No project description provided

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for KimSoungRyoul from gravatar.com KimSoungRyoul

Unverified details

These details have not been verified by PyPI
Meta
  • License: Apache-2.0
  • Author: kimsoungryoul
  • Requires: Python >=3.10

Project description

bentoml-extensions pdm-managed python

todo: plan for 2024

[Project]bentoml-extensions alpha release

  • FeatureStore Runner [ODM],
  • optimize cpu inference [ipex, ovms]

QuickStart

pip install bentoml bentomlx

todo ...

FeatureStore

  • pip install bentomlx[featurestore-redis]
  • pip install bentomlx[featurestore-aerospike]
import logging
from typing import Dict, TypedDict

import bentoml
import numpy as np
from bentoml.io import JSON


import bentomlx
from bentomlx.feature_repo import DBSettings


class IrisFeature(TypedDict, total=False):
    pk: str
    sepal_len: float | int
    sepal_width: float
    petal_len: float | int
    petal_width: float


# db_settings = DBSettings(namespace="test", hosts=["127.0.0.1:3000"], use_shared_connection=True)
db_settings = DBSettings()  # EXPORT ENV BENTOML_REPO_NAMESPACE=test; BENTOML_REPO__HOSTS=localhost:3000; BENTOML_REPO__USE_SHARED_CONNECTION=true

repo_runner = bentomlx.feature_repo.aerospike_fs(db_settings).to_repo_runner(entity_name="iris_features", embedded=True)

iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner()

svc = bentoml.Service("iris_classifier_svc", runners=[repo_runner, iris_clf_runner])

logger = logging.getLogger("bentoml")


@svc.api(
    input=JSON.from_sample(["pk1", "pk2", "pk3"]),
    output=JSON(),
)
async def classify(feature_keys: list[str]) -> Dict[str, list[int]]:
    # features: list[list[float]] = await repository.get_many.async_run(pks=feature_keys, _nokey=True) #  [[4.9, 3.0, 1.4, 0.2], [5.1 3.5 1.4 0.3], [5.5 2.5 4.  1.3]]
    # features: list[IrisFeature] = repo_runner.get_many.run(pks=feature_keys) # input_arr = [{"pk": "pk1": "sepal_len":4.9,  "sepal_width":3.  "petal_len":1.4, "petal_width": 0.2], ... ]
    features: np.array = repo_runner.get_many.run(pks=feature_keys, _numpy=True) # input_arr = np.array([[4.9, 3.0, 1.4, 0.2], [5.1 3.5 1.4 0.3], [5.5 2.5 4.  1.3]])
    result: np.ndarray = await iris_clf_runner.predict.async_run(features)
    return {"result": result.tolist()}

CPU Optimized Runner

  • bentomlx[ipex]
  • bentomlx[ovms] like a bentoml[triton]
import bentoml
import bentomlx


#iris_clf_runner = bentoml.ipex.get("iris_clf:latest").to_runner()
# change like this
iris_clf_runner = bentomlx.pytorch.get("iris_clf:latest").to_runner(intel_optimize=True)
xxx_runner = bentomlx.transformers.get("xxx:latest").to_runner(intel_optimize=True)
xxx_tf_runner = bentomlx.tensorflow.get("xxx:latest").to_runner(intel_optimize=True)


# support only in bentoml-extension
# model type such as ipex, tensorflow, onnx
xxx_ov_runner = bentomlx.openvino.get("xxx:latest").to_runner(intel_optimize=True)
# or
xxx_ov_runner = bentomlx.pytorch.get("xxx:latest").to_runner(openvino=True, post_quant=True)

# intel bert op
# https://www.intel.com/content/www/us/en/developer/articles/guide/bert-ai-inference-amx-4th-gen-xeon-scalable.html
# ?? need discussion about Out of ML serving framework responsibility
#https://github.com/intel/light-model-transformer/tree/main/BERT
xxx_ov_runner = bentomlx.experimental.light_model_transformer.bert.get("xxx:latest").to_runner(post_quant=True,quant_dtype=torch.float32)

Post(Runtime) Model Compression (oneapi nncl)

  • post quant ?
  • ...

스크린샷 2023-11-27 오후 3 18 18

❯ python fibo_main.py # mypyc
0.17745399475097656
0.1755237579345703
0.17790436744689941
0.18230915069580078

❯ python fibo_main.py # 3.11.7
1.2891952991485596
1.2943885326385498
1.2915637493133545
1.305750846862793

❯ pyenv global cinder-3.10-dev
❯ PYTHONJIT=1 python fibo_main.py
2.9099485874176025
2.918196678161621
2.929981231689453
2.9137821197509766

❯ pyenv global pypy3.10-7.3.15
❯ PYTHONJIT=1 python fibo_main.py
0.8286490440368652
0.8387455940246582
0.8492231369018555
0.84218430519104

BertOperator

Batch-Size 1

❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-large-uncased --warmup-time 5 --run-time 20
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-large-uncased --warmup-time 5 --run-time 20
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
|    | Model              | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:-------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-large-uncased | False  | False     | False          | False      |            1 |       128 |                    0.878 | 1139.490 ms    |

❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-large-uncased --bert-op --warmup-time 5 --run-time 20 -q
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-large-uncased --bert-op --warmup-time 5 --run-time 20 -q
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
|    | Model              | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:-------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-large-uncased | False  | True      | True           | False      |            1 |       128 |                    6.124 | 163.285 ms     |

Batch-Size 10

❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-large-uncased --warmup-time 5 --run-time 20 --batch-size 10
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-large-uncased --warmup-time 5 --run-time 20 --batch-size 10
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
|    | Model              | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:-------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-large-uncased | False  | False     | False          | False      |           10 |       128 |                    1.588 | 6296.495 ms    |


❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-large-uncased --bert-op  --warmup-time 5 --run-time 20 --batch-size 10 --quant
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-large-uncased --bert-op --warmup-time 5 --run-time 20 --batch-size 10 --quant
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
|    | Model              | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:-------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-large-uncased | False  | True      | True           | False      |           10 |       128 |                    5.959 | 1678.104 ms    |

bert-base-uncased

❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
config.json: 100%|██████████| 570/570 [00:00<00:00, 170kB/s]
model.safetensors: 100%|██████████| 440M/440M [00:12<00:00, 36.2MB/s]
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | False     | False          | False      |            1 |       128 |                    3.832 | 260.979 ms     |


❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --bert-op --quant
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --bert-op --quant
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
config.json: 100%|██████████| 570/570 [00:00<00:00, 168kB/s]
model.safetensors: 100%|██████████| 440M/440M [00:11<00:00, 37.0MB/s] 
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | True      | True           | False      |            1 |       128 |                   16.622 | 60.160 ms      |

bert-base-uncased (batch-size 10)

❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --bert-op --quant --batch-size 10
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --bert-op --quant --batch-size 10
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
config.json: 100%|██████████| 570/570 [00:00<00:00, 172kB/s]
model.safetensors: 100%|██████████| 440M/440M [00:12<00:00, 35.2MB/s]
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | True      | True           | False      |           10 |       128 |                   23.923 | 418.015 ms     |

diff ( origin bert, ipex bert, bert operator)

---------------- BatchSize 1 ------------
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | False     | False          | False      |            1 |       128 |                     4.89 | 204.520 ms     |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | True   | False     | False          | False      |            1 |       128 |                    5.243 | 190.739 ms     |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | True      | False          | False      |            1 |       128 |                     5.88 | 170.077 ms     |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | True      | True           | False      |            1 |       128 |                   15.444 | 64.752 ms      |

---------------- BatchSize 10 ------------
|    | Model              | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:-------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-large-uncased | False  | False     | False          | False      |           10 |       128 |                    1.588 | 6296.495 ms    |
|---:|:-------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-large-uncased | False  | True      | True           | False      |           10 |       128 |                    5.959 | 1678.104 ms    |

-------------- BatchSize 20 -------------
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | False     | True           | False      |           20 |       128 |                    5.441 | 3675.675 ms    |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | True      | True           | False      |           20 |       128 |                   16.334 | 1224.473 ms    |

|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | False     | True           | False      |           10 |       128 |                    5.727 | 1746.223 ms    |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | True      | True           | False      |           10 |       128 |                   17.384 | 575.239 ms     |


|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | False     | True           | False      |          100 |       128 |                     5.15 | 19417.498 ms   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | True      | True           | False      |          100 |       128 |                   18.638 | 5365.511 ms    |

origin bert

❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
config.json: 100%|██████████| 570/570 [00:00<00:00, 169kB/s]
model.safetensors: 100%|██████████| 440M/440M [00:12<00:00, 36.2MB/s] 
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | False     | False          | False      |            1 |       128 |                     4.89 | 204.520 ms     |

ipex optimized bert

❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --ipex
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --ipex
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
config.json: 100%|██████████| 570/570 [00:00<00:00, 175kB/s]
model.safetensors: 100%|██████████| 440M/440M [00:11<00:00, 38.7MB/s][W LegacyTypeDispatch.h:74] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())

/usr/local/lib/python3.8/dist-packages/intel_extension_for_pytorch/frontend.py:396: UserWarning: Conv BatchNorm folding failed during the optimize process.
  warnings.warn("Conv BatchNorm folding failed during the optimize process.")
/usr/local/lib/python3.8/dist-packages/intel_extension_for_pytorch/frontend.py:401: UserWarning: Linear BatchNorm folding failed during the optimize process.
  warnings.warn("Linear BatchNorm folding failed during the optimize process.")
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | True   | False     | False          | False      |            1 |       128 |                    5.243 | 190.739 ms     |

bert operator

❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --bert-op
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --bert-op
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
config.json: 100%|██████████| 570/570 [00:00<00:00, 187kB/s]
model.safetensors: 100%|██████████| 440M/440M [00:12<00:00, 35.4MB/s] 
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | True      | False          | False      |            1 |       128 |                     5.88 | 170.077 ms     |

bert operator (with quant)

❯ sudo docker run --rm --privileged bert-op-pytorch-demo numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --bert-op --quant
 
:: initializing oneAPI environment ...
   entrypoint.sh: BASH_VERSION = 5.0.17(1)-release
   args: Using "$@" for setvars.sh arguments: numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --bert-op --quant
:: compiler -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: mkl -- latest
:: tbb -- latest
:: oneAPI environment initialized ::
 
config.json: 100%|██████████| 570/570 [00:00<00:00, 159kB/s]
model.safetensors: 100%|██████████| 440M/440M [00:11<00:00, 39.1MB/s] 
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | True      | True           | False      |            1 |       128 |                   15.444 | 64.752 ms      |

origin bert VS bert operator (with quant, batch-size 20)

  • batch-size=1 : 성능차이가 10~20% 차이이지만
  • batch-size=20 : 3배 정도 성능 차이난다.
  • DNNL_CPU_RUNTIME=TBB|OMP 는 큰 차이를 확인 못함 이론상 tbb는 thread num이 늘어나도 성능저하 없는게 특징
❯ sudo docker run --rm --privileged bert-op-pytorch-demo-oneapi-tbb-onednn-v34pc numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --quant --batch-size 20
   config.json: 100%|██████████| 570/570 [00:00<00:00, 5.15MB/s]
model.safetensors: 100%|██████████| 440M/440M [00:07<00:00, 61.7MB/s] 
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | False     | True           | False      |           20 |       128 |                    5.441 | 3675.675 ms    |

❯ sudo docker run --rm --privileged bert-op-pytorch-demo-oneapi-tbb-onednn-v34pc numactl --all -- -m bert-base-uncased --warmup-time 5 --run-time 20 --bert-op --quant --batch-size 20
config.json: 100%|██████████| 570/570 [00:00<00:00, 5.25MB/s]
model.safetensors: 100%|██████████| 440M/440M [00:06<00:00, 63.2MB/s] 
|    | Model             | IPEX   | BERT Op   | Quantization   | BFloat16   |   Batch Size |   Seq Len |   Throughput [samples/s] | Latency [ms]   |
|---:|:------------------|:-------|:----------|:---------------|:-----------|-------------:|----------:|-------------------------:|:---------------|
|  0 | bert-base-uncased | False  | True      | True           | False      |           20 |       128 |                   16.334 | 1224.473 ms    |

pip install --index-url https://pypi.anaconda.org/intel/simple --extra-index-url https://pypi.org/simple

pip install dpnp numba-dpex dpctl intel-optimization-for-horovod==0.28.1.1 torch==2.0.1 torchvision==0.15.2 --extra-index-url=https://download.pytorch.org/whl/cpu intel_extension_for_pytorch==2.0.100 oneccl-bind-pt==2.0.0 --extra-index-url=https://pytorch-extension.intel.com/release-whl/stable/cpu/us/

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for KimSoungRyoul from gravatar.com KimSoungRyoul

Unverified details

These details have not been verified by PyPI
Meta
  • License: Apache-2.0
  • Author: kimsoungryoul
  • Requires: Python >=3.10

Release history Release notifications | RSS feed

This version

0.0.2

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bentomlx-0.0.2.tar.gz (647.3 kB view details)

Uploaded Source

Built Distribution

bentomlx-0.0.2-py3-none-any.whl (656.4 kB view details)

Uploaded Python 3

File details

Details for the file bentomlx-0.0.2.tar.gz.

File metadata

  • Download URL: bentomlx-0.0.2.tar.gz
  • Upload date:
  • Size: 647.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: pdm/2.12.4 CPython/3.10.7

File hashes

Hashes for bentomlx-0.0.2.tar.gz
Algorithm Hash digest
SHA256 c86a8074533874b72266dad895a9434275d3fae5d9af299af0be33cba9a4aaa7
MD5 b52a1f4517d2763c841d7c5e60a10311
BLAKE2b-256 f7463f3c69030291462641375ed2ee8b4afd371e7837df7264e548e2f613e0d3

See more details on using hashes here.

File details

Details for the file bentomlx-0.0.2-py3-none-any.whl.

File metadata

  • Download URL: bentomlx-0.0.2-py3-none-any.whl
  • Upload date:
  • Size: 656.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: pdm/2.12.4 CPython/3.10.7

File hashes

Hashes for bentomlx-0.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 440355fef8f041fd24b4a67a6bb62b6a13f1e8455fc3fead584be875a75e711e
MD5 b20b654248194e899931472466bed667
BLAKE2b-256 44304f47241e9a0e1d46dc512175b11aafe5edc16754bf4f53ad9e2e31d815d5

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page