A good choice to use Kaggle is this feature: Kaggle provides free access to NVidia K80 GPUs in kernels.
The tutorial for today is about kaggle and is new for me because I hear about this opportunity last year.
This platform that hosts data science and machine learning competitions can give the people a good area for development.
The official blog can tell you more about how can this platform works.
The kaggle A.P.I. can be found at GitHub.
Let's start the tutorial with the pip3 install tool:
[mythcat@desk kaggle]$ pip3 install kaggle --upgrade --user
Collecting kaggle
...
[mythcat@desk kaggle]$ mkdir ~/.kaggle/
[mythcat@desk kaggle]$ mv kaggle.json ~/.kaggle/kaggle.json
[mythcat@desk kaggle]$ kaggle
Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /home/mythcat/.kaggle/kaggle.json'
usage: kaggle [-h] [-v] {competitions,c,datasets,d,kernels,k,config} ...
kaggle: error: the following arguments are required: command
[mythcat@desk kaggle]$ chmod 600 /home/mythcat/.kaggle/kaggle.json[mythcat@desk kaggle]$ kaggle competitions list
ref deadline category reward teamCount
userHasEntered
--------------------------------------------- ------------------- --------------- --------- ---------
--------------
digit-recognizer 2030-01-01 00:00:00 Getting Started Knowledge 2305
False
titanic 2030-01-01 00:00:00 Getting Started Knowledge 17135
False
house-prices-advanced-regression-techniques 2030-01-01 00:00:00 Getting Started Knowledge 5532
False
imagenet-object-localization-challenge 2029-12-31 07:00:00 Research Knowledge 57
False
google-quest-challenge 2020-02-10 23:59:00 Featured $25,000 410
False
tensorflow2-question-answering 2020-01-22 23:59:00 Featured $50,000 810
False
data-science-bowl-2019 2020-01-22 23:59:00 Featured $160,000 1762
False
pku-autonomous-driving
...kaggle competitions {list, files, download, submit, submissions, leaderboard}
kaggle datasets {list, files, download, create, version, init}
kaggle kernels {list, init, push, pull, output, status}
kaggle config {view, set, unset}The process of using models need the datasets and you can use the kaggle datasets.
You can use the New Notebook button from that page to start using the datasets.
The datasets can be load from kaggle or can be uploaded.
The next window let you to Select new notebook settings.
I used Python with a notebook and I got the default source code:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# Any results you write to the current directory are saved as output.(read-only data) and output from kaggle with pandas module:
data = pd.read_csv("../input/lego-database/colors.csv")
data.head()You can see my online test I created with dataset Lego and python on my kaggle page.
