasks-per-node=4
 #SBATCH --mem=512M
-#SBATCH --partition=bdw
+#SBATCH --partition=cpu
+#SBATCH --qos=cpu
 #SBATCH --time=0-00:05:00
 ##SBATCH --account=<account>
@@ Linea 129: / Linea 131: @@
 ==== Virtualenv "machine-learning-cuda" ====
-Main packages included:
+Main packages included in 2022 condaenvs:
 <code>
 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:
-keras.datasets sklearn
+ - keras.datasets
+ - sklearn
+</code>
+Available versions of the "machine-learning-cuda" environment:
+^ condaenv                                 ^ build date  ^
+| machine-learning-cuda-11.6.0             | 2022.05.20  |
+| machine-learning-cuda-11.6.0-2022.05.20  | 2022.05.20  |
+| machine-learning-cuda-11.6.0-2022.03.09  | 2022.03.09  |
+| machine-learning-cuda-11.4.2             | 2022.05.20  |
+| machine-learning-cuda-11.4.2-2022.05.20  | 2022.05.20  |
+| machine-learning-cuda-11.4.2-2022.03.09  | 2022.03.09  |
+Main packages included in 2021 condaenvs:
+<code>
+conda install:
+ - cudatoolkit
+ - pip
+ - mpi4py
+ - h5py
+ - keras
+ - opencv
+ - opencv-python-headless
+ - tensorflow
+ - torchvision
+ - easydict
+ - pytorch
+pip install:
+ - keras.datasets
+ - sklearn
 </code>
@@ Linea 205: / Linea 252: @@
 #SBATCH --nodes=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:tesla:1
+#SBATCH --gres=gpu:p100:1
 #SBATCH --partition=gpu
+#SBATCH --qos=gpu
 #SBATCH --mem=16G
 #SBATCH --time=0-00:30:00
@@ Linea 269: / Linea 317: @@
 #SBATCH --mem=1G
 #SBATCH --partition=vrt
+#SBATCH --qos=vrt
 #SBATCH --time=0-01:00:00
 ##SBATCH --account=<account>
@@ Linea 286: / Linea 335: @@
 </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
 #SBATCH --job-name=cryolo
@@ Linea 296: / Linea 345: @@
 #SBATCH --ntasks-per-node=4
 #SBATCH --mem=4G
-#SBATCH --partition=bdw
+#SBATCH --partition=cpu
+#SBATCH --qos=cpu
 #SBATCH --time=0-01:00:00
 ##SBATCH --account=<account>
@@ Linea 318: / Linea 368: @@
 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:
@@ Linea 378: / Linea 429: @@
 </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>