clean out unneeded dependencies

Dockerfile

@@ -3,12 +3,10 @@ MAINTAINER ersilia
 
 RUN pip install rdkit
 RUN pip install git+https://github.com/bp-kelley/descriptastorus
-RUN pip install tqdm>=4.62.2
 RUN pip install typed-argument-parser==1.6.1
 RUN pip install scikit-learn
 RUN pip install torch
 RUN pip install pandas
-RUN pip install tensorboardX==2.0
 RUN pip install scipy>=1.4.1
 RUN pip install hyperopt
 RUN pip install protobuf==3.18.3

model/framework/code/chemprop/train/run_training.py

@@ -7,7 +7,7 @@
 import warnings
 warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning) 
 import pandas as pd
-from tensorboardX import SummaryWriter
+#from tensorboardX import SummaryWriter
 import torch
 from tqdm import trange
 from torch.optim.lr_scheduler import ExponentialLR
@@ -243,13 +243,13 @@ def run_training(args: TrainArgs,
 
     # Train ensemble of models
     for model_idx in range(args.ensemble_size):
-        # Tensorboard writer
-        save_dir = os.path.join(args.save_dir, f'model_{model_idx}')
-        makedirs(save_dir)
-        try:
-            writer = SummaryWriter(log_dir=save_dir)
-        except:
-            writer = SummaryWriter(logdir=save_dir)
+        # # Tensorboard writer
+        # save_dir = os.path.join(args.save_dir, f'model_{model_idx}')
+        # makedirs(save_dir)
+        # try:
+        #     writer = SummaryWriter(log_dir=save_dir)
+        # except:
+        #     writer = SummaryWriter(logdir=save_dir)
 
         # Load/build model
         if args.checkpoint_paths is not None:

model/framework/code/chemprop/train/train.py

@@ -2,7 +2,7 @@
 from typing import Callable
 
 import numpy as np
-from tensorboardX import SummaryWriter
+#from tensorboardX import SummaryWriter
 import torch
 import torch.nn as nn
 from torch.optim import Optimizer
@@ -25,7 +25,7 @@ def train(
     n_iter: int = 0,
     atom_bond_scaler: AtomBondScaler = None,
     logger: logging.Logger = None,
-    writer: SummaryWriter = None,
+    #writer: SummaryWriter = None,
 ) -> int:
     """
     Trains a model for an epoch.