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.
import svetoviz_webgpu as sv
data = np.random.rand(4, 4, 32, 32).astype(np.float32)
sv.array(data, gap_size=10)pytorch_debugger.py
PyTorch Debugger
Directly load your PyTorch module, interact, and debug activations in real-time.
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)model_viewer.py
Model Analysis
Deep-dive into model architecture. Explore layers down to a single parameter.
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
)model_debug.py
Model Debugger
Interact with your model and investigate activations in real-time.
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
)dataset_handler.py
Dataset Explorer
Efficiently stream and visualize large-scale HuggingFace datasets from local storage or remote server.
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}")image_browser.py
Image Browser
Browse through massive collections of images regardless of resolution.
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")Installation