Getting Started in 3 Steps

Follow the steps below to get started with Aim.

1. Install Aim on your training environment

Prerequisite: You need to have python3 and pip3 installed in your environment before installing Aim

$ pip install aim

2. Integrate Aim with your code

Integrate your Python script

import aim

# Save inputs, hparams or any other `key: value` pairs
aim.set_params(hyperparam_dict, name='hparams') # Passing name argument is optional

# ...
for step in range(10):
    # Log metrics to visualize performance
    aim.track(metric_value, name='metric_name', epoch=epoch_number)
# ...

See documentation here.

Integrate PyTorch Lightning

from aim.pytorch_lightning import AimLogger

# ...
trainer = pl.Trainer(logger=AimLogger(experiment='experiment_name'))
# ...

See documentation here.

Integrate Hugging Face

from aim.hugging_face import AimCallback

# ...
aim_callback = AimCallback(repo='/path/to/logs/dir', experiment='mnli')
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset if training_args.do_train else None,
    eval_dataset=eval_dataset if training_args.do_eval else None,
    callbacks=[aim_callback],
    # ...
)
# ...

See documentation here.

Integrate Keras & tf.keras

import aim

# ...
model.fit(x_train, y_train, epochs=epochs, callbacks=[
    aim.keras.AimCallback(repo='/path/to/logs/dir', experiment='experiment_name')

    # Use aim.tensorflow.AimCallback in case of tf.keras
    aim.tensorflow.AimCallback(repo='/path/to/logs/dir', experiment='experiment_name')
])
# ...

See documentation here.

Integrate XGBoost

from aim.xgboost import AimCallback

# ...
aim_callback = AimCallback(repo='/path/to/logs/dir', experiment='experiment_name')
bst = xgb.train(param, xg_train, num_round, watchlist, callbacks=[aim_callback])
# ...

See documentation here.

3. Run the training as usual and start Aim UI

$ aim up

Overview

Aim helps you to compare 1000s of training runs at once through its framework-agnostic python SDK and performant UI.

While using Aim SDK you create a Session object. It handles the tracking of metrics and parameters.

When the training code is instrumented with Aim SDK’s Python Library and ran, Aim creates the .aim repository in your specified path and stores the data. Otherwise the data is created and stored in working directory.

Additionally, Aim SDK also gives you flexibility to:

use multiple sessions in one training script to store multiple runs at once. When not initialized explicitly, Aim creates a default session.
use experiments to group related runs together. An experiment named default is created otherwise.
use integrations to automate tracking

Democratizing AI Dev tools

The mission…

Aim’s mission is to democratize AI dev tools. We believe that the best AI tools need to be:

open-source, open-data-format, community-driven, extensible
have great UI/UX, CLI and other interfaces for automation
performant both on UI and data

Our motivation…

Existing open-source tools (TensorBoard, MLFlow) are super-inspiring.

However we see lots of improvements to be made. Especially around issues like:

ability to handle 1000s of large-scale experiments
actionable, beautiful and performant visualizations
extensibility - how easy are the apis for extension/democratization?

These problems are a huge motivation.

We are inspired to build beautiful, scalable AI dev tools with great APIs. That’s what unites the Aim community.

Join us, help us build the future of AI tooling!

SDK Specifications

Session

Session is the main object that tracks and stores the ML training metadata (metrics and hyperparams).

Use Session arguments to define:

custom path for .aim directory
experiment name: each session is associated with an experiment
run name/message

Use Session methods to specify:

the metrics of your training run(s)
the hyperparameters of your training run(s)

Class aim.Session_()

Arguments

repo - Full path to parent directory of Aim repo - the .aim directory. By default current working directory.
experiment - A name of the experiment. By default default. Use experiments to group related runs together.
flush_frequency - The frequency per step to flush intermediate aggregated values of metrics to disk. By default per 128 step.
block_termination - If set to True process will wait until all the tasks are completed, otherwise pending tasks will be killed at process exit. By default True.
run - A name of the run. If run name is not specified, universally unique identifier will be generated.
system_tracking_interval - System resource usage tracking interval in seconds. By default 10 seconds. In order to disable system tracking set system_tracking_interval=0.

Methods

track()- Tracks the training run metrics associated with the Session
set_params() - Sets the params of the training run associated with the Session
flush() - Flushes intermediate aggregated metrics to disk. This method is called at a given frequency and at the end of the run automatically.
close() - Closes the session. If not invoked, the session will be automatically closed when the training is done.

Returns

Session object to attribute recorded training run to.

Example

Here are a few examples of how to use the aim.Session in code.

The Default Session

When no session is explicitely initialized, a default Session object is created by Aim.

When aim.track or aim.set_params are invoked, underneath the default session object’s track and set_param are called.

track

Session.track_(value, name=’metric_name’ [, epoch=epoch] [, **context_args])

Parameters

value - the metric value of type int/float to track/log
name - the name of the metric of type str to track/log (preferred divider: snake_case)
epoch - an optional value of the epoch being tracked
context_args - any set of other parameters passed would be considered as key-value context for metrics

Example

session_inst = aim.Session()

session_inst.track(0.01, name='loss', epoch=43, subset='train', dataset='train_1')
session_inst.track(0.003, name='loss', epoch=43, subset='val', dataset='val_1')

Once tracked this way, the following search expressions are enabled:

loss if context.subset in (train, val) # Retrieve all losses in both train and val phase
loss if context.subset == train and context.dataset in (train_1) # Retrieve all losses in train phase with given datasets

Please note that any key-value could be used to track this way and enhance the context of metrics and enable even more detailed search.

Search by context example here:

set_params

Session.set_params_(dict_value, name)

Parameters

dict_value - Any dictionary relevant to the training
name - A name for dictionaries

Example

session_inst = aim.Session()

 # really any dictionary can go here
hyperparam_dict = {
  'learning_rate': 0.0001,
  'batch_siz': 32,
}
session_inst.set_params(hyperparam_dict, name='params')

The following params can be used later to perform the following search experssions:

loss if params.learning_rate < 0.01 # All the runs where learning rate is less than 0.01
loss if params.learning_rate == 0.0001 and params.batch_size == 32 # all the runs where learning rate is 0.0001 and batch_size is 32

**Note:** If the set_params is called several times with the same name all the dictionaries will add up in one place on the UI.

flush

Session.flush_()

Aim calculates intermediate values of metrics for aggregation during tracking. This method is called at a given frequency(see Session) and at the end of the run automatically. Use this command to flush those values to disk manually.

Instrumentation

Use Python Library to instrument your training code to record the experiments.

The instrumentation only takes 2 lines:

# Import aim
import aim

# Initialize a new session
session_inst = Session()

Afterwards, simply use the two following functions to track metrics and any params respectively.

session_inst.set_params(hyperparam_dict, name='dict_name')

for iter, sample in enumerate(train_loader):
  session_inst.track(metric_val, name='metric_name', epoch=current_epoch)

Integrations

We have integrated Aim to Tensorflow, Keras and Pytorch Lightning to enable automatic tracking. It allows you to track metrics without the need for explicit track statements.

TensorFlow and Keras

Pass an instance of aim.tensorflow.AimCallback to the trainer callbacks list.

**Note:** Logging for pure keras is handled by aim.keras.AimCallback

Parameters

repo - Full path to parent directory of Aim repo - the .aim directory (optional)
experiment - A name of the experiment (optional)

Example

import aim

# ...
model.fit(x_train, y_train, epochs=epochs, callbacks=[
    aim.tensorflow.AimCallback(repo='/path/to/logs/dir', experiment='experiment_name')

    # Use aim.keras.AimCallback in case of pure keras
    aim.keras.AimCallback(repo='/path/to/logs/dir', experiment='experiment_name')
])
# ...

TensorFlow v1 full example here
TensorFlow v2 full example here
Keras full example here

PyTorch Lightning

Pass aim.pytorch_lightning.AimLogger instance as a logger to the pl.Trainer to log metrics and parameters automatically.

Parameters

repo - Full path to parent directory of Aim repo - the .aim directory (optional)
experiment - A name of the experiment (optional)
train_metric_prefix - The prefix of metrics names collected in the training loop. By default train_ (optional)
test_metric_prefix - The prefix of metrics names collected in the test loop. By default test_ (optional)
val_metric_prefix - The prefix of metrics names collected in the validation loop. By default val_ (optional)
flush_frequency - The frequency per step to flush intermediate aggregated values of metrics to disk. By default per 128 step. (optional)
system_tracking_interval - System resource usage tracking interval in seconds. By default 10 seconds. In order to disable system tracking set system_tracking_interval=0. (optional)

Example

from aim.pytorch_lightning import AimLogger

...
# Initialize Aim PL logger instance
aim_logger = AimLogger(experiment='pt_lightning_exp')

# Log parameters (optional)
aim_logger.log_hyperparams({
  "max_epochs": 10,
})

trainer = pl.Trainer(logger=aim_logger)
trainer.fit(model, train_loader, val_loader)
...

Full example here

Hugging Face

Pass aim.hugging_face.AimCallback instance as a callback to the transformers.Trainer to log metrics and parameters automatically.

Parameters

repo - Full path to parent directory of Aim repo - the .aim directory (optional)
experiment - A name of the experiment (optional)
system_tracking_interval - System resource usage tracking interval in seconds. By default 10 seconds. In order to disable system tracking set system_tracking_interval=0. (optional)

Example

from aim.hugging_face import AimCallback

# ...
# Initialize Aim callback instance
aim_callback = AimCallback(repo='/path/to/logs/dir', experiment='mnli')

# Initialize trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset if training_args.do_train else None,
    eval_dataset=eval_dataset if training_args.do_eval else None,
    compute_metrics=compute_metrics,
    tokenizer=tokenizer,
    data_collator=data_collator,
    callbacks=[aim_callback]
)
# ...

Full example here

XGBoost

Pass aim.xgboost.AimCallback instance as a callback to the xgboost.train to log metrics automatically.

Parameters

repo - Full path to parent directory of Aim repo - the .aim directory (optional)
experiment - A name of the experiment (optional)
system_tracking_interval - System resource usage tracking interval in seconds. By default 10 seconds. In order to disable system tracking set system_tracking_interval=0. (optional)
flush_frequency - The frequency per step to flush intermediate aggregated values of metrics to disk. By default per 128 step. (optional)

Example

from aim.xgboost import AimCallback

# ...
# Initialize Aim callback instance
aim_callback = AimCallback(repo='/path/to/logs/dir', experiment='experiment_name')

# Initialize trainer
bst = xgb.train(param, xg_train, num_round, watchlist, callbacks=[
  aim_callback,
])
# ...

Full example here

Command Line Interface

Aim CLI offers a simple interface to easily organize and record your experiments. Paired with the Python Library, Aim is a powerful utility to record, search and compare AI experiments. Here are the set of commands supported:

Command	Description
`init`	Initialize the `aim` repository.
`version`	Displays the version of aim cli currently installed.
`experiment`	Creates a new experiment to group similar training runs into.
`up`	Runs Aim web UI for the given repo

**This step is optional.** Initialize the aim repo to record the experiments.

$ aim init

Creates .aim directory to save the recorded experiments to. Running aim init in an existing repository will prompt the user for re-initialization.

**Beware:** Re-initialization of the repo clears .aim folder from previously saved data and initializes new repo. **Note:** This command is not necessary to be able to get started with Aim as aim is automatically initializes with the first aim function call.

Display the Aim version installed.

$ aim version

Create new experiments to organize the training runs. Here is how it works:

$ aim experiment COMMAND [ARGS]

Command	Args	Description
`add`	`-n` \| `--name <exp_name>`	Add new experiment with a given name.
`checkout`	`-n` \| `--name <exp_name>`	Switch/checkout to an experiment with given name.
`ls`		List all the experiments of the repo.
`rm`	`-n` \| `--name <exp_name>`	Remove an experiment with the given name.

**Disclaimer:** Removing the experiment also removes the recorded experiment runs data.

Start the Aim web UI locally.

$ aim up [ARGS]

Args	Description
`-h` \| `--host <host>`	Specify host address.
`-p` \| `--port <port>`	Specify port to listen to.
`--repo <repo_path>`	Path to parent directory of `.aim` repo. Current working directory by default
`--tf_logs <logs_dir_path>`	Use Aim to search and compare TensorBoard experiments. More details in TensorBoard Experiments
`--dev`	Run UI in development mode.

**Please make sure to run ``aim up`` in the directory where ``.aim`` is located.**

Use Cases

Searching Experiments

AimQL is a super simple, python-like search that enables rich search capabilities to search experiments. Here are the ways you can search on Aim:

Search by experiment name - experiment == {name}
Search by run - run.hash == "{run_hash}" or run.hash in ("{run_hash_1}", "{run_hash_2}") or run.archived is True
Search by param - params.{key} == {value}
Search by context - context.{key} == {value}

Search Examples

Display the losses and accuracy metrics of experiments whose learning rate is 0.001:
- loss, accuracy if params.learning_rate == 0.001
Display the train loss of experiments whose learning rate is greater than 0.0001:
- loss if context.subset == train and params.learning_rate > 0.0001

Check out this demo project deployment to play around with search.

TensorBoard Experiments

Easily run Aim on experiments visualized by TensorBoard. Here is how:

$ aim up --tf_logs path/to/logs

This command will spin up Aim on the TensorFlow summary logs and load the logs recursively from the given path. Use tf: prefix to select and display metrics logged with tf.summary in the dashboard, for example tf:accuracy.

Tensorboard search example here

Anonymized Telemetry

We constantly seek to improve Aim for the communty. Telementry data helps us immensely by capturing anonymous usage analytics and statistics. You will be notified when you run aim up. The telemetry is collected only on the UI. The python package does not have any telemetry associated with it.

Motivation

Aim UI uses segment’s analytics toolkit to collect basic info about the usage:

Anonymized stripped-down basic usage analytics;
Anonymized number of experiments and run. We constantly improve the storage and UI for performance in case of many experiments. This type of usage analytics helps us to stay on top of the performance problem.
Note: **no* analytics is installed on the Aim Python package.*

How to opt out

You can turn telemetry off by setting the AIM_UI_TELEMETRY_ENABLED environment variable to 0.

Contributor Guide