tfcaidm 0.0.0a4

• Reusable state-of-the-art deep learning model blocks
• Support for training multiple models in parallel
• High-level interface for customizing datasets, models, loss functions, training routines, etc.
• Reproducibility, performance logging, model checkpointing, and hyperparameter tracking

• AutoML / efficient hyperparameter search
• Distributed data and model training
• Vision transformer models
• Better documentation

• YAML configuration files
• Hyperparameter tuning
• Supported models
• Customizability
• Viewing results
• Benchmarks (coming soon)

Install using the conda virtual environment.

Where user is your account username.

user $ conda create --name tfcaidm python=3.7
user $ conda activate tfcaidm
user (tfcaidm) $ pip install tensorflow
user (tfcaidm) $ pip install jarvis-md
user (tfcaidm) $ pip install tfcaidm

from jarvis.utils.general import gpus
from tfcaidm import Jobs

# --- Define paths
YML_CONFIG = "pipeline.yml"
TRAIN_ROUTINE_PATH = "main.py"

# --- Submit a training job
Jobs(path=YML_CONFIG).setup(
    producer=__file__,
    consumer=TRAIN_ROUTINE_PATH,
).train_cluster()

from jarvis.train import params
from jarvis.utils.general import gpus
from tfcaidm import Trainer

# --- Autoselect GPU (use only on caidm cluster)
gpus.autoselect()

# --- Get hyperparameters (args passed by environment variables)
hyperparams = params.load()

# --- Train model (dataset and model created within trainer)
trainer = Trainer(hyperparams)
results = trainer.cross_validation(save=True)
trainer.save_results(results)

from jarvis.train import params
from jarvis.utils.general import gpus, overload
from tfcaidm import JClient
from tfcaidm import Model
from tfcaidm import Trainer

# --- Autoselect GPU (use only on caidm cluster)
gpus.autoselect()

# --- Get hyperparameters (args passed by environment variables)
hyperparams = params.load()

# --- Setup custom dataset generator (more details in notebooks)
@overload(JClient)
def create_generator(self, gen_data):
    for xs, ys in gen_data:

        # --- User defined code
        xs = DataAugment(xs)

        yield xs, ys

# --- Setup custom model (more details in notebooks)
@overload(Model)
def create(self):

    # --- User defined code
    model = ViT(...)
    model.compile(...)

    return model

# --- Train model (dataset and model created within trainer)
trainer = Trainer(hyperparams)
results = trainer.cross_validation(save=True)
trainer.save_results(results)

# See notebooks for a breakdown on customizability