In [0]:

    

# meminfo
!cat /proc/meminfo

# cpu information
!cat /proc/cpuinfo

# GPU information
from tensorflow.python.client import device_lib
device_lib.list_local_devices()

# memory footprint support libraries/code
!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi
!pip install gputil
!pip install psutil
!pip install humanize
import psutil
import humanize
import os
import GPUtil as GPU
GPUs = GPU.getGPUs()
# XXX: only one GPU on Colab and isn’t guaranteed
gpu = GPUs[0]
def printm():
 process = psutil.Process(os.getpid())
 print("Gen RAM Free: " + humanize.naturalsize( psutil.virtual_memory().available ), " | Proc size: " + humanize.naturalsize( process.memory_info().rss))
 print("GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))
printm()


    

In [1]:

    

# Run this cell and select the kaggle.json file downloaded
# from the Kaggle account settings page.

from google.colab import files
files.upload()


    

    






     
     

      Upload widget is only available when the cell has been executed in the
      current browser session. Please rerun this cell to enable.
      
       






    
Out[1]:





{}

In [2]:

    

# Let's make sure the kaggle.json file is present
!ls -lha kaggle.json


    

    




ls: cannot access 'kaggle.json': No such file or directory

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# Next, install kaggle API client
!pip install -q kaggle


    

In [4]:

    

# The Kaggle API client expects this file to be in ~/.kaggle,
# so move it there.
!mkdir -p ~./kaggle
!cp kaggle.json ~/.kaggle/

# This permissions change avoids a warning on Kaggle tool startup.
!chmod 600 ~/.kaggle/kaggle.json


    

    




cp: cannot stat 'kaggle.json': No such file or directory
chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory

In [5]:

    

# List available datasets
!kaggle datasets list


    

    




Traceback (most recent call last):
  File "/usr/local/bin/kaggle", line 11, in <module>
    load_entry_point('kaggle==1.5.1.1', 'console_scripts', 'kaggle')()
  File "/usr/local/lib/python2.7/dist-packages/pkg_resources/__init__.py", line 487, in load_entry_point
    return get_distribution(dist).load_entry_point(group, name)
  File "/usr/local/lib/python2.7/dist-packages/pkg_resources/__init__.py", line 2728, in load_entry_point
    return ep.load()
  File "/usr/local/lib/python2.7/dist-packages/pkg_resources/__init__.py", line 2346, in load
    return self.resolve()
  File "/usr/local/lib/python2.7/dist-packages/pkg_resources/__init__.py", line 2352, in resolve
    module = __import__(self.module_name, fromlist=['__name__'], level=0)
  File "/usr/local/lib/python2.7/dist-packages/kaggle/__init__.py", line 23, in <module>
    api.authenticate()
  File "/usr/local/lib/python2.7/dist-packages/kaggle/api/kaggle_api_extended.py", line 116, in authenticate
    self.config_file, self.config_dir))
IOError: Could not find kaggle.json. Make sure it's located in /root/.kaggle. Or use the environment method.

In [0]:

    

# Kaggle dataset download
# copy from Kaggle dataset website
!kaggle competitions download -c humpback-whale-identification

## ../input/train , test

#!mkdir train
#!unzip ./train.zip -d train

#!mkdir test
#!unzip ./test.zip -d test


    

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# Load the Drive helper and mount
from  google.colab import drive

# This will prompt for authorization
drive.mount('/content/drive')

# After executing the cell above, 
# Drive files will be present in "/content/drive/My Drive"
!ls "/content/drive/My Drive"


    

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import h5py

##Prep:

import numpy as np
import h5py

data_to_write = np.random.random(size=(100,20)) # or some such

## Write:

with h5py.File('name-of-file.h5', 'w') as hf:
    hf.create_dataset("name-of-dataset",  data=data_to_write)

## Read:

with h5py.File('name-of-file.h5', 'r') as hf:
    data = hf['name-of-dataset'][:]


    

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## EXAMPLE

#Read: HDF5 file with h5py
import h5py

with h5py.File('whale_train_image.h5', 'r') as hf:
    train_X = hf['whale_X'][:]