With the introduction of online platforms for technologies such as deep learning, the need for powerful GPUs on our local systems has decreased with a consequential degree. To induct heavy deep learning models for machine learning, some platforms can provide GPU hosted on cloud which can be accessed online. The best part is that a computer with any configuration which is capable of internet connectivity can use these tools. The following online platforms can be used to overcome the obstacle of finding the computer with an optimal configuration for running deep learning applications.
1. Google Colab
Google Colab is hands down the most popular and well crafted hosted GPU based machine learning development platform. It uses the Tesla K80 GPU for free usage up to 12 hours per day. Google Colab which is by default connected to any Google Drive can be used to store the datasets or documents required for deep learning development. Google Colab notebooks can be connected to the local runtime as well as the hosted GPU. The best part of Google Colab is that it is free and extra add-ons can be bought if required. Apart from its robustness, it is very easy to use due to its well-built interface which can be used to run any deep learning model possible. It is the perfect tool for programmers who are into deep learning models.
FloydGPU is a well-developed cloud-based platform that suffices all the needs and facilities required for any machine learning developer. With a reasonable amount of features as well as its multi-thread feature, it is certainly one of the most robust tools available. It is perfectly tailored for deep learning and provides reasonable storage options that enable the user to store large datasets. FloydGPU can reduce the consequential amount of time spent and at the same time also enhance its efficiency. Some of the primary features of FloydGPU are framework support, Jupyter notebook support, and version control.
Kaggle is one of the most influential resources for data science as it provides free datasets, articles as well as coding examples. Its induction of Nvidia K80 GP enables the users to run deep learning models with ease. It enables professionals to run deep learning applications with ease. Kaggle also provides a tremendous amount of multi-domain datasets that can be used directly into a project to develop any deep learning project. GPU usage is limited to 30 hours/week. Its use results in higher rates of training data on deep learning models. GPU kernels can be enabled whenever required to perform a particular task in deep learning.
4. Cloud X Lab
Cloud X lab helps its users to develop hands-on machine learning applications on a cloud GPU. Its users can build powerful AI applications. Cloud X Lab also provides some of the best tools for big data. The X lab is most ideal for analysts as well as big data engineers. It is great for learning purposes as well as hands-on practicing especially for students and professors working in this domain. With competitive pricing, it is a better fit for apache products. It is ideal for all novice programmers. It also provides some specific online courses and big data tools to perform practice on the cloud.
Jupyter Notebooks can easily run on the Azure Cloud Platform, which is developed by Microsoft. Azure notebooks do not have a GUI of its own but use that of Jupyter Notebooks. So it has the same shortcuts and code snippets as that of Jupyter Notebooks. The Azure platform is a phenomenal way to write scripts and can be used by anyone with prior experience working on Jupyter notebooks. Azure notebooks are very responsive as well as provide higher functionality towards its excellent tools.
The systems with lower configurations that are not equipped with the essential requirements to perform deep learning can utilize these platforms to conduct deep learning coding and application development on these online platforms. These systems not only provide a great IDE for development but also an external GPU that is much needed for heavier machine learning/deep learning applications to be deployed easily.
Share Your Views: