Re: [scipion-users] relion classification of xmipp binned particles

[Laura d. C. <su...@bc...>]

Hi Juha, 
   
 No, I tested on a machine with a single GPU. 
 Could you please tell us if you are Pre-reading particles into RAM, number of pooled particles and number of MPIs? Also GPU RAM on each node and exact error that you are getting. 
 You could also try to do resize and normalize with Relion preprocess protocol and tell us if it works.  
   
 thanks in advance  
 Laura  
  
   
 
 
 
                 
         Activo Mar, 27 Junio at  3:52 PM
          ,  Juha Huiskonen <ju...@st...>  Escrito:
           Hi Laura,   
 Is your test with multiple nodes or GPUs? I wonder what happens in Relion if there is just one stack, when it's splitting it. Also I guess memory can be an issue when opening a large single stack on GPUs? I have 160,000 particles that were originally 300 pix and now they are 100 pix. I was running 3D classification and it indeed crashed in the Estimating Initial Noise step
 
 
          
 Best wishes,  
 Juha 
         
  
 
 
  
 On Tue, Jun 27, 2017 at 2:28 PM, ldelcano <lde...@cn...> wrote: 
 
 
  
    
  
   
     Hi Jose Miguel, 
     I have tested with a project that I have. Particles 200 px
      resized to 66 px (factor 0.333) and normalized with Xmipp. Then I
      run Relion 2D classification and it arrives to Iteration 1 without
      errors.  I guess the error that Juha is reporting is on Estimating
      Initial Noise Expectra. I will try tomorrow with another dataset. 
     cheers 
     Laura 
     
  
     
     On 27/06/17 15:03, Jose Miguel de la
      Rosa Trevin wrote: 
     
     
       Laura, have you checked if the same error happens
        (or not) with a tutorial dataset, for example? 
       
 
         On Tue, Jun 27, 2017 at 3:02 PM, Laura
          del Caño <su...@bc...>
          wrote: 
           
             
               
                 
                   Hi Juha, 
                   
 
                   
                   could you send us protocol parameters for the
                    binning and normalization, as well as particle size
                    (and maybe logs) to try to reproduce the problem? 
                   
 
                   
                   thanks 
                   Laura 
                   
                   
                   
                     
 
                     
                   
                 
               
               
                 Activo
                  Mar, 27 Junio at 1:21 PM , Juha
                  Huiskonen <ju...@st...>
                  Escrito:
                   
                     
                       Dear all,
                         
 
                         
                         I need to run a fairly large classification
                          run. To speed it up, I have run
                          xmipp-crop/resize particles to achieve binning
                          by factor of 3 and then normalised the
                          particles again with xmipp-preprocess
                          particles.  
                         
 
                         
                         Without binning/renormalization the job
                          runs fine (albeit slowly). With
                          binning/renormalization I get a segmentation
                          fault referring to libfftw3 right when the
                          'Estimating initial noise spectra' starts. I
                          am running on GPU.  
                         
 
                         
                         The only difference I can see between the
                          two is that after binning/renormalization all
                          the particles are in one large stack, whereas
                          normally they are in several separate stacks.
                          Has anyone else seen this behaviour? Any
                          suggestions? 
                         
                           
                           
                             
                               
                                 
                                   
                                     
                                       
                                         
                                           
                                             Best wishes, 
                                               
                                             Juha 
                                               
                                             
 
                                             
                                           
                                         
                                       
                                     
                                   
                                 
                               
                             
                           
                         
                       
                     
                   
                 
               
              
             
             
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