get pytorch version version in ubuntu with code examples

PyTorch is a widely-used open-source deep learning framework developed by Facebook's AI Research lab. It is used for natural language processing, computer vision, and other applications. In this article, we will look at how to check the version of PyTorch installed on an Ubuntu system, and provide some code examples for doing so.

To check the version of PyTorch installed on your Ubuntu system, you can use the following command in the terminal:

pip show torch

This will display information about the PyTorch package, including its version number.

Alternatively, you can use the following command to check the version of PyTorch:

python -c "import torch; print(torch.__version__)"

Another way to check the PyTorch version is using the following command:

import torch
print(torch.version.__version__)

In this method, we are importing torch package and then checking the version of it.

Additionally, you can also check the version of PyTorch using the torch.version module. This module provides several attributes that can be used to check the version of PyTorch, such as torch.version.__version__, torch.version.git_version, and torch.version.cuda.

Here is an example of how to use the torch.version module to check the version of PyTorch:

import torch
print("PyTorch version: ", torch.version.__version__)
print("Git version: ", torch.version.git_version)
print("CUDA version: ", torch.version.cuda)

In this example, we import the torch package and then use the torch.version.__version__ attribute to print the version of PyTorch. Additionally, we are also printing the git version and cuda version.

In conclusion, checking the version of PyTorch installed on an Ubuntu system is a straightforward process that can be done using a few simple commands or python code. By using the above-mentioned method, you can easily check the version of PyTorch installed on your system.

In addition to checking the version of PyTorch, there are several other things you may want to do with PyTorch on an Ubuntu system.

One common task is to install PyTorch on an Ubuntu system. To install PyTorch, you can use the following command:

pip install torch torchvision

This command installs both the PyTorch package and the torchvision package, which provides additional functionality for computer vision tasks.

Another task that is often done with PyTorch is to perform deep learning tasks on the GPU. PyTorch makes it easy to perform deep learning on the GPU by providing a CUDA backend for its tensors. To use the GPU backend, you will need to have a CUDA-enabled GPU and the appropriate CUDA drivers installed on your system.

To check if PyTorch is using the GPU, you can use the following code snippet:

import torch
print(torch.cuda.is_available())

This will print 'True' if a CUDA-enabled GPU is detected and 'False' if it is not.

You can also check the version of CUDA being used by PyTorch by using the following command:

print(torch.version.cuda)

Additionally, you can also check the number of GPUs available on your system using torch library as shown below:

print(torch.cuda.device_count())

Another important feature of PyTorch is its ability to save and load models. PyTorch provides several ways to save and load models, including using the torch.save() and torch.load() functions, or using the torch.nn.Module.save() and torch.nn.Module.load() methods.

Here is an example of how to save a PyTorch model:

import torch

# Define the model
model = torch.nn.Linear(10, 1)

# Save the model
torch.save(model.state_dict(), 'model.pt')

And here is an example of how to load a PyTorch model:

import torch

# Define the model
model = torch.nn.Linear(10, 1)

# Load the model
model.load_state_dict(torch.load('model.pt'))

In conclusion, PyTorch is a powerful deep learning framework that can be used for a wide range of tasks on Ubuntu systems. In this article, we have looked at how to check the version of PyTorch, install PyTorch, use GPU with PyTorch, check the CUDA version, check number of GPUs available, saving and loading models. With the knowledge of these adjacent topics, you'll be able to use PyTorch more effectively on your Ubuntu system.

Popular questions

1. What command can be used to check the version of PyTorch installed on an Ubuntu system?

• The command pip show torch can be used to check the version of PyTorch installed on an Ubuntu system.

2. How can we check if PyTorch is using the GPU in an Ubuntu system?

• The following code snippet can be used to check if PyTorch is using the GPU:

import torch
print(torch.cuda.is_available())

This will print 'True' if a CUDA-enabled GPU is detected and 'False' if it is not.

3. How can we check the version of CUDA being used by PyTorch in an Ubuntu system?

• The following command can be used to check the version of CUDA being used by PyTorch:

print(torch.version.cuda)

4. How can we check the number of GPUs available on an Ubuntu system using PyTorch?

• The following command can be used to check the number of GPUs available on an Ubuntu system using PyTorch:

print(torch.cuda.device_count())

5. How can we save and load a PyTorch model on an Ubuntu system?

• PyTorch provides several ways to save and load models, including using the torch.save() and torch.load() functions, or using the torch.nn.Module.save() and torch.nn.Module.load() methods. Here is an example of how to save a PyTorch model:

import torch

# Define the model
model = torch.nn.Linear(10, 1)

# Save the model
torch.save(model.state_dict(), 'model.pt')

And here is an example of how to load a PyTorch model:

import torch

# Define the model
model = torch.nn.Linear(10, 1)

# Load the model
model.load_state_dict(torch.load('model.pt'))

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PyTorch