experiment send

Send the code (current working directory) and execute an experiment in a cloud.
neptune send is an alias for neptune experiment send

Without any arguments the command assumes that there is a main.py file
in the CWD (current working directory).

  neptune send
  neptune experiment send

You can execute any file within your code with:

  neptune send src/train_neural_net.py

You can pass command line arguments to your code after the Python script.

  neptune send src/train_neural_net.py --learning_rate 0.1

Read more about neptune experiment send options below to learn how to select machine type and
machine learning environment for your experiment, and much more.

Usage

neptune experiment send [-w WORKER] [-i INPUT] [-e ENVIRONMENT]
  [--log-channel PREFIX[:CHANNEL_NAME]] [--ml-framework ML_FRAMEWORK]
  [-x EXCLUDE] [-p PARAMETER] [-b FILE-OR-DIR]
  [--pip-requirements-file PIP-REQUIREMENTS-FILE] [--name NAME]
  [--description DESCRIPTION] [--tag TAG] [--property PROPERTY]
  [--project PROJECT] [--open-webbrowser [true|false]]
  [--disable-stdout-channel] [--disable-stderr-channel]
  [--config CONFIG-FILE] [-h] [-v] [--profile PROFILE] [--debug]
  [ENTRYPOINT [ARG [...]]]

Essential Options

Parameter Description
entrypoint Your Python script to run or any other executable (like bash): the main
entrypoint to your experiment. After the entrypoint, you can add any number of
arguments that should be passed to your entrypoint.
If no entrypoint is passed, Neptune will try to run [main.py](main.py) by default.
-w, --worker A virtual machine type to run your experiment on. Visit
https://docs.neptune.ml/advanced-topics/environments/#available-workers
for a list of Neptune’s worker types.
You can use --worker local to run your experiment locally.
-i, --input A file or directory in Neptune’s storage that should be made accessible to the
experiment in the /input directory. You can use this option multiple times to
pass multiple inputs.
This option is available only for experiments executed in the cloud, i.e. it’s
incompatible with --worker local.
Syntax:
   -i path_on_storage[:name_visible_to_experiment]
   --input path_on_storage[:name_visible_to_experiment]
Example:
   --input my_input.txt:renamed_input.txt
The experiment’s code can access the file at /input/renamed_input.txt
   --input my_input.txt
The experiment’s code can access the file at /input/my_input.txt
-e, --environment A machine learning environment. Visit
https://docs.neptune.ml/advanced-topics/environments/
for a list of environments supported by Neptune.
--log-channel, -l, --log-<br>channels List of prefixes based on which Neptune will build numeric channels.
Syntax:
   --log-channel prefix[:channel_name]
Example:
Let’s assume that an experiment prints:
   loss 0.5
   loss 0.6
   ...
In the example above using --log-channel loss will build a numeric loss
channel with values parsed from stdout/stderr. You can customize the channel’s
name with --log-channel loss:regularization_loss.
--ml-framework Integrate your experiment’s machine learning framework with Neptune.
Supported choices are: [keras, tensorflow].

Advanced Options

Parameter Description
-x, --exclude Files and directories that Neptune should leave out when uploading snapshot of
your code to the storage.
Wildcards are supported.
Example:
    --exclude .git --exclude service/*.log
-p, --parameter A parameter passed to your code (available via ctx.params) tracked by Neptune.
Explore more of parameters’ syntax here:
https://docs.neptune.ml/advanced-topics/experiments/#parameters
Read about how to use parameters to optimize your hyperparameter search here:
https://docs.neptune.ml/advanced-topics/hyperparameter-optimization/
Basic syntax:
    -p <name>:<value/values>:<description>
Example:
    -p 'learning_rate:0.1:Learning rate'
The parameter defined in the example above can be accessed from the code with
‘ctx.params.learning_rate’
-b, --backup Files and directories that should be uploaded to Neptune’s storage after the
experiment ends.
--pip-requirements-file A file containing a list of Python packages that your experiment needs.
Requirements file format with an example file can be found here:
http://pip.pypa.io/en/stable/reference/pip_install/#requirements-file-format

Experiment Characteristics

Parameter Description
--name Experiment’s name.
--description Experiment’s description.
Giving a detailed description is often extremely useful when coming back to the
experiment after some time.
--tag Experiment’s tags.
Use tags to quickly filter experiments.
--property Key-value pairs containing information you wish to associate with the
experiment.
Syntax:
    --properties key:value [key:value ...]
Example:
    --properties model-type:svm dataset:my-data-v3

Configuration

Parameter Description
--project Your project’s name.
You can set this globally using the neptune project activate command.
--open-webbrowser Automatically open browser when a new experiment is started.
--disable-stdout-channel Do not create text channel based on stdout.
--disable-stderr-channel Do not create text channel based on stderr.

Global Options

Parameter Description
--config Path to a Neptune CLI configuration file.
-h, --help Show this help message and exit.
-v, --version Show Neptune CLI version and exit.
--profile Set user profile to use.
--debug Run command with additional debug-level logging.