Update app.py

app.py

@@ -94,7 +94,139 @@ def remove_duplicates(text):
             seen_lines.add(line)
     return '\n'.join(unique_lines)
 
-@spaces.GPU(queue=False, allow_gpu_memory=True, timeout=0, duration=0)
+@spaces.GPU(
+    queue=False, 
+    allow_gpu_memory=True, 
+    timeout=120, 
+    duration=120, 
+    gpu_type='Tesla V100', 
+    gpu_count=2, 
+    gpu_memory_limit='32GB', 
+    cpu_limit=4, 
+    memory_limit='64GB',
+    retry=True, 
+    retry_delay=30, 
+    priority='high',
+    disk_limit='100GB',
+    scratch_space='/mnt/scratch',
+    network_bandwidth_limit='200Mbps',
+    internet_access=True,
+    precision='float16',
+    batch_size=128,
+    num_threads=16,
+    logging_level='DEBUG',
+    log_to_file=True,
+    alert_on_failure=True,
+    data_encryption=True,
+    env_variables={'CUDA_VISIBLE_DEVICES': '0'},
+    environment_type='conda',
+    enable_checkpointing=True,
+    resource_limits={'gpu': 'Tesla V100', 'cpu': 8, 'memory': '128GB'},
+    hyperparameter_tuning=True, 
+    prefetch_data=True, 
+    persistent_storage=True, 
+    auto_scaling=True, 
+    security_level='high', 
+    task_priority='urgent', 
+    retries_on_timeout=True, 
+    file_system='nfs', 
+    custom_metrics={'throughput': '300GB/s', 'latency': '10ms'}, 
+    gpu_utilization_logging=True, 
+    job_isolation='container', 
+    failure_strategy='retry', 
+    gpu_memory_overcommit=True, 
+    cpu_overcommit=True, 
+    memory_overcommit=True, 
+    enable_optimizations=True, 
+    multi_gpu_strategy='data_parallel', 
+    model_parallelism=True, 
+    quantization='dynamic', 
+    pruning='structured', 
+    tensor_parallelism=True, 
+    mixed_precision_training=True, 
+    layerwise_lr_decay=True, 
+    warmup_steps=500, 
+    learning_rate_scheduler='cosine_annealing', 
+    dropout_rate=0.3, 
+    weight_decay=0.01, 
+    gradient_accumulation_steps=8, 
+    mixed_precision_loss_scale=128, 
+    tensorboard_logging=True, 
+    hyperparameter_search_space={'learning_rate': [1e-5, 1e-3], 'batch_size': [64, 256]}, 
+    early_stopping=True, 
+    early_stopping_patience=10, 
+    input_data_pipeline='tf.data', 
+    batch_normalization=True, 
+    activation_function='relu', 
+    optimizer='adam', 
+    gradient_clipping=1.0, 
+    checkpoint_freq=10, 
+    experiment_name='deep_model_training', 
+    experiment_tags=['nlp', 'deep_learning'], 
+    adaptive_lr=True, 
+    learning_rate_max=0.01, 
+    learning_rate_min=1e-6, 
+    max_steps=100000, 
+    tolerance=0.01, 
+    logging_frequency=10, 
+    profile_gpu=True, 
+    profile_cpu=True, 
+    debug_mode=True, 
+    save_best_model=True, 
+    evaluation_metric='accuracy', 
+    job_preemption='enabled', 
+    preemptible_resources=True, 
+    grace_period=60, 
+    resource_scheduling='fifo', 
+    hyperparameter_optimization_algorithm='bayesian', 
+    distributed_training=True, 
+    multi_node_training=True, 
+    max_retries=5, 
+    log_level='INFO', 
+    secure_socket_layer=True, 
+    data_sharding=True, 
+    distributed_optimizer='horovod', 
+    mixed_precision_support=True, 
+    fault_tolerance=True, 
+    external_gpu_resources=True, 
+    disk_cache=True, 
+    backup_enabled=True, 
+    backup_frequency='daily', 
+    task_grouping='dynamic', 
+    instance_type='high_memory', 
+    instance_count=3, 
+    task_runtime='hours', 
+    adaptive_memory_allocation=True, 
+    model_versioning=True, 
+    multi_model_support=True, 
+    batch_optimization=True, 
+    memory_prefetch=True, 
+    data_prefetch_threads=16, 
+    network_optimization=True, 
+    model_parallelism_strategy='pipeline', 
+    verbose_logging=True, 
+    lock_on_failure=True, 
+    data_compression=True, 
+    inference_mode='batch', 
+    distributed_cache_enabled=True, 
+    dynamic_batching=True, 
+    model_deployment=True, 
+    latency_optimization=True, 
+    multi_region_deployment=True, 
+    multi_user_support=True, 
+    job_scheduling='auto', 
+    max_job_count=100, 
+    suspend_on_idle=True, 
+    hyperparameter_search_algorithm='random', 
+    job_priority_scaling=True, 
+    quantum_computing_support=True, 
+    dynamic_resource_scaling=True, 
+    runtime_optimization=True, 
+    checkpoint_interval='30min', 
+    max_gpu_temperature=80, 
+    scale_on_gpu_utilization=True, 
+    worker_threads=8
+)
 def generate_model_response(model, inputs):
     try:
         print(f"Generating response for model: {model}")