Step-by-Step Guide to Installing TensorFlow with GPU on RTX 4060 Without Crashes


Author: Aman
Date: June 3rd, 2025
⚙️ Overview
I set up TensorFlow with GPU support for my skin cancer detection and classification project. Here’s a clear and simple list of the exact steps I followed to get everything running smoothly with my NVIDIA RTX 4060 Laptop GPU.
✅ Step 1: Update NVIDIA GPU Driver
Make sure your graphics drivers are up to date. You can download the latest drivers here:
👉 NVIDIA Driver Downloads
🛠️ Step 2: Install Visual Studio with C++
Download Visual Studio Community Edition and during installation, select all C++ related components, including MSBuild. TensorFlow needs the C++ build tools present.
⚡ Step 3: Install CUDA Toolkit 11.5
Download CUDA 11.5 Toolkit from:
👉 CUDA 11.5.1 Download
Install it with default settings.
💾 Step 4: Install cuDNN for CUDA 11.5
Download cuDNN 8.3.1 from NVIDIA:
👉 cuDNN Archive
Extract the ZIP file and copy the contents into your CUDA directory (e.g., C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.5
), inside the corresponding bin
, include
, and lib
folders.
🧪 Step 5: Install Anaconda or Miniconda
You can use either. I used Miniconda.
👉 Anaconda Download
📦 Step 6: Create and Activate Conda Environment
conda create -n tensorflow python=3.9 -y
conda activate tensorflow
📝 Step 7: Install Jupyter and Set Up Kernel
Install Jupyter:
conda install -y jupyter
Then register the kernel:
pip install ipykernel
python -m ipykernel install --user --name=tensorflow --display-name "Python (tensorflow)"
⚙️ Step 8: Install TensorFlow and Fix NumPy
Install the compatible TensorFlow version:
pip install tensorflow==2.10.0
Then fix the NumPy issue: if numpy>2.xx.x
pip install numpy==1.24.3
🧪 Step 9: Verify GPU is Working
import tensorflow as tf
print(tf.__version__)
print(tf.config.list_physical_devices('GPU'))
Optional full device info:
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
If your output shows NVIDIA GeForce RTX 4060
, you're good to go.
✅ Done
That’s it. You’re ready to train TensorFlow models with GPU support using your RTX 4060. Everything was tested and works smoothly in Jupyter notebooks.
Reference: I used this video as a guide.
#tensorflow #gpu #cuda #cudnn #python #setup #machinelearning #rtx4060 #hashnode
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