Multiple wandb outputs
I noticed that when I’m training a model using theAcceleratelibrary that the number of syncable runs that get outputted to wandb is the same as the number of GPUs I configureAcceleratewith. If I have 4 GPUs available to me and configureAccelerateto utilize 2 of them, there will be two output directories in the/path/to/my/project/wandbdirectory that I can sync and view the various plots.These appear to be different directories but I was wondering if it were possible to have the wandb runs aggregated somehow so as to get a final single big picture of was took place during training?
2022-08-09T10:52:38Z
Make sure when initializing your trackers its under anif accelerator.is_main_processas shown in this example (docs need to be updated, will do so today):github.comhuggingface/accelerate/blob/main/examples/by_feature/tracking.py#L168-L172# New Code ## We need to initalize the trackers we use. Overall configurations can also be storedif args.with_tracking and accelerator.is_main_process:run = os.path.split(__file__)[-1].split(".")[0]accelerator.init_trackers(run, config)
2022-08-09T16:13:22Z
@muellerzrThank you very much for your response. Below is what I originally had:rom accelerate import Accelerator
import wandb
import torch

class MyRun:
    def __init__(self) -> None:
        
        self.tracker_project_name = 'my_project'
        self.tracker_run_name = 'my_run'
        self.is_accelerate = True
        self.tracker_name = 'wandb'
        
        if self.is_accelerate:
            self.accelerator = Accelerator(log_with=[self.tracker_name])
            self.accelerator.init_trackers(self.tracker_project_name)
            self.accelerator.trackers[0].run.name = self.tracker_run_name
        
        # if self.is_accelerate:
        #     self.accelerator = Accelerator(log_with=[self.tracker_name])

        # if self.is_accelerate and self.accelerator.is_main_process:
        #     self.accelerator.init_trackers(self.tracker_project_name)
        #     self.accelerator.trackers[0].run.name = self.tracker_run_name
            
        else:
            wandb.init(project=self.tracker_project_name)
            wandb.run.name = self.tracker_run_name
            self.device = torch.device("cpu")

myrun = MyRun()The output I get is:accelerate launch tmp.py
wandb: Tracking run with wandb version 0.12.17
wandb: W&B syncing is set to `offline` in this directory.  
wandb: Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
wandb: Tracking run with wandb version 0.12.17
wandb: W&B syncing is set to `offline` in this directory.  
wandb: Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
wandb: Tracking run with wandb version 0.12.17
wandb: W&B syncing is set to `offline` in this directory.  
wandb: Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
wandb: Waiting for W&B process to finish... (success).
wandb: Waiting for W&B process to finish... (success).ed)
wandb: Waiting for W&B process to finish... (success).ed)
wandb:                                                                                
wandb:                                                                                
wandb:                                                                                
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /home/aclifton/rf_fp/wandb/offline-run-20220809_100211-1nh4853d
wandb: Find logs at: ./wandb/offline-run-20220809_100211-1nh4853d/logs
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /home/aclifton/rf_fp/wandb/offline-run-20220809_100211-3sq46azq
wandb: Find logs at: ./wandb/offline-run-20220809_100211-3sq46azq/logs
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /home/aclifton/rf_fp/wandb/offline-run-20220809_100211-apk9120l
wandb: Find logs at: ./wandb/offline-run-20220809_100211-apk9120l/logsYou can see that three offline-run directories are created. If I change the code to:from accelerate import Accelerator
import wandb
import torch

class MyRun:
    def __init__(self) -> None:
        
        self.tracker_project_name = 'my_project'
        self.tracker_run_name = 'my_run'
        self.is_accelerate = True
        self.tracker_name = 'wandb'
        
        # if self.is_accelerate:
        #     self.accelerator = Accelerator(log_with=[self.tracker_name])
        #     self.accelerator.init_trackers(self.tracker_project_name)
        #     self.accelerator.trackers[0].run.name = self.tracker_run_name
        
        if self.is_accelerate:
            self.accelerator = Accelerator(log_with=[self.tracker_name])

        if self.is_accelerate and self.accelerator.is_main_process:
            self.accelerator.init_trackers(self.tracker_project_name)
            self.accelerator.trackers[0].run.name = self.tracker_run_name
            
        else:
            wandb.init(project=self.tracker_project_name)
            wandb.run.name = self.tracker_run_name
            self.device = torch.device("cpu")

myrun = MyRun()I get the following output:accelerate launch tmp.py
wandb: Tracking run with wandb version 0.12.17
wandb: W&B syncing is set to `offline` in this directory.  
wandb: Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
wandb: Tracking run with wandb version 0.12.17
wandb: W&B syncing is set to `offline` in this directory.  
wandb: Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
wandb: Tracking run with wandb version 0.12.17
wandb: W&B syncing is set to `offline` in this directory.  
wandb: Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
wandb: Waiting for W&B process to finish... (success).
wandb: Waiting for W&B process to finish... (success).ed)
wandb: Waiting for W&B process to finish... (success).ed)
wandb:                                                                                
wandb:                                                                                
wandb:                                                                                
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /home/aclifton/rf_fp/wandb/offline-run-20220809_100510-14o40pkk
wandb: Find logs at: ./wandb/offline-run-20220809_100510-14o40pkk/logs
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /home/aclifton/rf_fp/wandb/offline-run-20220809_100510-17jusbof
wandb: Find logs at: ./wandb/offline-run-20220809_100510-17jusbof/logs
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /home/aclifton/rf_fp/wandb/offline-run-20220809_100510-ydjk3ywn
wandb: Find logs at: ./wandb/offline-run-20220809_100510-ydjk3ywn/logsI see that there are still three offline-run directories created, which corresponds to having configured accelerate to use 3 out of the 4 GPUs I have available to me.What I presume would be happening if I were to write out a full training loop, would be each GPU sees different parts of the training data. As such, each call to something likewandb.log(my_var)would log the value ofmy_varbased off which portion of the data it was calculated on (which would theoretically be different for each GPU).Synicing these different offline-run directories towandbwould show three different trends ofmy_var, one for each portion of the data that was put on the GPUs. I’m more interested in the aggregate ofmy_varto get an overall complete picture of the training session. Ifmy_varwas something like the training classification accuracy of my model, I’d be able to answer the question “what is the training accuracy” from the aggregate and not from the 3 individual training accuracies logged in the offline-run directories.Is it possible to do something like this? I appreciate all your help and I apologize if my questions are naive. I’m a new user ofAccelerate.
2022-08-16T15:10:03Z
Hey@aclifton314, you can use “grouping” in wandb to achieve what you’re trying to do I think.One way is to pass the “group” argument to wandb.init with your own name or id for the experiment, e.g. “my_experiment_1”. Then you will be able to group runs from the same run into one in the UI, e.g. your plots can show the average loss instead of the loss for the 3 individual GPUs used in “my_experiment_1”docs.wandb.aiGroup RunsGroup training and evaluation runs into larger experimentsTo pass arguments to wandb.init using Accelerate, you can use theinit_kwargskeyword, and pass it a nested dictionary, in the below case, this passes an argument to the group argument in wandb.initaccelerator.init_trackers("my_project", config=hps, init_kwargs={"wandb":{"group":"my_experiment_1"}})
2022-08-16T15:10:59Z
Also for distributed training, make sure you are using wandb 0.13 (released last week), as it improves support for distributed training
2022-08-22T20:12:57Z
@morganI’m usingaccelerate v 0.9.0and get the following error:Traceback (most recent call last):
  File "/home/aclifton/rf_fp/run_training.py", line 544, in <module>
    run_training_pipeline(config_files_dict_list)
  File "/home/aclifton/rf_fp/run_training.py", line 32, in run_training_pipeline
    rffp_run = rffprun.RFFPRun(run_config_file_path)
  File "/home/aclifton/rf_fp/rffprun.py", line 56, in __init__
    self.accelerator.init_trackers(self.tracker_project_name, init_kwargs={self.tracker_name:{'group': self.tracker_run_name}})
TypeError: init_trackers() got an unexpected keyword argument 'init_kwargs'Do I need to upgradeaccelerateor is there another way to initialize those keyword arguments for wandb?
2022-08-22T20:22:57Z
aclifton314:Do I need to upgradeaccelerateor is there another way to initialize those keyword arguments for wandb?You should update your Accelerate version to 0.12.0. init kwargs come from a recent update
2022-08-22T20:26:16Z
@muellerzr@morgandone and done! works great! Thank you both!