PyTorch Optimizers

August 29, 2022


PyTorch is the fastest growing deep learning framework and it is also used by many top fortune companies. It integrates many algorithms, methods, and classes into a single line of code to ease your day. PyTorch has many optimizer classes such as AdaDelta, Adam, and SGD to name a few. 

The optimizer takes the parameters we want to update, the learning rate we want to use and optimizers update weights through its step() method.

In this blog, we are going to see 13 such optimizers which are supported by the PyTorch framework.

Table of contents

  • Torch.optim
  • AdaDelta Class
  • AdaGrad Class
  • Adam Class
  • AdamW Class
  • SparseAdam Class
  • Adamax Class
  • LBFGS Class
  • RMSprop Class
  • Rprop Class
  • SGD Class
  • ASGD Class
  • NAdam Class
  • RAdam Class
  • Conclusion


torch.optim is a PyTorch package containing various optimization algorithms. Most commonly used methods for optimizers are already supported, and the interface is pretty simple enough so that more complex ones can be also easily integrated in the future.Now to use torch.optim you have to construct an optimizer object that can hold the current state and also update the parameter based on gradients.

import torch.optim as optim


optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)


optimizer = optim.Adam([var1, var2], lr=0.0001)



AdaDelta Class 

It implements the Adadelta algorithm and the algorithms were proposed in ADADELTA: An Adaptive Learning Rate Method paper. In Adadelta you don’t require an initial learning rate constant to start with, You can use it without any torch method by defining function like this:

def Adadelta(weights, sqrt, deltas, rho, batch_size):

     eps_stable = 1e-5

     for weight, sqr, delta in zip(weights, sqrs, deltas):

         g = weight.grad / batch_size

         sqr[:] = rho * sqr + (1. - rho) * nd.square(g)

         cur_delta = nd.sqrt(delta + eps_stable) / nd.sqrt(sqr + eps_stable) * g

         delta[:] = rho * delta + (1. - rho) * cur_delta * cur_delta

         # update weight in place.

         weight[:] -= cur_delta 


With help of PyTorch you can do same with just a single line of code as shown below:

torch.optim.Adadelta(params, lr=1.0, rho=0.9, eps=1e-06, weight_decay=0)


AdaGrad Class

Adagrad (short for adaptive gradient) penalizes the learning rate for parameters that are frequently updated, instead, it gives more learning rate to sparse parameters, parameters that are not updated as frequently. You can implement it without any class like this:

def Adagrad(data):

    gradient_sums = np.zeros(theta.shape[0])

     for t in range(num_iterations):

         gradients = compute_gradients(data, weights)

         gradient_sums += gradients ** 2

         gradient_update = gradients / (np.sqrt(gradient_sums + epsilon))

         weights = weights - lr * gradient_update

     return weights 


In several problems many times the most critical information is present in the data that is not as frequent. So, if the use case you are working on is related to sparse data, Adagrad can be useful. You can call the optimizer algorithm by using the below command with the help torch:


torch.optim.Adagrad(params, lr=0.01, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10)


But there are some drawbacks too like it is computationally expensive and the learning rate is also decreasing which makes it slow in training.


Adam Class

Adam is One of the most popular optimizers also known as adaptive Moment Estimation, it combines the good properties of Adadelta and RMSprop optimizer into one and hence tends to do better for most of the problems. You can simply call this class using the below command:


torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)


AdamW Class

This time the authors suggested an improved version of the Adam class called AdamW in which weight decay is performed only after controlling the parameter-wise step size. The weight decay or regularization term does not end up in moving averages and in outputs, it is only proportional to the weight itself. The authors show practically that AdamW yields better training loss, which means the models generalize much better than models trained with Adam allowing the remake to compete with SGD with momentum.


In PyTorch, you can simply call this algorithm using below command:


 torch.optim.AdamW(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.01, amsgrad=False)



SparseAdam Class

SparseAdam Implements a lazy version of the Adam algorithm which is suitable for sparse tensors.

In this variant of adam optimizer, only moments that show up in the gradient get updated, and only those portions of the gradient get applied to the parameters. 


In PyTorch, you can simply call this algorithm using below command:


torch.optim.SparseAdam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08)


Adamax class

It Implements the Adamax algorithm (a variant of Adam supported infinity norm).


In PyTorch, you can simply call this algorithm using below command:


torch.optim.Adamax(params, lr=0.002, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)




This class Implements the L-BFGS algorithm, which is heavily inspired by minFunc (minFunc – unconstrained differentiable multivariate optimization in Matlab


In PyTorch, you can simply call this with the help of the torch method:


torch.optim.LBFGS(params, lr=1, max_iter=20, max_eval=None, tolerance_grad=1e-07, tolerance_change=1e-09, history_size=100, line_search_fn=None)



RMSprop Class

This class Implements the RMSprop algorithm, which was Proposed by G. Hinton.


The centered version first appears in Generating Sequences with Recurrent Neural Networks. The implementation here takes the square root of the gradient average before adding epsilon (note that TensorFlow interchanges these two operations). The effective learning rate is thus \gamma/(\sqrt{v} + \epsilon)γ/(v​+ϵ) where \gammaγ is the scheduled learning rate and v is the weighted moving average of the squared gradient.


To implement this optimizer in PyTorch, you can use below mentioned command:


torch.optim.RMSprop(params, lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False)


Rprop Class

This class Implements the resilient backpropagation algorithm.


In PyTorch, you can use below mentioned command to call the optimizer:


torch.optim.Rprop(params, lr=0.01, etas=(0.5, 1.2), step_sizes=(1e-06, 50))



SGD Class

Implements stochastic gradient descent (optionally with momentum).

Nesterov momentum is predicated on the formula from On the importance of initialization and momentum in deep learning.


Implementation of the algorithm:


def SGD(data, batch_size, lr):

     N = len(data)


     mini_batches = np.array([data[i:i+batch_size]

     for i in range(0, N, batch_size)])

     for X,y in mini_batches:

         backprop(X, y, lr) 


In PyTorch, you can implement algorithms by calling below command:


torch.optim.SGD(params, lr=<required parameter>, momentum=0, dampening=0, weight_decay=0, nesterov=False)


To use algorithms in building deep neural network model, you can use as per below example:


optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)


loss_fn(model(input), target).backward()




ASGD Class

It Implements Averaged Stochastic Gradient Descent(ASGD) algorithm.


It has been proposed in Acceleration of stochastic approximation by averaging paper.


In PyTorch, you can implement algorithms by calling below command:



torch.optim.ASGD(params, lr=0.01, lambd=0.0001, alpha=0.75, t0=1000000.0, weight_decay=0)


NAdam Class

This class implements the NAdam optimization algorithm.


For further details regarding the algorithm, you can refer to Incorporating Nesterov Momentum into Adam paper.

In PyTorch, you can implement algorithms by calling below command:


torch.optim.NAdam(params, lr=0.02, betas=(0.9,0.999), eps=1e-08, weight_decay=0, momentum_decay=0.004, foreach=None)



RAdam Class

It implements the RAdam optimization algorithm.


For more details regarding the algorithms, you can refer to On the variance of the adaptive learning rate and beyond.


In PyTorch, you can implement algorithms by calling below command

torch.optim.NAdam(params, lr=0.001, betas=(0.9,0.999), eps=1e-08, weight_decay=0, foreach=None)





In this blog, we saw 13 optimizers supported by the PyTorch framework and their implementations. A PyTorch framework is a powerful tool when it comes to deep learning models whether it is a research problem or business problem. For more details and in-depth details on parameters, you can refer to their official documentation here.


Latest Blogs
This is a decorative image for: Comparison between Cloud-Based and On Premises GPUs
October 6, 2022

Comparison between Cloud-Based and On Premises GPUs

Cloud GPUs vs On Premises GPUs

Cloud GPUs are typically more powerful than on-premises GPU instances. The cost of renting a cloud GPU is generally lower than the cost of purchasing an on-premise GPU. 

Cloud platforms offer fast access to high performance compute and deep learning algorithms, which makes it simpler to start using machine learning models and get early insights into your data. 

Cloud GPUs are better for machine learning because they have lower latency, which is important because the time it takes a neural network to learn from data affects its accuracy. Furthermore, cloud GPUs allow users to take advantage of large-scale training datasets without having to build and maintain their own infrastructure.

On Premises GPUs are better for machine learning if you need high performance or require access to cutting-edge technologies not available in the public cloud. For example, on-premises hardware can be used for deep learning applications that require high memory bandwidth and low latency.

Cloud GPUs: Cloud GPUs are remote data centers where you can rent unused GPU resources. This allows you to run your models on a massive scale, without having to install and manage a local machine learning cluster.

Lower TCO: Cloud GPUs require no upfront investment, making them ideal for companies that are looking to reduce their overall capital expenses. Furthermore, the cost of maintenance and upgrades is also low since it takes place in the cloud rather than on-premises.

Scalability & Flexibility: With cloud-based GPU resources, businesses can scale up or down as needed without any penalty. This ensures that they have the resources they need when demand spikes but also saves them money when there is little or no demand for those resources at all times.

Enhanced Capacity Planning Capabilities: Cloud GPU platforms allow businesses to better plan for future demands by providing estimates of how much processing power will be required in the next 12 months and beyond based on past data points such as workloads run and successes achieved with similar models/algorithms etc... 

Security & Compliance : Since cloud GPUs reside in a remote datacenter separate from your business' core systems, you are ensured peace of mind when it comes to security and compliance matters (eigenvector scanning / firewalls / SELinux etc...) 

Reduced Total Cost Of Ownership (TCO) over time due to pay-as-you-go pricing model which allows you only spend what you actually use vs traditional software licensing models where significant upfront investments are made.

Cloud GPUs: Cloud GPUs offer significant performance benefits over on-premises GPUs. They are accessible from anywhere, and you don't need to own or manage the hardware. This makes them a great choice for data scientists who work with multiple data sets across different platforms.

Numerous Platforms Available for Use: The wide variety of available platforms (Windows, Linux) means that you can run your models using the most popular machine learning libraries and frameworks across different platforms without having to worry about compatibility issues between them.

This is a decorative image for: Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian
October 4, 2022

Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

As the rupee is inching closer to 82 per dollar, the strong dollar has directly impacted the costs of cloud services for Indian businesses. The high cost of storage and computing power, along with bandwidth charges from overseas vendors, has led to a huge increase in the effective rate of these services. This is especially true for startups and SMEs that rely on cloud computing to store and process user data. With the strong dollar continuing to impact the cost of cloud services, it is essential for Indian companies to evaluate their options and adopt local alternatives wherever possible. This blog post will discuss how the strong dollar impacts cloud costs, as well as potential Indian alternatives you can explore in response to this global economic trend. 

What is a Strong Dollar?

A strong US dollar($) is a term used to describe a situation where a US’s currency has appreciated in value compared to other major currencies. This can be due to a variety of factors, including interest rate changes, a country’s current account deficit, and investor sentiment. When a currency appreciates, it means that it is worth more. A strong dollar makes imports more expensive, while making exports cheaper. Strong dollars have been a growing trend in the past couple of years. As the US Federal Reserve continues to hike interest rates, the dollar strengthens further. The rising value of the dollar means that the cost of cloud services, especially from hyperscalers based in the US, will rise as well. 

Increase in Cloud Costs Due to Strong Dollar

Cloud services are essential for modern businesses, as they provide easy access to software, storage, and computing resources. Cloud services are delivered over the internet and are typically charged on a per-use basis. This makes them incredibly convenient for businesses, as they can pay for only the resources they actually use. Cloud computing allows businesses to scale their resources up or down, depending on their current business needs. This makes it suitable for startups, where demand is uncertain, or large enterprises with global operations. Cloud computing is also inherently scalable and allows businesses to quickly react to changing business needs. Cloud computing is a very competitive industry and providers offer attractive prices to attract customers. However, these prices have been impacted by the strong dollar. The dollar has strengthened by 15-20% against the Indian rupee in the last few years. As a result, the costs of services such as storage and bandwidth have increased for Indian companies. Vendors charge their Indian customers in Indian rupees, taking into account the exchange rate. This has resulted in a significant rise in the costs of these services for Indian companies.

Why are Cloud Services Becoming More Expensive?

Cloud services are priced in US dollars. When the dollar is strong, the effective price of services will be higher in Indian rupees, as the cost is not re-adjusted. There are a couple of reasons for this price discrepancy. First, Indian customers will have to pay the same prices as American customers, despite a weaker Indian rupee. Second, vendors have to ensure that they make a profit.

Possible Indian Alternatives to Cloud Services

If you're looking for a cost-effective substitute for services provided by the U.S.-based suppliers, consider E2E Cloud, an Indian cloud service provider. When it comes to cloud services, E2E Cloud provides everything that startups and SMEs could possibly need.

The table below lists some of these services and compares their cost against their US equivalents. 

According to the data in the table above, Indian E2E Cloud Services are much cheaper than their American equivalents. The difference in price between some of these options is substantial. When compared to the prices charged by suppliers in the United States, E2E Cloud's bandwidth costs are surprisingly low. Although not all E2E Cloud services will be noticeably less expensive. Using Indian services, however, has an additional, crucial perk: data sovereignty.


The price of cloud services will rise as the US Dollar appreciates. Indian businesses will need to find ways to counteract the strong dollar's impact on their bottom lines. To do this, one must use E2E Cloud. The availability of E2E Cloud services in INR currency is a bonus on top of the already substantial cost savings. An effective protection against the negative effects of a strong dollar.

This is a decorative image for: Actions CEOs can take to get the value in Cloud Computing
September 28, 2022

Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.


In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure