init kwargs (#3)

Author: ayulockin

mmf/configs/defaults.yaml

@@ -50,10 +50,10 @@ training:
         # it will log the run to your user account.
         entity: null
         # Project name to be used while logging the experiment with wandb
-        wandb_projectname: mmf_${oc.env:USER,}
+        project: mmf
         # Experiment/ run name to be used while logging the experiment
         # under the project with wandb
-        wandb_runname: ${training.experiment_name}
+        name: ${training.experiment_name}
         # Specify other argument values that you want to pass to wandb.init(). Check out the documentation
         # at https://docs.wandb.ai/ref/python/init to see what arguments are available.
         # job_type: 'train'

mmf/trainers/callbacks/logistics.py

@@ -60,10 +60,8 @@ def __init__(self, config, trainer):
 
             self.wandb_logger = WandbLogger(
                 entity=config.training.wandb.entity,
-                project=config.training.wandb.wandb_projectname,
                 config=config,
-                name=config.training.wandb.wandb_runname,
-                save_dir=log_dir,
+                project=config.training.wandb.project,
             )
 
     def on_train_start(self):

mmf/utils/logger.py

@@ -2,7 +2,6 @@
 
 import collections
 import functools
-import itertools
 import json
 import logging
 import os
@@ -11,12 +10,14 @@
 from functools import wraps
 from typing import Any, Callable, Dict, Optional, Union
 
+import omegaconf
 import torch
 from mmf.common.registry import registry
 from mmf.utils.configuration import get_mmf_env
 from mmf.utils.distributed import get_rank, is_main, is_xla
 from mmf.utils.file_io import PathManager
 from mmf.utils.timer import Timer
+from omegaconf import OmegaConf
 from termcolor import colored
 
 
@@ -226,17 +227,19 @@ def summarize_report(
     if not is_main() and not is_xla():
         return
 
+    # Log the learning rate if available
+    if wandb_logger and "lr" in extra.keys():
+        wandb_logger.log_metrics(
+            {"train/learning_rate": float(extra["lr"])}, commit=False
+        )
+
     if tb_writer:
         scalar_dict = meter.get_scalar_dict()
         tb_writer.add_scalars(scalar_dict, current_iteration)
 
     if wandb_logger:
         metrics = meter.get_scalar_dict()
-        wandb_logger.log_metrics({**metrics, "trainer/global_step": current_iteration}, commit=False)
-
-    # Log the learning rate if available
-    if wandb_logger and 'lr' in extra.keys():
-        wandb_logger.log_metrics({"train/learning_rate": float(extra["lr"])})
+        wandb_logger.log_metrics({**metrics, "trainer/global_step": current_iteration})
 
     if not should_print:
         return
@@ -401,10 +404,8 @@ class WandbLogger:
 
     Args:
         entity: An entity is a username or team name where you're sending runs.
-        name: Display name for the run.
-        save_dir: Path where data is saved (./save/logs/wandb/ by default).
-        project: Display name for the project.
         config: Configuration for the run.
+        project: Name of the W&B project.
 
     Raises:
         ImportError: If wandb package is not installed.
@@ -413,10 +414,8 @@ class WandbLogger:
     def __init__(
         self,
         entity: Optional[str] = None,
-        name: Optional[str] = None,
-        save_dir: Optional[str] = None,
-        project: Optional[str] = None,
         config: Optional[Dict] = None,
+        project: Optional[str] = None,
     ):
         try:
             import wandb
@@ -428,15 +427,15 @@ def __init__(
 
         self._wandb = wandb
 
-        self._wandb_init = dict(
-            entity=entity, name=name, project=project, dir=save_dir, config=config
-        )
+        self._wandb_init = dict(entity=entity, config=config, project=project)
 
-        init_kwargs = dict(
-            itertools.islice(
-                config.training.wandb.items(), 4, len(config.training.wandb)
-            )
-        )
+        wandb_params = config.training.wandb
+        with omegaconf.open_dict(wandb_params):
+            wandb_params.pop("enabled")
+            wandb_params.pop("entity")
+            wandb_params.pop("project")
+
+        init_kwargs = OmegaConf.to_container(wandb_params, resolve=True)
         self._wandb_init.update(**init_kwargs)
 
         self.setup()
@@ -473,7 +472,8 @@ def log_metrics(self, metrics: Dict[str, float], commit=True):
 
         Args:
             metrics (Dict[str, float]): A dictionary of metrics to log.
-            commit (bool): Save the metrics dict to the wandb server and increment the step. (default: True)
+            commit (bool): Save the metrics dict to the wandb server and
+                           increment the step. (default: True)
         """
         if not self._should_log_wandb():
             return

website/docs/notes/logging.md

@@ -8,13 +8,13 @@ sidebar_label: Weights and Biases Logging
 
 MMF now has a `WandbLogger` class which lets the user to log their model's progress using [Weights and Biases](https://wandb.ai/site). Enable this logger to automatically log the training/validation metrics, system (GPU and CPU) metrics and configuration parameters.
 
-## First time setup 
+## First time setup
 
 To set up wandb, run the following:
 ```
 pip install wandb
 ```
-In order to log anything to the W&B server you need to authenticate the machine with W&B **API key**. You can create a new account by going to https://wandb.ai/signup which will generate an API key. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. You only need to supply your key once, and then it is remembered on the same device. 
+In order to log anything to the W&B server you need to authenticate the machine with W&B **API key**. You can create a new account by going to https://wandb.ai/signup which will generate an API key. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. You only need to supply your key once, and then it is remembered on the same device.
 
 ```
 wandb login
@@ -28,42 +28,42 @@ training:
     # Weights and Biases control, by default Weights and Biases (wandb) is disabled
     wandb:
         # Whether to use Weights and Biases Logger, (Default: false)
-        enabled: false
+        enabled: true
         # An entity is a username or team name where you're sending runs.
         # This is necessary if you want to log your metrics to a team account. By default
         # it will log the run to your user account.
         entity: null
         # Project name to be used while logging the experiment with wandb
-        wandb_projectname: mmf_${oc.env:USER,}
+        project: mmf
         # Experiment/ run name to be used while logging the experiment
         # under the project with wandb
-        wandb_runname: ${training.experiment_name}
+        name: ${training.experiment_name}
         # Specify other argument values that you want to pass to wandb.init(). Check out the documentation
         # at https://docs.wandb.ai/ref/python/init to see what arguments are available.
         # job_type: 'train'
         # tags: ['tag1', 'tag2']
 env:
     wandb_logdir: ${env:MMF_WANDB_LOGDIR,}
-``` 
+```
 
 * To enable wandb logger the user needs to change the following option in the config.
 
 	`training.wandb.enabled=True`
 
-* To give the `entity` which is the name of the team or the username, the user needs to change the following option in the config. In case no `entity` is provided, the data will be logged to the `entity` set as default in the user's settings. 
+* To give the `entity` which is the name of the team or the username, the user needs to change the following option in the config. In case no `entity` is provided, the data will be logged to the `entity` set as default in the user's settings.
 
 	`training.wandb.entity=<teamname/username>`
 
-* To give the current experiment a project and run name, user should add these config options.
+* To give the current experiment a project and run name, user should add these config options. The default project name is `mmf` and the default run name is `${training.experiment_name}`.
 
-	`training.wandb.wandb_projectname=<ProjectName>` <br>
-	`training.wandb.wandb_runname=<RunName>`
+	`training.wandb.project=<ProjectName>` <br>
+	`training.wandb.name=<RunName>`
 
 * To change the path to the directory where wandb metadata would be stored (Default: `env.log_dir`):
 
 	`env.wandb_logdir=<dir_name>`
 
-* To provide extra arguments to `wandb.init()`, the user just needs to define them in the config file. Check out the documentation at https://docs.wandb.ai/ref/python/init to see what arguments are available. An example is shown in the config parameter shown above. 
+* To provide extra arguments to `wandb.init()`, the user just needs to define them in the config file. Check out the documentation at https://docs.wandb.ai/ref/python/init to see what arguments are available. An example is shown in the config parameter shown above. Make sure to use the same key name in the config file as defined in the documentation.
 
 ## Current features