Package: onespace
♾️ OneSpace ♾️A high-level Python framework to automate the project lifecycle of Machine and Deep Learning ProjectsOverviewOneSpaceenables you to train high performace production ready Machine Learning And Deep Learning Models effortlessly with less than five lines of code.OneSpaceprovides you a unified workspace so you can work on all kinds of Machine Learning and Deep Learning problem statements without having to leave your workspace environment. It don't just run everything as a black box to present you results, instead it makes the complete model training process easily explainable using artifacts, logs and plots.OneSpacealso provides you the optional parameters to pass database credentials which will create a table with the project name in the database of your choice and will log all the training activities and the parameters in the database.Following are the major contents to follow, you can jump to any section:InstallationUsageGetting Started with OneSpace (Tutorials)TabularTraining a Regression Model With Tabular DataTraining a Classification Model With Tabular DataComputer VisionTraining an Image Classification Model with OneSpace(Tensorflow)Training an Image Classification Model with OneSpace(PyTorch)ContributingConclusion🔗 Project LinkOneSpaceis being distributed through PyPI. Check out the PyPI Packagehere1.InstallationTo avoid any dependency conflict, make sure to create a new Python virtual environment and then install via Pip!pipinstallonespace2.UsageGet theconfig.pyandtraining.pyfiles ready. You can get it from this repo or from the following tutorials section.Preparetraining.pyimportconfig# In case, you renamed config.py to something else, make sure to use the same name herefromonespace.tabular.regressionimportExperiment# Importing Experiment class to train a regression modeldeftraining(config):exp=Experiment(config)exp.run_experiment()if__name__=="__main__":training(config)Now run the following command in your terminal to start the training job:pythontraining.pyPlease following along with thesequick tutorials👇 to understand the complete setup and training process.3.Getting Started with OneSpaceEnsure you havePython 3.7+installed.Create a new Python conda environment for the OneSpace:$ conda create -n venv  # create venv
$ conda activate venv  # activate venv
$ pip install onespace # install onespaceCreate a new Python virtual environment with pip for the OneSpace:$ python3 -m venv venv  # create venv
$ . venv/bin/activate   # activate venv
$ pip install onespace # install onespaceTabularIn this section, you will learn to train a Machine Learning model withOneSpace:Training a Regression Model With Tabular Data:First step is to setup the initial directory. This should present a following tree structure at the beginning of the training:│.env
│tabularConfig.py
│training.py
│
└───data_folderinsurance.csvNow let's discuss these files one by one.envThis file should only be used when the database integration is required. You will be required to hold the database credentials in.envas shown belowMONGO_CONN_STR="Connection String for MongoDB"MYSQL_HOST="database_host(could be an ip or domain)"MYSQL_USER="database_user_name"MYSQL_PASS="database_password"MYSQL_DB="database_name"tabularConfig.pyThis file is used to setup the configuration for Experiment. It has standard format as shown below. Parameters could be updated according to the training job requirements#--------------------------------------------------------------------------------------------------------------------------------# Parameters#--------------------------------------------------------------------------------------------------------------------------------data_dir="data_folder"csv_file_name="insurance.csv"target_column="charges"autopilot=False# True if you want to automatically configure everything for the training job and run the job without user interaction else, False.eda=False# Exploratory Data Analysis (EDA)metrics='r2_score'# selection_for_classificaton = ['accuracy', 'f1_score', 'recall', 'precision']# selection_for_regression = ['r2_score', 'mean_absolute_error','mean_squared_error', 'mean_absolute_percentage_error',# 'median_absolute_error', 'explained_variance_score']validation_split=0.20scaler="RobustScaler"# available_selections = ['MinMaxScaler', 'StandardScaler', 'MaxAbsScaler', 'RobustScaler']imputer="SimpleImputer"# available_selections = ['KNNImputer', ''SimpleImputer']PloynomialFeatures=Falseremove_outliers=Falsehandle_imbalance=False# Only applicable to the classification problems.pca=False# Principal Component Analysis (PCA).feature_selection=False# This will use recursive feature elimination (RFE)#--------------------------------------------------------------------------------------------------------------------------------# Artifacts (Directory names to store the results & resources, can be customized according to the user requirements)#--------------------------------------------------------------------------------------------------------------------------------project_name='Insurance Project'artifacts_dir="Artifacts"pipelines_dir="Pipelines"plots_dir="Plots"model_name="my_test_model"experiment_name="light model testing"#--------------------------------------------------------------------------------------------------------------------------------# Logs (Directory names to record logs, can be customized according to the user requirements)#--------------------------------------------------------------------------------------------------------------------------------logs_dir="Logs"csv_logs_dir="CSV Logs"csv_logs_file="csv_logs_file"comments="making comparision for optimizers"executed_by='hasanain'#--------------------------------------------------------------------------------------------------------------------------------# Database Integration#--------------------------------------------------------------------------------------------------------------------------------# Please Note that before making any change in this section, create a .env file and store the mongo db connection string or MySQL credentials in the environment variables# Guideline for creating .env is available on project description main pagefromdotenvimportload_dotenvload_dotenv()db_integration_mysql=Falsedb_integration_mongodb=Falsetraining.pyimporttabularConfigfromonespace.tabular.regressionimportExperimentdeftraining(config):exp=Experiment(config)exp.run_experiment()if__name__=="__main__":training(tabularConfig)Run the following command in the terminal to start the training jobpythontraining.pyNow let theOneSpacetake care of your end-to-end model training and evaluation process.After the training job is completed, the directories in the workspace should look like as follows:│.env
│tabularConfig.py
│training.py
│
├───cachedir
│└───joblib
│└───sklearn
│└───pipeline
│└───_fit_transform_one
├───data_folder
│insurance.csv
│
├───Tabular
│└───Regression
│└───InsuranceProject
│├───Artifacts
││└───Pipelines_on_20221012_at_004113
││GradientBoostingRegressor_on_20221012_at_004124.pkl
││LGBMRegressor_on_20221012_at_004126.pkl
││StackingRegressor_on_20221012_at_004128.pkl
││VotingRegressor_on_20221012_at_004128.pkl
││
│├───Logs
││├───Final
│││csv_logs_file.csv
│││
││├───ModelComparision
│││modelsComparisionAfterTuning_on_20221012_at_004128..csv
│││modelsComparisionBeforTuning_on_20221012_at_004116..csv
│││
││└───Preprocessing
││preprocessed_on_20221012_at_004113..csv
││
│└───Plots
│├───EDA
││└───EDA_on_20221012_at_004111
││barplot.png
││boxplot.png
││corr_heatmap.png
││countplot.png
││histogram.png
││
│└───Evaluation
│└───Evaluation_on_20221012_at_004120
│GradientBoostingRegressor_feature_importance.png
│GradientBoostingRegressor_residplot.png
│LGBMRegressor_feature_importance.png
│LGBMRegressor_residplot.png
│
└───__pycache__tabularConfig.cpython-310.pycTraining a Classification Model With Tabular Data:First step is to setup the initial directory. This should present a following tree structure at the beginning of the training:│.env
│tabularConfig.py
│training.py
│
└───data_foldertitanic.csvNow let's discuss these files one by one.envThis file should only be used when the database integration is required. You will be required to hold the database credentials in.envas shown belowMONGO_CONN_STR="Connection String for MongoDB"MYSQL_HOST="database_host(could be an ip or domain)"MYSQL_USER="database_user_name"MYSQL_PASS="database_password"MYSQL_DB="database_name"tabularConfig.pyThis file is used to setup the configuration for Experiment. It has standard format as shown below. Parameters could be updated according to the training job requirements#--------------------------------------------------------------------------------------------------------------------------------# Parameters#--------------------------------------------------------------------------------------------------------------------------------data_dir="data_folder"csv_file_name="titanic.csv"target_column="Survived"autopilot=False# True if you want to automatically configure everything for the training job and run the job without user interaction else, False.eda=True# Exploratory Data Analysis (EDA)metrics='f1_score'# selection_for_classificaton = ['accuracy', 'f1_score', 'recall', 'precision']# selection_for_regression = ['r2_score', 'mean_absolute_error','mean_squared_error', 'mean_absolute_percentage_error',# 'median_absolute_error', 'explained_variance_score']validation_split=0.20scaler="RobustScaler"# available_selections = ['MinMaxScaler', 'StandardScaler', 'MaxAbsScaler', 'RobustScaler']imputer="SimpleImputer"# available_selections = ['KNNImputer', ''SimpleImputer']PloynomialFeatures=Falseremove_outliers=Falsehandle_imbalance=False# Only applicable to the classification problems.pca=False# Principal Component Analysis (PCA).feature_selection=False# This will use recursive feature elimination (RFE)#--------------------------------------------------------------------------------------------------------------------------------# Artifacts (Directory names to store the results & resources, can be customized according to the user requirements)#--------------------------------------------------------------------------------------------------------------------------------project_name='Titanic Project1.0'artifacts_dir="Artifacts"pipelines_dir="Pipelines"plots_dir="Plots"model_name="my_test_model"experiment_name="light model testing"#--------------------------------------------------------------------------------------------------------------------------------# Logs (Directory names to record logs, can be customized according to the user requirements)#--------------------------------------------------------------------------------------------------------------------------------logs_dir="Logs"csv_logs_dir="CSV Logs"csv_logs_file="csv_logs_file"comments="making comparision for optimizers"executed_by='hasanain'#--------------------------------------------------------------------------------------------------------------------------------# Database Integration#--------------------------------------------------------------------------------------------------------------------------------# Please Note that before making any change in this section, create a .env file and store the mongo db connection string or MySQL credentials in the environment variables# Guideline for creating .env is available on project description main pagefromdotenvimportload_dotenvload_dotenv()db_integration_mysql=Falsedb_integration_mongodb=Falsetraining.pyimporttabularConfigfromonespace.tabular.classificationimportExperimentdeftraining(config):exp=Experiment(config)exp.run_experiment()if__name__=="__main__":training(tabularConfig)Run the following command in the terminal to start the training jobpythontraining.pyNow let theOneSpacetake care of your end-to-end model training and evaluation process.After the training job is completed, the directories in the workspace should look like as follows:│.env
│tabularConfig.py
│training.py
│
├───cachedir
│└───joblib
│└───sklearn
│└───pipeline
│└───_fit_transform_one
├───data_folder
│titanic.csv
│
├───Tabular
│└───Classification
│└───TitanicProject1.0
│├───Artifacts
││└───Pipelines_on_20221012_at_003107
││ExtraTreesClassifier_on_20221012_at_003128.pkl
││RandomForestClassifier_on_20221012_at_003130.pkl
││StackingClf_on_20221012_at_003136.pkl
││VotingClf_on_20221012_at_003136.pkl
││
│├───Logs
││├───Final
│││csv_logs_file.csv
│││
││└───Preprocessing
││preprocessed_on_20221012_at_003107..csv
││
│└───Plots
│├───EDA
││└───EDA_on_20221012_at_003048
││boxplot.png
││corr_heatmap.png
││countplot.png
││histogram.png
││pairplot.png
││pie_bar.png
││violinplot.png
││
│└───Evaluation
│└───Evaluation_on_20221012_at_003124
│auc_roc_plot.png
│ExtraTreesClassifier_confusion_matrix_count.png
│ExtraTreesClassifier_confusion_matrix_pct.png
│ExtraTreesClassifier_feature_importance.png
│RandomForestClassifier_confusion_matrix_count.png
│RandomForestClassifier_confusion_matrix_pct.png
│RandomForestClassifier_feature_importance.png
│StackingClf_confusion_matrix_count.png
│StackingClf_confusion_matrix_pct.png
│VotingClf_confusion_matrix_count.png
│VotingClf_confusion_matrix_pct.png
│
└───__pycache__tabularConfig.cpython-310.pycComputer VisionThis API enable users to create image classification models with Tensorflow or PyTorch.
More services for computer vision API are being developed.Training an Image Classification Model with OneSpace(Tensorflow):First step is to setup the initial directory. This should present a following tree structure at the beginning of the training:│.env
│tensorflowConfig.py
│training.py
│└───data_folder├───train│├───ants││0013035.jpg││1030023514_aad5c608f9.jpg││1095476100_3906d8afde.jpg││1099452230_d1949d3250.jpg││116570827_e9c126745d.jpg│││└───bees│1092977343_cb42b38d62.jpg│1093831624_fb5fbe2308.jpg│1097045929_1753d1c765.jpg│1232245714_f862fbe385.jpg│129236073_0985e91c7d.jpg│└───val├───ants│10308379_1b6c72e180.jpg│1053149811_f62a3410d3.jpg│1073564163_225a64f170.jpg│└───bees1032546534_06907fe3b3.jpg10870992_eebeeb3a12.jpg1181173278_23c36fac71.jpgNow let's discuss these files one by one.envThis file should only be used when the database integration is required. You will be required to hold the database credentials in.envas shown belowMONGO_CONN_STR="Connection String for MongoDB"MYSQL_HOST="database_host(could be an ip or domain)"MYSQL_USER="database_user_name"MYSQL_PASS="database_password"MYSQL_DB="database_name"tensorflowConfig.pyThis file is used to setup the configuration for Experiment. It has standard format as shown below. Parameters could be updated according to the training job requirements#--------------------------------------------------------------------------------------------------------------------------------# Parameters#--------------------------------------------------------------------------------------------------------------------------------data_dir="data_folder"# main directory for datatrain_folder_name="train"# name of the training folder. Pass "None" if there is no training and validation folder availableval_folder_name="val"# name of the validation folder pass "None" if there is no training and validation folder availabletransfer_learning=False# Pass "False" if you want to train the model from scratchmodel_architecture="MobileNetV3Small"# available_selections = ["VGG16", "MobileNetV3Large", "MobileNetV3Small", "DenseNet201", "EfficientNetV2L", "EfficientNetV2S", "ResNet50", "ResNet50V2", "ResNet152V2", "VGG19", "Xception"]freeze_all=True# Will freeze the weights for all the layers except for last one (Dense Layer)freeze_till=None# selection = [-1,-2,-3,-4,-5] => if you want to freeze weights until a specific layer, select any value from the list and observe the trainable pramsaugmentation=False# Pass True if the augmentation should be applied on the images. It helps with regularization.epochs=2# Number of Epochsbatch_size=64input_shape=(224,224,3)activation="relu"# available_selections = ["relu","selu", "swish", "elu", "gelu", "tanh"] => This param is used when there is no transferlearning involved and model is being trained from scratchactivation_output="softmax"# available_selections = ["softmax","sigmoid", "hard_sigmoid"]loss_function="binary_crossentropy"# available_selection = ["binary_crossentropy","categorical_crossentropy" , "sparse_categorical_crossentropy"]metrics=["AUC","Recall"]# available_selection = ["accuracy", "AUC", "Precision", "Recall" ]lr_scheduler='InverseTimeDecay'#availabel_selection = ["InverseTimeDecay","CosineDecay", "ExponentialDecay", "CosineDecayRestarts", "PolynomialDecay"] # can be observed on tensorboardoptimizer="Adam"# available_selection = ["SGD", "Adam", "RMSprop", "Adadelta", "Adagrad", "Adamax", "Nadam", "Ftrl"]validation_split=0.20# The proportion of data should be used for validation. This param won't be used if a seperate Validation Data Folder is being passedes_patience=5# Early Stopping Patience#--------------------------------------------------------------------------------------------------------------------------------# Artifacts (Directory names to store the results & resources, can be customized according to the user requirements)#--------------------------------------------------------------------------------------------------------------------------------project_name='AntsBeesProject_tf'artifacts_dir="Artifacts"model_dir="Models"plots_dir="Plots"model_name="my_test_model"experiment_name="Exp for Demo"plot_name="results_plot"model_ckpt_dir="Model Checkpoints"callbacked_model_name="model_ckpt"#--------------------------------------------------------------------------------------------------------------------------------# Logs (Directory names to record logs, can be customized according to the user requirements)#--------------------------------------------------------------------------------------------------------------------------------logs_dir="Logs"tensorboard_root_log_dir="Tensorboard Logs"csv_logs_dir="CSV Logs"csv_logs_file="cv_test_logs.csv"comments="Running a demo with transfer learning"executed_by='Hasanain'#--------------------------------------------------------------------------------------------------------------------------------# Database Integration#--------------------------------------------------------------------------------------------------------------------------------# Please Note that before making any change in this section, create a .env file and store the mongo db connection string or MySQL credentials in the environment variables# Guideline for creating .env is available on project description main pagefromdotenvimportload_dotenvload_dotenv()db_integration_mysql=Falsedb_integration_mongodb=Falsetraining.pyimporttensorflowConfigfromonespace.tensorflow.cvimportExperimentdeftraining(config):exp=Experiment(config)exp.run_experiment()if__name__=="__main__":training(tensorflowConfig)Run the following command in the terminal to start the training jobpythontraining.pyNow let theOneSpacetake care of your end-to-end model training and evaluation process.After the training job is completed, the directories in the workspace should look like as follows:│.env
│tensorflowConfig.py
│training.py
│├───ComputerVision
│└───TensorFlow
│└───AntsBeesProject_tf
│├───Artifacts
││├───Images
│││├───TrainingImages
││││img_grid__on_20221012_at_090752.png
│││││││└───ValidationImages
│││img_grid__on_20221012_at_090752.png
│││
││├───Models
│││├───ModelCheckpoints
││││model_ckpt__on_20221012_at_090755_.h5
││││
│││└───TrainedModels
│││my_test_model__on_20221012_at_090800_.h5
│││
││└───Plots
││├───models
│││my_test_model__on_20221012_at_091731.png
│││
││└───results
││results_plot__on_20221012_at_091731.png
││
│└───Logs
│├───CSVLogs
││cv_test_logs.csv
││
│└───TensorboardLogs
│└───logs__on_20221012_at_090755
│├───train
││events.out.tfevents.1665540475.MY-LAPTOP.143340.0.v2
││
│└───validation
│events.out.tfevents.1665540477.MY-LAPTOP.143430.1.v2
│
├───data_folder
│├───train
││├───ants
│││0013035.jpg
│││1030023514_aad5c608f9.jpg
│││1095476100_3906d8afde.jpg
│││1099452230_d1949d3250.jpg
│││116570827_e9c126745d.jpg
││└───bees
││1092977343_cb42b38d62.jpg
││1093831624_fb5fbe2308.jpg
││1097045929_1753d1c765.jpg
││1232245714_f862fbe385.jpg
││129236073_0985e91c7d.jpg
││
│└───val
│├───ants
││10308379_1b6c72e180.jpg
││1053149811_f62a3410d3.jpg
││1073564163_225a64f170.jpg
││
│└───bees
│1032546534_06907fe3b3.jpg
│10870992_eebeeb3a12.jpg
│1181173278_23c36fac71.jpg
│
└───__pycache__tensorflowConfig.cpython-310.pycTraining an Image Classification Model with OneSpace(PyTorch):First step is to setup the initial directory. This should present a following tree structure at the beginning of the training:│.env
│pytorchConfig.py
│training.py
│└───data_folder├───train│├───ants││0013035.jpg││1030023514_aad5c608f9.jpg││1095476100_3906d8afde.jpg││1099452230_d1949d3250.jpg││116570827_e9c126745d.jpg│││└───bees│1092977343_cb42b38d62.jpg│1093831624_fb5fbe2308.jpg│1097045929_1753d1c765.jpg│1232245714_f862fbe385.jpg│129236073_0985e91c7d.jpg│└───val├───ants│10308379_1b6c72e180.jpg│1053149811_f62a3410d3.jpg│1073564163_225a64f170.jpg│└───bees1032546534_06907fe3b3.jpg10870992_eebeeb3a12.jpg1181173278_23c36fac71.jpgNow let's discuss these files one by one.envThis file should only be used when the database integration is required. You will be required to hold the database credentials in.envas shown belowMONGO_CONN_STR="Connection String for MongoDB"MYSQL_HOST="database_host(could be an ip or domain)"MYSQL_USER="database_user_name"MYSQL_PASS="database_password"MYSQL_DB="database_name"pytorchConfig.pyThis file is used to setup the configuration for Experiment. It has standard format as shown below. Parameters could be updated according to the training job requirements#--------------------------------------------------------------------------------------------------------------------------------# Parameters#--------------------------------------------------------------------------------------------------------------------------------data_dir="data_folder"train_folder_name="train"val_folder_name="val"transfer_learning=Truemodel_architecture="MobileNet_v3_small"# available_selections = ["AlexNet", "ConvnNeXt", "DenseNet121", "DenseNet201", "EfficientNet_b7", "EfficientNet_v2_s", "EfficientNet_v2_m", "EfficientNet_v2_l", "Wide_Resnet50_2",#"GoogleNet", "Inception_v3", "MnasNet0_5", "MnasNet1_3", "MobileNet_v2", "MobileNet_v3_large", "MobileNet_v3_small", "RegNet_y_32gf", "ResNet18",#"ResNet34", "ResNet50", "ResNet152", "ResNext101_32x8d", "ShuffleNet_v2_x1_5", "SqueezeNet1_0", "VGG11", "VGG13", "VGG16", "VGG19", "VisionTransformer"]augmentation=Falseepochs=1batch_size=32input_shape=(224,224,3)lr_scheduler="ExponentialLR"# available_selections = ["OneCycleLR","StepLR", "LambdaLR", "ExponentialLR"]optimizer="RMSprop"# available_selections = ["SGD", "Adam", "Adadelta", "Adagrad", "RMSprop"]validation_split=0.20grad_clip=1# Should be a value ranging from 0.5 to 1.0 OR Noneweight_decay=0.001# Should be a value ranging from 0.5 to 1.0#--------------------------------------------------------------------------------------------------------------------------------# Artifacts (Directory names to store the results & resources, can be customized according to the user requirements)#--------------------------------------------------------------------------------------------------------------------------------project_name='AntsBeensProject_pytorch'artifacts_dir="Artifacts"model_dir="Models"plots_dir="Plots"model_name="antbees_model"experiment_name="Demo Experiment"plot_name="results_plot"model_ckpt_dir="Model Checkpoints"callbacked_model_name="model_ckpt"#--------------------------------------------------------------------------------------------------------------------------------# Logs (Directory names to record logs, can be customized according to the user requirements)#--------------------------------------------------------------------------------------------------------------------------------logs_dir="Logs"tensorboard_root_log_dir="Tensorboard Logs"csv_logs_dir="CSV Logs"csv_logs_file="cv_test_logs.csv"comments="This is a Demo"executed_by='Hasanain'#--------------------------------------------------------------------------------------------------------------------------------# Database Integration#--------------------------------------------------------------------------------------------------------------------------------# Please Note that before making any change in this section, create a .env file and store the mongo db connection string or MySQL credentials in the environment variables# Guideline for creating .env is available on project description main pagefromdotenvimportload_dotenvload_dotenv()db_integration_mysql=Falsedb_integration_mongodb=Falsetraining.pyimportpytorchConfigfromonespace.pytorch.cvimportExperimentdeftraining(config):exp=Experiment(config)exp.run_experiment()if__name__=="__main__":training(pytorchConfig)Run the following command in the terminal to start the training jobpythontraining.pyNow let theOneSpacetake care of your end-to-end model training and evaluation process.After the training job is completed, the directories in the workspace should look like as follows:│.env
│pytorchConfig.py
│training.py
│├───ComputerVision
│└───PyTorch
│└───AntsBeensProject_pytorch
│├───Artifacts
││├───Images
│││├───TrainingImages
││││img_grid__on_20221012_at_093114.png
│││││││└───ValidationImages
│││img_grid__on_20221012_at_093114.png
│││
││├───Models
│││├───BaseModels
││││MobileNet_v3_small__on_20221012_at_093140_.pth
││││
│││└───TrainedModels
│││antbees_model__on_20221012_at_093227_.pth
│││
││└───Plots
││└───results
││results_plot__on_20221012_at_093227.png
││
│└───Logs
│├───CSVLogs
││cv_test_logs.csv
││
│└───TensorboardLogs
│└───logs__on_20221012_at_093052
││events.out.tfevents.1665541852.My-Laptop.86003.0
││projector_config.pbtxt
││
│├───00000
││└───default
││metadata.tsv
││sprite.png
││tensors.tsv
││
│└───1665541924.1492205
│events.out.tfevents.1665541924.My-Laptop.86003.1
│
├───data_folder
│├───train
││├───ants
│││0013035.jpg
│││1030023514_aad5c608f9.jpg
│││1095476100_3906d8afde.jpg
│││1099452230_d1949d3250.jpg
│││116570827_e9c126745d.jpg
│││
││└───bees
││1092977343_cb42b38d62.jpg
││1093831624_fb5fbe2308.jpg
││1097045929_1753d1c765.jpg
││1232245714_f862fbe385.jpg
││129236073_0985e91c7d.jpg
││
│└───val
│├───ants
││10308379_1b6c72e180.jpg
││1053149811_f62a3410d3.jpg
││1073564163_225a64f170.jpg
││
│└───bees
│1032546534_06907fe3b3.jpg
│10870992_eebeeb3a12.jpg
│1181173278_23c36fac71.jpg
│
└───__pycache__pytorchConfig.cpython-310.pyc4.ContributingYes, Please! We believe that there is alot of oportunity to make Machine Learning more interesting and less complicated for the comunity, so let's make it more efficient, let's go with low-code!!5.ConclusionAll the services which are being provided byOneSpacecould be managed in a single directory without having to leave your workspace. A user only needs to take care of two things before running a training job:Setup the relavent configurations inconfig.py.Check if you are importing the right module intraining.py.Now typepython training.pyon the terminal and hit enter.Voila, you have a trained production ready model saved in the workspace, Great Work!Please give this repository a star if you find our work useful, Thank you! 🙏Copyright © 2022 OneSpaceLet's connect onLinkedIn