> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Demo Setting up PyTorch > This guide covers setting up a PyTorch development environment on Ubuntu, including installation and verification steps. Welcome to this comprehensive guide on configuring a PyTorch development environment. In this tutorial, you'll learn how to create an isolated Python virtual environment on an Ubuntu machine, install all required dependencies (including PyTorch, TorchVision, and TorchAudio), and verify the installation. This step-by-step approach ensures that your environment is reproducible for collaboration or deployment. Before you start, visit the [PyTorch “Get Started” page](https://pytorch.org/get-started/) to choose your operating system, package manager, and CUDA version. The page dynamically generates the installation commands. For instance, for a nightly CPU build you may receive: ```bash theme={null} pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu ``` Alternatively, if you prefer Conda with CUDA 11.8 support, you might run: ```bash theme={null} conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia ``` For a typical installation using CUDA 11.8, the command is: ```bash theme={null} pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 ``` In the following sections, we detail how to set up your PyTorch environment on an Ubuntu system. *** ## Step 1: Update Your System and Verify Python Installation Begin by updating your package list to ensure that you have the latest versions of available packages: ```bash theme={null} root@ubuntu-host:~ ➜ apt-get update Get:1 http://old-releases.ubuntu.com/ubuntu lunar InRelease [267 kB] Get:2 http://old-releases.ubuntu.com/ubuntu lunar-updates InRelease [109 kB] Get:3 http://old-releases.ubuntu.com/ubuntu lunar-backports InRelease [99.9 kB] Get:4 http://old-releases.ubuntu.com/ubuntu lunar-security InRelease [109 kB] Get:5 http://old-releases.ubuntu.com/ubuntu lunar/multiverse amd64 Packages [289 kB] Get:6 http://old-releases.ubuntu.com/ubuntu lunar/main amd64 Packages [1,797 kB] Get:7 http://old-releases.ubuntu.com/ubuntu lunar/universe amd64 Packages [18.7 MB] Get:8 http://old-releases.ubuntu.com/ubuntu lunar/restricted amd64 Packages [181 kB] Get:9 http://old-releases.ubuntu.com/ubuntu lunar-updates/restricted amd64 Packages [325 kB] Get:10 http://old-releases.ubuntu.com/ubuntu lunar-updates/main amd64 Packages [531 kB] Get:11 http://old-releases.ubuntu.com/ubuntu lunar-updates/multiverse amd64 Packages [11.6 kB] Get:12 http://old-releases.ubuntu.com/ubuntu lunar-backports/universe amd64 Packages [4,203 B] Get:13 http://old-releases.ubuntu.com/ubuntu lunar-security/universe amd64 Packages [1,017 kB] Get:14 http://old-releases.ubuntu.com/ubuntu lunar-security/main amd64 Packages [429 kB] Get:15 http://old-releases.ubuntu.com/ubuntu lunar-security/restricted amd64 Packages [8,203 B] Fetched 25.3 MB in 4s (7,220 kB/s) Reading package lists... Done root@ubuntu-host:~ ➜ ``` It appears that Python is not installed yet. Install Python 3 (version 3.11 in this guide) and verify the installation with: ```bash theme={null} root@ubuntu-host:~ ⟶ python3 --version Python 3.11.4 root@ubuntu-host:~ ⟶ which python3 /usr/bin/python3 ``` *** ## Step 2: Install pip and the venv Package Pip is essential for managing Python packages. Install pip along with build dependencies: ```bash theme={null} root@ubuntu-host:~ ⟶ apt-get install -y python3-pip python3-dev python3-venv ``` During the installation, you will encounter output similar to this: ```plaintext theme={null} Setting up libgprofng0:amd64 (2.40-2ubuntu4.1) ... Setting up python3-pip (23.0.1+dfsg-1ubuntu0.2) ... ... root@ubuntu-host:~ # ``` Verify pip's installation: ```bash theme={null} root@ubuntu-host:~ ⟶ pip3 --version pip 23.0.1 from /usr/lib/python3/dist-packages/pip (python 3.11) ``` *** ## Step 3: Create and Activate a Virtual Environment Isolating your project in a virtual environment prevents conflicts between package versions. Create a new virtual environment named "venv": ```bash theme={null} root@ubuntu-host:~ ⟶ python3 -m venv venv ``` Check that the `venv` directory has been created: ```bash theme={null} root@ubuntu-host:~ ⟶ ls -l total 4 drwxr-xr-x 5 root root 4096 Dec 18 14:12 venv ``` Inside the `venv` folder, you will find several subdirectories and files: ```bash theme={null} root@ubuntu-host:~ ⟶ ls -l venv/ total 16 drwxr-xr-x 2 root root 4096 Dec 18 14:12 bin drwxr-xr-x 2 root root 4096 Dec 18 14:12 include drwxr-xr-x 3 root root 4096 Dec 18 14:12 lib lrwxrwxrwx 1 root root 14 Dec 18 14:12 lib64 -> lib -rw-r--r-- 1 root root 149 Dec 18 14:12 pyvenv.cfg ``` Activate the virtual environment with: ```bash theme={null} root@ubuntu-host:~ ⟶ source venv/bin/activate ``` Your prompt should now indicate that you are working within the virtual environment. You can safely install packages using pip without affecting the global Python installation. *** ## Step 4: Install PyTorch, TorchVision, and TorchAudio With the virtual environment activated, install PyTorch and its related libraries via pip. This command also pulls in necessary NVIDIA libraries if a GPU is detected: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 (venv) ➜ pip3 install torch torchvision torchaudio ``` The installation will display output similar to: ```plaintext theme={null} Collecting torch Downloading torch-2.5.1-cp311-cp311-manylinux1_x86_64.whl (906.5 MB) Collecting torchvision Downloading torchvision-0.20.1-cp311-cp311-manylinux1_x86_64.whl (7.2 MB) Collecting torchaudio Downloading torchaudio-2.5.1-cp311-cp311-manylinux1_x86_64.whl (3.4 MB) ... ``` This confirms that PyTorch along with TorchVision and TorchAudio (plus their dependencies) have been installed in your isolated environment. *** ## Step 5: Verify the Installation To check that all packages installed correctly, list the installed packages using: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 (venv) ➜ pip3 list ``` For reproducibility, you can generate a requirements file: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 (venv) ➜ pip3 freeze > requirements.txt ``` Review the generated file: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 (venv) ➜ cat requirements.txt ``` The file should contain entries like: ```plaintext theme={null} filelock==3.16.1 fsspec==2024.10.0 Jinja2==3.1.4 MarkupSafe==3.0.2 ... torch==2.5.1 torchaudio==2.5.1 torchvision==0.20.1 ``` Next, validate that PyTorch operates as expected by opening a Python interpreter: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 (venv) ➜ python3 ``` Inside the interactive shell, run: ```python theme={null} import torch print(torch.__version__) # Expected output: 2.5.1+cu124 (or similar) print(torch.rand(2, 4)) # Generates a random 2x4 tensor ``` Optionally, check for CUDA-enabled GPU availability: ```python theme={null} print(torch.cuda.is_available()) ``` This returns True if a CUDA device is available, else it returns False. Exit the interpreter by pressing Ctrl+D. *** ## Step 6: Deactivate the Virtual Environment Once you've completed testing, deactivate the virtual environment to return to the global Python state: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 (venv) ➜ deactivate ``` Running `pip3 list` in the global environment will now display only basic packages (e.g., pip, setuptools, wheel) without the additional PyTorch and NVIDIA libraries. To double-check, reactivate your virtual environment and list its installed packages: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 ➜ source venv/bin/activate root@ubuntu-host:~ via 🐍 v3.11.4 (venv) ➜ pip3 list ``` *** ## Step 7: Reproducing Your Virtual Environment Reproducibility is key when collaborating or migrating between machines. First, create a new virtual environment (named "venv2"): ```bash theme={null} root@ubuntu-host:~ via v3.11.4 ↣ python3 -m venv venv2 ``` Verify both virtual environments exist: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 ➜ ls -l total 12 -rw-r--r-- 1 root root 595 Dec 18 14:16 requirements.txt drwxr-xr-x 6 root root 4096 Dec 18 14:14 venv drwxr-xr-x 5 root root 4096 Dec 18 14:24 venv2 ``` Activate the new environment: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 ➜ source venv2/bin/activate ``` Your new environment is minimal. Install all dependencies using the previously generated `requirements.txt`: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 (venv2) ➜ pip3 install -r requirements.txt ``` After installation, validate the PyTorch version by starting Python: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 (venv2) ➜ python3 ``` Then execute: ```python theme={null} import torch print(torch.__version__) # Expected output: 2.5.1+cu124 (or similar) ``` Exit the interpreter and deactivate the environment: ```bash theme={null} root@ubuntu-host:~ via 🐍 v3.11.4 (venv2) ➜ deactivate ``` Generating a `requirements.txt` file helps ensure your project’s environment can be perfectly replicated on another machine, thereby improving collaboration efficiency. *** ## Conclusion In this guide, you learned how to: * Update an Ubuntu system and verify Python installation. * Install pip and create a Python virtual environment. * Install PyTorch along with TorchVision and TorchAudio in an isolated environment. * Verify the installation and generate a reproducible `requirements.txt` file. * Reproduce the virtual environment on another instance. By following these steps, you ensure that your development environment is consistent and easily shareable. Happy coding with PyTorch! *** ## Additional Resources * [PyTorch Documentation](https://pytorch.org/docs/stable/index.html) * [Python Virtual Environments Guide](https://docs.python.org/3/library/venv.html) * [Understanding CUDA in PyTorch](https://pytorch.org/docs/stable/notes/cuda.html)