mango
Parallel Hyperparameter Tuning in Python
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- MetaTuner-on-bcancer.ipynb
- MetaTuner-on-functions.ipynb
- 1D_Mango_Different_Parallelization.ipynb
- 1D_Mango_Visualizations.ipynb
- Convex_Function_1D_HyperOpt.ipynb
- Convex_Function_1D_Parallel_5.ipynb
- Convex_Function_1D_Random.ipynb
- Convex_Function_1D_Serial.ipynb
- Convex_Function_Uniform_1D_Random.ipynb
- Convex_Function_Uniform_1D_Serial.ipynb
- Final_Brann_Benchmark_All_Plots.ipynb
- Final_Camel_Batch_Mango.ipynb
- Final_Camel_Batch_Mango_Clustering.ipynb
- Final_Camel_HyperOpt_All_Plots_working.ipynb
- Final_Camel_Random.ipynb
- Final_Camel_Serial_Mango.ipynb
- Final_Camel_Serial_Mango_Only_Alpha.ipynb
- Final_Convex_Function_1D_HyperOpt.ipynb
- Final_Convex_Function_Uniform_1D_Parallel_Mango_Working.ipynb
- Final_Convex_Function_Uniform_1D_Serial_Mango_Plots.ipynb
- Final_Convex_Function_Uniform_1D_Serial_Random.ipynb
- Final_SVM_HyperOpt_All_Plots_working.ipynb
- Final_SVM_Parallel_Mango_Clustering.ipynb
- Final_SVM_Parallel_Mango_working.ipynb
- Final_SVM_Random.ipynb
- Final_SVM_Serial_Mango.ipynb
- Final_XGBoost_HyperOpt_All_Plots.ipynb
- Final_XGBoost_Parallel_Mango.ipynb
- Final_XGBoost_Parallel_Mango_Clustering.ipynb
- Final_XGBoost_Random.ipynb
- Final_XGBoost_Serial_Mango.ipynb
- Final_XGBoost_Serial_Mango_Alpha.ipynb
- Hyperopt bitfarm.ipynb
- Parameter_Spaces_Evaluated.ipynb
- Time_Calculations.ipynb
- Xgboost_XGBClassifier_Serial.ipynb
- demo_video.png
- Mango_CogMI_paper.pdf
- Mango_cogml_slides.pdf
- Mango_github_slides.pdf
- Mango_video_svm.mp4
- combined_data
- data1
- data20
- prophet.py
- test_data
- train_data
- validate_data
- __init__.py
- celery.py
- CeleryTasks.py
- prophet.py
- xgboosttree.py
- Activity_NAS.ipynb
- data_utils.py
- hardware_utils.py
- README.md
- requirements.txt
- README.md
- Test.txt
- Train.txt
- Train_Valid.txt
- Valid.txt
- data_utils.py
- geometry_helpers.py
- hardware_utils.py
- README.md
- requirements.txt
- TinyOdom_OxIOD.ipynb
- ARIMA_Example_1.ipynb
- Prophet_Example_1.ipynb
- XGBoost_Example_1.ipynb
- XGBoost_Example_2.ipynb
- XGBoost_Example_3.ipynb
- XGBoost_Example_4.ipynb
- __init__.py
- Bitfarm_Function.ipynb
- Branin_Benchmark.ipynb
- Constrained Optimization.ipynb
- Convex_Function_Example.ipynb
- Convex_Function_Example_1D.ipynb
- Convex_Function_Example_Random_Bayesian_Comparison.ipynb
- Debugging_Mango.ipynb
- EarlyStopping.ipynb
- Failure_Handling.ipynb
- Failure_Handling_Parallel.ipynb
- Getting_Started.ipynb
- getting_started.py
- KNN_Celery.ipynb
- knn_celery.py
- KNN_Example.ipynb
- knn_serial.py
- Multiple_Local_Optima_Example.ipynb
- NAS_Mnist.ipynb
- NeuralNetwork_Simple.ipynb
- Prophet_Celery.ipynb
- Prophet_Classifier.ipynb
- requirements.txt
- Rosenbrock_Valley_Example.ipynb
- simple_celery.py
- Simple_Function.ipynb
- simple_parallel.py
- SVM_Example.ipynb
- SVM_Example_Screen_shot.ipynb
- Test functions for constrained optimization.ipynb
- warmup.ipynb
- Xgboost_Example.ipynb
- Xgboost_XGBClassifier.ipynb
- __init__.py
- distribution.py
- domain_space.py
- parameter_sampler.py
- __init__.py
- base_predictor.py
- bayesian_learning.py
- __init__.py
- metatuner.py
- scheduler.py
- tuner.py
- conftest.py
- test_config.py
- test_domain_space.py
- test_tuner.py
- .gitignore
- dev_notes.md
- development.md
- LICENSE
- License.txt
- poetry.lock
- pyproject.toml
- README.md
# Installation Guide
1. Get the code
git clone https://github.com/ARM-software/mango
Downloads the entire project code from GitHub to your computer.
cd mango
Moves into the project folder you just downloaded.
2. Official Install Script
Easy RecommendedPrerequisites
- Python 3 Python is required to use pip.
pip install arm-mango
Installs the package published on PyPI directly โ no need to clone the source.
After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.
Pulled directly from this repo's README.
3. Python
EasyPrerequisites
pip install arm-mango
Installs the package published on PyPI directly โ no need to clone the source.
$ pip3 install .
Installs the Python libraries listed in requirements.txt (or similar).
If it runs without errors and prints output in the terminal, it worked.
Pulled directly from this repo's README.
// repository documentation
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