<jupyter_start><jupyter_text>Super Resolution Experiments  Train<jupyter_code>import os

for scale in [16,]:
    #MLFlow
    experiment_name='conditionalDDPM'; run_name=f'ls_aedownup{scale:02d}'

    #Dataset
    dataset=f'ls_aedownup{scale:02d}'; image_size=128

    #Model
    timesteps=1000; dim=128; n_layers=4; model_type='c'; clip = 1

    #Hyperparameters
    train_num_steps=2000; lr=0.000001; batch_size=16

    os.system(f"python train_ls.py --dataset={dataset} --experiment_name={experiment_name} --run_name={run_name} --image_size={image_size} --timesteps={timesteps} --dim={dim} --n_layers={n_layers} --model_type={model_type} --clip={clip} --train_num_steps={train_num_steps} --lr={lr} --batch_size={batch_size} ")<jupyter_output>2022/06/22 16:36:42 WARNING mlflow.tracking.context.registry: Encountered unexpected error during resolving tags: 'getpwuid(): uid not found: 1001030000'
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

  | Name  | Type              | Params
--------------------------------------------
0 | model | GaussianDiffusion | 117 M 
--------------------------------------------
117 M     Trainable params
0         Non-trainable params
117 M     Total params
468.746   Total estimated model params size (MB)
/usr/local/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:112: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 64 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  f"The dataloader, {name}, does not have many[...]
{'fraction_non_alphanumeric': 0.1595197255574614, 'fraction_numerical': 0.04745568896512293, 'mean_word_length': 3.6052631578947367, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '93507', 'license': 'MIT', 'n_tokens_mistral': 628, 'n_tokens_neox': 563, 'n_words': 151, 'path': 'lightsheet_exp.ipynb', 'repo_name': 'marcoaversa/conditionalDDPM', 'size': '1749'}