docs/Profiling-Python.md · UnityTechnologies/ml-agents

Fetch the repository succeeded.

richgiteeai

Watch

Unwatch Watching Releases Only Ignoring

2 Star 4 Fork 3

UnityTechnologies/ml-agents

Code Issues 0 Pull Requests 0 Wiki Insights Pipelines

Service

Tencent Cloud Serverless

Create your Gitee Account

Explore and code with more than 14 million developers,Free private repositories !:)

Already have an account? Sign in

文件

master

Branches (96)

Tags (30)

Manage

master

develop-tweaksacsettings

hh/new_env_styles

develop-yaml-json-loading-errors

develop

develop-debug-multienv-hang

develop-image-decompress-timer

develop-env-args2

develop-pyinstaller

hh/new_env_styles-startup-rename

develop-env-args

develop-argparse-env-args

develop-remove-docopt

develop-one-demo-location

develop-sensor-prototype

develop-trainHatCube

develop-mypy-envs

develop-fix-multigpu-save

develop-fix-flake8

develop-make-odd-monoB

0.9.3

0.9.2

0.9.1

0.9.0

0.8.2

0.8.1

0.8.0

0.7.0

0.6.0a

0.6.0

0.5.0a

0.5.0

0.4.0b

0.4.0a

0.4.0

0.3.1b

0.3.1a

0.3.1

0.3.0b

0.3.0a

Clone or Download

Clone/Download

HTTPS SSH SVN SVN+SSH Download ZIP

Prompt

To download the code, please copy the following command and execute it in the terminal

To ensure that your submitted code identity is correctly recognized by Gitee, please execute the following command.

git config --global user.name userName 
git config --global user.email userEmail

When using the SSH protocol for the first time to clone or push code, follow the prompts below to complete the SSH configuration.

1 Generate RSA keys.

2 Obtain the content of the RSA public key and configure it in SSH Public Keys

To use SVN on Gitee, please visit the usage guide

When using the HTTPS protocol, the command line will prompt for account and password verification as follows. For security reasons, Gitee recommends configure and use personal access tokens instead of login passwords for cloning, pushing, and other operations.

Username for 'https://gitee.com': userName

Password for 'https://userName@gitee.com': # Private Token

Branches 96

Tags 30

ml-agents

docs

Profiling-Python.md

Profiling-Python.md 2.44 KB

# Profiling in Python

As part of the ML-Agents tookit, we provide a lightweight profiling system,
in order to identity hotspots in the training process and help spot regressions from changes.

Timers are hierarchical, meaning that the time tracked in a block of code can be further split into other blocks if
desired. This also means that a function that is called from multiple places in the code will appear in multiple
places in the timing output.

All timers operate using a "global" instance by default, but this can be overridden if necessary (mainly for testing).

## Adding Profiling

There are two ways to indicate code should be included in profiling. The simplest way is to add the `@timed`
decorator to a function or method of interested.

```python
class TrainerController:
 # ....
 @timed
 def advance(self, env: EnvManager) -> int:
 # do stuff
```

You can also used the `hierarchical_timer` context manager.

``` python
with hierarchical_timer("communicator.exchange"):
 outputs = self.communicator.exchange(step_input)
```

The context manager may be easier than the `@timed` decorator for profiling different parts of a large function, or
profiling calls to abstract methods that might not use decorator.

## Output
By default, at the end of training, timers are collected and written in json format to
`{summaries_dir}/{run_id}_timers.json`. The output consists of node objects with the following keys:
 * name (string): The name of the block of code.
 * total (float): The total time in seconds spent in the block, including child calls.
 * count (int): The number of times the block was called.
 * self (float): The total time in seconds spent in the block, excluding child calls.
 * children (list): A list of child nodes.
 * is_parallel (bool): Indicates that the block of code was executed in multiple threads or processes (see below). This
 is optional and defaults to false.

### Parallel execution
For code that executes in multiple processes (for example, SubprocessEnvManager), we periodically send the timer
information back to the "main" process, aggregate the timers there, and flush them in the subprocess. Note that
(depending on the number of processes) this can result in timers where the total time may exceed the parent's total
time. This is analogous to the difference between "real" and "user" values reported from the unix `time` command. In the
timer output, blocks that were run in parallel are indicated by the `is_parallel` flag.

Copy Edit Raw Blame History

Ervin T authored 2019年07月31日 12:24 +08:00 . Release 0.9.0 docs checklist and cleanup - v2 (#2372)