Peter Roelants peterroelants

## rasterize_rectilinear_dask_16384_success.ipynb

      
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                peterroelants
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              November 30, 2020 20:32
            
          
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## quadmesh_rectilinear_dask_16384_failed.ipynb

      
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                / quadmesh_rectilinear_dask_16384_failed.ipynb
            
            
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              November 30, 2020 20:30
            
          
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## quadmesh_rectilinear_dask_4096_working.ipynb

      
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                / quadmesh_rectilinear_dask_4096_working.ipynb
            
            
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              November 30, 2020 20:34
            
          
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## xarray_dask_quadmesh_shade_outofmemory.ipynb

      
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                / xarray_dask_quadmesh_shade_outofmemory.ipynb
            
            
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              November 30, 2020 10:22
            
              
                QuadMesh not making use of Dask out-of-core processing
              
          
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## xarray_dask_shade_working.ipynb

      
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                / xarray_dask_shade_working.ipynb
            
            
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              November 30, 2020 10:14
            
              
                XArray + DaskArray working and tested for shade
              
          
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## dask_type_error_array.ipynb

      
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              November 26, 2020 17:35
            
              
                Datashader task typeerror
              
          
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## gridworld.ipynb

      
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                peterroelants
                / gridworld.ipynb
            
            
              Created
              July 2, 2019 07:30
            
              
                GridWorld
              
          
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## tf2_error.txt
2.0.0-dev20190205
2019-02-05 18:09:28.956373: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-02-05 18:09:28.959894: I tensorflow/stream_executor/platform/default/dso_loader.cc:161] successfully opened CUDA library libcuda.so.1 locally
2019-02-05 18:09:29.161041: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1010] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-02-05 18:09:29.168088: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1010] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-02-05 18:09:29.168686: I tensorflow/compiler/xla/service/service.cc:162] XLA service 0x1a03ef0 executing computations on platform CUDA. Devices:
2019-02-05 18:09:29.168697: I tensorflow/compiler/xla/service/service.cc:169]   StreamExecuto

## RANSAC.ipynb

      
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                / RANSAC.ipynb
            
            
              Created
              November 6, 2018 15:16
            
              
                RANSAC
              
          
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## dataset_example.py
dataset = tf.data.Dataset.from_tensor_slices(mnist_data)
dataset = dataset.shuffle(
    buffer_size=1000, reshuffle_each_iteration=True
).repeat(count=None).batch(batch_size)
	2.0.0-dev20190205
	2019-02-05 18:09:28.956373: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
	2019-02-05 18:09:28.959894: I tensorflow/stream_executor/platform/default/dso_loader.cc:161] successfully opened CUDA library libcuda.so.1 locally
	2019-02-05 18:09:29.161041: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1010] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
	2019-02-05 18:09:29.168088: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1010] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
	2019-02-05 18:09:29.168686: I tensorflow/compiler/xla/service/service.cc:162] XLA service 0x1a03ef0 executing computations on platform CUDA. Devices:
	2019-02-05 18:09:29.168697: I tensorflow/compiler/xla/service/service.cc:169] StreamExecuto
	dataset = tf.data.Dataset.from_tensor_slices(mnist_data)
	dataset = dataset.shuffle(
	buffer_size=1000, reshuffle_each_iteration=True
	).repeat(count=None).batch(batch_size)