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| title: Dynamic Tab Loading Examples | |
| emoji: 🏢 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.34.2 | |
| app_file: app.py | |
| pinned: true | |
| license: apache-2.0 | |
| short_description: Exploring different loading methods for a HF Space | |
| # Dynamic Space Loading | |
| --- | |
| ## 1. **Sending Data To/From IFrames** | |
| ### **A. Standard Web (HTML/JS) Context** | |
| - **IFrames are sandboxed:** By default, an iframe is isolated from the parent page for security reasons. | |
| - **postMessage API:** | |
| - The standard way to communicate between a parent page and an iframe (and vice versa) is using the [window.postMessage](https://developer.mozilla.org/en-US/docs/Web/API/Window/postMessage) API. | |
| - This requires both the parent and the iframe to have JavaScript code that listens for and sends messages. | |
| - Example: | |
| - Parent: `iframeEl.contentWindow.postMessage({data: "hello"}, "https://iframe-domain.com")` | |
| - IFrame: `window.parent.postMessage({data: "hi back"}, "https://parent-domain.com")` | |
| - **Limitations in Gradio:** | |
| - Gradio does not expose a built-in way to inject custom JS for postMessage into the iframe or parent. | |
| - If you control both the parent and the iframe (i.e., both are your own apps), you could add custom JS to both and use postMessage. | |
| - If the iframe is a third-party app (like a Hugging Face Space you don’t control), you cannot inject JS into it, so you cannot send/receive data programmatically. | |
| ### **B. Gradio Context** | |
| - **No built-in Gradio API for iframe communication.** | |
| - **You can use gr.HTML to inject a script into the parent,** but you cannot inject into the iframe if you don’t control its code. | |
| --- | |
| ## 2. **Sending Data Between Tabs in Gradio** | |
| - **Tabs in Gradio are just layout elements:** All components in all tabs exist in the same Python process and can share state. | |
| - **You can use gr.State or any shared variable:** | |
| - For example, you can have a gr.State object that is updated in one tab and read in another. | |
| - You can also use hidden components or callbacks to pass data between tabs. | |
| --- | |
| ## 3. **Summary Table** | |
| | Method | Parent ↔ IFrame | Tab ↔ Tab (Gradio) | | |
| |-----------------------|:--------------:|:------------------:| | |
| | postMessage (JS) | Yes (if you control both) | N/A | | |
| | gr.State | No | Yes | | |
| | Hidden Components | No | Yes | | |
| | gradio API | No | Yes | | |
| --- | |
| ## 4. **Practical Recommendations** | |
| - **For arbitrary Hugging Face Spaces in iframes:** | |
| - You cannot send/receive data programmatically unless the Space itself is designed to listen for postMessage. | |
| - **For your own Spaces:** | |
| - You can add JS to both parent and iframe to use postMessage. | |
| - **For Gradio tabs:** | |
| - Use gr.State or shared components for seamless data transfer. | |
| --- | |
| This is a very insightful and advanced question! Here’s a breakdown of what’s possible, what’s not, and what’s practical with Gradio, Hugging Face Spaces, and Python environments: | |
| --- | |
| ## 2. **GPU Spaces (transformers/diffusers) Loading/Unloading** | |
| ### **A. In a Single Python Process (One Space, One App)** | |
| - **You can load multiple models/pipelines in one Gradio app.** | |
| - You can have a dropdown or tabs to select which model/task/pipeline to use. | |
| - You can load/unload models on demand (though loading large models is slow). | |
| - You can keep all models in memory (if you have enough GPU RAM), or load/unload as needed. | |
| - **You cannot have truly separate environments** (e.g., different Python dependencies, CUDA versions, or isolated memory) in a single Space. | |
| - All code runs in the same Python process/environment. | |
| - All models share the same GPU/CPU memory pool. | |
| #### **Example:** | |
| ```python | |
| from transformers import pipeline | |
| import gradio as gr | |
| # Preload or lazy-load multiple pipelines | |
| pipe1 = pipeline("text-generation", model="gpt2") | |
| pipe2 = pipeline("image-classification", model="google/vit-base-patch16-224") | |
| def run_model(input, model_choice): | |
| if model_choice == "Text Generation": | |
| return pipe1(input) | |
| elif model_choice == "Image Classification": | |
| return pipe2(input) | |
| # ... more models | |
| gr.Interface( | |
| fn=run_model, | |
| inputs=[gr.Textbox(), gr.Dropdown(["Text Generation", "Image Classification"])], | |
| outputs="auto" | |
| ).launch() | |
| ``` | |
| - You can use tabs or dropdowns to switch between models/tasks. | |
| --- | |
| ### **B. Multiple Gradio Apps in One Space** | |
| - You can define multiple Gradio interfaces in one script and show/hide them with tabs or dropdowns. | |
| - **But:** They still share the same Python process and memory. | |
| --- | |
| ### **C. True Isolation (Multiple Environments)** | |
| - **Not possible in a single Hugging Face Space.** | |
| - You cannot have multiple Python environments, different dependency sets, or isolated GPU memory pools in one Space. | |
| - Each Space is a single container/process. | |
| --- | |
| ### **D. What About Docker or Subprocesses?** | |
| - Hugging Face Spaces (hosted) do not support running multiple containers or true subprocess isolation with different environments. | |
| - On your own infrastructure, you could use Docker or subprocesses, but this is not supported on Spaces. | |
| --- | |
| ## 3. **Best Practices for Multi-Model/Multi-Task Apps** | |
| - **Lazy-load models:** Only load a model when its tab is selected, and unload it when switching (if memory is a concern). | |
| - **Use a single environment:** Install all dependencies needed for all models in your `requirements.txt`. | |
| - **Warn users about memory:** If users switch between large models, GPU memory may fill up and require manual cleanup (e.g., `torch.cuda.empty_cache()`). | |
| --- | |
| ## 4. **Summary Table** | |
| | Approach | Isolation | Multiple Models | Multiple Envs | GPU Sharing | Supported on Spaces | | |
| |----------------------------------|:---------:|:--------------:|:-------------:|:-----------:|:------------------:| | |
| | Single Gradio app, many models | No | Yes | No | Yes | Yes | | |
| | Multiple Gradio apps in one file | No | Yes | No | Yes | Yes | | |
| | Multiple Spaces (one per app) | Yes | Yes | Yes | Isolated | Yes | | |
| | Docker/subprocess isolation | Yes | Yes | Yes | Isolated | No (on Spaces) | | |
| --- | |
| ## 4. **What’s Practical?** | |
| - **For most use cases:** | |
| - Use a single app with tabs/dropdowns to select the model/task. | |
| - Lazy-load and unload models as needed to manage memory. | |
| - **For true isolation:** | |
| - Use multiple Spaces (one per app/model) or host your own infrastructure with Docker. | |
| --- | |
| ## 5. **Properly Unloading Models, Weights, and Freeing Memory in PyTorch/Diffusers** | |
| When working with large models (especially on GPU), it's important to: | |
| - **Delete references to the model and pipeline** | |
| - **Call `gc.collect()`** to trigger Python's garbage collector | |
| - **Call `torch.cuda.empty_cache()`** (if using CUDA) to free GPU memory | |
| ### **Best Practice Pattern** | |
| Here’s a robust pattern for loading and unloading models in a multi-model Gradio app: | |
| ```python | |
| import torch | |
| import gc | |
| from diffusers import DiffusionPipeline | |
| model_cache = {} | |
| def load_diffusion_model(model_id, dtype=torch.float32, device="cpu"): | |
| pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype) | |
| pipe = pipe.to(device) | |
| pipe.enable_attention_slicing() | |
| return pipe | |
| def unload_model(model_key): | |
| # Remove from cache | |
| if model_key in model_cache: | |
| del model_cache[model_key] | |
| # Run Python garbage collection | |
| gc.collect() | |
| # Free GPU memory if using CUDA | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| ``` | |
| ### **How to Use in a Gradio Tab** | |
| ```python | |
| import gradio as gr | |
| model_id = "LPX55/FLUX.1-merged_lightning_v2" | |
| model_key = "flux" | |
| device = "cpu" # or "cuda" if available and desired | |
| def do_load(): | |
| if model_key not in model_cache: | |
| model_cache[model_key] = load_diffusion_model(model_id, torch.float32, device) | |
| return "Model loaded!" | |
| def do_unload(): | |
| unload_model(model_key) | |
| return "Model unloaded!" | |
| def run_inference(prompt, width, height, steps): | |
| if model_key not in model_cache: | |
| return None, "Model not loaded!" | |
| pipe = model_cache[model_key] | |
| image = pipe( | |
| prompt=prompt, | |
| width=width, | |
| height=height, | |
| num_inference_steps=steps, | |
| ).images[0] | |
| return image, "Success!" | |
| with gr.Blocks() as demo: | |
| status = gr.Markdown("Model not loaded.") | |
| load_btn = gr.Button("Load Model") | |
| unload_btn = gr.Button("Unload Model") | |
| prompt = gr.Textbox(label="Prompt", value="A cat holding a sign that says hello world") | |
| width = gr.Slider(256, 1536, value=768, step=64, label="Width") | |
| height = gr.Slider(256, 1536, value=1152, step=64, label="Height") | |
| steps = gr.Slider(1, 50, value=8, step=1, label="Inference Steps") | |
| run_btn = gr.Button("Generate Image") | |
| output_img = gr.Image(label="Output Image") | |
| output_msg = gr.Textbox(label="Status", interactive=False) | |
| load_btn.click(do_load, None, status) | |
| unload_btn.click(do_unload, None, status) | |
| run_btn.click(run_inference, [prompt, width, height, steps], [output_img, output_msg]) | |
| demo.launch() | |
| ``` | |
| --- | |
| ### **Key Points** | |
| - **Always delete the model from your cache/dictionary.** | |
| - **Call `gc.collect()` after deleting the model.** | |
| - **Call `torch.cuda.empty_cache()` if using CUDA.** | |
| - **Do this every time you switch models or want to free memory.** | |
| --- | |
| ### **Advanced: Unloading All Models** | |
| If you want to ensure all models are unloaded (e.g., when switching tabs): | |
| ```python | |
| def unload_all_models(): | |
| model_cache.clear() | |
| gc.collect() | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| ``` | |
| --- | |
| ### **Summary Table** | |
| | Step | CPU | GPU (CUDA) | | |
| |---------------------|-----|------------| | |
| | Delete model object | ✅ | ✅ | | |
| | `gc.collect()` | ✅ | ✅ | | |
| | `torch.cuda.empty_cache()` | ❌ | ✅ | | |
| --- | |