An Introduction To The Need For Free GPU Cloud Compute
GPUs were once used solely for video games. Now, they power machine learning models around the world with their unique configuration and processing power. Getting free GPU cloud hours has become a need for many machine learning practitioners and hobbyists.
In brief summary, your traditional CPUs are good for complex calculations performed sequentially, while GPUs are excellent for many simple parallel calculations performed across multiple cores. GPUs take advantage of the fact that their hardware structure and architecture is meant to do shallow calculations in parallel faster than a CPU can do them in sequence.
That makes them the perfect fit to train deep neural networks. The new RAPIDS framework also allows us to extend this to regular machine learning work and to data visualization tasks. This has led to speedups that can take algorithms that normally take upwards of 30 minutes, and reduce them to speeds of 3 seconds.
How do we take best advantage of this scenario? Fortunately, there are many GPU cloud providers that are offering free GPU cloud compute time so you can run experiments and try out these new processes.
1 – Google Colab
Google Colab offers you the opportunity to easily upload Python Notebooks into the cloud and interact with Github/Git to pull repositories to modify or to push work in Colab files to Github. If you have a Google Drive account, you can easily access your Colab notebooks in your Google Drive. You’ll be able to easily switch into GPU runtime mode by clicking Runtime on the top of the menu bar.
Specs:
Free access to Tesla K80 GPU
Up to 12 hours of consecutive runtime per day
12 GB of RAM
2- Kaggle GPU (30 hours a week)
Kaggle is a platform that allows data scientists and machine learning engineers the ability to demonstrate their capabilities with creating accurate models.
They offer 30 hours a week of free GPU time through their Kernels. The hardware they use are NVIDIA TESLA P100 GPUs. The intent of Kaggle is to offer them for deep learning, and they don’t accelerate workflows with other processes — though it’s possible you might try using RAPIDS with pandas and sci-kit learn like functions.
While the GPU time is offered for free, they do offer certain recommendations. You should, as with Google Compute, monitor when you’re using GPU time and switch it off when you’re not. Even if it’s monetarily free, you’ll want to be careful with the time you’re allotted. The limit of six hours of consecutive runtime means that you won’t be able to train complex state-of-the-art models that often take days to fully train.
Specs:
Free access to NVIDIA TESLA P100 GPUs
Up to 30 hours a week of free GPU time, with six hours of consecutive runtime
13 GB of RAM
3- Google Cloud GPU
For each Google account that you register with Google Cloud, you can get $300 USD worth of GPU credit. That can get you over 850 hours of GPU training time on their Nvidia Tesla T4. In practice though, you’ll want to try more powerful GPU instances with Google Cloud since you can get a baseline free with Google Colaboratory. You’d be able to train relatively powerful models in that time, or use it to practice machine learning work with RAPIDS. This tutorial goes over the setup of the GPU.
Note that when you set up the virtual machine, if you don’t turn it off when you’re not using it, you’ll still get billed, and you’ll get billed if you go past the $300 USD quota, so be careful to avoid unneeded charges.
Tired of using Google/Microsoft infrastructure or want to try something new? Gradient offers free community GPU cloud usage attached to their notebooks. This blog post offers a more in-depth perspective on their community notebooks.
6- Twitter Search for Free GPU Cloud Hours
You can always keep an eye out for promo codes and other cloud providers offering free GPU Cloud Hours by looking at Twitter and searching for relevant keywords.
With the right search query, you’ll be alerted to the latest offerings. I’ll try to retweet a few if you want to follow my personal Twitter account.
7-An alternative: build your own machine learning computer with GPU
If you’re tired of more limited cloud compute constraints, from cost to execution time limits, one solution might be to go as far as building our your own machine. Your only constraint is the power cost, which can be higher than expected with these powerful machines.
Still, you’ll be able to fully control your configuration and the hardware you use. It can be very cost-efficient, since you can run your own machine 24/7 — and you can build your own machine learning GPU rig for less than $1,000.
Data scientist roles are often one of the most highly-paid and highly-rewarding jobs out there. Glassdoor has cited data scientist at the #1 position for most-satisfying job in the United States. With the explosive growth of unstructured data, there has never been a greater need for data scientists.
This has prompted a wave of questioning about how to be a data scientist, with upwards of 600 people a month searching for that on Google.
Data science combines statistical knowledge, programming chops and domain expertise/communication skills. You’ll work on dealing with large amounts of data and get as much insight at scale as you can.
Job Prerequisites
To become a data scientist, you have to have a solid understanding of statistics, mathematics and the theory behind different algorithms.
You also have to have enough programming chops, usually in a language such as Python or R to iterate with data science models.
You also have to be able to communicate your findings to top executives. You need to have enough domain expertise to understand your data and the implications of it.
Typically, most roles will need advanced degrees and programming experience. STEM degrees are preferred. However, some companies will hire undergraduates straight from school — and advanced degrees, while preferred, are not a hard prerequisite. You can do data science without a PhD or even a Master’s degree.
Data Science Salary
Author screenshot
Based on a Kaggle survey, data scientists and the adjacent field of machine learning engineers earn the highest median salary ($120,000 USD) in the United States of America. Australia closely follows at about $110,000 USD. Other countries fall swiftly down the median, with data scientists earning close to $15,000 USD in both Russia and India.
While it’s clear that you can earn a lot being a data scientist, it’s also true that there are nuances.
The division in the United States makes this clear. States like California and New York have the highest volume of data science jobs. California data scientists average about $140,000 USD in yearly salary. Washington and New York State follow up in the $115,000 USD to $120,000 USD range. New Jersey, Maryland, and North Carolina are around there as well.
California is home to Silicon Valley and the growing startups in San Francisco. Washington state headquarters both Microsoft and Amazon. New York state and adjacent states like New Jersey host large vibrant startup ecosystems including Silicon Alley. While all these figures need to be adjusted for cost of living (different states like Kansas come first due to their low cost of living in another analysis), they show a key tenet of raw data science salaries: to earn as much as possible, you’ll have to go to where data is most valuable.
Factors That Increase Your Data Science Salary
Source: Pixabay
I wrote this handy guide from Springboard on factors that increase your data science salary after doing some research. The most important ones are the data science tools you have experience with, the industry you work in, the location you choose to work in (as discussed above), the data science role you choose, your experience and degrees, and the individual negotiation for each salary.
Understanding big data tools like Spark and data visualization tools like D3.js, a powerful and advanced custom library for strong visualization might increase your yearly salary by between $8,000 USD and $15,000 USD.
It’s not just data science in general that drives your salary, it’s also the individual components you’re familiar with. Premiums are paid for data scientists who know how to handle large amounts of data in a distributed fashion, and those who can work with powerful data visualization libraries.
If you have up to 15 data science tools mastered, it can increase your salary about $30,000 USD.
You’ll also want to work in an industry that has access to a lot of valuable data. This tends to be software or social media companies who pay the highest for data scientists (think Facebook or Google).
You’ll want to make sure you’re working as a data scientist or data engineer, not a data analyst. Most intro-level roles in the data space are data analyst roles. It will affect your future salary if you stay in data analyst roles or only apply to them.
As discussed, your location is key as well. If you want the absolutely highest raw salary, you’ll have to move to the United States, and you’re likely going to be working in one of the tech hubs there (either San Francisco/Silicon Valley, New York City, or the DC area). However, you should note that the amount of salary you can gain on location, while high, may not be as high as other factors that don’t need you to move.
Finally, your level of experience can make a dramatic difference. Having ten or more years of experience can add around $30,000 USD to your yearly salary as a data scientist. And while degrees might not be a hard prerequisite, those with advanced degrees do tend to earn more as data scientists.
Data Science Curriculum/Checklist
Source: Pixabay
First, you’ll want to start with enough programming knowledge so you can play around with the different concepts and libraries. In practice, a lot of the statistics and mathematics is abstracted away by different programming libraries. It’s best to learn some of the basics of statistics and programming at the same time. If you had to focus on one area, start with the programming practice.
Most machine learning and data science libraries (including Pandas, Numpy and scikit-learn, the mainstays of data science) are compiled in Python. You’ll want to start there, and work with Anaconda so you can manage different packages and dependencies. Once you’re in, you can find different courses to practice your Python programming, and practice live in the Jupyter Notebook offered, which is an intuitive and easy-to-access editor for code that can be run locally and uploaded or given version control quite easily by hooking it up with Git and a Github account.
While you can work on Jupyter Notebook in a local context by yourself and seldom do anything but upload your finished experiments and files to Github (something I’ve often done), building in the habit of working with version control is a great practice.
It’s the default method of collaboration between different programmers, who must ensure that code doesn’t conflict — so if you want to work on a data science team, or any software team for that matter, it’s always good to start with good habits.
You’ll also want to use version control to revert back in case something goes wrong and to maintain a steady thread of progress.
Programming
R vs Python
A large part of the data science ecosystem debate is whether or not to use R or Python as an intro-level programming language to get started. In this article for The Next Web, I wrote that it was ideal to know both. Realistically, if you had to choose, I would go with Python. We’ll start there, but I’ll add some R resources in case.
This interactive course is given by Microsoft on the edX platform, and is completely free to access. You will need to pay $99 USD if you want to have a verified certificate on your profile.
I wrote this list of resources to learn Python, going from beginner to advanced. Go through and pick out the resources that are data science and machine learning-specific.
You’ll want to practice your SQL as well if you’re looking to become a data scientist. A large amount of data is still held in structured SQL tables. Practicing with SQL will help you extract that data and work with it.
SQLZoo works partly as a Wiki, partly as a set of interactive exercises. I use this to sharpen my SQL skills when I need to practice.
Pandas
Pandas dataframes are the default unit of data wrangling in data science work. Pandas allows you to organize your data in a tabular, structural fashion similar to a SQL table or an Excel spreadsheet. It also allows you to use Python to programmatically treat data.
This handy guide goes over the Pandas library and different things you can do with it from grouping to aggregation functions. It’s a handy interactive guide to Pandas — and it’s how I first started getting familiar with the library and data science in general.
This guide helps define data wrangling, why it’s important, and introduces a few new functions and situations in Pandas to get you comfortable with it.
Statistics
Once you’re able to source data, you’ll need the statistical ability to be able to draw insight from the data you’ve collected.
As you’re learning the programming you need, you need to be able to understand statistics to manipulate data, understand it, and evaluate different models. This often involves at least a basic understanding of probability, frequentist and Bayesian statistics.
This interactive video-filled course will help you catch up on frequentist statistics, confidence intervals, p-values, and more. It’ll serve as a refresher if you’ve encountered these concepts in university, and a learning opportunity if you haven’t.
This iPython Notebook allows you to directly work with probability concepts in your own version of Jupyter Notebook should you desire. It expresses probability ideas in very readable Python code, helping to combine both your programming practice and statistics knowledge.
This post introduces Bayesian theory with a lot of visualizations. It can take the visualizations to really crystalize Bayesian thinking, especially since it involves a lot of segmentation on probability.
This tutorial uses a Python library to explain Bayesian reasoning through a model of click-throughs on ads. Use it to understand Bayesian inference in practice.
Mathematics
Once you’re done with the statistics, it can be good to understand some of the mathematics behind data science and machine learning even if most of the detail is not something you’ll confront everyday given how abstracted away most of the math is.
This book is offered as a free PDF, covering several sections of machine learning math in detail from analytic geometry to vector calculus.
Machine Learning
Now that you’ve refreshed or embraced statistics and programming concepts, it’s time to take it all together and learn the machine learning algorithms you can use on your data.
Starting with foundational concepts in machine learning such as the difference between supervised and unsupervised learning (and semi-supervised) we can then drill down into the different categories of machine learning algorithms and broadly see how the logic works with a set of visualizations.
This Towards Data Science Medium post then dives a bit deeper into ten specific machine learning algorithms, giving code implementations of a few so you can see them in practice on data.
Data Modelling/Evaluation
After all the work on different algorithms, it’s time to refresh what makes for a good data model. How do you know if your model is working? This section of resources will help you put that together.
The article summarizes the data science methodology. In this section, it focuses on how evaluating your model fits with the broader work of machine learning and data science.
Matplotlib is the default data visualization library embedded in Python, and something designed to be used off-the-bat with Pandas. You can use its visualizations to get a quick sense of the data yourself without needing to export it. This guide goes over the basics of Matplotlib and how it’s constructed.
Seaborn is a Python library that provides more compelling data visualizations than the default Matplotlib library. Use this tutorial to get familiar with it.
This D3.js introduction helps get you started with the powerful JavaScript library. The related chart collection helps collect tons of examples of different charts you can use to visualize your data in R, Python and more.
Kaggle, the online data science competition platform, offers a variety of datasets you can use to practice your data science skills. The datasets feature ranking and comments so you can follow the most trending datasets. You can study what others have done with them as inspiration for your own projects.
A Github repository that hosts a wide variety of open, public databases. They are organized by their domain. This is a great definitive resource for free datasets.
This website hosts datasets, some of them quite large, hosted on IPFS (the interplanetary file system). This is a distributed, decentralized protocol of storing data that goes beyond HTTP’s standard server-client relationships. In theory, this means that datasets downloaded through IPFS might be faster to get. After all, you’ll be working with a swarm of hosts rather than just a single one.
Amazon Web Services, which helps host much of the content on the web today, also has this registry that helps people find open data hosted on its cloud services. It includes examples of what people have done with that data.
Google hosts the above datasets on BigQuery, its big data storage solution. They include the complete revision history of Wikipedia up to April 2010, and weather information from the NOAA since late 1929.
Data Science Courses/Bootcamps
Source: Pixabay
The curriculum might be a bit too much to handle as a learner — and that’s perfectly fine. It’s meant as a bare-bones categorization of the material you need to learn to get into data science. However, if you want to refine your learning, there are a few options out there. I’ve linked to a list of bootcamps and courses. Be aware that I worked for Springboard.
Springboard offers a variety of mentored bootcamps where you’re given personal attention from a data science expert and career coaching. It also comes with a job guarantee. Either get a job or your tuition back once you’re accepted.
Udemy offers a variety of data science courses created by different independent teachers on its platform.
Data Science Interview Questions
The data science interview tends to fall into many steps, with some being technical and some being non-technical. I wrote this guide for Springboard on the data science interview process to fully flesh it out. I’ve added some sample questions you might expect, some with solutions, under each section.
Initial Recruiter Call
Before you’re assessed by a hiring manager, you’ll usually have a call with a recruiter to determine if you’re a fit with the company. They’ll ask general questions about your motivations and career path and see if you’re a fit with what the hiring manager wants.
Sample questions
Why this company?
What interests you about the role?
What are your salary expectations?
Technical Interview
A hiring manager will ask you technical questions related to your knowledge of statistics and programming. Here are about 109 data science questions with solutions. For programming, you can try HackerRank challenges as well to stay sharp before your interview.
Technical Case Study
Part of the interview process will involve either an in-depth review of a project you worked on or a case study where you work with your (hopefully) future team. This will involve detailed questions about work you’ve done or how you’d approach a project. You might have to do a take-home assignment or to work on a problem with the hiring manager.
Behavioral Interview
The behavioral part of the interview will test your management and communication skills as well as fit with the team. It’s usually done by the hiring manager rather than the recruiter.
Job Boards And Resources
There are many data science specific job resources and career sites out there worth following. Here are a few where you can find resources and data science job postings.
Kaggle features a host of different resources for data scientists, including datasets that are free and public for use, a customized version of a Python kernel that allows for automated version control as well as collaboration with other Kaggle users and a host of competitions that can help you practice and show your data science skills.
Hacker News Jobs is a great spot to cleanly aggregate machine learning and data science job positions from technologists who post on Hacker News “Who’s Hiring?” threads.
What’s great about these postings is that you’ll often find a lot of context and a direct connection to a hiring manager, who will often leave their email directly on a posting to make themselves available for connection. You can easily search for data science specific postings.
AngelList is the world’s largest repository of startups, many of whom are looking to hire for data science roles. You can filter specifically for data science roles, location and industry.
Do You Need A Degree Or Not?
This is an ongoing discussion. Advanced degrees help increase your data science salary and some hiring managers display a bias towards those degrees. Many hiring positions demand a minimum of a bachelor’s degree.
While the data science community often draws from the same ethos of do-it-yourself learning-by-doing that typifies the open source community, it can be a more gated process because of the statistics and math knowledge needed, as well as the communication skills data scientists need to develop.
Work experience can fill a lot of gaps here, but to get into the industry, it’s possible you might have to start with a data analyst role then move up in a data scientist role, or settle for a junior data science role or internship if you have no experience and no degree.
Despite the emergence of Masters programs targeted for data science, the truth is that you don’t absolutely need a degree to succeed in data science.
This role at Spotify involves a lot of teamwork and data exploration. It focuses on data modeling. Data engineers help to bring pipelines of data for you to model properly. This role is more focused on the product analytics team, and as a result, is cross-functional in nature. While there is a demand for degrees, most of the other requirements involve applied experience.
This entry level role doesn’t need a degree — rather just the skills that make up the data science curriculum. The focus is on communication, tools, and the different models that make up data science roles.
Candid Data Science Career Advice On How To Be A Data Scientist
Here’s some candid career advice from different data scientists in the field to help you with how to be a data scientist:
Jess Zhang (Inference Data Scientist @ Airbnb): Throw out the first number and do your research when it comes to negotiations. Look through courses and continually refresh and learn so you have a toolbox you can rely on. Find somebody who believes in you, sometimes through networking at data science meetups.
In my earlier corporate life, I often used Excel to look through thousands of lines of spreadsheet goodness. I assumed what I was doing was “big data”, and I prided myself on my association with a trendy buzzword.
I know better now. A lot better.
If you’ve ventured here, you’re probably looking into data science, the mysterious science that seems to verge on mysticism in the press. The virtues of data are constantly praised as innovative and disruptive. They seem like the domain of an exclusive few practitioners lifting numbers into actionable insight.
It seems that data scientists create many of the most exciting projects at the cutting-edge of technology. The people you may know on LinkedIn appear thanks to data mining. Amazon’s book recommendations rely on computers to mine your book preferences and select the one book that is most likely to appeal to you. Facebook finds what posts you like, and serves you more of the same. Google finds out who you are, and filters search results and ads for you.
If I like computers, the search term Python will return me the programming language. If I like snakes, it will return me a whole bunch of snakes.
This is all down to the magic of data mining. You’re here because you want to look behind the veil and learn how to do all this.
It’s hard, but not as hard as you think. Data science, at its’ core, is all about using computing power to parse through huge data sets.
Learn Data Mining with code(love)
Here are nine free, brilliant resources to do just that.
1- Coursera’s Specialization in Data Mining (level: beginner)
Coursera brings the best from the University of Illinois at Urbana-Champaign, ranked in the top 5 for computer science schools in America. It’s a useful introduction to data mining–the application of data science and computing power to find patterns in large collections of data.
2- A UCLA professor’s overview of data mining (level: beginner)
The coding language R is the workhorse of scientific data analysis and visualization. Codeschool offers an interactive and gamified approach to learn it, similar to Codecademy. Working with R will give you insight into how to move and dance with digital data, a skill that is the foundation of data science.
Kaggle is a platform for crowdsourced data challenges. The website has a ton of resources on how to get started with data science. This particular link leads to their guide on Python, one of the most versatile programming languages for data analysis.
W3 hosts a bunch of interactive tutorials on the basics of programming. This set of tutorials goes through SQL, a language that allows you to access data from most web databases. The tutorials will give you a glimpse into how data is structured for many websites and they will give you enough knowledge so that you would know how to play with data.
Have you ever wanted to play with big data? Learn the basics here and experiment with them. Hadoop helps distribute data across multiple servers, helping to process large amounts of data as seemlessly as possible.
8- Machine Learning on Coursera with Andrew Ng (level: intermediate)
Learn about data mining and the algorithms you can create to make your data analysis job so much easier from a master in the field: the founder of Coursera Andrew Ng, a Stanford professor who has recently become Baidu’s chief scientist.
9- A Programmer’s Guide to Data Mining (level: advanced)
If you can work with Python at a proficient level, this book will help you implement different algorithms that will sort, filter, and manipulate your data for you. A must-read for people looking into the practical applications of data mining.
I hoped that helped get you set on the path to data mining. What resources do you think I’m missing? Comment below. 🙂
I remember the first time I said the word “big data” with pride when describing my work. It, like every good buzzword, meant nothing to me, but conveyed a lot to my imagined prospective audience. It said something about my intelligence that I was working in “big data”, plying away at Excel sheets with way too many lines—a sure sign of a “big data” expert!
I know better now. After doing some research, I’m proud to say that I knew absolutely nothing about the topic at the time. In many ways, I still don’t—but I know enough to talk about the basics of “big data” and what it really represents, so you can explore with me.
The first step is to realize that big data represents data that is so large and complex that conventional data tools such as the table-based SQL cannot handle the load. Big data is not simply a big dataset that can be handled with Excel. Think of, for example, someone tracking every time someone commented on Ahnold’s accent on social media, their location, and other user attributes, in a mad quest to find who had the best “get to the choppa!” or “there is no bathroom!” quote variations: you’d quickly go mad trying to pass through every single one of those data points in a relational table or in an Excel file, even if you worked for a large Arnold-watching company, and had a set data process.
An easy rule of thumb to describe this is to say that big data refers to data sets that become difficult for an organization with a conventional data process to handle. This can be on several orders of magnitude. A smaller business may struggle with a lower threshold than a larger one. Nevertheless, it is the beginning of the struggle, and the search for alternatives to bread-and-butter SQL/Excel that is at the core of big data.
Traditional data tends to group data into tables, and operates with a smaller number of servers. Big data tends to ungroup data, and organize and analyze data through parallel processing across a larger number of servers.
When people in the field comment about the possibilities offered by big data, they are espousing the collection of unfathomable amounts of details we are now leaving on the web which was impossible five or ten years ago—because there were not so many details on the web, and there were no tools to collect them. Now with smartphones, sensors, and social media, data points are multiplying on an exponential level. Those who would take a dragnet over all of this data, pry them through tools not traditionally used in data collection that spread the volume and velocity of data over several servers instead of one or two, and then emerge with finely combed and actionable insights despite the overbearingly massive amount of data, are dealing with big data. This includes the NSA, but also data scientists who won the 2012 election, and health analysts working to ensure better care for all.
Please contribute to big data by commenting or forwarding me your terabytes of favorite Ahnold quotes.
It’s probably big data: new tools and terms
Hadoop
NoSQL
MapReduce
MongoDB
Look at me in very not-tabled Javascript Object Notation, a favorite of web-based Big Data databases:
JSON in relation to Big Data
It’s probably not big data
Your Excel spreadsheets of political enemies, no matter how many you have
Your Excel spreadsheets of dateable people, no matter how many you have
Your SQL tables of your favorite Arnold movies, and quotes contained within
Your handwritten list of things you would do for a Klondike bar
