Welcome back to the oneAPI channel.
The essence of AI is that we moved from coding algorithms
to having computers learn the algorithms on their own using trial and error on an ever
increasing amount of data.
This trial and error process is sometimes very heavy, takes a long time and is very
expensive, And even when we allocate the best compute
power, the process sometimes does not get us to where we want to be
The reasons is that models many times have millions of parameters to tune.
But, before we can even start to learn all of these parameters, we need to set the hyperparameters
that controls the training process. It is the hyperparameters that allow the model
to learn anything and converge.
SigOpt can help you find the best set of hyperparameters for your specific model.. let me show you
SigOpt can assist you regardless of the platform you are using, the model you are running or
the application. You can run PyTorch or XGBoost, on a CPU or
a GPU, for a recommendation system, fraud detection, financial application or whatever
The journey to converge an AI system is long Every model has tunable parameters, configurations,
magic numbers, and many decisions to take And SigOpt can help you along this whole journey
Model tuning and evaluation is obvious
But SigOpt can also help you to understand convergence, so that you can stop training
earlier and save a lot of time And while you are training your model, SigOpt
automatically tracks all of the metadata that you need to understand your model
And all of that is visualized on the SigOpt dashboard,
so that you can get a perspective on how training is affecting your model and more importantly
all of your metrics
SigOpt can help you choose the right model architecture
It can help you with data and features engineering while tracking and logging everything that
has been done
And even to better define your success criteria..
or evaluating the quality and re-tuning for deployment
And it’s all being done in a non intrusive approach, let me show you in a minute..
The best way to get started is to sign up to sigopt.com
Tons of materials here, If you scroll down, there are many videos
you can watch If you go to resources, blog
Pretty interesting and impressive test cases you can read and get a feeling on the capabilities
and value
Under documentations you can find many getting started tutorials
In many formats, jupyter notebooks and more
And even real time experiments that demonstrates how sigOpt can help you converge
Here to maximize a function , track the progress in real time
The sigOpt github page holds many
And this is the simplest example I found to demonstrate how easy it is..
This example is using XGBoost.. So we import the right libraries
And load the scikit-learn iris data-set
Classification using XGBoost
And running the model.. So far nothing new.
Now let’s import sigopt
Log the dataset, the model and the parameters (here it is just one parameter.. n_estimators)
into sigopt
Use sigopt tracked parameters
And track the metric value and name for each run..
So we added just few lines to our model code
Here it is… Experiment yaml file has directives for parameters
ranges and more And we are running sigopt, optimize, with
the yaml file, and our model…
While the model’s iterations are running,
we can track the progress in real time,,
In the sigOpt page, go to experiment, choose your experiment
3 runs out of 50 already completed We can see the convergence as it happens
Ask for more graphs This is a very simple example, so nothing
fancy happens here But you can see the progress in real time..
So whatever kind of AI you are doing, sigOpt is the magic powder that can help you
get to results and to conclusions faster and cheaper
Many companies and experts are already using it and there’s lots of materials online,
check out the SigOpt summit on the SigOpt YouTube channel
Sign up today and get started right away, Link is here
Enjoy, subscribe, thank you