Cup stats & predictions

The Future of Football Cup Argentina: Innovations on Horizon

The Football Cup Argentina continues to evolve, embracing innovations that promise to elevate the tournament further. Future developments may include enhanced fan engagement platforms, sustainable practices for hosting events, and cutting-edge technology integration.

Potential Innovations Include:

• Sustainable Stadiums: Eco-friendly venues that reduce environmental impact while providing top-notch facilities for fans and players alike.

• Digital Fan Experiences: Interactive apps and virtual experiences that bring fans closer to their favorite teams and players from anywhere in the world.................

• Ethical Betting Practices: Ensuring responsible gambling through transparent practices and support systems for bettors..............

Fostering Youth Development: Nurturing Future Stars

The future of Argentine football lies in its youth development programs. By investing in young talent, clubs ensure a steady pipeline of skilled players ready to take on professional challenges. These programs focus on holistic development, emphasizing technical skills, physical fitness, and mental resilience.

• Youth Academies: State-of-the-art facilities dedicated to nurturing young talent through comprehensive training programs.
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• Mentorship Programs:
  Mentorship from experienced professionals guiding young players in their journey towards professional success.
  

• Talent Scouting Networks:
  National networks dedicated to identifying promising talents across regions.
  

Economic Impact: Boosting Local Economies

The Football Cup Argentina not only captivates fans but also significantly impacts local economies. Hosting matches brings increased tourism, boosts hospitality industries, and creates job opportunities within communities.

• Tourism Surge:
  Influx of visitors leading to higher demand for accommodations, dining, and entertainment.
  

• Hospitality Industry Growth:
  Rising need for hotels, restaurants, and local businesses catering to visitors.
  

• Creative Employment Opportunities:
  New jobs created across various sectors including security services,<|repo_name|>YuriyKlimchuk/diffusion-models<|file_sep|>/demos/rag/README.md # Retrieval Augmented Generation This directory contains demos showing how retrieval augmented generation (RAG) can be used to generate answers from an external knowledge source. ## Running locally bash docker build . -t rag-demo docker run --rm -it -p8000:8000 rag-demo ## Deploying The model can be deployed using `flyctl`. bash flyctl launch --name rag-demo --image flyio/fly-rag-demo flyctl secrets set OPENAI_API_KEY=xxx flyctl secrets set FAISS_INDEX_URL=xxx <|repo_name|>YuriyKlimchuk/diffusion-models<|file_sep|>/scripts/generate_dataset.py import argparse import json import os import random from typing import List import datasets from datasets import DatasetDict def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--dataset-path", required=True) parser.add_argument("--num-shards", type=int) parser.add_argument("--shard-id", type=int) parser.add_argument("--output-dir", required=True) parser.add_argument("--seed", type=int) args = parser.parse_args() return args def generate_split( split_name, dataset_path, num_shards, shard_id, output_dir, ): if split_name == "train": subset = "train" size = "full" subset_size = "full" num_examples = None num_shards = num_shards or random.randint(8_000_000 // len(os.listdir(dataset_path)), len(os.listdir(dataset_path)) + 1) shard_size = None skip_size = None # train split needs special handling because we want: # * consistent shard sizes (for fine-tuning) across runs # * even distribution across shards (for evaluation) # * ability to control number shards per run (for tuning hyperparameters) # # To achieve this we shuffle all examples once using a fixed seed, # then divide them into fixed-size shards. # # To make sure shards have consistent content across runs we first sort examples by text length. # This way we always get full shards first. elif split_name == "validation": subset = "validation" size = "large" subset_size = "small" num_examples = int(5e4) num_shards = num_shards or random.randint(1000 // len(os.listdir(dataset_path)), len(os.listdir(dataset_path)) + 1) shard_size = int(num_examples / num_shards) skip_size = random.randint(0, shard_size) else: raise ValueError(f"Unsupported split {split_name}") dataset = datasets.load_dataset( "text", data_files=os.path.join(dataset_path, f"{subset}.{size}.jsonl") ) if split_name == "train": dataset["train"] = dataset["train"].shuffle(seed=args.seed).map(lambda e: {"text": e["text"][:2048]}) dataset["train"] = dataset["train"].sort("text_length") shard_size = int(len(dataset["train"]) / num_shards) print(f"Splitting {split_name} into {num_shards} shards") shard_dataset = dataset[split_name].shard(num_shards=num_shards).select(range(shard_id * shard_size + skip_size, (shard_id + 1) * shard_size + skip_size)) print(f"Shard {shard_id} contains {len(shard_dataset)} examples") if not os.path.exists(output_dir): os.makedirs(output_dir) output_file = os.path.join(output_dir, f"{split_name}-{subset}_{subset_size}-{shard_id+1}-of-{num_shards}.jsonl") shard_dataset.to_jsonl(output_file) def main(): args = parse_args() print("Generating train split") generate_split("train", args.dataset_path, args.num_shards, args.shard_id, args.output_dir) print("Generating validation split") generate_split("validation", args.dataset_path, args.num_shards, args.shard_id, args.output_dir) if __name__ == "__main__": main() <|file_sep|>#include "arguments.h" #include "dataloader.h" #include "model.h" #include "../third_party/tensorflow/include/tensorflow/cc/client/client_session.h" #include "../third_party/tensorflow/include/tensorflow/cc/saved_model/loader.h" #include "../third_party/tensorflow/include/tensorflow/core/public/session_options.h" #include "../third_party/absl/flags/flag.h" namespace mlperf { namespace image_classification { using tensorflow::client::Session; using tensorflow::GraphDef; using tensorflow::ImportGraphDefOptions; using tensorflow::NodeDef; using tensorflow::NodeDefBuilder; using tensorflow::OpDef; using tensorflow::OpRegistry; using tensorflow::Output; using tensorflow::PartialTensorShape; using tensorflow::SavedModelBundle; using tensorflow::Scope; using tensorflow::Status; using tensorflow::Tensor; using tensorflow::TensorFlowDataType; class Benchmark { public: explicit Benchmark(const Arguments& arguments); ~Benchmark(); private: void RunInferenceLoop(); }; Benchmark::Benchmark(const Arguments& arguments) : arguments_(arguments) { #ifdef _WIN32 #else std::setenv("CUDA_VISIBLE_DEVICES", "", true); #endif #ifdef _WIN32 #else #if !defined(__APPLE__) #if defined(__aarch64__) // For Jetson GPU devices. #define TENSORFLOW_USE_ROCM // Or define this manually before including any TensorFlow headers. #endif // __aarch64__ #endif // __APPLE__ #endif // _WIN32 #if defined(TENSORFLOW_USE_ROCM) #define TF_USE_ROCM // Or define this manually before including any TensorFlow headers. #endif #ifdef _WIN32 #else // Enable CPU instructions. #define EIGEN_USE_THREADS // Or define this manually before including any TensorFlow headers. #endif #include "../third_party/tensorflow/include/tensorflow/core/platform/env.h" // Set up CPU options. tensorflow::SessionOptions session_options; session_options.config.mutable_gpu_options()->set_allow_growth(true); session_options.config.set_intra_op_parallelism_threads(arguments_.num_threads); #ifdef _WIN32 #else #if defined(__APPLE__) #else // Not Apple. tensorflow::ConfigProto cuda_config_proto; cuda_config_proto.set_cuda_thread_mode(tensorflow::ConfigProto_DeviceLocality_CUDA_THREAD_MODE_UNRESTRICTED); session_options.config.MergeFrom(cuda_config_proto); #endif // __APPLE__ #endif // _WIN32 // Load graph definition. tensorflow::GraphDef graph_def; Status status = #if defined(TENSORFLOW_USE_ROCM) tensorflow::LoadSavedModel(tensorflow::SessionOptions(), /*run_metadata=*/nullptr, arguments_.saved_model_dir.c_str(), {"serve"}, &graph_def); #else tensorflow::LoadSavedModel(session_options /* options */, /*run_metadata=*/nullptr, arguments_.saved_model_dir.c_str(), {"serve"}, &graph_def); #endif if (!status.ok()) { #if defined(_WIN32) #define SSTR( x ) static_cast< std::ostringstream & >( ( std::ostringstream() << std::dec << x ) ).str() #else #define SSTR(x) std::to_string(x) #endif #ifndef NDEBUG #ifdef _WIN32 #define LOG(msg) std::cout << msg << std::endl; #else #define LOG(msg) std::cout << msg << std::endl; #endif #undef LOG_IF_ERROR_ #define LOG_IF_ERROR_(expr) do { auto err__##__LINE__##__FILE__##__FUNCTION__##__LINE__##x0 __attribute__((unused)) = (expr); if (!err__##__LINE__##__FILE__##__FUNCTION__##__LINE__##x0.ok()) { LOG(err__##__LINE__##__FILE__##__FUNCTION__##__LINE__##x0.ToString()); } } while (0); LOG_IF_ERROR_(status); LOG("Failed loading SavedModel"); #undef LOG_IF_ERROR_ #define LOG_IF_ERROR_(expr) status.Update(expr) #endif // NDEBUG } #ifdef NDEBUG tensorflow::StatusOr> session = #if defined(TENSORFLOW_USE_ROCM) tensorflow_session(tensorflow_session_options()); #else tensorflow_session(session_options /* options */); #endif if (!session.ok()) { #undef