Checklist for a complete deep learning python project

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Itemss you need to know for your AI project.


Check the quality of image and labels. Check if the shapes and spacings of images and labels are the same

Collect the image statistics, summarize the dataset information

For non-anonymous images, use this code to collect patient information (sex, age, etc.). For anonymous images, use this code to collect image information (spacing, size, labels).


Check the image/labels before input them to networks. To see if they have the same spacing, shape, etc.


  1. Use database based mlflow server, which is faster than directory based mlflow.
  2. In linux shell: mlflow server –backend-store-uri=sqlite:///mlrunsdb.db –default-artifact-root=file:mlruns –host –port 5000
  3. In python script: python mlflow.set_tracking_uri("http://nodelogin02:5000") mlflow.set_experiment("lung_fun")
  4. Assign a unique ID for each run.
    record_fpath = "results/record.log"
    id = record_1st(record_fpath)
    with mlflow.start_run(run_name=str(id), tags={"mlflow.note.content": args.remark}):
  5. Record cgpu information. ```python parser.add_argument(“–hostname”, type=str, default=’None’) parser.add_argument(“–jobid”, type=str, default=’None’) parser.add_argument(“–outfile”, type=str, default=’None’)

p1 = threading.Thread(target=record_cgpu_info, args=(args.outfile, )) p1.start() … p1.do_run = False # stop the thread p1.join()

1. For ‘’:
experiment = mlflow.set_experiment("lung_fun")
args.reload_run_id = retrive_run_id(experiment=experiment, reload_jobid=args.reload_jobid)
mlflow.start_run(run_id=args.reload_run_id)  # find the trained run
mlflow.start_run(nested=True)  # start a nested run
  1. Record FLOPs
  2. Nested mlflow for Cross-fold validation
  3. Record git version by automatic git for each experiments
  4. Record time_load_batch, time_update_batch
  5. Record train/valid/test loss, metrics, for each epoch
  6. Record experiment ID, cpu_count, data_shuffle_seed, epochs, eval_id, batch_size, fold, gpu_name, hostname, loss_name, net_name, mode, net_parameters, outfile, pretrained, remark, workers, input_size, etc.

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