chunk
stringlengths 11
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stringlengths 37
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|---|---|---|
It is not always easy to decide when and what code you should compile, in
order to make optimal use of the compiler, we recommend you to check out the
documentation. Later in this blog we’ll go a bit deeper in the design of the
compiler and why this makes JAX such a powerful framework.
### Training an MLP in 5 minutes using a TPU
Now that we learned about the most important transformations, we are ready to
put that knowledge into practice. We will implement an MLP from scratch to
classify MNIST images and train it super fast on a TPU. Our neural network
will have an input layer of 728 input variables, followed by two hidden layers
with 512 and 256 neurons respectively and an output layer with a node for each
class.
#### Model initialisation | scraping/output/-5836825137693104580.txt | [
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] |
#### Model initialisation
The first thing we will need to do is create a structure that represents our
model. As input of our initialisation function we have a list with the number
of nodes in each layer of our neural network. We have an input layer that is
equal to the number of pixels of an image, followed by two hidden layers with
512 and 256 neurons respectively and an output layer that is equal to the
number of classes. We use JAX numpy arrays to initialise the model on the
accelerator, avoiding to manually copy that data.
Note that generating random numbers is slightly different from numpy. We want
to be able to generate random numbers on parallel accelerators and we need a
random number generator that works well with the functional programming
paradigm. Numpy’s algorithm to generate random numbers isn’t very suited for
these purposes. Check out the JAX design notes and documentation for more
information.
#### Prediction | scraping/output/-5836825137693104580.txt | [
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#### Prediction
Our next step is to write a prediction function that will assign labels to a
batch of images. We will use automatic vectorization to transform a function
that takes in a single image as input and outputs a label into a function that
predicts labels for a batch of inputs. Writing a prediction function is not
super hard, we flow through the hidden layers of the network and apply weights
and biases via a matrix multiplication and vector addition and apply the RELU
activation function. At the end we calculate the output label using the
RealSoftMax function. Once we have our function to label a single image we can
transform it using vmap so it can process a batch of inputs.
#### Loss Function
The loss function takes a batch of images and calculates the mean absolute
error. We call our batched predictions and calculate the label for each image,
compare this against the one-hot encoded ground truth labels and calculate the
mean number of errors.
#### Update function | scraping/output/-5836825137693104580.txt | [
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#### Update function
Now that we have our prediction and loss function, we will implement an update
function to iteratively update our model in each training step. Our update
function takes in a batch of images and its ground truth labels together with
the current model and a learning rate. We calculate both the value of the loss
and the value of its gradient. We update the model using the learning rate and
the loss gradients. As we want to compile this function, we have to convert
the updated model to a pytree. We also return the value of the loss for
monitoring the accuracy. | scraping/output/-5836825137693104580.txt | [
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] |
Now that we have the update function, we are going to compile so it can run on
a TPU and greatly improve its performance. The nested functions called in the
update function will also be compiled and optimised. The reason why we only
apply the compile transformation on update and not on each function separately
is that we want to give the compiler as much information to work with, so it
can optimize the code as much as possible.
#### Training Our Model
We can define an accuracy function (and optionally other metrics) and create a
training loop using our update function and initial model as input. We are now
ready to train our model using a TPU or GPU.
### Conclusion | scraping/output/-5836825137693104580.txt | [
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] |
### Conclusion
Phew, we have learned a lot today. First we started describing JAX as a
framework with composable function transformations. The four core
transformations are automatic vectorisation, automatic parallelisation over
multiple accelerators, automatic differentiation of python functions and JIT-
compiling functions to run them on accelerators. We went deeper in on the
inner workings of JAX and learned how it is able to create such efficient
functions that work both on GPUs and TPUs by compiling to an IR that is then
transformed to XLA calls. This approach allows researchers to experiment with
new machine learning techniques without having to worry about a low level,
highly optimised version of their code. We hope software engineers are excited
as well so that new libraries can be built on top of JAX and potential
accelerators can be quickly adopted.
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December 16, 2020
# Designing better Python interfaces with keyword-only arguments
Contributors
Robbe Sneyders
Principal ML Engineer
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Thank you! Your submission has been received! | scraping/output/-7946979701029462078.txt | [
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Keyword-only arguments are one of my favorite features introduced in Python 3.
But while they’ve been available since Python 3 was released in 2008, they
still seem rarely used today. Probably because their true value is not always
fully understood. This is hardly surprising though, since the only rationale
provided in PEP 3102, which introduces the feature, is to offer more
flexibility in the ordering of the arguments. However, keyword-only arguments
can help you design better interfaces.
### Positional and Keyword arguments | scraping/output/-7946979701029462078.txt | [
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] |
### Positional and Keyword arguments
Let’s start by setting the scene and have a look at the two kinds of arguments
Python supports in function signatures: positional and keyword arguments.
We’ll discuss them using the copytree example, which we’ll develop throughout
this blogpost. copytree recursively copies an entire directory tree from the
src to the dest directory .
def copytree(src, dest):
...
We can call this function with positional arguments, which are assigned
implicitly based on their position.
copytree(‘foo/’, ‘bar/’)
Or we can call it using keyword arguments, which are explicitly assigned based
to their respective keywords. The order of keyword arguments does not matter
and can be changed freely.
copytree(src=‘foo/’, dest=‘bar/)
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] |
copytree(src=‘foo/’, dest=‘bar/)
Both of these function calls are equivalent and the caller can decide which
version to use. Using positional arguments seems the most natural here, as
keywords can feel superfluous when the meaning of the arguments can be derived
from their position. When adding additional arguments however, the situation
changes and using positional arguments can quickly become unclear.
def copytree(src, dest, dirs_exist_ok, symlinks):
...
copytree(‘foo/’, ‘bar/’, True, False)
Without looking at the function definition, it’s impossible to tell what the
True and False arguments are doing here. A better way to call the function
would be:
copytree(‘foo/’, ‘bar/’, dirs_exist_ok=True, symlinks=False)
Anyone who looks at this function call can immediately tell what it’s doing
without having to look at the function definition. | scraping/output/-7946979701029462078.txt | [
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copytree(‘foo/’, ‘bar/’, dirs_exist_ok=True, symlinks=False)
Anyone who looks at this function call can immediately tell what it’s doing
without having to look at the function definition.
Each caller is still free to call this function with positional arguments
though, which can lead to unclarities and inconsistencies across the codebase.
Keyword-only arguments can help enforce the proper usage of your functions.
### Keyword-only arguments
To understand the syntax of keyword-only arguments, we’ll have to take a small
detour explaining ‘varargs’ arguments first.
def f(x, y, *args):
...
In this function signature, *args is called a varargs argument. It swallows
all positional arguments provided on top of x and y, allowing for a dynamic
number of arguments. This is easily understood when looking at the following
example call and the resulting values for each argument.
f(1, 2, 3, 4, 5)
x = 1, y = 2, args = [3, 4, 5]
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f(1, 2, 3, 4, 5)
x = 1, y = 2, args = [3, 4, 5]
Since Python 3, we can define additional arguments after the varargs argument.
Since the varargs argument swallows all positional arguments, it’s impossible
to fill these additional arguments with positional values.
def f(x, y, *args, flag):
...
In this case, the flag argument therefore needs to be provided by keyword. It
is a keyword-only argument.
We might not want to use a varargs argument though. If our function only
expects a limited amount of arguments, a varargs argument can swallow any
mistakenly provided arguments, which might lead to unexpected function
behavior. Luckily, we can keep the keyword-only behavior while dropping the
varargs argument.
def f(x, y, *, flag):
...
This function will only take two positional arguments and requires the flag
argument to be provided by keyword. Applying this to our previous example
leads to the following function definition.
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] |
This function will only take two positional arguments and requires the flag
argument to be provided by keyword. Applying this to our previous example
leads to the following function definition.
def copytree(src, dest, *, dirs_exist_ok, symlinks):
...
Which enforces callers to provide the dirs_exist_ok and symlinks arguments by
keyword.
copytree(‘foo/’, ‘bar/’, dirs_exist_ok=True, symlinks=False)
Satisfying, isn’t it?
### Bonus: Positional-only arguments
Using keyword-only arguments prevents us from calling functions with all
positional arguments.
copytree(‘foo/’, ‘bar/’, True, False)
But it still allows calling the function with all keyword arguments.
copytree(src=‘foo/’, dest=‘bar/’, dirs_exist_ok=True,
symlinks=False)
| scraping/output/-7946979701029462078.txt | [
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copytree(‘foo/’, ‘bar/’, True, False)
But it still allows calling the function with all keyword arguments.
copytree(src=‘foo/’, dest=‘bar/’, dirs_exist_ok=True,
symlinks=False)
While this is less of a problem, I would still argue that this is less clear
than a combination of both positional and keyword arguments. Positional
arguments implicitly provide additional clarity on how the function behaves.
They rely on the natural logic of order, which can be understood at a glance.
Just look at this example where we reorder the keyword arguments… and weep.
copytree(dest=‘bar/’, src=‘foo/’, dirs_exist_ok=True,
symlinks=False)
On top of the reordering problem, the src and dest argument names can now
impossibly be changed without breaking the calling code, even though they were
never intended to be called by name. | scraping/output/-7946979701029462078.txt | [
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] |
On top of the reordering problem, the src and dest argument names can now
impossibly be changed without breaking the calling code, even though they were
never intended to be called by name.
For example, let’s say we want to rename the dest argument to dst to be more
consistent with our src argument name. Making this change will break any
calling code that specifies dest with a keyword argument.
def copytree(src, dst, *, dirs_exist_ok, symlinks):
...
copytree(src=‘foo/’, dest=‘bar/’, dirs_exist_ok=True,
symlinks=False)
TypeError: copytree() got an unexpected keyword argument 'dest'
This can especially impose a problem for public interfaces, in libraries for
instance. That’s why PEP 570 introduced positional-only arguments, whose
behavior is equivalent but opposite to keyword-only arguments.
def copytree(src, dest, /, *, dirs_exist_ok, symlinks):
...
| scraping/output/-7946979701029462078.txt | [
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] |
def copytree(src, dest, /, *, dirs_exist_ok, symlinks):
...
Here all arguments left of the / are positional-only arguments. This completes
our search for the perfect function definition, which can now only be called
using:
copytree(‘foo/’, ‘bar/’, dirs_exist_ok=True, symlinks=False)
Unfortunately, position-only arguments are only available starting from Python
3.8, so if you’re on a lower version, you’ll have to manage without them. But
while they are a great additional tool to build cleaner interfaces, keyword-
only arguments already go a long way by themselves!
### Designing Python interfaces
With these tools in our toolbox, let’s have a look at how we can use them to
design some beautiful interfaces. What follows is an opinionated set of
guidelines. They are not all encompassing and you might want to deviate from
them occasionally. | scraping/output/-7946979701029462078.txt | [
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] |
First of all, the meaning of positional arguments depends on a clear function
name, so choose it wisely. Good function signatures consist of the same
components as natural language sentences. The function name is the verb,
describing the action of the function. The positional keywords are the object
or subject of the action. And keyword arguments are the adjectives, which
describe and modify the action of the function.
In general, required arguments make for good positional arguments, while
keyword arguments should most often be optional. This fits well with our
natural language comparison, where a sentence will still make sense when
removing the adjectives, but not so much when removing the object or subject. | scraping/output/-7946979701029462078.txt | [
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] |
Note that this mostly works in one way though. While required arguments should
almost always be positional, positional arguments don’t always need to be
required. Especially when clear defaults can be provided, they can be made
optional instead. Just look at some of the functions in the Python time
module. When calling the gmtime function for instance, the secs argument is
optional and will default to None if not explicitly provided, which results in
the current time being returned.
time.gmtime([secs])
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time.gmtime([secs])
For functions that require a lot of arguments, it might be an option to turn
some of these required arguments into keyword arguments if their meaning is
not clearly implied by their position. This is also recognized by pylint,
which will stop throwing too-many-arguments warnings in this case. However if
you find yourself in this situation, it might be better to check if some
logical defaults can be provided for some of the arguments, rendering them
optional, or if the code can be refactored so less arguments are needed.
There’s probably an infinite amount of cases where you want to deviate from
these guidelines, which is OK. As long as you know why it makes sense to
deviate, they will still help you write clear function signatures.
### Conclusion | scraping/output/-7946979701029462078.txt | [
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] |
### Conclusion
I’ve been using and advocating for keyword-only arguments in the codebases I
touch for quite some time now and I’m happy to see that a lot of my colleagues
agree with their usefulness and their usage is spreading in ML6. For everyone
else, I hope this blogpost could convince you as well that keyword-only
arguments can help you design better interfaces.
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implemented a formal channel to report serious misconduct here. | scraping/output/-2552123161754320726.txt | [
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Infrastructure | scraping/output/2811074158401184053.txt | [
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Smart energy: how to make your home battery smart and save money?
Learn how to make your home battery smarter by connecting it to external
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July 16, 2023
By
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] |
July 16, 2023
By
Juta Staes
Placeholder tag
Multimodal AI: overview + experiments with DALL-E & CLIP
In this blogpost, our ML engineer Juta will dive into the world of multimodal
AI. Multimodal AI is able to reason about different modalities, such as text
and images, at the same time and leads to more general understanding across
domains.
September 7, 2021
By
Juta Staes
Large Language Model
Foundation Models
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] |
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# Computer Vision
AI solutions for crystal clear vision
Learn moreContact us
Our AI solutions process images and video, recognize objects and scenes, and
generate descriptions and labels for visual content. In other words, thanks to
computer vision, you never lose sight of your business value.
## Object detection | scraping/output/-5396960046312052695.txt | [
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## Object detection
We develop custom, high-performance machine learning models for object
detection, even in challenging real-world circumstances. We tailor our
approach - data pre-processing, modeling, tuning and setup - to fit each
unique use case for optimal results.
## Video analysis
Unlock the full potential of your video data with our expert object tracking
techniques. We help you detect and segment objects, identify activities, and
overcome the unique challenges of video analysis, like resource management and
model architecture.
## Edge vision
Edge Processing of video can reduce network traffic and increase data
security. With our Edge Video Analysis (EVA) project, subsidized by Vlaio, we
aim to develop a high-performing solution for anonymizing and re-identifying
video on the edge.
## Visual inspection
Monitor, steer and improve your quality control with advanced machine vision
technology. Our algorithms detect various defects, optimizing production
processes. | scraping/output/-5396960046312052695.txt | [
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## Visual inspection
Monitor, steer and improve your quality control with advanced machine vision
technology. Our algorithms detect various defects, optimizing production
processes.
## Client cases
Discover how our expertise in Computer vision leads businesses to success
Placeholder tag
Transforming Cosmec’s In-Line Quality Inspection with AI
By accurately detecting broken bricks over 90% of the time, Cosmec
significantly reduced waste and boosted production efficiency.
January 4, 2024
By
Jan Gerlo
This is some text inside of a div block.
Manufacturing
This is some text inside of a div block.
Placeholder tag
Accelerating Keypoint's Digital Real Estate Management Platform with AI
By embedding artificial intelligence in its platforms, Keypoint is able to
reduce fraud cases and will guarantee all home repairs are completed properly
and at a fair price. Next to that, water damage claims will be prevented
through the implementation of smart sensors and AI. | scraping/output/-5396960046312052695.txt | [
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] |
December 30, 2021
By
Simon Logghe
This is some text inside of a div block.
Public & Professional Services
This is some text inside of a div block.
Placeholder tag
Accolade Wines is saving 1 million litres of wine per year with AI
ML6 helped Accolade Wines to implement a ML process to capture real time
insights during the manufacturing process and prevent wine loss from happening
with predictive analytics, enabling all 3 bottling lines to further reduce
losses and saving another 1 million litres of wine per year or the consumption
of 50 000 people per year. A 200% ROI for Accolade Wines.
September 8, 2021
By
Jeffrey Hagen
This is some text inside of a div block.
Manufacturing
This is some text inside of a div block.
Placeholder tag
Building autonomous combines with CNH Industrial | scraping/output/-5396960046312052695.txt | [
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By
Jeffrey Hagen
This is some text inside of a div block.
Manufacturing
This is some text inside of a div block.
Placeholder tag
Building autonomous combines with CNH Industrial
ML6 helped to move an algorithm to a multispectral camera. ML6 helped
downscale and optimize the algorithm and helped get it embedded by working
together with the camera provider. Through the collaboration, CNH was able to
go into production much faster. They now also have the necessary expertise to
deploy algorithms on an embedded system for future use cases.
March 5, 2021
By
This is some text inside of a div block.
This is some text inside of a div block.
Large Language Model
Foundation Models
Corporate
People
Structured Data
Chat GPT
Sustainability
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
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Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
## Typical challenges
Building a computer vision solution can be complex process. Let's take a look
at some common challenges.
## Data collection & diversity
Computer vision algorithms need labeled data to learn, but getting this data
can be difficult and expensive. That’s why it’s important to use the data
effectively, which means getting good quality data and making sure it covers a
wide range of situations. This leads to more accurate and robust computer
vision models.
## Object detection, segmentation and tracking
Finding and separating objects in an image is important for many computer
vision tasks. It can be challenging to track objects over time, especially if
they're hidden or have complicated shapes, but there are ways to overcome
these challenges and improve the accuracy of computer vision models. | scraping/output/-5396960046312052695.txt | [
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] |
## Image classification and recognition
Computer vision aims to accurately and efficiently identify objects in images,
even if some objects are hard to tell apart. With proper training on large
datasets, computer vision models can achieve high accuracy and improve our
ability to analyze visual information.
## Model selection
Choosing the right model is crucial for successful computer vision solutions.
It requires a deep understanding of algorithms and fine-tuning for specific
use cases. While transfer learning can be useful, adapting pre-trained models
to new domains is important for optimal results.
## Latency
Computer vision applications often need to process data quickly, but the
algorithms can be demanding. This means that it's important to find ways to
make the algorithms faster without losing accuracy. Finding the right balance
between speed and accuracy is key.
## Interpretability and explainability | scraping/output/-5396960046312052695.txt | [
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## Interpretability and explainability
Explaining how computer vision models work can be tough because they often use
complex neural networks. But there are ways to make these networks more
interpretable, so stakeholders can understand and use the models more
effectively.
## High level outline of the solution
Data acquisition
To start building a computer vision solution, you first need to collect and
prepare data. This includes cleaning and annotating images or videos, and
gathering a diverse dataset that represents all possible cases.
Model architecture
Convolutional Neural Networks (CNNs) and Transformer-based algorithms are
commonly used models for computer vision. These models are designed to detect
and analyze features and patterns in images using specific architectures.
Training | scraping/output/-5396960046312052695.txt | [
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] |
Training
For the computer vision model to work, it must be trained using the collected
data. This process includes minimizing the error between predicted and actual
results, which requires powerful hardware, such as GPUs.
Evaluation
After the model is trained, it is evaluated to see how well it performs using
various evaluation metrics such as precision, recall, mIoU and mAP.
Deployment
Once trained and evaluated, the model can be used to perform tasks such as
image classification, object detection, and image segmentation. Depending on
the resources and infrastructure available, deployment can be on-premise or in
the cloud.
Incremental training and adaptation
When new data becomes available or the model's application changes, it may
need to be fine-tuned or adapted. Retraining the model on new data or updating
its parameters can be part of this process.
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] |
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Business Development unit 🤝 * Multiple locations
# AI Client Executive
## Love taking ownership to further accelerate businesses with AI? 🚀
Join us
## Your challenge
At ML6, we design tailor-made AI solutions for our clients across industries
such as ASML, Randstad, VRT, GfK, and many more.
As we’re growing fast and we continue to make more impact, we’re hiring AI
Client Executives. In this role, you will have an immediate impact on our
international business. | scraping/output/-7471444671965897141.txt | [
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As we’re growing fast and we continue to make more impact, we’re hiring AI
Client Executives. In this role, you will have an immediate impact on our
international business.
What about you? You get the opportunity to lead full sales cycles! You will
identify new business opportunities with artificial intelligence, develop deep
client relationships, retain them and act as a trusted advisor.
In short, if you have a background in consultancy, experience with developing
and retaining large accounts, acquiring new ones, and love to translate their
business needs to technical solutions, you are the one we’re looking for!
You will: | scraping/output/-7471444671965897141.txt | [
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* Identify and acquire new business opportunities: gain a deep understanding of your vertical and develop relations with partners, the local ecosystem and business networks to discover new business opportunities and close them
* Develop deep client relationships and retain them: engage with clients to understand their needs and formulate solutions, follow up during projects, meet them or a regular basis and come up with new ideas on how to support their business goals
* Co-create proposals, prepare commercial offers and reply together with our Advisory & Delivery team on tenders
* Act as a trusted advisor on key client accounts and manage and nurture the relationship: work with clients on a strategic level to identify how we can best support them in realizing their ambitions and accelerating their innovation roadmap
* Manage entire complex sales-cycles and pitch the value directly to C-level executives | scraping/output/-7471444671965897141.txt | [
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* Manage entire complex sales-cycles and pitch the value directly to C-level executives
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* Work with our Advisory & Delivery unit and with our Cloud Alliance Managers to develop new offerings and services to bring to the market to better respond to client needs | scraping/output/-7471444671965897141.txt | [
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* A Master’s degree in a business or other related domain
* Five or more years of experience in consulting and complex solutions sales
* Proven track record in account development, planning, retention, relationship building and acquiring new accounts
* Familiar with leading account strategy in generating and developing business growth opportunities, working collaboratively with stakeholders - all to maximize business results in territory and open up opportunities with large enterprise accounts
* Understand each customer's technology footprint, strategic growth plan and business drivers, technology strategy, and competitive landscape
* Experienced with proposal building, contracting and negotiation
* Passionate & with understanding of technology, data and AI
* Experience in strategically managing and nurturing business relationships with key accounts
* Love to create real value for your clients and have a consultative selling approach | scraping/output/-7471444671965897141.txt | [
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* Experience in strategically managing and nurturing business relationships with key accounts
* Love to create real value for your clients and have a consultative selling approach
* Excellent verbal and written communication skills in English and Dutch (German for Germany) and comfortable speaking to C-level executives
* Goal oriented team player, as you will work together with several experts from different units
* Analytical and have great problem solving skills
* Love taking ownership to further accelerate businesses with AI | scraping/output/-7471444671965897141.txt | [
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##
Why ML6?
Looking for a dynamic environment where you can create real business impact
with AI? Look no further. Don’t expect a big corporate organisation with pre-
defined jobroles and plans for the coming years. Expect the unexpected.
Growth, change and impact. Expect to work for the most innovative and biggest
companies across countries. Expect exponential personal growth in your career.
We provide an attractive salary package for your expertise and an optimised
benefits program where we stay true to our caring and learning goals. In a
nutshell, we've built a place where we truly love working, we think you will
too.
Scroll down to find out more about why others choose ML6! 👇
Department
Business Development unit 🤝
Locations
Office Amsterdam 📍, Office Berlin 📍, Office Ghent 📍
Employment type
Full-time
## Why others choose ML6 🤝
* ### Create intelligence with a lasting positive impact | scraping/output/-7471444671965897141.txt | [
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Office Amsterdam 📍, Office Berlin 📍, Office Ghent 📍
Employment type
Full-time
## Why others choose ML6 🤝
* ### Create intelligence with a lasting positive impact
As a leader in AI, we work hard to ensure that AI is designed, developed and
deployed to create positive impact. We take our responsibility to build secure
and sustainable technology seriously and have integrated ethics within our way
of working. This shows in every client case and do good projects we do.
* ### Innovate and share knowledge
Learning is part of our DNA. Whether it's through our team's expertise,
creating spin-offs or the latest tech breakthrough, our culture will empower
you to deliver top-notch AI solutions to our customers. We organise chapter
conferences, guest lectures, meet ups, dedicate time to research, blogposts, a
personal training catalog and so much more.
* ### We achieve more together | scraping/output/-7471444671965897141.txt | [
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* ### We achieve more together
Expect to build lasting relationships that extend beyond the office walls.
Thanks to our collaborative and diverse culture, we achieve the extraordinary.
From our ML6 agents to our ML6 Maffia, we’ve created a safe environment where
mutual trust and open feedback is cherished. To foster our interpersonal
connections, we love to see each other's pretty faces at the office.
* ### Fast growing and international
We're gearing up for our next growth milestone. Don’t expect a big corporate
organisation with pre-defined jobroles and plans for the coming years. Expect
the unexpected. Growth, change and impact. Expect to work for the most
innovative and biggest companies across countries. Expect exponential personal
growth in your career.
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* ### Byte size Benefits
Last but not least, we provide an attractive salary package for your expertise
and an optimised benefits program where we stay true to our caring and
learning goals. In a nutshell, we've built a place where we truly love
working, we think you will too.
## FAQ
* ### Can I work remotely at ML6?
There is a reason why we don’t work (fully) remotely at ML6 and why we love
being present at our offices so often. At ML6, our culture is based on
interpersonal connections and our strength lies in our personal collaboration.
With our clients, and with each other. We work from home about 2 days a week.
Our offices are situated in Berlin, Amsterdam and Ghent.
* ### What to expect when joining ML6? | scraping/output/-7471444671965897141.txt | [
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* ### What to expect when joining ML6?
Don’t expect a big corporate organisation with pre-defined jobroles and a
defined plan for the coming years. Expect the unexpected. Growth, change and
impact. Learn from engaged experts with a like minded passion for technology.
Expect to work for the most innovative and biggest companies across countries.
We are all T-shaped consultants working on a shared goal.
* ### Am I a fit for the company?
We are an AI Consulting company on a mission. Guiding the AI revolution
towards positive impact. You can become part of this mission. We are always on
the look for business minded people with a consultative approach. We dedicate
great effort in building a caring culture full of learning opportunities that
accelerates us to accomplish our main purpose. You can be in the driver’s seat
of your career. You can be the next ML6 agent to create value for our
customers and company.
Are you up for the challenge? | scraping/output/-7471444671965897141.txt | [
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Are you up for the challenge?
* ### What does the application process look like?
It depends on the role that you're applying for. For our technical roles, we
always start with a technical matching (ex. coding challenge) and get to know
interview. Every role at ML6 receives a presentation challenge where we will
evaluate how you can contribute to our innovative projects by demonstrating
your skills & creativity. As you approach the finish line, we schedule a final
interview (physically) to reflect on your dream job at ML6.
* ### How is ML6 structured?
We work in Units and with Unit Coaches. For more information about our
structure, we recommend you to watch this video.
* ### What are examples of projects that ML6 worked on? | scraping/output/-7471444671965897141.txt | [
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We work in Units and with Unit Coaches. For more information about our
structure, we recommend you to watch this video.
* ### What are examples of projects that ML6 worked on?
At ML6, we boost and increase revenue growth for other companies, by
implementing AI solutions. We do this for a broad range of industries. From
manufacturing, to healthcare or media and entertainment industries (and lots
more!).
You can find some of our client cases and testimonials here.
* ### I would love to meet up with someone from ML6
You can find all of our upcoming meet ups and events here. If you would like
to schedule a first get-to-know, we recommend you to apply officially via our
jobsite where we will follow up with you soon.
* ### What about the ethical aspect of AI?
At ML6 we take our responsibility to build secure, reliable and sustainable
technology seriously and integrate ethics within our way of working. We have
our own Ethical Advisory board to discuss sensitive projects. | scraping/output/-7471444671965897141.txt | [
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* ### Why is ML6 called ML6?
ML stands for our main expertise, Machine Learning. Next to that, we don't
take ourselves always very seriously. Our people are 'ML6 agents', wink to
MI6. Seen the James Bond movies? Our meeting rooms for example are named after
them. After all, we can be a bit geeky at times ;-)
* ### What does a typical AI project look like?
This really depends on the scope of the project. A project can run for a
couple of months, to even years. Our ML6ies work on projects together with a
team of colleagues. This team can have different skills like data engineering,
software, machine learning and project management to deliver top notch service
to our clients. They sometimes work on different projects simultaneously or
combine project work with an internal project or research topic.
Contact Julie Plusquin Talent Partner – Talent & Culture 🫶
## Colleagues
Sophie Decock
Head of Business Development
## Join us
* Alliance Manager - Azure | scraping/output/-7471444671965897141.txt | [
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Contact Julie Plusquin Talent Partner – Talent & Culture 🫶
## Colleagues
Sophie Decock
Head of Business Development
## Join us
* Alliance Manager - Azure
Business Development unit 🤝 * Multiple locations
* AI Client Partner
Business Development unit 🤝 * Multiple locations
More jobs
Office Amsterdam 📍 Office Berlin 📍 Office Ghent 📍
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* AI Client Partner
Business Development unit 🤝 * Multiple locations
More jobs
Office Amsterdam 📍 Office Berlin 📍 Office Ghent 📍
## About ML6
We guide the AI revolution towards positive impact. 🌐
Exciting developments are happening in the world of AI, offering unprecedented
opportunities for businesses to innovate and gain a competitive edge. Now more
than ever is the time to create intelligence with lasting positive impact. And
you can be a part of this!
1. We deliver end-to-end projects where we accompany organisations through the entire AI journey
2. We build bespoke hybrid ML solutions
3. We navigate and set up advisory projects
As ML6 highly values diversity, inclusion and transparent communication, we've
implemented a formal channel to report serious misconduct here.
Examples of serious misconduct (not limited to): safety of the individual
(aggression, discrimination, sexual harassment), data privacy issues, public
health, tax and fraud.
Join us | scraping/output/-7471444671965897141.txt | [
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Examples of serious misconduct (not limited to): safety of the individual
(aggression, discrimination, sexual harassment), data privacy issues, public
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AI has great potential to accelerate efforts to protect our environment, such
as reducing emissions or making more efficient use of scarce resources. Let’s
dive into some examples of use cases - with no ambition to be exhaustive -
where AI can help tackle important challenges.
In a series of blogposts, we will look at 4 dimensions, in line with the EU
Green Deal actions, covered in the visual below. In this second blogpost, we
will deepdive into the topics of transport and mobility and environment and
oceans.
The four dimensions in line with the EU green Deal actions
### Transport and Mobility | scraping/output/1646628569221613545.txt | [
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The four dimensions in line with the EU green Deal actions
### Transport and Mobility
Transport is a critical part of our infrastructure, but it comes with costs
such as greenhouse gas emissions, pollution, noise, and congestion. With
transport emissions representing 25% of total EU greenhouse gas emissions, the
transport and mobility sector faces essential changes on the way to becoming
climate-neutral (EU). Let’s explore where AI technology can help this
transition:
#### Journey planning and traffic prediction/management | scraping/output/1646628569221613545.txt | [
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AI allows for predicting and optimising traffic more accurately by making
sense of complex variables influencing traffic flow and adjusting and planning
traffic flows and journeys in real time. ML6 worked on a project with the City
of London, for example, where we visualized data from road sensors and
predicted congestion 40 minutes ahead of time, and calculated ripple effects
to recommend appropriate actions to the authorities. AI based prediction of
traffic volume can be a first step to optimising routes to be more cost- and
fuel-efficient, adjusting tolls with smart pricing based on congestion,
identifying key bottlenecks for strategic improvement, or helping to solve the
last mile delivery problem. Another important way AI can improve resource use
and sustainability in trucking and shipment is by optimising backhauls, i.e.
empty return trips of unloaded trucks. Multiple AI apps are already tackling
this problem, helping companies and drivers to book backhauls efficiently, | scraping/output/1646628569221613545.txt | [
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empty return trips of unloaded trucks. Multiple AI apps are already tackling
this problem, helping companies and drivers to book backhauls efficiently,
decreasing empty mileage and reducing emissions. | scraping/output/1646628569221613545.txt | [
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#### Connected and Autonomous Vehicles & deliveries
Autonomous driving could bring significant mobility changes. While autonomous
vehicles are still in their infancy, with a long process towards full autonomy
that could take many years, there is potential for autonomous driving for
sustainability. Vehicles driven by AI technology can implement eco-driving and
energy-saving features and provide more efficient routing. Shared mobility and
Mobility as a Service could reduce the need for ownership of cars by sharing
cars and rides. This being said, we can currently not assess the net impact of
autonomous vehicles, as it will depend on how customer patterns will develop
in the future. Increased (empty) trips or increased car ownership could have a
negative impact on sustainability, if consumer behaviours change in that
direction.
#### Battery efficiency and EV charging | scraping/output/1646628569221613545.txt | [
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#### Battery efficiency and EV charging
The number of electric vehicles is expected to grow exponentially. This growth
brings along the challenge of organising charging infrastructure,scheduling
battery charging, and improving battery efficiency. ML6 and Sarolea worked on
the latter, using sensor data from electric motorbikes to improve battery
performance.
##
### Environment & oceans
The agriculture and water sectors have a vital role in preserving the health
of our Earth’s natural systems, including biodiversity conservation, ocean
health, freshwater quality, biogeochemical flows, forests and land system
change, and related impacts on the security of food and water supply. Also
here, AI can play a role in managing and preserving essential resources:
#### Protecting Oceans | scraping/output/1646628569221613545.txt | [
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The use of AI can help to detect illegal activities or suspicious activity in
the ocean, such as overfishing or oil spills with the help of satellite data
and computer vision, as well as to protect wildlife (e.g. whale detection).
Another way AI can help conserve oceans is by fighting plastic pollution by
tracking and measuring in much more detail how plastic waste gets into oceans.
UNEP’s CounterMEASURE project, for example, developed a model to identify
sources and pathways of plastic pollution in the Mekong River using geospatial
data and images of plastic waste (Source). We can use the created knowledge to
inform policy decisions and actions to reduce plastic pollution. Lastly, in a
Christmas project last year, ML6 created an ML model to detect coral, so
researchers can better predict the coral's coverage and track its health to
better target protective measures. Such models could ultimately help to assess
vulnerable reefs and provide early warning and actions to protect them. | scraping/output/1646628569221613545.txt | [
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#### Detecting illegal deforestation
Illegal logging has significant economic, social and environmental impacts.
For the environment, it is associated with deforestation, climate change and a
loss of biodiversity (Source). Machine learning methods have become
increasingly powerful and accurate to spot even selectively cut down trees
from satellite images. As such, ML can help detect illegal logging activity in
rainforests, protecting the indispensable trees that counter climate change.
#### Water resource prediction and management | scraping/output/1646628569221613545.txt | [
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#### Water resource prediction and management
AI can be used to managewater resources. On the demand side, predicting water
use is crucial for reliable water management, increasingly made possible with
cheaper sensors that can supply more precise information. We can use AI to
decrease water wastage or loss on the supply side. Leakage is a major issue in
water distribution systems, and AI can help detect burst pipes or leaks by
analysing pressure and sensor signals. A near real-time prediction could help
to raise alarms and decrease wastage. Another critical area is water
contamination and reducing pollutants by monitoring the water quality with AI.
### Closing statement | scraping/output/1646628569221613545.txt | [
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### Closing statement
While this series of blog posts did not aim to give a comprehensive overview
of use cases or domains where AI could help drive sustainability for the green
transition, we believe it can be a starting point to inspire uses of AI to
help drive forward towards a more sustainable future. In order to ensure AI
can have an actual impact, we must put the right processes and structures in
place to ensure that an AI solution is not only developed with a strong
ethical framework and sustainable practices in mind but also makes it into
production and is ultimately adopted and used.
Of course, AI alone will not be the silver bullet to move to a sustainable
world. If we want to transform the current course we are taking, many
different factors will have to come together - not least a sense of shared
responsibility and true commitment from the private and public sectors.
## Related posts
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November 24, 2021
# The beauty of efficient neural networks
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Share this post | scraping/output/-4766375058050370193.txt | [
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Share this post
SotA machine learning models are growing so large in size, that it’s
impossible for hardware to keep up. What are the symptoms of this growing
problem and is there an antidote?
In this blog post, we’ll start by discussing the semantics of efficient models
and why you would want to use them. Then we’ll take a deep dive into what
techniques you can use to end up with smaller, faster and more sustainable
machine learning models.
Source: unsplash.com/alina.grubnyak
### Efficiency and machine learning models
A machine learning model is more efficient if we can achieve one or more of
the following:
* reduce the memory footprint
* decrease the amount of compute needed | scraping/output/-4766375058050370193.txt | [
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A machine learning model is more efficient if we can achieve one or more of
the following:
* reduce the memory footprint
* decrease the amount of compute needed
without resulting in a significant drop in performance. The reduction in
memory footprint or compute can be at training time, inference time, or both.
It’s important to note that these improvements don’t always go hand in hand
with each other. It is for example possible that a model needs more compute to
train, but takes up less memory and is faster at inference time.
### Why do we need it?
A first reason follows from an observation of the growth in the size of SotA
machine learning models. When comparing GPT with DeepSpeed, we see a
10.000-fold increase in the number of parameters of a model in only 2 years'
time.
Size of SotA NLP models through time (visuals from EMNLP tutorial on high-
performance NLP) | scraping/output/-4766375058050370193.txt | [
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Size of SotA NLP models through time (visuals from EMNLP tutorial on high-
performance NLP)
Thus, models are growing so fast in size, that it’s impossible for hardware to
keep up. This has a number of drawbacks. For one, this imposes a financial
barrier on the models which in turn makes them less accessible to people with
regular hardware. A recent poll on Twitter suggests that even most Ph.D.
researchers only have basic hardware at their disposal.
Furthermore, when you train your model you can expect to have very powerful
GPUs available. However, in production you’ll often be constrained by the
limitations of the target hardware. This is especially important when the goal
is to deploy the machine learning model on-premise or on edge. | scraping/output/-4766375058050370193.txt | [
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There are multiple reasons to invest time into optimizing your model for
efficiency. For example when your model is too large for the target hardware
and it doesn’t fit in memory, you can use model compression techniques to
reduce the size of your weights file. This is especially useful for on-premise
and edge setups because if you can successfully compress your model, you can
keep on using the state of the art without any hardware upgrades. Another use
case is to speed up the inference time of your model. Most efficient model
techniques reduce the number of computations needed to make a prediction or
make them less expensive. Lastly, it can significantly reduce the energy
consumed by a model which can decrease the costs for long-term running models
in the cloud or notable battery life improvements for edge devices.
### Pruning
Pruning is a family of methods that focuses on removing redundant stuff while
only minimally impacting performance. | scraping/output/-4766375058050370193.txt | [
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### Pruning
Pruning is a family of methods that focuses on removing redundant stuff while
only minimally impacting performance.
Pruning is a pretty old concept and was already used in the ‘80s to try and
reduce the size of decision trees. So it might not come as a surprise that
there exist many different types of pruning, and they can be differentiated
by:
* The pruning criteria, i.e. how do we decide what to prune?
The selection can be based on any easy or complex function of the weights, the
activations, the gradients or combination of any of them. A lot of research
has been done on this topic in recent years and a nice summary paper on the
topic that was recently released by Uber can be found here. However, let’s not
get lost in the details, in general the most popular pruning criteria is
simply removing the smallest weights in terms of absolute value.
* What is pruned, e.g. connections, channels or entire layers. | scraping/output/-4766375058050370193.txt | [
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* What is pruned, e.g. connections, channels or entire layers.
Connection, channel and layer pruning refer to the smallest unit that can be
pruned in a single step, based on the pruning criteria. The choice of which
pruning method to choose is a trade-off between size reduction and speed
improvement. With connection pruning, you can remove more connections without
a significant drop in accuracy as you only remove the connections that
minimally contribute to making a prediction. With a more structured approach
such as channel pruning or layer pruning you will respectively need to choose
the least important channels or layers to prune which will inevitably also
contain some more important connections and thus you’ll be able to prune fewer
connections before experiencing a significant drop in accuracy. | scraping/output/-4766375058050370193.txt | [
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] |
On the other hand, connection pruning will only introduce a lot of 0’s in the
matrix multiplications but as the multiplications still have to be done they
will barely impact inference speed. With channel pruning entire matrix
multiplications can be skipped, and with layer pruning even entire steps of
the forward pass. Thus, the latter 2 methods will have a more significant
impact on inference time.
* When the pruning happens, e.g. before or after training. | scraping/output/-4766375058050370193.txt | [
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* When the pruning happens, e.g. before or after training.
The standard approach is to first train a large neural network and then prune
away unnecessary components based on the results of the training process. The
holy grail of pruning however is to find these smaller network structures
before training, as this can significantly reduce the training costs. This is
better known as the lottery ticket hypothesis and has been a hot research
topic since its introduction in 2018. However, a technique that’s
generalizable and easily applicable still has to be developed, and thus for
the time being pruning after training is still the way to go.
* How many times we prune.
Here the choices are once or iteratively. In general, the iterative approach
with a fine-tuning step in between yields the best results as can be seen in
the figure below. However, there’s no free lunch as this approach also takes
up the most time and computation. | scraping/output/-4766375058050370193.txt | [
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] |
Pruning results from this paper, with vs without retraining and one-shot vs
iterative.
### Practical advice when pruning
So what if you want to try pruning yourself? Well, this depends on if you’re
using TensorFlow or PyTorch.
At the moment, TensorFlow only supports one kind of pruning, connection
pruning. They do give the option to only prune some types of layers, for
example only the dense layers. Providing other pruning methods is on their
roadmap, but for now there are no pruning methods available that will help you
speed up your model.
PyTorch however, supports both structured and unstructured pruning methods and
provides an easily extensible pruning class that gives you the power to
implement your own pruning methods if you’d ever want to. On top of the
standard pruning methods that come with PyTorch, you can find multiple open-
source pruning toolkits that provide even more options. So with respect to
pruning, PyTorch takes the cake.
### Quantization | scraping/output/-4766375058050370193.txt | [
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### Quantization
A second technique that is well-known and really powerful is quantization. The
basic idea is surprisingly simple: it’s a reduction in the precision of the
numbers used to represent the weights of a model. So for example, assume we
have a model where the weights are saved as 32-bit floats, then by converting
them to 8-bit integers we already have a 4x reduction in size, and at least a
50% speed improvement. It’s quite surprising if you think about the fact that
you can represent 2²⁴ more numbers with 32 bits vs 8 bits. But apparently ML
models don’t care that much, since most of the time we can achieve similar
performance. | scraping/output/-4766375058050370193.txt | [
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A distinction can be made between uniform and
non-uniform quantization. Uniform means that the quantization levels are
equally spaced, non-uniform if they’re unequally spaced.
Uniform quantization is the easier of the two, you only have one degree of
freedom and that is, how many bits am I going to use to represent the weights
after quantization.
However, with non-uniform quantization you have the extra degree of freedom on
how are you going to divide the quantization levels?
An interesting approach, that has been successful in research, is to do a
k-nearest neighbor search and divide the quantization levels accordingly. This
has the benefit that lots of connections will share the same weight which
allows for a large compression of the original network. However, due to some
optimization issues, there is still more research needed for this approach to
become applicable in the wild. | scraping/output/-4766375058050370193.txt | [
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] |
Another smart trick we can use is quantization aware training, which simulates
inference-time quantization during training in the forward passes. This
induces some quantization error which is accumulated in the total loss of the
model and they try to reduce it by adjusting the parameters accordingly. This
makes our model more robust to quantization later on.
Both TensorFlow and PyTorch support quantization and quantization aware
training.
### Knowledge distillation | scraping/output/-4766375058050370193.txt | [
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### Knowledge distillation
One of the central observations behind knowledge distillation is that there’s
a difference in requirements during training and inference time. In many
cases, the goal at training time is to learn and extract structure from large
amounts of data. This is where large models, the “teacher”, with a lot of
capacity are able to shine. At inference time however, these large models are
cumbersome to work with (due to the typically more stringent latency and
memory requirements) and smaller models, the “student”, would be much more
suitable.
In knowledge distillation, the goal is to combine the best of both worlds. In
the first step, a large model is trained on a large amount of data. In a
second step, the knowledge captured by the large models is “distilled” into a
much smaller model. But what do we mean by knowledge? And how can we “distill”
this knowledge into a smaller model? | scraping/output/-4766375058050370193.txt | [
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When talking about knowledge in the context of knowledge distillation, we’re
talking about the learned relationship between input and output vectors. This
relationship can tell us a lot about the way a model generalizes to unseen
data (one of the reasons why large models typically perform better than
smaller ones is because they are able to generalize better).
Take classification for example. Although the output probability for the
target class will by far be the highest, the output probabilities for the
other classes still contain a lot of information. It’s exactly these relative
differences between the small probabilities of the incorrect classes that can
provide a lot of information about how the model generalizes. | scraping/output/-4766375058050370193.txt | [
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] |
Finally, the knowledge learned by the teacher should somehow be transferred to
the student. This is done by adding an extra loss term to the usual loss
function called the distillation loss. Again taking the example of
classification, we’re not only interested in the hard label (i.e. the one-hot
encoded vector representing the class) but also in the soft label which is
generated by the teacher (in order to magnify the small differences between
probabilities of incorrect classes, a temperature parameter is added to the
final softmax layer where the higher the temperature, the smoother the output
distribution.
Knowledge distillation makes use of an extra distillation loss term in the
loss function (no dogs were harmed during distillation).
### Smart optimization tricks | scraping/output/-4766375058050370193.txt | [
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Knowledge distillation makes use of an extra distillation loss term in the
loss function (no dogs were harmed during distillation).
### Smart optimization tricks
The previous approaches all have in common that they change the network
somehow (remove some parts, reduce the precision of the weights or even train
a smaller one altogether). The two techniques discussed below don’t really
change the network itself, but they do make the optimization procedure more
efficient in terms of the amount of memory that is required at training time.
#### Gradient checkpointing | scraping/output/-4766375058050370193.txt | [
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] |
#### Gradient checkpointing
In gradient checkpointing, we are basically reducing the amount of memory
needed at the cost of an increase in compute needed. This trade-off is
especially useful since it’s typically much easier to wait a bit longer for
your model to be trained than to get some more VRAM.
The way gradient checkpointing works is by not keeping all activations in
memory all the time, but only keeping checkpoints in between. Whenever certain
activations are needed again (e.g. when calculating gradients in the backward
pass) you simply recalculate them again starting from the closest checkpoint.
Without gradient checkpointing, all activations are kept in memory
(visualisation from this blogpost).
With gradient checkpointing, activations are only stored at certain
checkpoints and are recalculated when necessary (visualisation from this
blogpost).
#### Gradient accumulation | scraping/output/-4766375058050370193.txt | [
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With gradient checkpointing, activations are only stored at certain
checkpoints and are recalculated when necessary (visualisation from this
blogpost).
#### Gradient accumulation
If you’ve ever tried to fine-tune a relatively large transformer-based model
(e.g. BERT, T5, …) on a modest GPU, you have without a doubt encountered your
fair share of out-of-memory exceptions. The issue is that in many cases you
want to fine-tune these models using larger batch sizes than you can fit in
memory. Enter gradient accumulation. As the name implies, it allows you to
accumulate gradients for a number of training steps before doing any weight
updates. This way you can simulate larger batch sizes without actually needing
the memory requirements to fit them in memory.
Gradients are accumulated for and weight updates are only performed after a
number of training steps, effectively simulating larger batch sizes using a
smaller amount of memory.
### Beyond general techniques | scraping/output/-4766375058050370193.txt | [
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