tfcaidm 0.0.0a4

• 可重复使用的最先进的深度学习模型块
• 支持并行训练多个模型
• 用于自定义数据集、模型、损失函数、训练例程等的高级界面。
• 再现性、性能记录、模型检查点和超参数跟踪

• AutoML / 高效的超参数搜索
• 分布式数据和模型训练
• 视觉变压器模型
• 更好的文档

• YAML 配置文件
• 超参数调优
• 支持的机型
• 可定制性
• 查看结果
• 基准（即将推出）

使用conda虚拟环境安装。

user您的帐户用户名在哪里。

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