Install prerequisites guide

2025-08-18 11:45 UTC gpt-5-thinking Open in ChatGPT ↗

Got it 👍 The screenshot you shared lists the prerequisites for installing on Windows:

Python 3.11
- Download from the official Python site.
- During installation, make sure to check “Add Python to PATH”.
Git
- Download from Git for Windows.
- This is needed for cloning repositories and managing code.
CUDA 11.8 or 12.1 with cuDNN (only required if you’re using an NVIDIA GPU and want GPU acceleration)
- Download CUDA Toolkit from NVIDIA CUDA downloads.
- Download cuDNN from NVIDIA cuDNN (you’ll need an NVIDIA developer account).
- Make sure to match the CUDA version with the cuDNN version.

⚡ If you don’t have an NVIDIA GPU (or don’t want GPU acceleration), you can usually skip CUDA/cuDNN and just run everything on CPU — it will work but slower.

Would you like me to give you a step-by-step installation guide (with exact download links and setup order) for Windows so you don’t miss anything?

Yes ✅ you can quickly check each prerequisite with simple commands in Command Prompt (cmd). Here’s a script you can run step by step:

@echo off
echo ==== Checking Python ====
python --version

echo ==== Checking Git ====
git --version

echo ==== Checking CUDA ====
nvcc --version

echo ==== Checking cuDNN ====
:: This part is trickier, cuDNN doesn’t have a simple command.
:: We check if cudnn64*.dll exists in CUDA directories.
where cudnn64*.dll

pause

What this does:

Python → shows version (should be 3.11.x).
Git → shows version (any recent version is fine).
CUDA → nvcc prints version (should be 11.8 or 12.1).
cuDNN → checks if the DLL file exists in your system PATH.

👉 Save that as check_env.bat and double-click it (or run from cmd).

Do you want me to also include a PowerShell version that gives cleaner output (like ✅ / ❌)?

Perfect 👍 thanks for running the script. Here’s what your output shows:

Python ❌ → Not found ('python' is not recognized).
🔹 You need to install Python 3.11 and make sure to tick “Add to PATH” during installation.
🔹 Or, if it’s already installed, Windows might only recognize py instead of python. Try running:
Terminal window
```
py --version
```
If it shows Python 3.11.x, then you’re fine.
Git ✅ → Installed (git version 2.50.1.windows.1).
CUDA ✅ → Installed (CUDA 12.1 is detected).
cuDNN ❓ → The check failed because my script used where, but it wasn’t recognized in your environment. (Strange, since where is normally a built-in command in cmd.)
To check manually:
1. Open C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin
2. Look for a file like cudnn64_8.dll

⚡ Next Steps:

Run py --version and confirm if Python 3.11 is installed.
If not, install Python 3.11 and re-run the script.
Check if cudnn64_8.dll exists in your CUDA bin folder.

Do you want me to fix the cuDNN check command so you can re-run it properly?

Thanks for checking again 👍

Your screenshot shows:

'py' is not recognized as an internal or external command,
operable program or batch file.

👉 This means Python is not installed at all (or at least not correctly added to PATH).

Since the project you’re trying to install requires Python 3.11, here’s what you should do:

🔧 Install Python 3.11 on Windows

Go to the official download page:
Python 3.11.9 for Windows
Download Windows installer (64-bit).
Run the installer and make sure to:
- ✅ Check “Add Python to PATH” before clicking Install.
- Choose “Customize installation” if you want to confirm pip is installed too (recommended).
After installation, open a new Command Prompt and verify:
Terminal window
```
python --version
```
You should see something like:
```
Python 3.11.9
```
Also check that pip works:
Terminal window
```
pip --version
```

⚡ Once Python is installed, you’ll have:

Git ✅
CUDA ✅ (12.1 installed)
Python ✅ (after install)
cuDNN ❓ (still to confirm, but we can check once Python is working).

Do you want me to also give you the cuDNN check and installation steps so we can confirm your GPU is fully ready after Python is fixed?

Looks like Windows still can’t find Python on your PATH. Let’s fix that fast.

1) Try a brand-new terminal

Close all CMD/PowerShell windows and open a fresh Command Prompt, then run:

python --version

If it still says not recognized, continue.

2) Add Python 3.11 to PATH (quick command)

Replace Chris if your username is different, then run these two commands in CMD:

setx PATH "%PATH%;C:\Users\Chris\AppData\Local\Programs\Python\Python311\"
setx PATH "%PATH%;C:\Users\Chris\AppData\Local\Programs\Python\Python311\Scripts\"

Close CMD, open a new one, then:

python --version
pip --version

If you installed to a different folder, adjust the paths. Common alt path: C:\Program Files\Python311\ and ...\Python311\Scripts\

3) (Optional) Re-run installer to include launcher + PATH

If step 2 doesn’t work, rerun the Python 3.11 installer:

Choose Modify → ensure “pip” and “py launcher” are checked.
Check “Add python.exe to PATH” (or choose Repair).

Then try:

py -3.11 --version
python --version

4) Quick sanity checks

where python
where pip

These should print the paths to your python/pip. If they don’t, the PATH isn’t set to the right folder.

Once python and pip work, we’ll verify cuDNN next.

No worries—Windows still can’t find Python (and pip). Let’s get it working.

Step 1 — See if Python is actually installed

Try these exact commands in CMD (copy/paste):

"%LocalAppData%\Programs\Python\Python311\python.exe" --version
"C:\Program Files\Python311\python.exe" --version

If one of those prints Python 3.11.x, Python is installed but not on PATH. Go to Step 2.
If both say “The system cannot find the path…”, the install didn’t complete. Skip to Step 3.

Step 2 — Add Python 3.11 to PATH (so `python`/`pip` work)

Run these two commands in CMD (adjust the folder if you used C:\Program Files\Python311):

setx PATH "%PATH%;%LocalAppData%\Programs\Python\Python311"
setx PATH "%PATH%;%LocalAppData%\Programs\Python\Python311\Scripts"

Close CMD, open a new CMD, then verify:

python --version
pip --version
where python
where pip

If you installed under C:\Program Files\Python311, use:
setx PATH "%PATH%;C:\Program Files\Python311"
setx PATH "%PATH%;C:\Program Files\Python311\Scripts"

Step 3 — Reinstall (if Step 1 showed nothing)

Download Python 3.11 (64-bit) from the official site.
Run the installer and check all of these:
- Add python.exe to PATH
- pip
- py launcher
Finish install (or choose Repair if it shows that option).
Open a new CMD and run:
Terminal window
```
python --version
pip --version
```

Quick extra checks (optional)

If python still doesn’t work: Settings → Apps → App execution aliases → make sure python.exe and python3.exe (App Installer) are OFF (these aliases can interfere).
Confirm Git and CUDA:
Terminal window
```
git --version
nvcc --version
```

When python and pip both show versions, tell me and I’ll walk you through verifying cuDNN next.

Nice — Python and pip are now working 🎉
(Your screenshot shows Python 3.11.9 and pip 24.0.)

Want to finish the prereq check? Run these:

1) CUDA (toolkit) check

nvcc --version
nvidia-smi

You should see CUDA 12.1 and your GPU listed.

2) cuDNN check (CMD)

Try the quick “does the DLL exist?” checks:

dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin\cudnn*.dll"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\lib\x64\cudnn*.lib"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\include\cudnn*.h"

If those paths don’t exist on your machine, search all CUDA folders:

dir /s /b "C:\Program Files\NVIDIA*\*\cudnn*.dll"

If no files are found: cuDNN isn’t installed/copied yet. Download the cuDNN package that matches your CUDA 12.1, unzip, and copy:

bin\* → C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin
lib\x64\* → ...\v12.1\lib\x64
include\* → ...\v12.1\include

(Optional) One clean PowerShell check

This gives ✅ / ❌ summaries:

$ok = @{ }
$ok.Python = (python --version) -match "3\.11"
$ok.Git    = (git --version)     -match "git version"
$ok.CUDA   = (nvcc --version)    -match "release 12\.1"
$cuDNN = Get-ChildItem "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1" -Recurse -Filter "cudnn*.dll" -ErrorAction SilentlyContinue
$ok.cuDNN  = $cuDNN.Count -gt 0
$ok.GetEnumerator() | ForEach-Object { "{0}: {1}" -f $_.Key, ($(if($_.Value){"✅"}else{"❌"})) }

3) (Optional) Python-level GPU sanity test (after cuDNN)

If you plan to use PyTorch:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
python - << "PY"
import torch
print("torch:", torch.__version__)
print("CUDA available:", torch.cuda.is_available())
if torch.cuda.is_available(): print(torch.cuda.get_device_name(0))
PY

Post the outputs of the cuDNN check and I’ll confirm if you’re fully set.

Great—nvcc confirms CUDA 12.1 is installed.
nvidia-smi not found usually means the NVIDIA driver tools aren’t on PATH (or the driver isn’t installed/finished).

Quick checks

Try running it by full path (copy/paste in CMD):

"C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi.exe"

If this prints a table with your GPU, the file exists—just not on PATH.

Add it to PATH:

setx PATH "%PATH%;C:\Program Files\NVIDIA Corporation\NVSMI"

Close CMD, open a new one, then:

nvidia-smi

If the path above doesn’t exist

Your GPU driver may be missing or incomplete. Install (or reinstall) the latest NVIDIA Game Ready or Studio driver for your GPU from NVIDIA. During install, choose Custom → Perform a clean installation.
After installing, verify:
Terminal window
```
nvidia-smi
```

Next (cuDNN)

Once nvidia-smi works, check cuDNN files:

dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin\cudnn*.dll"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\lib\x64\cudnn*.lib"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\include\cudnn*.h"

Tell me what you see, and I’ll confirm you’re fully set.

Got it — CUDA toolkit is installed, but the GPU driver (or its tools) aren’t. That’s why nvcc works yet nvidia-smi and the NVSMI folder are missing. Let’s verify and fix it.

1) Confirm Windows sees your NVIDIA GPU

Run these in CMD:

wmic path win32_VideoController get name
devmgmt.msc

In Device Manager → Display adapters, you should see your NVIDIA GPU (not “Microsoft Basic Display Adapter”).
If there’s a yellow warning or only Intel/Basic is listed, the NVIDIA driver isn’t installed/enabled.

2) Check if `nvidia-smi.exe` exists anywhere

where /R "C:\Program Files" nvidia-smi.exe
where /R "C:\Windows" nvidia-smi.exe

If nothing is found, the driver tools truly aren’t present.

3) Install the correct NVIDIA GPU driver (this is separate from CUDA)

Go to NVIDIA’s driver page and download the latest DCH driver for your exact GPU and Windows version (choose Game Ready or Studio — either is fine for CUDA).
Run the installer → Custom → check Perform a clean installation.
For laptops, if the NVIDIA installer fails or the GPU isn’t detected, first install the GPU driver from your laptop/OEM support page, then update with NVIDIA’s package.

(Optional but thorough) Use DDU (Display Driver Uninstaller) in Safe Mode first, then install the driver cleanly.

4) Verify after install

Open a new CMD and run:

"C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi.exe"
nvidia-smi

You should see a table with your GPU, driver version, and CUDA version.
If it works, add to PATH (optional):

setx PATH "%PATH%;C:\Program Files\NVIDIA Corporation\NVSMI"

5) Finish cuDNN check

dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin\cudnn*.dll"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\lib\x64\cudnn*.lib"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\include\cudnn*.h"

If those don’t exist, install cuDNN for CUDA 12.1 and copy the bin/lib/include files into the matching CUDA folders.

If Step 3 completes but nvidia-smi still won’t appear:

Post the output of wmic path win32_VideoController get name and what you see in Device Manager.
Tell me if this is a desktop card or a laptop (brand/model), and I’ll give exact driver links and any OEM-specific steps.

Aha — even basic commands like where and devmgmt.msc aren’t found. That means your PATH is missing the Windows system folders, so lots of tools (including nvidia-smi) won’t be seen even if installed.

Quick temporary fix (for this CMD only)

Paste this in CMD to restore core paths for the current window:

set PATH=C:\Windows\System32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Windows\System32\OpenSSH\;%PATH%

Now try:

where where
devmgmt.msc

Permanent fix (update PATH system-wide)

Open the Environment Variables editor:

rundll32.exe sysdm.cpl,EditEnvironmentVariables

In System variables → select Path → Edit. Ensure these entries exist (each as its own line):

C:\Windows\System32
C:\Windows
C:\Windows\System32\Wbem
C:\Windows\System32\WindowsPowerShell\v1.0\
C:\Windows\System32\OpenSSH\

Also keep your dev tools:

C:\Users\Chris\AppData\Local\Programs\Python\Python311\
C:\Users\Chris\AppData\Local\Programs\Python\Python311\Scripts\
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin
C:\Program Files\NVIDIA Corporation\NVSMI

Click OK on all dialogs, then open a new CMD and verify:

where python
where pip
where nvcc
where nvidia-smi

If `nvidia-smi` still isn’t found

Try the full path:

"C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi.exe"

If that file doesn’t exist, your GPU driver didn’t install properly. Install the latest NVIDIA Game Ready/Studio driver for your GPU (Custom → Perform a clean installation). On laptops, install the OEM driver first if NVIDIA’s installer won’t detect the GPU.

Then check cuDNN

After PATH and driver are fixed:

dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin\cudnn*.dll"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\lib\x64\cudnn*.lib"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\include\cudnn*.h"

Send me the output of where nvidia-smi (or whether the NVSMI folder exists) after fixing PATH, and we’ll finish the setup.

Your Windows system folders aren’t on PATH, so even built-ins like where and rundll32.exe fail. Let’s fix PATH first, then finish NVIDIA.

A) Restore core PATH (temporary for this CMD window)

Paste this exactly in Command Prompt:

set "PATH=C:\Windows\System32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Windows\System32\OpenSSH\;%PATH%"

Quick sanity:

where where
where rundll32.exe

You should now see paths.

B) Open the Environment Variables editor (with full path)

C:\Windows\System32\rundll32.exe sysdm.cpl,EditEnvironmentVariables

In the window that opens:

System variables → Path → Edit…
Make sure these entries exist (each as its own line, top section is fine):

C:\Windows\System32
C:\Windows
C:\Windows\System32\Wbem
C:\Windows\System32\WindowsPowerShell\v1.0\
C:\Windows\System32\OpenSSH\

Keep your dev tools too, e.g.:

C:\Users\Chris\AppData\Local\Programs\Python\Python311\
C:\Users\Chris\AppData\Local\Programs\Python\Python311\Scripts\
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin
C:\Program Files\NVIDIA Corporation\NVSMI

Click OK on all dialogs.

C) Open a new Command Prompt and verify

where python
where pip
where nvcc
where nvidia-smi

If nvidia-smi still isn’t found, try the full path:
Terminal window
```
"C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi.exe"
```
- If this file exists → add C:\Program Files\NVIDIA Corporation\NVSMI to PATH (already listed above).
- If it doesn’t exist → the NVIDIA GPU driver isn’t installed correctly. Install the latest Game Ready/Studio driver for your exact GPU (Custom → Clean installation). For laptops, use the OEM driver first if NVIDIA’s installer can’t detect the GPU.

D) After `nvidia-smi` works, check cuDNN

dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin\cudnn*.dll"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\lib\x64\cudnn*.lib"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\include\cudnn*.h"

Send me what you get for where nvidia-smi (and whether that NVSMI folder exists) and we’ll wrap it up.

Got it — if Edit… under System variables → Path is greyed out, that usually means you don’t have admin rights for system-wide variables. No problem, we can set it just for your user, which works fine.

Option 1 — Add PATH entries for your user

In the same Environment Variables window, look at the top half (“User variables for Chris”).
If there’s already a variable named Path, select it → click Edit.
- If it doesn’t exist, click New… and name it Path.

Add these entries (each on its own line):

C:\Windows\System32
C:\Windows
C:\Windows\System32\Wbem
C:\Windows\System32\WindowsPowerShell\v1.0\
C:\Windows\System32\OpenSSH\
C:\Users\Chris\AppData\Local\Programs\Python\Python311\
C:\Users\Chris\AppData\Local\Programs\Python\Python311\Scripts\
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin
C:\Program Files\NVIDIA Corporation\NVSMI

Save → OK → OK → close everything.

Open a new Command Prompt and run:

where python
where pip
where nvcc
where nvidia-smi

Option 2 — Use commands to add PATH (user-only)

If you prefer doing it straight from CMD, run these one at a time:

setx PATH "%PATH%;C:\Windows\System32"
setx PATH "%PATH%;C:\Windows"
setx PATH "%PATH%;C:\Windows\System32\Wbem"
setx PATH "%PATH%;C:\Windows\System32\WindowsPowerShell\v1.0\"
setx PATH "%PATH%;C:\Windows\System32\OpenSSH\"
setx PATH "%PATH%;C:\Users\Chris\AppData\Local\Programs\Python\Python311\"
setx PATH "%PATH%;C:\Users\Chris\AppData\Local\Programs\Python\Python311\Scripts\"
setx PATH "%PATH%;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin"
setx PATH "%PATH%;C:\Program Files\NVIDIA Corporation\NVSMI"

Then close CMD, open a new one, and re-test with:

where python
where pip
where nvcc
where nvidia-smi

👉 Question for you: do you want me to write out a single combined setx command that appends all of those paths at once, instead of copy-pasting line by line?

Beautiful 🎉 — your PATH is fixed now!

From your screenshot:

✅ python → C:\Users\Chris\AppData\Local\Programs\Python\Python311\python.exe
✅ pip → in Python311\Scripts
✅ nvcc → CUDA 12.1 found
✅ nvidia-smi → located in C:\Windows\System32\nvidia-smi.exe (driver utilities installed!)

That means your GPU driver, CUDA toolkit, Python, and pip are all working now. 🚀

Next step: Check cuDNN

Run these in CMD:

dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin\cudnn*.dll"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\lib\x64\cudnn*.lib"
dir "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\include\cudnn*.h"

If you see files, cuDNN is installed.
If not, you’ll need to download cuDNN 8.x for CUDA 12.1 from NVIDIA, unzip, and copy the files:
- bin\* → ...\CUDA\v12.1\bin
- lib\x64\* → ...\CUDA\v12.1\lib\x64
- include\* → ...\CUDA\v12.1\include

⚡ Do you want me to link you directly to the correct cuDNN download for CUDA 12.1, and show exactly where to copy the files?

Perfect ✅ — your screenshot shows all the right cuDNN files in place:

DLLs → in bin\
LIBs → in lib\x64\
Headers → in include\

That means Python 3.11, pip, Git, CUDA 12.1, and cuDNN are all correctly installed and on PATH 🎉

🔥 Next step: Verify inside Python

Let’s check that CUDA + cuDNN are visible from Python. Run this:

python

Then inside the Python shell:

import torch
print("Torch version:", torch.__version__)
print("CUDA available:", torch.cuda.is_available())
if torch.cuda.is_available():
    print("GPU:", torch.cuda.get_device_name(0))

If you don’t have PyTorch yet, install it with the CUDA 12.1 build:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

👉 Do you want me to also show you a TensorFlow test script (in case you’re using that instead of PyTorch), or are you planning to stick with PyTorch?

You’re inside the Python REPL, so pip install … is being treated as Python code (hence the SyntaxError). Let’s do it the right way.

1) Exit the REPL

At the >>> prompt, type:

exit()

(or press Ctrl+Z, then Enter)

2) (Optional but recommended) Create a fresh venv

In Command Prompt (not the Python prompt):

python -m venv .venv
.\.venv\Scripts\activate
python -V

3) Install PyTorch with CUDA 12.1 wheels

python -m pip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

These wheels bundle the CUDA 12.1 runtime; they’ll work with your driver and don’t depend on your local CUDA toolkit.

4) Verify GPU access

python - << "PY"
import torch
print("Torch:", torch.__version__)
print("CUDA available:", torch.cuda.is_available())
if torch.cuda.is_available():
    print("GPU:", torch.cuda.get_device_name(0))
PY

If CUDA available: True and your GPU name prints, you’re all set. If it’s False, tell me the output and we’ll troubleshoot (it’s usually a venv issue or a mismatched wheel).

Yep — based on everything you showed, you’ve got all three Windows prerequisites working:

✅ Python 3.11 — Python 3.11.9 (python --version) and pip present.
✅ Git — git version 2.50.1.windows.1 (from your earlier run).
✅ CUDA 12.1 + cuDNN — nvcc --version → 12.1.66; nvidia-smi present; cuDNN files exist in:
- ...\CUDA\v12.1\bin\cudnn*.dll
- ...\CUDA\v12.1\lib\x64\cudnn*.lib
- ...\CUDA\v12.1\include\cudnn*.h

If you want a one-shot verification, paste this PowerShell block (shows ✅/❌):

$ok = @{ }
$ok.Python = (python --version) -match "3\.11"
$ok.Git    = (git --version)     -match "git version"
$ok.CUDA   = (nvcc --version)    -match "release 12\.1"
$cuDNN = Get-ChildItem "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1" -Recurse -Filter "cudnn*.dll" -ErrorAction SilentlyContinue
$ok.cuDNN  = $cuDNN.Count -gt 0
$ok.GetEnumerator() | ForEach-Object { "{0}: {1}" -f $_.Key, ($(if($_.Value){"✅"}else{"❌"})) }

Optional GPU test in Python (PyTorch):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
python - << "PY"
import torch
print("Torch:", torch.__version__)
print("CUDA available:", torch.cuda.is_available())
if torch.cuda.is_available():
    import torch.backends.cudnn as cudnn
    print("GPU:", torch.cuda.get_device_name(0))
    print("cuDNN:", cudnn.version())
PY

You’re good to proceed with the install from the video.

Nice — I can see you ran the PowerShell check script 👍

But right now, it’s only showing you the script itself (no output). That happens if you pasted everything at once but didn’t press Enter on the last line. PowerShell is still waiting.

To get the ✅ / ❌ output:

Paste the full block again:

$ok = @{ }
$ok.Python = (python --version) -match "3\.11"
$ok.Git    = (git --version)     -match "git version"
$ok.CUDA   = (nvcc --version)    -match "release 12\.1"
$cuDNN = Get-ChildItem "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1" -Recurse -Filter "cudnn*.dll" -ErrorAction SilentlyContinue
$ok.cuDNN  = $cuDNN.Count -gt 0
$ok.GetEnumerator() | ForEach-Object { "{0}: {1}" -f $_.Key, ($(if($_.Value){"✅"}else{"❌"})) }

Then press Enter at the very end.

You should see something like:

Python: ✅
Git: ✅
CUDA: ✅
cuDNN: ✅

Do you want me to also write a Python test script that confirms CUDA + cuDNN are detected directly inside torch? That way you’ll know your environment is 100% ready before installing the project.

Perfect 🎉 — now the PowerShell check actually ran, and the results show:

CUDA: ✅
cuDNN: ✅
Python: ✅
Git: ✅

That means all four prerequisites are installed and working correctly on your system:

Python 3.11 ✅
Git ✅
CUDA 12.1 ✅
cuDNN ✅

👉 At this point, your environment is ready to install and run the project from the video.

Do you want me to walk you through the next steps of actually cloning and setting up the repo (so you don’t hit dependency/version issues), or do you already have the repo and just want to start testing it?

Ah — good catch 👍 The ? symbols show up because your PowerShell font/encoding doesn’t support the ✅/❌ characters I used.

The script itself worked fine — it’s just the display symbol that’s off.

Let’s switch it to plain text output instead:

$ok = @{ }
$ok.Python = (python --version) -match "3\.11"
$ok.Git    = (git --version)     -match "git version"
$ok.CUDA   = (nvcc --version)    -match "release 12\.1"
$cuDNN = Get-ChildItem "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1" -Recurse -Filter "cudnn*.dll" -ErrorAction SilentlyContinue
$ok.cuDNN  = $cuDNN.Count -gt 0
$ok.GetEnumerator() | ForEach-Object { "{0}: {1}" -f $_.Key, ($(if($_.Value){"OK"}else{"MISSING"})) }

That will print something like:

Python: OK
Git: OK
CUDA: OK
cuDNN: OK

So you’ll have a clear ✅/❌ equivalent without relying on Unicode rendering.

Do you want me to also add a Python-level check (PyTorch + cuDNN) so we can confirm CUDA is not just installed, but actually usable in your ML framework?

Perfect 👌 — you’ve now got Python 3.11, Git, CUDA 12.1, and cuDNN all correctly installed and verified.

That means your system is fully prepped for whatever project you’re about to install (from the video). 🚀

Next steps are usually:

Clone the repo
Terminal window
```
git clone <repo-url>
cd <repo-folder>
```
Create a virtual environment (recommended)
Terminal window
```
python -m venv .venv
.\.venv\Scripts\activate
```
Install dependencies
Most repos will have a requirements.txt:
Terminal window
```
pip install -r requirements.txt
```
Run the project following its README / video guide.

👉 Do you want me to write out the exact setup commands tailored for that YouTube repo you linked earlier, so you can just copy-paste them?