# I build WebGPU PyTorch debugger, dataset browser and NumPy viewer

> Source: <https://svetoviz.com>
> Published: 2026-07-22 07:13:49+00:00

Get empowered by **Svetovid**, whose four heads represent omniscience: the ability to see through space and time in all directions at once.
Maintain unified perspective across your tensors, models, and massive datasets.
Peer into the architecture of your neural networks and navigate high-dimensional data with absolute clarity.

array_inspector.py

## NumPy Inspector

Visualize any NumPy array with instant dimension awareness. Support for high-dimensional, large arrays.

``` python
import svetoviz_webgpu as sv

data = np.random.rand(4, 4, 32, 32).astype(np.float32)

sv.array(data, gap_size=10)
```

[Learn More](content/guides/numpy.html)

pytorch_debugger.py

## PyTorch Debugger

Directly load your PyTorch module, interact, and debug activations in real-time.

``` python
import svetoviz_webgpu as sv

img = Image.open("sample_image.jpg").convert("RGB")
img_np = np.array(img).astype(np.float32) / 255.0

# Rearrange from (H, W, C) to (C, H, W) and add batch dim
input_tensor = torch.from_numpy(img_np).permute(2, 0, 1).unsqueeze(0)

# 2. Define the 2D Convolutional module
conv2d = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)

def terminal_callback(buffer, message, images, files):
    # 3. Process the image through the module
    output = conv2d(input_tensor)

    # Log the feature map shape to the Svetoviz terminal
    buffer.send_system_message(f"Processed image. Shape: {list(output.shape)}")

# 4. Start the interactive session
sv.pytorch(module=conv2d, terminal_callback=terminal_callback)
```

[Learn More](content/guides/pytorch.html)

model_viewer.py

## Model Analysis

Deep-dive into model architecture. Explore layers down to a single parameter.

``` python
import svetoviz_webgpu as sv

image_processor = DetrImageProcessor.from_pretrained(
    "facebook/detr-resnet-50",
    revision="no_timm",
    device_map="cpu")
model = DetrForObjectDetection.from_pretrained(
    "facebook/detr-resnet-50",
    revision="no_timm",
    device_map="cpu")

sv.model(
    model=model,
    image_processor=image_processor
)
```

[Learn More](content/guides/models.html)

model_debug.py

## Model Debugger

Interact with your model and investigate activations in real-time.

``` python
import svetoviz_webgpu as sv

tokenizer = GPT2Tokenizer.from_pretrained('gpt2', device_map="cpu")
model = GPT2LMHeadModel.from_pretrained('gpt2', device_map="cpu")

def terminal_callback(buffer, message, images, files):
    text = message  # "Replace me by any text you'd like."
    buffer.send_user_message(text)
    inputs = tokenizer(text, return_tensors="pt")

    # Generate continuation
    outputs = model.generate(**inputs, max_new_tokens=50)

    # Decode to string
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
    buffer.send_system_message(decoded)

sv.model(
    model=model,
    tokenizer=tokenizer,
    terminal_callback=terminal_callback
)
```

[Learn More](content/guides/models.html)

dataset_handler.py

## Dataset Explorer

Efficiently stream and visualize large-scale HuggingFace datasets from local storage or remote server.

``` python
import svetoviz_webgpu as sv

hf_name = "eltorio/ROCOv2-radiology"
dataset = load_dataset(hf_name, split="train")
view = DatasetView(
    name=hf_name,
    dataset=dataset,
    image_columns=["image"],
    cell_width=400,
    cell_height=400
)
sv.save_to_disc(view=view,
                compression="png_0",
                directory=f"/Volumes/Untitled/{hf_name}")

sv.load_from_disc(directory=f"/Volumes/Untitled/{hf_name}")
```

[Learn More](content/guides/datasets.html)

image_browser.py

## Image Browser

Browse through massive collections of images regardless of resolution.

``` python
import svetoviz_webgpu as sv

view = DirectorySpiralView(directory="/Desktop/nasaimages")

sv.save_to_disc(view=view,
                compression="jpeg_0",
                directory="/Volumes/Untitled/universe")

sv.load_from_disc(directory="/Volumes/Untitled/universe")
```

[Learn More](content/guides/images.html)

Installation