ml-logger 0.0.21

A print and debugging utility that makes your error printouts look nice

When running AWS experiments in batch, sometimes it is much easier if all of the logging are done towards a dedicated logging server. This way it is easy to mount the drive via fstp or smba, and manage the data (deletion, move and copying) more efficiently.

ML_Logger does exactly this.

ML_Logger has a local client and a http logging server. It runs both locally (without explicitly setting up a server) as well as to a remote log server using its http end-point. ML_Logger supports simple scalar, numpy tensors, images, and many other mem types.

Compared with sftp and smba

Use this so that you don’t have to setup sftp and smba :)

Usage

To install ml_logger, do:

pip install ml-logger

To kickstart a logging server, run

python -m ml_logger.server

In your project files, do:

from ml_logger import ML_Logger
logger = ML_Logger(log_directory="/tmp/logs/ml_logger_test/")

logger.log(index=3, note='this is a log entry!')
logger.flush()

# Images
face = scipy.misc.face()
face_bw = scipy.misc.face(gray=True)
logger.log_image(index=4, color_image=face, black_white=face_bw)
    image_bw = np.zeros((64, 64, 1))
    image_bw_2 = scipy.misc.face(gray=True)[::4, ::4]

# now print a stack
for i in range(10):
    logger.log_image(i, animation=[face] * 5)

This version of logger also prints out a tabular printout of the data you are logging to your stdout. - can silence stdout per key (per logger.log call) - can print with color: logger.log(timestep, some_key=green(some_data)) - can print with custom formatting: logger.log(timestep, some_key=green(some_data, percent)) where percent - uses the correct unix table characters (please stop using | and +. Use ``│``, ``┼`` instead)

A typical print out of this logger look like the following:

from ml_logger import ML_Logger

logger = ML_Logger(log_directory=f"/mnt/bucket/deep_Q_learning/{datetime.now(%Y%m%d-%H%M%S.%f):}")

logger.log_params(G=vars(G), RUN=vars(RUN), Reporting=vars(Reporting))

outputs the following

example_real_log_output

═════════════════════════════════════════════════════
              G
───────────────────────────────┬─────────────────────
           env_name            │ MountainCar-v0
             seed              │ None
      stochastic_action        │ True
         conv_params           │ None
         value_params          │ (64,)
        use_layer_norm         │ True
         buffer_size           │ 50000
      replay_batch_size        │ 32
      prioritized_replay       │ True
            alpha              │ 0.6
          beta_start           │ 0.4
           beta_end            │ 1.0
    prioritized_replay_eps     │ 1e-06
      grad_norm_clipping       │ 10
           double_q            │ True
         use_dueling           │ False
     exploration_fraction      │ 0.1
          final_eps            │ 0.1
         n_timesteps           │ 100000
        learning_rate          │ 0.001
            gamma              │ 1.0
        learning_start         │ 1000
        learn_interval         │ 1
target_network_update_interval │ 500
═══════════════════════════════╧═════════════════════
             RUN
───────────────────────────────┬─────────────────────
        log_directory          │ /mnt/slab/krypton/machine_learning/ge_dqn/2017-11-20/162048.353909-MountainCar-v0-prioritized_replay(True)
          checkpoint           │ checkpoint.cp
           log_file            │ output.log
═══════════════════════════════╧═════════════════════
          Reporting
───────────────────────────────┬─────────────────────
     checkpoint_interval       │ 10000
        reward_average         │ 100
        print_interval         │ 10
═══════════════════════════════╧═════════════════════
╒════════════════════╤════════════════════╕
│      timestep      │        1999        │
├────────────────────┼────────────────────┤
│      episode       │         10         │
├────────────────────┼────────────────────┤
│    total reward    │       -200.0       │
├────────────────────┼────────────────────┤
│ total reward/mean  │       -200.0       │
├────────────────────┼────────────────────┤
│  total reward/max  │       -200.0       │
├────────────────────┼────────────────────┤
│time spent exploring│       82.0%        │
├────────────────────┼────────────────────┤
│    replay beta     │        0.41        │
╘════════════════════╧════════════════════╛

from ml_logger import ML_Logger

logger = ML_Logger('/mnt/slab/krypton/unitest')
logger.log(0, some=Color(0.1, 'yellow'))
logger.log(1, some=Color(0.28571, 'yellow', lambda v: f"{v * 100:.5f}%"))
logger.log(2, some=Color(0.85, 'yellow', percent))
logger.log(3, {"some_var/smooth": 10}, some=Color(0.85, 'yellow', percent))
logger.log(4, some=Color(10, 'yellow'))
logger.log_histogram(4, td_error_weights=[0, 1, 2, 3, 4, 2, 3, 4, 5])

colored output: (where the values are yellow)

╒════════════════════╤════════════════════╕
│        some        │        0.1         │
╘════════════════════╧════════════════════╛
╒════════════════════╤════════════════════╕
│        some        │     28.57100%      │
╘════════════════════╧════════════════════╛
╒════════════════════╤════════════════════╕
│        some        │       85.0%        │
╘════════════════════╧════════════════════╛
╒════════════════════╤════════════════════╕
│  some var/smooth   │         10         │
├────────────────────┼────────────────────┤
│        some        │       85.0%        │
╘════════════════════╧════════════════════╛

TODO:

• [ ] Integrate with visdom, directly plot locally.

  • (better to keep it separate, because visdom is shitty.)

  • ml_logger does NOT know the full data set. Therefore we should not expect it to do the data processing such as taking mean, reservoir sampling etc. Where should this happen though?

  • just log to visdom for now. Use the primitive plot.ly plotting inteface.

  • data: keys/values