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https://github.com/spockoo/pylegend
A fusion between Python and legend, a name that suggests that the code is both modern and mythical. 3 months of work, with tons of errors to establish the calculations necessary for the superposition, I want to publish my work and improve it and share it under Apache 2.0 License. Designed to work with NBminer!
https://github.com/spockoo/pylegend
crypto crypto-tools how-to-farm-crypto kerastuner matplotlib matrix-multiplication mining nbminer numpy performance-optimization pickle project quantum quantumcircuits quantumcomputing qubits scikit-learn tensorflow
Last synced: 3 months ago
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A fusion between Python and legend, a name that suggests that the code is both modern and mythical. 3 months of work, with tons of errors to establish the calculations necessary for the superposition, I want to publish my work and improve it and share it under Apache 2.0 License. Designed to work with NBminer!
- Host: GitHub
- URL: https://github.com/spockoo/pylegend
- Owner: spockoo
- License: apache-2.0
- Created: 2024-08-25T16:12:36.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T17:11:12.000Z (4 months ago)
- Last Synced: 2024-09-23T17:25:48.646Z (3 months ago)
- Topics: crypto, crypto-tools, how-to-farm-crypto, kerastuner, matplotlib, matrix-multiplication, mining, nbminer, numpy, performance-optimization, pickle, project, quantum, quantumcircuits, quantumcomputing, qubits, scikit-learn, tensorflow
- Homepage:
- Size: 26.4 KB
- Stars: 2
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
README
README
Overview
This script involves various functionalities including configuring GPU memory for TensorFlow, creating and handling weight matrices, simulating quantum circuits using Cirq, managing file operations, building and tuning machine learning models, and visualizing data using Matplotlib in mining operation, collecting data in .txt and create his quantum circuit in his execution folder, i search constantly a way to up performances on each function and in the code structure. Here a Summary of what he do and what he need for work, no joke this code upgraded my performance in mining using nbminer by 90%-100%, i love Qubit.
Key Components
1. GPU Memory Configuration
python
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def configure_gpu_memory(log_file_path):
"""
Configure GPU memory to use dynamic growth.
"""
# Lists physical devices and configures GPU memory growth
This function configures TensorFlow to dynamically allocate GPU memory, which helps in managing memory efficiently. Logs are written to a specified file path.
2. Weight Matrix Functions
python
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def get_weight_matrix():
return np.array([...])
python
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def create_weight_matrix(num_shares, decay_rate=0.1):
weights = np.zeros((num_shares, num_shares))
# Generates a weight matrix with exponential decay
These functions create and return weight matrices used for reward calculation based on shares. get_weight_matrix() returns a predefined matrix, while create_weight_matrix() generates a matrix with exponential decay based on the number of shares and decay rate.
3. Reward Calculation
python
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def calculate_rewards(shares, weight_matrix):
"""
Calculates rewards based on shares and a weight matrix.
"""
# Computes rewards by applying weight matrix to shares
Calculates rewards for miners based on their shares and the provided weight matrix.
4. Quantum Circuit Simulation
python
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def simulate_quantum_circuit_optimized(num_qubits, depth, repetitions=1000):
"""
Simulates an optimized quantum circuit with specified parameters.
"""
# Creates and simulates a quantum circuit with given qubits and depth
Simulates a quantum circuit using Cirq with a specified number of qubits and circuit depth. The circuit is optimized and then simulated.
5. File Operations
python
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def save_file(obj, file_path, log_file_path, mode='wb'):
"""
Saves an object to a file.
"""
# Serializes and writes object to file
python
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def load_file(file_path, log_file_path, mode='rb'):
"""
Loads an object from a file.
"""
# Reads and deserializes object from file
Functions to save and load objects using pickle. They also log operations to a specified file path.
6. Machine Learning Model Building and Tuning
python
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def build_model(hp, input_shape):
model = Sequential([...])
# Builds and compiles a neural network model
python
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def hyperparameter_tuning(X, y):
"""
Performs hyperparameter tuning using Keras Tuner.
"""
# Tunes hyperparameters for model training
Functions to build and tune a machine learning model using Keras and Keras Tuner.
7. Data Collection and Processing
python
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def collect_data_from_miner(log_file_path, X, y):
"""
Collects data from a mining process.
"""
# Reads and processes data from the mining process
python
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def extract_hashrate(line):
"""
Extracts hashrate from a mining process output line.
"""
# Parses hashrate from process output
Functions to collect data from a mining process, extract relevant metrics, and handle the data for further processing.
8. Model Evaluation and Visualization
python
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def cross_val_score_with_cv(X, y, model=None, scoring=None, cv=5):
"""
Evaluates a model using cross-validation.
"""
# Performs cross-validation and calculates the mean score
python
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def update_graph(frame, log_file_path, fig, ax1, ax2, ax3, ax4, ax5, ax6):
"""
Updates graphs with new data and simulations.
"""
# Updates plots with new data, quantum circuit simulations, and model metrics
Functions for evaluating models using cross-validation and updating visualizations based on new data and simulations.
9. Mining Control
python
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def start_mining(log_file_path):
"""
Starts a mining process.
"""
# Executes mining command and logs the output
python
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def stop_mining(log_file_path):
"""
Stops a mining process.
"""
# Terminates the mining process
Functions to start and stop a mining process, including command execution and process management.
Dependencies
The code relies on the following libraries:
numpy
tensorflow
cirq
keras_tuner
scikit-learn
matplotlib
pickle
Ensure these libraries are installed before running the script.
Usage
1- Configure GPU Memory: Run configure_gpu_memory() to set up GPU memory growth.
2- Create Weight Matrix: Use create_weight_matrix() to generate the weight matrix for rewards.
3- Simulate Quantum Circuit: Call simulate_quantum_circuit_optimized() to simulate quantum circuits.
4- File Operations: Use save_file() and load_file() for saving and loading objects.
5- Build and Tune Models: Use build_model() and hyperparameter_tuning() for creating and optimizing models.
6- Collect Data and Train Models: Call collect_data_from_miner() and train_tf_model() to manage data and model training.
7-Evaluate and Visualize: Use cross_val_score_with_cv() and update_graph() for model evaluation and visualization.
Thank you. *Just Corrected no closed comment section, now the code is functionnal.