ERROR: Could not find a version that satisfies the requirement torch==2.1.0

from chutes.image import Image

# Use compatible base images
image = Image(
    username="myuser",
    name="my-image",
    tag="1.0"
).from_base("nvidia/cuda:12.4.1-runtime-ubuntu22.04")

# Specify compatible package versions
image.run_command("pip install torch>=2.4.0 torchvision --index-url https://download.pytorch.org/whl/cu124")

# Alternative: Use conda for complex dependencies
image.run_command("conda install pytorch torchvision pytorch-cuda=12.4 -c pytorch -c nvidia")

Uploading build context... 2.3GB

# Create .dockerignore to exclude unnecessary files
# .dockerignore content:
"""
__pycache__/
*.pyc
.git/
.pytest_cache/
large_datasets/
*.mp4
*.avi
"""

# Or use specific file inclusion
image.add("app.py", "/app/app.py")
image.add("requirements.txt", "/app/requirements.txt")

Permission denied: '/usr/local/bin/pip'

# Run commands as root when needed
image.set_user("root")
image.run_command("apt-get update && apt-get install -y curl")

# Set proper ownership
image.run_command("chown -R chutes:chutes /app")

# Use USER directive correctly
image.set_user("chutes")

# Optimize startup time
@chute.on_startup()
async def setup(self):
    # Move heavy operations to background
    asyncio.create_task(self.load_model_async())

async def load_model_async(self):
    """Load model in background to avoid startup timeout."""
    self.model = load_large_model()
    self.ready = True

@chute.cord(public_api_path="/health")
async def health_check(self):
    """Health check endpoint."""
    return {"status": "ready" if hasattr(self, 'ready') else "loading"}

RuntimeError: CUDA out of memory. Tried to allocate 2.00 GiB

import torch
import gc

# Clear GPU cache
torch.cuda.empty_cache()
gc.collect()

# Use gradient checkpointing
model.gradient_checkpointing_enable()

# Reduce batch size
@chute.cord(public_api_path="/generate")
async def generate(self, request: GenerateRequest):
    # Process in smaller batches
    batch_size = min(request.batch_size, 4)

    # Use mixed precision
    with torch.cuda.amp.autocast():
        outputs = model.generate(**inputs)

    return outputs

# Optimize node selector
node_selector = NodeSelector(
    gpu_count=1,
    min_vram_gb_per_gpu=24,  # Increase VRAM requirement
    include=["a100", "h100"]
)

MemoryError: Unable to allocate array

# Increase RAM in node selector
node_selector = NodeSelector(
    gpu_count=1,
    min_vram_gb_per_gpu=24
)

# Use memory-efficient data loading
import torch.utils.data as data

class MemoryEfficientDataset(data.Dataset):
    def __init__(self, file_paths):
        self.file_paths = file_paths

    def __getitem__(self, idx):
        # Load data on-demand instead of pre-loading
        return load_data(self.file_paths[idx])

FileNotFoundError: Model file not found: /models/pytorch_model.bin

from huggingface_hub import snapshot_download
import os

@chute.on_startup()
async def setup(self):
    """Download model if not present."""
    model_path = "/models/my-model"

    if not os.path.exists(model_path):
        # Download model during startup
        snapshot_download(
            repo_id="microsoft/DialoGPT-medium",
            local_dir=model_path,
            token=os.getenv("HF_TOKEN")  # If private model
        )

    self.model = load_model(model_path)

ValueError: Unsupported model format

# Pin compatible versions
image.run_command("pip install transformers==4.36.0 torch==2.1.0 safetensors==0.4.0")

# Use format conversion
from transformers import AutoModel
import torch

# Convert to compatible format
model = AutoModel.from_pretrained("model-name")
torch.save(model.state_dict(), "/models/converted_model.pt")

import time
import torch

@chute.cord(public_api_path="/generate")
async def generate(self, request: GenerateRequest):
    start_time = time.time()

    # Profile different stages
    load_time = time.time()
    inputs = prepare_inputs(request.text)
    prep_time = time.time() - load_time

    # Inference timing
    inference_start = time.time()
    with torch.no_grad():
        outputs = self.model.generate(**inputs)
    inference_time = time.time() - inference_start

    # Post-processing timing
    post_start = time.time()
    result = postprocess_outputs(outputs)
    post_time = time.time() - post_start

    total_time = time.time() - start_time

    self.logger.info(f"Timing - Prep: {prep_time:.2f}s, Inference: {inference_time:.2f}s, Post: {post_time:.2f}s, Total: {total_time:.2f}s")

    return result

# Enable optimizations
model.eval()
model = torch.compile(model)  # PyTorch 2.0+ optimization

# Use efficient data types
model = model.half()  # Use FP16

# Batch processing
@chute.cord(public_api_path="/batch_generate")
async def batch_generate(self, requests: List[GenerateRequest]):
    # Process multiple requests together
    batch_inputs = [prepare_inputs(req.text) for req in requests]
    batch_outputs = self.model.generate_batch(batch_inputs)
    return [postprocess_outputs(output) for output in batch_outputs]

@chute.on_startup()
async def setup(self):
    """Warm up model to reduce cold start."""
    self.model = load_model()

    # Warm-up inference
    dummy_input = "Hello world"
    _ = self.model.generate(dummy_input)

    self.logger.info("Model warmed up successfully")

# Use model caching
@chute.cord(public_api_path="/generate")
async def generate(self, request: GenerateRequest):
    # Cache compiled model
    if not hasattr(self, '_compiled_model'):
        self._compiled_model = torch.compile(self.model)

    return self._compiled_model.generate(request.text)

HTTPException: 401 Unauthorized

# Check API key configuration
chutes account info

# Set API key correctly
chutes auth login
# or
export CHUTES_API_KEY="your-api-key"

# Verify key is working
chutes chutes list

HTTPException: 403 Forbidden

# Check account permissions
chutes account info

# Contact support if you need additional permissions
# Ensure you're using the correct username in deployments

import logging
from chutes.chute import Chute

# Configure detailed logging
logging.basicConfig(level=logging.DEBUG)

chute = Chute(
    username="myuser",
    name="debug-app"
)

@chute.on_startup()
async def setup(self):
    self.logger.info("Application starting up")

    # Log system information
    import torch
    if torch.cuda.is_available():
        for i in range(torch.cuda.device_count()):
            props = torch.cuda.get_device_properties(i)
            self.logger.info(f"GPU {i}: {props.name} ({props.total_memory // (1024**3)}GB)")

@chute.cord(public_api_path="/debug")
async def debug_info(self):
    """Debug endpoint for system information."""
    import psutil
    import torch

    info = {
        "cpu_percent": psutil.cpu_percent(),
        "memory_percent": psutil.virtual_memory().percent,
        "gpu_memory": {}
    }

    if torch.cuda.is_available():
        for i in range(torch.cuda.device_count()):
            allocated = torch.cuda.memory_allocated(i)
            total = torch.cuda.get_device_properties(i).total_memory
            info["gpu_memory"][f"gpu_{i}"] = {
                "allocated_gb": allocated / (1024**3),
                "total_gb": total / (1024**3),
                "utilization": (allocated / total) * 100
            }

    return info

# Enable remote debugging for development
import os

if os.getenv("DEBUG_MODE"):
    import debugpy
    debugpy.listen(("0.0.0.0", 5678))
    print("Waiting for debugger to attach...")
    debugpy.wait_for_client()

import traceback
from fastapi import HTTPException

@chute.cord(public_api_path="/generate")
async def generate(self, request: GenerateRequest):
    try:
        result = self.model.generate(request.text)
        return result
    except torch.cuda.OutOfMemoryError:
        self.logger.error("GPU out of memory", exc_info=True)
        raise HTTPException(
            status_code=503,
            detail="Service temporarily unavailable due to memory constraints"
        )
    except Exception as e:
        self.logger.error(f"Unexpected error: {str(e)}", exc_info=True)
        raise HTTPException(
            status_code=500,
            detail="Internal server error"
        )

# Make node selector more flexible
node_selector = NodeSelector(
    gpu_count=1,
    min_vram_gb_per_gpu=16,  # Reduce if too restrictive
    # Don't restrict VRAM to allow larger GPUs
    include=["a100", "l40", "a6000"],  # Include more GPU types
    exclude=[]  # Remove exclusions
)

# Optimize for concurrency
node_selector = NodeSelector(
    gpu_count=2,  # Multiple GPUs for parallel processing
    min_vram_gb_per_gpu=24
)

# Implement request queuing
import asyncio
from asyncio import Semaphore

class RateLimitedChute(Chute):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.semaphore = Semaphore(5)  # Limit concurrent requests

    @chute.cord(public_api_path="/generate")
    async def generate(self, request: GenerateRequest):
        async with self.semaphore:
            return await self._generate_impl(request)

# Check chute status
chutes chutes get myuser/my-chute

# Check logs for errors
chutes chutes logs myuser/my-chute

# Test health endpoint
curl https://your-chute-url/health

# Implement async processing for long-running tasks
@chute.job()
async def process_long_task(self, task_id: str, input_data: dict):
    """Background job for long-running tasks."""
    try:
        result = await long_running_process(input_data)
        # Store result in database or file system
        store_result(task_id, result)
    except Exception as e:
        self.logger.error(f"Task {task_id} failed: {e}")
        store_error(task_id, str(e))

@chute.cord(public_api_path="/start_task")
async def start_task(self, request: TaskRequest):
    """Start a background task and return task ID."""
    task_id = generate_task_id()
    await self.process_long_task(task_id, request.data)
    return {"task_id": task_id, "status": "started"}

@chute.cord(public_api_path="/task_status/{task_id}")
async def get_task_status(self, task_id: str):
    """Get status of a background task."""
    return get_task_status(task_id)