Software Engineer Intern at a tech services company with 11-50 employees
Real User
Top 5
2024-08-27T17:27:00Z
Aug 27, 2024
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.
Associate Machine Learning Engineer at a tech services company with 501-1,000 employees
Real User
Top 10
2024-07-15T07:12:21Z
Jul 15, 2024
As we know there are newer models coming in with newer functionalities. Such new functionalities can be embedded in PyTorch by itself, so as users, we don't have to update anything. Considering the newer models that are coming into the AI world along with the new functionalities, one should be able to ensure that upgrades are made to the existing structures. When the new upgrades come in, the tool should make it easier for users to use them. It should be made easier for users to move from one version to another.
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.
Financial Analyst 4 (Supply Chain & Financial Analytics) at Juniper Networks
MSP
Top 5
2024-03-28T09:56:01Z
Mar 28, 2024
I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques. I would also like to see some improvement in parallel processing. We can take advantage of the GPU and compute it.
On the production side of things, having more production tooling frameworks would be helpful. TensorFlow has a lot of elaborate frameworks e.g. for serving models, and that's one area where PyTorch could improve.
We've built this course as an introduction to deep learning. Deep learning is a field of machine learning utilizing massive neural networks, massive datasets, and accelerated computing on GPUs. Many of the advancements we've seen in AI recently are due to the power of deep learning. This revolution is impacting a wide range of industries already with applications such as personal voice assistants, medical imaging, automated vehicles, video game AI, and more.
In this course, we'll be...
The analyzing and latency of compiling could be improved to provide enhanced results.
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.
As we know there are newer models coming in with newer functionalities. Such new functionalities can be embedded in PyTorch by itself, so as users, we don't have to update anything. Considering the newer models that are coming into the AI world along with the new functionalities, one should be able to ensure that upgrades are made to the existing structures. When the new upgrades come in, the tool should make it easier for users to use them. It should be made easier for users to move from one version to another.
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've had issues with stability when I use a lot of data and try out different combinations of modeling techniques. I would also like to see some improvement in parallel processing. We can take advantage of the GPU and compute it.
On the production side of things, having more production tooling frameworks would be helpful. TensorFlow has a lot of elaborate frameworks e.g. for serving models, and that's one area where PyTorch could improve.
The training of the models could be faster. However, with PyTorch, modern training becomes a bit slower because it is within the models at Python.
None come to mind.
There is not enough documentation about some methods and parameters. It is sometimes difficult to find information.