- Ebook Foundations of Computer Science: http://i.stanford.edu/~ullman/focs.html
Penulis: admin
Machine Learning Free Book
Some free ebooks to learn Machine Learning and related fields
Statistics
- Cameron Davidson-Pilon, Probabilistic Programming & Bayesian Methods for Hackers,
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning, Springer
Machine Learning
- Jeffrey Stanton, An Introduction to Data Science version 3 , 2013
- Nils J. Nilsson , Introduction to Machine Learning, http://ai.stanford.edu/~nilsson/mlbook.html
- Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/copy.html
- David Barber, Bayesian Reasoning and Machine Learning, http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online
- Mohit Deshpande, Pablo Farias Navarro, Machine Learning for Human Beings, https://pythonmachinelearning.pro/free-ebook-machine-learning-for-human-beings/
- Alex Smola and S.V.N. Vishwanathan, Introduction to Machine Learning, http://alex.smola.org/drafts/thebook.pdf
- Allen B. Downey, Think Stats, Probability and Statistics for Programmers, O’Reilly,
- Allen B. Downey., Think Stats 2nd Edition, http://greenteapress.com/wp/think-stats-2e/,
- Allen B. Downey, Think Bayes,
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning, [Website]
- Ron Zacharski, A Programmer’s Guide to Data Mining, http://guidetodatamining.com/
- Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman, Mining of Massive Datasets http://infolab.stanford.edu/~ullman/mmds/book.pdf
- Steven Bird, Ewan Klein, and Edward Loper, Natural Language Processing with Python, http://www.nltk.org/book_1ed/
- Richard Szeliski, Computer Vision: Algorithms and Applications, http://szeliski.org/Book/
- Hal Daume, A Course in Machine Learning, http://ciml.info/
- Max Welling, A First Encounter with Machine Learning, https://www.ics.uci.edu/~welling/teaching/273ASpring10/IntroMLBook.pdf
- Carl Edward Rasmussen and Christopher K. I. Williams, Gaussian Processes for Machine Learning, http://www.gaussianprocess.org/gpml
- Amnon Shashua, Introduction to Machine Learning, [PDF]
- Julie Steele, Understanding the Chief Data Officer, O’Reilly,
- Ron Zacharski, A Programmer’s Guide to Data Mining, 2015
- D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds), Machine Learning, Neural and Statistical Classification
- David MacKay, Information Theory, Pattern Recognition and Neural Networks
- Jake VanderPlas, Python Data Science Handbook
- Andriy Burkov, Machine Learning Engineering [Draft]
- Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006 [PDF]
Data Engineering
- DJ Patil, Hilary Mason, Data Driven Creating a Data Culture, [Website], O’Reilly
- DJ Patil, Data Jujitsu: The Art of Turning Data into Product
- DJ Patil, Building Data Science Teams, O’Reilly
Neural Network
- David Kriesel, A Brief Introduction to Neural Networks, [PDF]
- Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jess, Hagan, Neural Network Design 2nd Edition
Deep Learning
- Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning,
- Michael Nielsen, Neural Networks and Deep Learning, http://neuralnetworksanddeeplearning.com/
- Mohit Deshpande, Deep Learning with Python for Human Beings, https://pythonmachinelearning.pro/free-ebook-deep-learning-with-python/
Sources
- Big Data Made Simple: Learning more like a human: 18 free eBooks on Machine Learning
- https://github.com/TechBookHunter/Free-Machine-Learning-Books
- https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html
- KD Nuggets: 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python R and more
- Free Machine Learning Books https://github.com/TechBookHunter/Free-Machine-Learning-Books
- Free Deep Learning Books, https://github.com/TechBookHunter/Free-Deep-Learning-Books
Google Code-In 2017: My Story
Weeks before GCI (Google Code-In) even started, I keep debating with myself whether to join GCI 2017 or not. I was a GCI 2016 participant and my experience with it was not so good. It was kinda a traumatic experience for me.
Long story short, I decided to join. The first thing I have to do is chose an organization I’m interested in. I already knew which organization I’d contribute to, even before I joined; Zulip.
But joining GCI more than a week late (I had some internet problems) ruins my plan. Zulip is a huge community. There sure were a lot of participants. That means I have to do a lot of tasks in order to, well, win? I never expect myself to be a finalist, let alone winning, but I want to push myself to the limit. The competition would be too tough for me, so I prefer to chose other organization.
I scroll through the available organizations and observe them. Surprisingly, a few organizations caught my eyes. OpenWISP, LiquidGalaxy, and CloudCV, to name a few. I feel like I was sorta qualified for them. Not only that, they’re all new organizations! A good thing to forget my past, GCI 2016.
I choose CloudCV as the organization I want to work with. I chose it because it’s related to Machine Learning, a thing that I’ve been interested in for the past several months. Perfect.
CloudCV is a young open source organization which builds some platforms for AI and/or Machine Learning. The goal of CloudCV is to make AI research more reproducible. CloudCV has 3 main projects, EvalAI, Origami, and Fabrik.
Fabrik’s page |
CloudCV’s task choices, however, were so limited. At one point, it even only had 7 tasks choices (not counting the beginner tasks)! I mostly give my contributions to Fabrik, such as adding neural network models to its model zoo. Adding a model to Fabrik’s model zoo was like a gambling game for me. When you’re lucky, it was so easy you feel like you’ve done nothing. But other times it’s really hard I feel like I want to give up.
The first thing I have to do when I want to add a new model to Fabrik is to find a neural network model. At this time of writing, Fabrik only supports 3 frameworks, Caffe, Keras, and Tensorflow. However, Fabrik still has some problems with tensorflow models. I don’t have any experience with Caffe so I prefer to go with keras.
After cloning a model I want to add, I have to make sure that the model works perfectly. Some models work well in keras 2, while some others don’t. Some works in tensorflow 1.4.1, some don’t, etcetera. After running the model smoothly, I have to make a JSON file from it. Then, I have to make sure that Fabrik supports the layers in the model.
Sometimes Fabrik throw me an error and I have to find another model. If Fabrik keeps throwing errors, I have to change the model I want to import, and start working from zero again. Repeat.
In this blog post, I’ve listed some models I’ve tried to add to Fabrik. There’s more to it though. Right now I have a collection of more than 20 different neural networks models, only because I keep getting errors on most models I tried! Almost all of them use keras as their framework.
Another thing I did was finding AI challenges on the internet. I already know one website; kaggle! But this task makes me even more creative and I scoured the internet for every possible AI challenge I can find. Some of them can be found here.
I also made some graphics for CloudCV:
A logo for Origami |
An illustration for Fabrik |
I enjoyed working with CloudCV. I like the atmosphere, the community, the nice and helping people, and pretty much everything, even the timezones. Most students in other organization usually have problems with a huge time zone difference with their mentors and ended up being awake all night long. In CloudCV, I was thankful to have mentors whose timezones were close to mine.
One thing that bugs me a little is that CloudCV only had a few mentors. I counted all the mentors whose name appeared on the task pages, and there were only 9 mentors!
A random screenshot of my terminal |
Working with CloudCV gave me the experience about programming in the real world. Programming isn’t all about coding. Sometimes when you find a problem, you gotta solve it yourself because StackOverflow doesn’t have all the answer. Setting up a development environment is the hardest of all. Package versions aren’t just numbers, but it plays an important role in a project.
In the future, I hope to contribute more to CloudCV whenever I have enough time.
I got into the leaderboard and I’m pretty happy with that. Thank you to everyone who has helped me through contributing to CloudCV, including my family, other students, and of course, and my mentors. Thanks for dealing with my dumb questions and dealing with me in general.
ps: if you want to ask me questions about GCI, feel free to, I’d be happy to answer.
Keras Neural Networks and Fabrik
A screenshot of Fabrik |
I tried to import several keras neural networks to Fabrik, and this is the result:
These are the models I successfully imported:
Model Link | Successfully Generated the JSON Model? | Problem | Error Message |
---|---|---|---|
https://github.com/ykamikawa/SegNet | Yes | Error when importing | ValueError: Unknown layer: MaxPoolingWithArgmax2D |
https://github.com/zhixuhao/unet | Yes | Error when exporting | ValueError: `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 64, 64, 512), (None, 63, 63, 512)] |
https://github.com/yihui-he/u-net | Yes | Error when exporting | ValueError: `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, -1, 19, 256), (None, 0, 20, 256)] |
https://github.com/aitorzip/Keras-ICNet | Yes | Error when importing | ValueError: bad marshal data (unknown type code) |
https://github.com/preddy5/segnet | Yes | Error when importing | Cannot import layer of Layer type |
https://github.com/k3nt0w/FCN_via_keras/ | No | ValueError: The input must have 3 channels; got `input_shape=(3, 224, 224)` | |
https://github.com/0bserver07/Keras-SegNet-Basic | No | ValueError: total size of new array must be unchanged |
How to Save a Keras Model to a JSON file
To save a keras model to a json file, simply use this code:
model_json = model.to_json()
with open(“model.json”, “w”) as json_file:
json_file.write(model_json)
where model is a keras model object.
Image Captioning Models and Fabrik
No
|
Name
|
URL
|
Framework
|
Note
|
Fabrik
|
---|---|---|---|---|---|
1 | NeuralTalk | https://github.com/karpathy/neuraltalk | Python + numpy | obsoleted by NeuralTalk2 | |
2 | NeuralTalk2 | https://github.com/karpathy/neuraltalk2 | Torch | Torch incompatible with Fabriq | |
3 | Show and Tell | https://github.com/tensorflow/models/tree/master/research/im2txt | Tensorflow | Tensorflow parser Fabriq incompatible with Fabriq | |
4 | Keras Image Caption | https://github.com/LemonATsu/Keras-Image-Caption | Keras |
requires python 3.4+
fail with python 3.6
|
|
5 | https://github.com/amaiasalvador/imcap_keras | Keras | need MSCOCO | ||
6 | Neural Image Captioning | https://github.com/oarriaga/neural_image_captioning | Keras | TimeDistributed Layer incompatible with | |
Machine Learning And Artificial Intelligence Challenges in 2018
Host | Challenge Name | URL | Prize | Deadline |
Visual Geometry Group | Visual Domain Decathlon Challenge | http://www.robots.ox.ac.uk/~vgg/decathlon/ | ||
Driven Data | Concept to Clinic | https://concepttoclinic.drivendata.org/ | 100000 | Early 2018 |
Driven Data | Predicting Poverty | https://www.drivendata.org/competitions/50/worldbank-poverty-prediction/ | 15000 | 28 Februari 2018 |
Kaggle | Mercari Price Suggestion Challenge | https://www.kaggle.com/c/mercari-price-suggestion-challenge | 100000 | |
Kaggle | Toxic Comment Classification Challenge | https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge | 35000 | 20 Februari 2018 |
Kaggle | Nomad2018 Predicting Transparent Conductors | https://www.kaggle.com/c/nomad2018-predict-transparent-conductors | EUR 5000 | |
Kaggle | Statoil/C-CORE Iceberg Classifier Challenge | https://www.kaggle.com/c/statoil-iceberg-classifier-challenge | 50000 | 23 Januari 2018 |
Kaggle | TensorFlow Speech Recognition Challenge | https://www.kaggle.com/c/tensorflow-speech-recognition-challenge | 25000 | 16 Januari 2018 |
Crowd AI | WWW 2018 Challenge: Learning to Recognize Musical Genre | https://www.crowdai.org/challenges/www-2018-challenge-learning-to-recognize-musical-genre | ||
Crowd AI | AI-generated music challenge | https://www.crowdai.org/challenges/ai-generated-music-challenge | ||
Crowdanalytix | Business Analytics for Beginners Using R – Part I | https://www.crowdanalytix.com/contests/business-analytics-for-beginners-using-r—part-i | ||
https://community.topcoder.com/longcontest/?module=ViewProblemStatement&rd=17036&pm=14735 | ||||
Innocentive | Machine Tagging Challenge | https://www.innocentive.com/ar/challenge/9934063 | ||
Innocentive | PET Imaging Probes for Visualization and Quantification of Oligonucleotide Exposure | https://www.innocentive.com/ar/challenge/9934086 | ||
Topcoder | Computer Vision – Duplicated Receipts Detector – Improvement of The Initial PoC | https://www.topcoder.com/challenges/30061112/?type=develop | ||
Topcoder | Road Detector | https://community.topcoder.com/longcontest/?module=ViewProblemStatement&rd=17036&pm=14735 | ||
https://community.topcoder.com/longcontest/?module=ViewStandings&rd=17036 | ||||
Hackerrank | Correlation and Regression Lines – A Quick Recap #1 | https://www.hackerrank.com/challenges/correlation-and-regression-lines-6/problem | ||
DataScience | Power Consumption Forecasts | https://www.datascience.net/fr/challenge/32/details | ||
Analytics Vidhya | Data Science Interview Preparation Test | https://datahack.analyticsvidhya.com/contest/data-science-interview-preparation-test/ | ||
Quora | Quora Challenges | https://www.quora.com/challenges | ||
Coda Lab | LiTS – Liver Tumor Segmentation Challenge | https://competitions.codalab.org/competitions/17094 | ||
Microsoft et al | MS COCO | http://cocodataset.org/ | ||
Kaggle & IEEE | IEEE Signal Processing Cup | https://signalprocessingsociety.org/get-involved/signal-processing-cup | ||
Kaggle & Google | Google Landmark Retrieval Challenge | https://www.kaggle.com/c/landmark-retrieval-challenge | ||
Kaggle & Google | Google Landmark Recognition Challenge | https://www.kaggle.com/c/landmark-recognition-challenge | ||
Kaggle | Humpback Whale Identification Challenge | https://www.kaggle.com/c/whale-categorization-playground | ||
Kaggle | Digit Recognizer | https://www.kaggle.com/c/digit-recognizer | ||
Crowd AI | AI-generated music challenge | https://www.crowdai.org/challenges/ai-generated-music-challenge | ||
Crowd AI | Mapping Challenge | https://www.crowdai.org/challenges/mapping-challenge | ||
RTE | Winter electricity demand forecast – a deterministic approach [Part 1] | https://www.datascience.net/fr/challenge/33/details | ||
RTE | Winter electricity demand forecast – a probabilistic approach [Part 2] | https://www.datascience.net/fr/challenge/34/details | ||
Coda Lab | Shallow Globe | https://competitions.codalab.org/competitions/18113 | ||
Coda Lab | AutoML2018 challenge PAKDD2018 | https://competitions.codalab.org/competitions/17767 | ||
Driven Data | Power Laws: Forecasting Energy Consumption | https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/ | ||
Driven Data | Power Laws: Detecting Anomalies in Usage | https://www.drivendata.org/competitions/52/anomaly-detection-electricity/ | ||
Driven Data | Power Laws: Optimizing Demand-side Strategies | https://www.drivendata.org/competitions/53/optimize-photovoltaic-battery/ | ||
Kaggle | Dog Breed Identification | https://www.kaggle.com/c/dog-breed-identification | ||
Robust Vision Challlenge | http://www.robustvision.net/ | |||
KITTI Vision Benchmark Suite | http://www.cvlibs.net/datasets/kitti/ | |||
Clickbait Challenge | http://www.clickbait-challenge.org/ | |||
Coda Lab | Example-based Single-Image Super-Resolution Challenge | https://competitions.codalab.org/competitions/18025 | ||
Hackerearth | various challenges | https://www.hackerearth.com/challenges/ | ||
Challenge Data | https://challengedata.ens.fr/en/season/4/challenge_data_2018.html | |||
Crowdanalytix | Identifying Superheroes from Product Images | https://www.crowdanalytix.com/contests/identifying-superheroes-from-product-images | ||
Kaggle | iMaterialist Challenge (Furniture) at FGVC5 | https://www.kaggle.com/c/imaterialist-challenge-furniture-2018 | ||
Kaggle | TalkingData AdTracking Fraud Detection Challenge | https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection | ||
Kaggle | DonorsChoose.org Application Screening | https://www.kaggle.com/c/donorschoose-application-screening | ||
Kaggle | Plant Seedlings Classification | https://www.kaggle.com/c/plant-seedlings-classification | ||
Kaggle | iNaturalist Challenge at FGVC5 | https://www.kaggle.com/c/inaturalist-2018 | ||
CrowdAI / EPFL | AI-generated music challenge | https://www.crowdai.org/challenges/ai-generated-music-challenge | ||
CrowdAI | Mapping Challenge | https://www.crowdai.org/challenges/mapping-challenge | ||
CrowdAI – CLEF | LifeCLEF 2018 Expert | https://www.crowdai.org/challenges/lifeclef-2018-expert | ||
CrowdAI / CLEF | LifeCLEF 2018 Geo – Location Based Species Recommendation | https://www.crowdai.org/challenges/lifeclef-2018-geo | ||
CrowdAI / CLEF | ImageCLEF 2018 Tuberculosis – Severity scoring | https://www.crowdai.org/challenges/imageclef-2018-tuberculosis-severity-scoring | ||
a | https://www.crowdai.org/challenges/imageclef-2018-tuberculosis-tbt-classification | |||
a | https://www.crowdai.org/challenges/imageclef-2018-caption-concept-detection | |||
a | https://www.crowdai.org/challenges/imageclef-2018-vqa-med | |||
a | https://www.crowdai.org/challenges/imageclef-2018-caption-caption-prediction | |||
Alibaba Cloud & Met Office |
Future Challenge
Helping Balloons Navigate the Weather
|
https://tianchi.aliyun.com/competition/introduction.htm?raceId=231622&_lang=en_US | ||
ICPR2018 | ICPR MTWI 2018 CHALLENGE 3: End to End Text Detection and Recognition of Web Images | https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5624d780ALjPWJ&raceId=231652 | ||
ICPR2018 | ICPR MTWI 2018 CHALLENGE 2: Text Detection of Web Images | https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5624d780ALjPWJ&raceId=231651 | ||
ICPR2018 | ICPR MTWI 2018 CHALLENGE 1: Text Recognition of Web Images | https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5624d780ALjPWJ&raceId=231650 | ||
Alibaba | FashionAI Global Challenge 2018—Attributes Recognition of Apparel | https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5624d780ALjPWJ&raceId=231649 | ||
CAINIAO | CAINIAO MSOM data-driven research competition | https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5624d780ALjPWJ&raceId=231623 | ||
Tianchi | Sina Weibo Interaction-prediction-Challenge the Baseline | https://tianchi.aliyun.com/getStart/introduction.htm?spm=5176.100066.0.0.56ecd780V1l8Q4&raceId=231574 | ||
IJCAI-18 | IJCAI-18 Alimama Sponsored Search Conversion Rate(CVR) Prediction Contest | https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.56ecd780V1l8Q4&raceId=231647 | ||
Tian Chi | FashionAI Global Challenge—Key Points Detection of Apparel | https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.56ecd780V1l8Q4&raceId=231648 | ||
Tianchi | Clothes Matching Challenge on Taobao.com-Challenge the Baseline | https://tianchi.aliyun.com/getStart/introduction.htm?spm=5176.100066.0.0.56ecd780V1l8Q4&raceId=231575 | ||
DCASE | Acoustic scene classification | http://dcase.community/challenge2018/task-acoustic-scene-classification | ||
DCASE | General Purpose audio tagging of Freesound content with AudioSet labels | http://dcase.community/challenge2018/task-general-purpose-audio-tagging and https://www.kaggle.com/c/freesound-audio-tagging | ||
DCASE | Bird audio detection | http://dcase.community/challenge2018/task-bird-audio-detection | ||
DCASE | Large-scale weakly labeled semi-supervised sound event detection in domestic environments | http://dcase.community/challenge2018/task-large-scale-weakly-labeled-semi-supervised-sound-event-detection | ||
DCASE | Monitoring of domestic activities on multi-channel acoustics | http://dcase.community/challenge2018/task-monitoring-domestic-activities | ||
Agorize | Smart City Innovation Award | https://www.agorize.com/en/challenges/le-monde-smart-cities-2018-world | ||
Open AI | Open AI Retro Contest | https://contest.openai.com/ | ||
IEEE Rebooting Computing | Low-Power Image Recognition Challenge (LPIRC 2018) | https://rebootingcomputing.ieee.org/lpirc | ||
VIST-NAACL-2018 | Visual Storytelling Challenge (NAACL 2018) | https://evalai.cloudcv.org/web/challenges/challenge-page/76/overview | ||
Automatic Visual Advertisements VQA – CVPR2018 | Automatic Understanding of Visual Advertisements | https://evalai.cloudcv.org/web/challenges/challenge-page/86/overview | ||
z | VQA Challenge 2018 | https://evalai.cloudcv.org/web/challenges/challenge-page/80/overview | ||
z | Leaf Segmentation Challenge | https://competitions.codalab.org/competitions/18405 | ||
z | DeepGlobe Land Cover Classification Challenge | https://competitions.codalab.org/competitions/18468 | ||
z | Chalearn LAP Inpainting Competition Track 2 – Video decaptioning | https://competitions.codalab.org/competitions/18421 | ||
z | Chalearn LAP Inpainting Competition Track 3 – Fingerprint Denoising and Inpainting | https://competitions.codalab.org/competitions/18426 | ||
z | Chalearn LAP Inpainting Competition Track 1 – Inpainting of still images | https://competitions.codalab.org/competitions/18423 | ||
Kaggle / Google | Open Images Challenge 2018 | https://storage.googleapis.com/openimages/web/challenge.html | ||
Principal Financial Group | IEEE Investment Ranking Challenge | https://www.crowdai.org/challenges/ieee-investment-ranking-challenge | ||
z | x | t | y | |
Stanford ML Group |
Bone X-Ray Deep Learning Competition
|
https://stanfordmlgroup.github.io/competitions/mura/ | t | y |
Berkeley |
WAD 2018 Challenges
|
http://bdd-data.berkeley.edu/wad-2018.html | t | y |
Hackerearth |
Deep Learning Beginner Challenge
|
https://www.hackerearth.com/challenge/competitive/deep-learning-beginner-challenge/ | y | |
x
|
x | t | y | |
x |
x
|
z | t | y |
x |
x
|
z | t | y |
Alibaba |
Alibaba Global Scheduling Algorithm Competition
|
https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5f1cd780HajDHE&raceId=231663 | t | y |
IEEE ICDM 2018 |
IEEE ICDM 2018 Global A.I. Challenge on MeteorologyCatch Rain If You Can
|
https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.47cbd780fgnIJX&raceId=231662 | t | y |
Instalasi Eval AI di Ubuntu 17.10
Artikel ini adalah adaptasi dari prosedur instalasi di https://github.com/Cloud-CV/EvalAI
apt-get install openssh-server
apt-get install net-tools
Instalasi software dependencies:
apt-get install python2.7
apt-get install git
apt-get install postgresql# Success. You can now start the database server using:
# /usr/lib/postgresql/9.6/bin/pg_ctl -D /var/lib/postgresql/9.6/main -l logfile startapt-get install rabbitmq-server
apt-get install virtualenv
apt-get install python-psycopg2 (thanks to https://stackoverflow.com/questions/28253681/you-need-to-install-postgresql-server-dev-x-y-for-building-a-server-side-extensi)
apt-get install libpq-dev
apt-get install python-dev
apt-get install build-essential
# clone the EvalAI code
git clone https://github.com/Cloud-CV/EvalAI.git evalai
# Create a python virtual environment and install python dependencies.
cd evalai
virtualenv venv
source venv/bin/activate # run this command everytime before working on project
pip install -r requirements/dev.txt
Proses sampai tahap ini berhasil, selanjutnya masih perlu diujicoba:
cp settings/dev.sample.py settings/dev.py
Use your postgres username and password for fields USER and PASSWORD in dev.py file.
Create an empty postgres database and run database migration.
sudo -i -u (username)
createdb evalai
python manage.py migrate –settings=settings.dev
Seed the database with some fake data to work with.
python manage.py seed –settings=settings.dev
This command also creates a superuser(admin), a host user and a participant user with following credentials.
SUPERUSER- username: admin password: password
HOST USER- username: host password: password
PARTICIPANT USER- username: participant password: password
That’s it. Now you can run development server at http://127.0.0.1:8000 (for serving backend)
python manage.py runserver –settings=settings.dev
Open a new terminal window with node(6.9.2) and ruby(gem) installed on your machine and type
npm install
Install bower(1.8.0) globally by running:
npm install -g bower
Now install the bower dependencies by running:
bower install
If you running npm install behind a proxy server, use
npm config set proxy http://proxy:port
Now to connect to dev server at http://127.0.0.1:8888 (for serving frontend)
gulp dev:runserver
That’s it, Open web browser and hit the url http://127.0.0.1:8888.
(Optional) If you want to see the whole game into play, then start the RabbitMQ worker in a new terminal window using the following command that consumes the submissions done for every challenge:
python scripts/workers/submission_worker.py
Percobaan iNaturalist
Menghitung Bandwidth Fair Use Policy Indihome 2016
Pada tulisan ini akan dihitung berapa bandwidth yang dapat dipakai untuk paket 10 Mbps Indihome jika diaktifkan terus menerus pada kondisi ideal.
PT Telkom Indonesia menerapkan kebijakan Fair Use Policy (FUP) pada Indihome berikut ini mulai Februari 2016.
Asumsi:
- Paket 10 Mbps
- 1 bulan adalah 30 hari
- Koneksi aktif download setiap saat hanya dibatasi kecepatan Indihome
- B = byte, b = bit, 1 byte = 8 bit
Perhitungan:
Paket ini akan mengalami 3 macam kecepatan:
- 10 Mbps ketika pemakaian <300 GB
- 75% x 10 Mbps = 7.5 Mbps ketika pemakaian < 400 GB
- 40% x 10 Mbps = 4. Mbps ketika pemakaian > 400 GB
Durasi kecepatan 10 Mbps adalah:
300 GB / 10 Mbps = 300 GB / 10 Mbps x 8 b/B = 240000 detik
Durasi kecepatan 7.5 Mbps adalah:
100 GB / 7.5 MBps = 100 GB / 7.5 Mbps x 8 b/B = 106666 detik
Durasi kecepatan 4 Mbps adalah:
30 hari – 240000 detik – 106666 detik = 30x24x60x60 – 240000 – 106666 = 2245333 detik
Berikut ini ringkasannya:
Kecepatan | Durasi | Total byte | |
segmen 1 | 10 Mbps | 240000 detik | 300 GB |
segmen 2 | 7.5 Mbps | 106666 detik | 100 GB |
segmen 3 | 4 Mbps | 2245333 detik | 1122,7 GB |
Total | 2592000 detik | 1522.7 GB |
Jadi total download selama 30 hari adalah 1522.7 GB , dengan kecepatan rata-rata adalah 433127 byte /s atau sekitar 3.46 Mbps
Referensi
- Software untuk menghitungnya dapat dilihat di https://bitbucket.org/waskita/fair-usage-policy-2016
- Deskripsi FUP Indihome dapat dibaca di http://www.indihome.co.id/faq