PyTorch Reviews

We are using it for deep learning to implement the deep learning algorithms. It is also quite good compared to TensorFlow. Many of them use PyTorch more than TensorFlow.

It's a framework for deep learning algorithms, allowing us to implement various parameters.

We use PyTorch libraries, which are working well. It's very easy.

We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3. We also faced a few version compatibility issues with CUDA drivers.

I have been using PyTorch for a year.

You can easily scale it up.

There is a huge community support.

The documentation is good.

I recommend the solution.

Overall, I rate the solution a nine out of ten.

PyTorch is used to develop machine learning models. I use the products depending on the kind of project I work on, and though I use PyTorch, I am more of a Python person. The use of the products depends on the kind of project I deal with, and though I use PyTorch, I am more of a Python person who uses it for data engineering. I use PyTorch when I am developing or writing my research, but not at the start of my work. The use of the product depends on the project I work on in my company. Python is mostly enough to deal with the boom due to the introduction of generative AI, but when there is a need for fine-tuning, we go for PyTorch in our company.

If you compare PyTorch with TensorFlow, I would say that PyTorch gives one more option to help build customized stuff, especially when building your own logic.

The product has certain shortcomings in the automation of machine learning. With automated machine learning, you just need to provide the dataset, and the tool does everything for you. The automated machine learning part can help since all you need to do is provide the datasets and let the solution build the models for you, and then, it can also work to improve it further. An automation process needs to be associated with the machine learning part. PyTorch is mostly used for deep learning models, so automation processes can be good, but they are difficult to implement. If it is possible to implement automation features in the product, then it would be better for the tool.

I have been using PyTorch for ten months to a year. I used the tool during the PoC phase in my company, and we are doing it again right now for our clients. I am a user of the tool.

You just go and install the tool on your laptop, so there is no user management or something like that when it comes to PyTorch. PyTorch is not like Amazon or other AWS products where you have to keep on adding users or create accounts for users. We just need to install the tool and work with it.

My company only has 300 employees, out of which only 10 to 20 people work in the data center where PyTorch is used.

If I face some issues, I use PyTorch's community support. I haven't contacted PyTorch's technical support team.

My company has used Pinecone's PoC phase, but not for production. My company has banking clients like Citibank and Citizens Bank, who have some generative AI-related use cases for which we are building some solutions, and for them, we store the vectors in Pinecone. I use Pinecone from a PoC point of view and not in the production use case. Other than Pinecone, my company uses PGVector.

The product's initial setup phase is easy. The deployment of the product depends on the hardware you have in your company because it requires GPUs. If you work with a single GPU, it is not at all a problem to install the product, but if you have multiple GPUs, then it might have some complexities. I would say it is easy to deploy the product. Compared to the tool's previous versions, the current version of the product is much better. In the tool's previous versions, there were some issues during the deployment process, and I have seen some complexities. Though there are some complexities with the deployment process, I feel it is okay.

The installation part is okay. At present you can install the product directly, but at the starting phase, you need to install CUDA separately, and even after that there might arise some version issues and mismatches in the tool.

My company chose PyTorch based on the use cases we have to deal with and the requirements of our clients. PyTorch is used as a common framework. For the building models, you either have PyTorch or TensorFlow, which are the best options right now in the market. PyTorch or TensorFlow are the open-source frameworks you can find.

Whether I would recommend the product or not depends on the kind of work someone is doing. If you are just getting into data science or deep learning this week, learning and building these models, then directly starting with PyTorch is okay for you, but I would say that it would be better if you first learned all the basic concepts before getting into machine learning. It is important to gain knowledge about Python and all the machine learning libraries and then get into deep learning before starting to use PyTorch.

When it comes to PyTorch, you use it to build models. If you have a machine learning or AI model, and you just use them, then the basics of PyTorch can be helpful. If you are into the building of models or creating new models, then you need to have more programming knowledge.

If you are a programmer, then learning to use the product would be easy. If you are from a non-programming background, then I would not suggest the product to you.

Compared to TensorFlow, I like PyTorch's abilities in terms of its programming, flexibility and ability to customize based on the needs of the users.

I rate the tool an eight out of ten.

I work within the field of NLP and data logistics, but most of the information is stored in the description. I mostly use NLP algorithms to extract the sentiment or the similarity if they have a similarity score calculation. For example, something like the windshield cracking, or something extremely severe like a garage burning, or multiple car damage. 

The NLP tasks don't deal with the financial data directly but use extra information to get some insights. It's not really a good practice in India to use deep learning models directly on financial data because of audit reasons.

I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code. It's one of the easiest tools and takes the hint on its own. I was quite happy about its existence.

None come to mind.

I have been using PyTorch for about three months.

I think PyTorch is a stable solution. I had to upgrade the box it was running on several times to handle the load from many models I was running parallely, but that's not specific to PyTorch. But I think that was more of an environmental issue and not a problem with PyTorch. There was a transition period where the initial models weren't production-ready, and I had slated Caffe2. But I think the current version is pretty good and pretty stable.

Initially, I was using the Keras addon, and these are like high-level wrappers for TensorFlow. I started to look at PyTorch, and it was quite easy to learn and easy to implement.

PyTorch is a new insert, and I'm the only one using it. Most of my teammates are on TensorFlow because that's something that has been stable for a long time. I'm just showing them that it's easy to use PyTorch. 

I was also experimenting with MXNet, which is part of Amazon Deep Learning for three months. I've been providing proof of concepts for existing models using these different frameworks that are available in the marketplace.

The initial setup is quite easy as it is just a library install. The model building itself is fairly simple because it's pythonic. It was more natural and followed how Python should be used. I would say that it helps the structure.

We just followed all these open source guys out there. They had like a lab for data science and a data lab in our company, which is basically an incident technology solutions provider. Just like bringing an analytics practice into the mix. But it's still a work in progress, and we just use a Python server that has all the latest policies installed. You just query whatever you want. The Ops and the support team take care of it.

PyTorch is an open-source solution.

I would recommend this solution because PyTorch is like a godsend.

On a scale from one to ten, I would give PyTorch a ten.

It is mostly used for everything that is connected to machine learning, data science, training, and other similar things.

Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful. 

There is not enough documentation about some methods and parameters. It is sometimes difficult to find information.

I have been using PyTorch for almost two years.

It is quite stable. Currently, I am the only user.

It is scalable.

They don't have support. It is an open-source kind of thing. It is not really open source, but openly available.

It is more or less straightforward. I sometimes have problems installing it. The deployment duration depends on the size of the model. It could take one hour. Sometimes, the whole process can take more than several weeks.

It was an in-house installation.

It is free.

I also considered TensorFlow. I don't remember why exactly I chose PyTorch instead of TensorFlow, but I think I didn't evaluate much. It was just like one of the two options, so I went for PyTorch.

I would definitely recommend this solution. I would rate PyTorch an eight out of ten.