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benchmarkai-client-lib-0.1
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مشاهده بیشترتوضیحات
A client library for posting metrics that uses the Benchmark AI framework.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | benchmarkai-client-lib-0.1 |
| نام | benchmarkai-client-lib |
| نسخه کتابخانه | 0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Edison Gustavo Muenz |
| ایمیل نویسنده | edisongustavo@gmail.com |
| آدرس صفحه اصلی | https://github.com/MXNetEdge/benchmark-ai |
| آدرس اینترنتی | https://pypi.org/project/benchmarkai-client-lib/ |
| مجوز | Apache |
A small library for publishing metrics when running a workflow using the Benchmark AI infrastructure.
# Installation
```
pip install benchmarkai-client-lib
```
# API
The library provides a single method:
```
def emit(metrics: Dict[str, float])
```
PS: This library also works with Python 2, which has no type system.
## Example usage
```python
from benchmarkai import emit
def my_fancy_benchmark():
import time
begin = time.time()
for epoch in range(100):
execute_training()
elapsed_time = time.time() - begin
emit({"alexnet.batchsize16.latency": elapsed_time})
```
## Performance
As the author you don't need to worry about `emit()` being slow since it does 2 things:
- serializes `metrics` into JSON
- writes this string into a file (see the internal details)
So it will be as slow as these operations can be, which are quite fast.
## Accepted values
The dictionary provided by the `metrics` parameter is in the form:
`"metric-name" => value`
- `metric-name` is a `string`
- `value` is a `float`
# Running tests
To set up the environment to run the tests, the file `test-environment.yml` is provided. This conda environment file
has all dependencies required to run the tests.
```
$ conda env create -f test-environment.yml
$ conda activate benchmark-ai-client-library-tests
$ tox
```
# Internal details
## Modes
The library can be used in these modes as of now:
1. Stdout printing (this is the **default** mode)
1. Benchmark AI infrastructure
To control which behavior is desired, the environment variable `BENCHMARK_AI` can be set to one of the values:
- `stdout`: The **default** mode. In this configuration, the `emit()` method is equivalent to:
```python
def emit(metrics):
import json
print(json.dumps(metrics))
```
- `fifo`: When running inside Benchmark AI infrastructure this is the mode used.
### Fifo mode
In this mode, the `emit()` method writes JSON string to a file that was created as a
[named pipe](https://docs.python.org/3.7/library/os.html#os.mkfifo). It does **NOT** write to `stdout`.
Keep in mind that the client library does **NOT** create the *named pipe* file, this is delegated to the
"daemon process", which will be running alongside the benchmark process in the Benchmark AI infrastructure.
The daemon process is responsible to:
- create the *named pipe*
- read the contents from this pipe
- post the contents of this pipe to the metrics servers (eg.: Elastic Search, Cloudwatch, etc.)
The first time that `emit()` is called, it checks for the existence of the file and uses that. If it doesn't exist, it
waits for some time before giving up and throwing an exception `FifoNotCreatedInTimeError`.
#### Fifo mode configurations
These environment variables control the behavior of the client library:
- `BENCHMARK_AI_FIFO_FILEPATH`: The path to the file that will be created as a *named pipe*.
Default value: `/tmp/benchmark-ai-fifo`
- `BENCHMARK_AI_FIFO_MAX_WAIT_TIME`: The maximum amount of time that the client library will wait for the
*named pipe file* to be created.
Type is **float**, unit is **seconds**, default value: `10`.
- `BENCHMARK_AI_FIFO_WAIT_TIME_STEP`: The amount of time between each check that the client library will wait before
checking for the existence of the *named pipe file* again.
Type is **float**, unit is **seconds**, default value: `0.5`.
**IMPORTANT**: These environment variables are for internal usage of the Benchmark AI infrastructure. They are listed
here for documentation purposes only.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.* | Python |
نحوه نصب
نصب پکیج whl benchmarkai-client-lib-0.1:
pip install benchmarkai-client-lib-0.1.whl
نصب پکیج tar.gz benchmarkai-client-lib-0.1:
pip install benchmarkai-client-lib-0.1.tar.gz