Container Runtime¶
개요¶
Container Runtime is a set of preconfigured customizable environments built for machine learning on Snowpark Container Services, covering interactive experimentation and batch ML workloads such as model training, hyperparameter tuning, batch inference and fine tuning. They include the most popular machine learning and deep learning frameworks. Used with Snowflake notebooks, they provide an end-to-end ML experience.
실행 환경¶
The Container Runtime provides an environment populated with packages and libraries that support a wide variety of ML development tasks inside Snowflake. In addition to the pre-installed packages, you can import packages from external sources like public PyPI repositories, or internally-hosted package repositories that provide a list of packages approved for use inside your organization.
Executions of your custom Python ML workloads and supported training APIs occur within Snowpark Container Services, which offers the ability to run on CPU or GPU compute pools. When using the Snowflake ML APIs, the Container Runtime distributes the processing across available resources.
분산 처리¶
Snowflake ML 모델링 및 데이터 로딩 APIs는 사용 가능한 컴퓨팅 성능을 최대한 활용하여 리소스 활용을 극대화하는 Snowflake ML의 분산 처리 프레임워크 상단에 구축됩니다. 기본적으로 이 프레임워크는 멀티 GPU 노드에서 모든 GPUs를 사용하므로 오픈 소스 패키지에 비해 성능이 크게 향상되고 전체 런타임이 단축됩니다.
데이터 로딩을 포함한 머신 러닝 워크로드는 Snowflake가 관리하는 컴퓨팅 환경에서 실행됩니다. 이 프레임워크를 사용하면 모델 훈련이나 데이터 로딩 등 해당 작업의 특정 요구 사항에 따라 리소스를 동적으로 확장할 수 있습니다. 각 작업에 대한 GPU 및 메모리 할당을 포함한 리소스 수는 제공된 APIs를 통해 손쉽게 구성할 수 있습니다.
최적화된 데이터 로딩¶
Container Runtime은 다중 코어 또는 GPU를 최대한 활용하여 Snowflake 데이터 원본(테이블, DataFrames, 데이터 세트 포함)을 PyTorch 및 TensorFlow 와 같은 널리 사용되는 ML 프레임워크에 연결할 수 있는 데이터 커넥터 API 세트를 제공합니다. 데이터 로딩이 완료되면 오픈 소스 패키지 또는 아래에 설명된 배포 버전을 포함한 Snowflake ML API를 사용하여 데이터를 처리할 수 있습니다. 이러한 APIs는 snowflake.ml.data 네임스페이스에서 제공됩니다.
snowflake.ml.data.data_connector.DataConnector 클래스는 Snowpark DataFrames 또는 Snowflake ML 데이터 세트를 TensorFlow 또는 PyTorch DataSets 또는 Pandas DataFrames 에 연결합니다. 다음 클래스 메서드 중 하나를 사용하여 커넥터를 인스턴스화합니다.
DataConnector.from_dataframe: Snowpark DataFrame 을 허용합니다.
DataConnector.from_dataset: 이름과 버전으로 지정된 Snowflake ML 데이터 세트를 받습니다.
DataConnector.from_sources: 소스 목록을 받으며, 각 소스는 DataFrame 또는 데이터 세트일 수 있습니다.
커넥터를 인스턴스화(예를 들어 data_connector 인스턴스 호출)한 후, 다음 메서드를 호출하여 원하는 종류의 출력을 생성합니다.
data_connector.to_tf_dataset: TensorFlow와 함께 사용하기에 적합한 TensorFlow 데이터 세트를 반환합니다.data_connector.to_torch_dataset: PyTorch와 함께 사용하기에 적합한 PyTorch 데이터 세트를 반환합니다.
이러한 APIs에 대한 자세한 내용은 Snowflake ML API 참조 섹션을 참조하십시오.
오픈 소스로 개발하기¶
널리 사용되는 ML 패키지로 미리 채워져 있는 기본 CPU 및 GPU 이미지와 pip 를 사용하여 추가 라이브러리를 설치할 수 있는 유연성을 통해 사용자는 데이터를 Snowflake 밖으로 옮기지 않고도 익숙하고 혁신적인 오픈 소스 프레임워크를 Snowflake Notebooks 내에서 사용할 수 있습니다. 데이터 로딩, 학습 및 하이퍼 매개 변수 최적화를 위해 Snowflakedml 분산 API를 사용하여 처리 규모를 확장할 수 있으며, 인터페이스를 약간 변경하여 익숙한 OSS 패키지의 API를 사용하여 확장 구성이 가능하도록 할 수 있습니다.
다음 코드는 이러한 API를 사용하여 XGBoost 분류기를 만드는 예입니다.
from snowflake.snowpark.context import get_active_session
from snowflake.ml.data.data_connector import DataConnector
import pandas as pd
import xgboost as xgb
from sklearn.model_selection import train_test_split
session = get_active_session()
# Use the DataConnector API to pull in large data efficiently
df = session.table("my_dataset")
pandas_df = DataConnector.from_dataframe(df).to_pandas()
# Build with open source
X = df_pd[['feature1', 'feature2']]
y = df_pd['label']
# Split data into test and train in memory
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=34)
# Train in memory
model = xgb.XGBClassifier()
model.fit(X_train, y_train)
# Predict
y_pred = model.predict(X_test)
CPU 컨테이너 런타임에는 GPU 컨테이너 런타임과 다른 패키지가 있습니다. 다음 섹션에서는 각 컨테이너 런타임 내에서 사용 가능한 패키지를 나열합니다.
Snowflake Runtime 패키지¶
Snowflake 런타임 CPU 패키지¶
다음은 Snowflake ML 런타임 CPU 버전에서 사용할 수 있는 패키지입니다.
패키지 |
버전 |
|---|---|
absl-py |
1.4.0 |
aiobotocore |
2.23.2 |
aiohappyeyeballs |
2.6.1 |
aiohttp |
3.12.15 |
aiohttp-cors |
0.8.1 |
aioitertools |
0.12.0 |
aiosignal |
1.4.0 |
altair |
5.5.0 |
annotated-types |
0.7.0 |
anyio |
4.10.0 |
appdirs |
1.4.4 |
arviz |
0.22.0 |
asn1crypto |
1.5.1 |
asttokens |
3.0.0 |
async-timeout |
5.0.1 |
attrs |
25.3.0 |
bayesian-optimization |
1.5.1 |
blinker |
1.9.0 |
boto3 |
1.39.8 |
botocore |
1.39.8 |
cachetools |
5.5.2 |
CausalPy |
0.5.0 |
certifi |
2025.8.3 |
cffi |
1.17.1 |
charset-normalizer |
3.4.3 |
click |
8.2.1 |
clikit |
0.6.2 |
cloudpickle |
3.0.0 |
cmdstanpy |
1.2.5 |
colorama |
0.4.6 |
colorful |
0.5.7 |
comm |
0.2.3 |
단점 |
0.4.7 |
contourpy |
1.3.2 |
crashtest |
0.3.1 |
cryptography |
43.0.3 |
cycler |
0.12.1 |
데이터 세트 |
4.0.0 |
debugpy |
1.8.16 |
decorator |
5.2.1 |
Deprecated |
1.2.18 |
dill |
0.3.8 |
distlib |
0.4.0 |
etuples |
0.3.10 |
evaluate |
0.4.5 |
exceptiongroup |
1.3.0 |
executing |
2.2.0 |
fastapi |
0.116.1 |
filelock |
3.19.1 |
FLAML |
2.3.6 |
Flask |
3.1.2 |
fonttools |
4.59.2 |
frozenlist |
1.7.0 |
fsspec |
2025.3.0 |
gitdb |
4.0.12 |
GitPython |
3.1.45 |
google-api-core |
2.25.1 |
google-auth |
2.40.3 |
googleapis-common-protos |
1.70.0 |
graphviz |
0.21 |
grpcio |
1.74.0 |
grpcio-status |
1.62.3 |
grpcio-tools |
1.62.3 |
gunicorn |
23.0.0 |
h11 |
0.16.0 |
h5netcdf |
1.6.4 |
h5py |
3.14.0 |
hf-xet |
1.1.9 |
holidays |
0.79 |
httpcore |
1.0.9 |
httpstan |
4.13.0 |
httpx |
0.28.1 |
huggingface-hub |
0.34.4 |
hypothesis |
6.138.7 |
idna |
3.10 |
importlib_metadata |
8.0.0 |
importlib_resources |
6.5.2 |
ipykernel |
6.30.1 |
ipython |
8.37.0 |
itsdangerous |
2.2.0 |
JayDeBeApi |
1.2.3 |
jedi |
0.19.2 |
Jinja2 |
3.1.6 |
jmespath |
1.0.1 |
Joblib |
1.5.2 |
jpype1 |
1.6.0 |
jsonschema |
4.25.1 |
jsonschema-specifications |
2025.4.1 |
jupyter_client |
8.6.3 |
jupyter_core |
5.8.1 |
kiwisolver |
1.4.9 |
lightgbm |
4.5.0 |
lightgbm-ray |
0.1.9 |
llvmlite |
0.44.0 |
logical-unification |
0.4.6 |
markdown-it-py |
4.0.0 |
MarkupSafe |
3.0.2 |
marshmallow |
3.26.1 |
matplotlib |
3.10.5 |
matplotlib-inline |
0.1.7 |
mdurl |
0.1.2 |
miniKanren |
1.0.5 |
mlruntimes_service |
1.8.0 |
modin |
0.35.0 |
mpmath |
1.3.0 |
msgpack |
1.1.1 |
multidict |
6.6.4 |
multipledispatch |
1.0.0 |
multiprocess |
0.70.16 |
narwhals |
2.2.0 |
nest-asyncio |
1.6.0 |
networkx |
3.4.2 |
nltk |
3.9.1 |
numba |
0.61.2 |
numpy |
1.26.4 |
nvidia-nccl-cu12 |
2.27.7 |
opencensus |
0.11.4 |
opencensus-context |
0.1.3 |
opentelemetry-api |
1.26.0 |
opentelemetry-exporter-otlp |
1.26.0 |
opentelemetry-exporter-otlp-proto-common |
1.26.0 |
opentelemetry-exporter-otlp-proto-grpc |
1.26.0 |
opentelemetry-exporter-otlp-proto-http |
1.26.0 |
opentelemetry-exporter-prometheus |
0.47b0 |
opentelemetry-proto |
1.26.0 |
opentelemetry-sdk |
1.26.0 |
opentelemetry-semantic-conventions |
0.47b0 |
패키징 |
24.2 |
pandas |
2.3.2 |
parso |
0.8.5 |
pastel |
0.2.1 |
patsy |
1.0.1 |
pexpect |
4.9.0 |
pillow |
10.4.0 |
platformdirs |
4.4.0 |
plotly |
6.3.0 |
prometheus_client |
0.22.1 |
prompt_toolkit |
3.0.52 |
propcache |
0.3.2 |
prophet |
1.1.7 |
proto-plus |
1.26.1 |
프로토콜 버퍼 |
4.25.8 |
psutil |
7.0.0 |
ptyprocess |
0.7.0 |
pure_eval |
0.2.3 |
py-spy |
0.4.1 |
py4j |
0.10.9.7 |
pyarrow |
21.0.0 |
pyasn1 |
0.6.1 |
pyasn1_modules |
0.4.2 |
pycparser |
2.22 |
pydantic |
2.11.7 |
pydantic-settings |
2.10.1 |
pydantic_core |
2.33.2 |
pydeck |
0.9.1 |
Pygments |
2.19.2 |
PyJWT |
2.10.1 |
pylev |
1.4.0 |
pymc |
5.25.1 |
pyOpenSSL |
25.1.0 |
pyparsing |
3.2.3 |
pysimdjson |
6.0.2 |
pystan |
3.10.0 |
pytensor |
2.31.7 |
python-dateutil |
2.9.0.post0 |
python-dotenv |
1.1.1 |
pytimeparse |
1.1.8 |
pytz |
2025.2 |
PyYAML |
6.0.2 |
pyzmq |
27.0.2 |
ray |
2.47.1 |
referencing |
0.36.2 |
regex |
2025.7.34 |
요청 |
2.32.5 |
retrying |
1.4.2 |
rich |
13.9.4 |
rpds-py |
0.27.1 |
rsa |
4.9.1 |
s3fs |
2025.3.0 |
s3transfer |
0.13.1 |
safetensors |
0.6.2 |
scikit-learn |
1.5.2 |
scipy |
1.15.3 |
seaborn |
0.13.2 |
shap |
0.48.0 |
six |
1.17.0 |
slicer |
0.0.8 |
smart_open |
7.3.0.post1 |
smmap |
5.0.2 |
sniffio |
1.3.1 |
snowbooks |
1.76.7rc1 |
Snowflake |
1.7.0 |
snowflake-connector-python |
3.17.2 |
snowflake-ml-python |
1.11.0 |
snowflake-snowpark-python |
1.37.0 |
snowflake-telemetry-python |
0.7.1 |
snowflake._legacy |
1.0.1 |
snowflake.core |
1.7.0 |
snowpark-connect |
0.20.3 |
sortedcontainers |
2.4.0 |
sqlglot |
27.9.0 |
sqlparse |
0.5.3 |
stack-data |
0.6.3 |
stanio |
0.5.1 |
starlette |
0.47.3 |
statsmodels |
0.14.5 |
Streamlit |
1.39.1 |
sympy |
1.13.1 |
tenacity |
9.1.2 |
threadpoolctl |
3.6.0 |
tokenizers |
0.21.4 |
toml |
0.10.2 |
tomlkit |
0.13.3 |
toolz |
1.0.0 |
torch |
2.6.0+cpu |
torchvision |
0.21.0+cpu |
tornado |
6.5.2 |
tqdm |
4.67.1 |
traitlets |
5.14.3 |
transformers |
4.55.4 |
typing-inspection |
0.4.1 |
typing_extensions |
4.15.0 |
tzdata |
2025.2 |
tzlocal |
5.3.1 |
urllib3 |
2.5.0 |
uvicorn |
0.35.0 |
virtualenv |
20.34.0 |
watchdog |
5.0.3 |
wcwidth |
0.2.13 |
webargs |
8.7.0 |
Werkzeug |
3.1.3 |
wrapt |
1.17.3 |
xarray |
2025.6.1 |
xarray-einstats |
0.8.0 |
xgboost |
2.1.4 |
xgboost-ray |
0.1.19 |
xxhash |
3.5.0 |
yarl |
1.20.1 |
zipp |
3.23.0 |
Snowflake ML 런타임 GPU 패키지¶
다음은 Snowflake ML 런타임 GPU 버전에서 사용할 수 있는 패키지입니다.
패키지 |
버전 |
|---|---|
absl-py |
1.4.0 |
accelerate |
1.10.1 |
aiobotocore |
2.23.2 |
aiohappyeyeballs |
2.6.1 |
aiohttp |
3.12.15 |
aiohttp-cors |
0.8.1 |
aioitertools |
0.12.0 |
aiosignal |
1.4.0 |
airportsdata |
20250811 |
altair |
5.5.0 |
annotated-types |
0.7.0 |
anyio |
4.10.0 |
appdirs |
1.4.4 |
arviz |
0.22.0 |
asn1crypto |
1.5.1 |
astor |
0.8.1 |
asttokens |
3.0.0 |
async-timeout |
5.0.1 |
attrs |
25.3.0 |
bayesian-optimization |
1.5.1 |
blake3 |
1.0.5 |
blinker |
1.9.0 |
boto3 |
1.39.8 |
botocore |
1.39.8 |
cachetools |
5.5.2 |
CausalPy |
0.5.0 |
certifi |
2025.8.3 |
cffi |
1.17.1 |
charset-normalizer |
3.4.3 |
click |
8.2.1 |
clikit |
0.6.2 |
cloudpickle |
3.0.0 |
cmdstanpy |
1.2.5 |
colorama |
0.4.6 |
colorful |
0.5.7 |
comm |
0.2.3 |
compressed-tensors |
0.9.3 |
단점 |
0.4.7 |
contourpy |
1.3.2 |
crashtest |
0.3.1 |
cryptography |
43.0.3 |
cuda-bindings |
12.9.2 |
cuda-pathfinder |
1.1.0 |
cuda-python |
12.9.2 |
cudf-cu12 |
25.6.0 |
cuml-cu12 |
25.6.0 |
cupy-cuda12x |
13.6.0 |
cuvs-cu12 |
25.6.1 |
cycler |
0.12.1 |
dask |
2025.5.0 |
dask-cuda |
25.6.0 |
dask-cudf-cu12 |
25.6.0 |
데이터 세트 |
4.0.0 |
debugpy |
1.8.16 |
decorator |
5.2.1 |
Deprecated |
1.2.18 |
depyf |
0.18.0 |
dill |
0.3.8 |
diskcache |
5.6.3 |
distlib |
0.4.0 |
distributed |
2025.5.0 |
distributed-ucxx-cu12 |
0.44.0 |
distro |
1.9.0 |
dnspython |
2.7.0 |
einops |
0.8.1 |
email-validator |
2.3.0 |
etuples |
0.3.10 |
evaluate |
0.4.5 |
exceptiongroup |
1.3.0 |
executing |
2.2.0 |
fastapi |
0.116.1 |
fastapi-cli |
0.0.8 |
fastapi-cloud-cli |
0.1.5 |
fastrlock |
0.8.3 |
filelock |
3.19.1 |
FLAML |
2.3.6 |
Flask |
3.1.2 |
fonttools |
4.59.2 |
frozenlist |
1.7.0 |
fsspec |
2025.3.0 |
gguf |
0.17.1 |
gitdb |
4.0.12 |
GitPython |
3.1.45 |
google-api-core |
2.25.1 |
google-auth |
2.40.3 |
googleapis-common-protos |
1.70.0 |
graphviz |
0.21 |
grpcio |
1.74.0 |
grpcio-status |
1.62.3 |
grpcio-tools |
1.62.3 |
gunicorn |
23.0.0 |
h11 |
0.16.0 |
h5netcdf |
1.6.4 |
h5py |
3.14.0 |
hf-xet |
1.1.9 |
holidays |
0.79 |
httpcore |
1.0.9 |
httpstan |
4.13.0 |
httptools |
0.6.4 |
httpx |
0.28.1 |
huggingface-hub |
0.34.4 |
hypothesis |
6.138.7 |
idna |
3.10 |
importlib_metadata |
8.0.0 |
importlib_resources |
6.5.2 |
interegular |
0.3.3 |
ipykernel |
6.30.1 |
ipython |
8.37.0 |
itsdangerous |
2.2.0 |
JayDeBeApi |
1.2.3 |
jedi |
0.19.2 |
Jinja2 |
3.1.6 |
jiter |
0.10.0 |
jmespath |
1.0.1 |
Joblib |
1.5.2 |
jpype1 |
1.6.0 |
jsonschema |
4.25.1 |
jsonschema-specifications |
2025.4.1 |
jupyter_client |
8.6.3 |
jupyter_core |
5.8.1 |
kiwisolver |
1.4.9 |
lark |
1.2.2 |
libcudf-cu12 |
25.6.0 |
libcuml-cu12 |
25.6.0 |
libcuvs-cu12 |
25.6.1 |
libkvikio-cu12 |
25.6.0 |
libraft-cu12 |
25.6.0 |
librmm-cu12 |
25.6.0 |
libucx-cu12 |
1.18.1 |
libucxx-cu12 |
0.44.0 |
lightgbm |
4.5.0 |
lightgbm-ray |
0.1.9 |
llguidance |
0.7.30 |
llvmlite |
0.44.0 |
lm-format-enforcer |
0.10.12 |
locket |
1.0.0 |
logical-unification |
0.4.6 |
markdown-it-py |
4.0.0 |
MarkupSafe |
3.0.2 |
marshmallow |
3.26.1 |
matplotlib |
3.10.5 |
matplotlib-inline |
0.1.7 |
mdurl |
0.1.2 |
miniKanren |
1.0.5 |
mistral_common |
1.8.4 |
mlruntimes_service |
1.8.0 |
modin |
0.35.0 |
mpmath |
1.3.0 |
msgpack |
1.1.1 |
msgspec |
0.19.0 |
multidict |
6.6.4 |
multipledispatch |
1.0.0 |
multiprocess |
0.70.16 |
narwhals |
2.2.0 |
nest-asyncio |
1.6.0 |
networkx |
3.4.2 |
ninja |
1.13.0 |
nltk |
3.9.1 |
numba |
0.61.2 |
numba-cuda |
0.11.0 |
numpy |
1.26.4 |
nvidia-cublas-cu12 |
12.6.4.1 |
nvidia-cuda-cupti-cu12 |
12.6.80 |
nvidia-cuda-nvcc-cu12 |
12.9.86 |
nvidia-cuda-nvrtc-cu12 |
12.6.77 |
nvidia-cuda-runtime-cu12 |
12.6.77 |
nvidia-cudnn-cu12 |
9.5.1.17 |
nvidia-cufft-cu12 |
11.3.0.4 |
nvidia-curand-cu12 |
10.3.7.77 |
nvidia-cusolver-cu12 |
11.7.1.2 |
nvidia-cusparse-cu12 |
12.5.4.2 |
nvidia-cusparselt-cu12 |
0.6.3 |
nvidia-ml-py |
12.575.51 |
nvidia-nccl-cu12 |
2.21.5 |
nvidia-nvjitlink-cu12 |
12.6.85 |
nvidia-nvtx-cu12 |
12.6.77 |
nvtx |
0.2.13 |
openai |
1.102.0 |
opencensus |
0.11.4 |
opencensus-context |
0.1.3 |
opencv-python-headless |
4.11.0.86 |
opentelemetry-api |
1.26.0 |
opentelemetry-exporter-otlp |
1.26.0 |
opentelemetry-exporter-otlp-proto-common |
1.26.0 |
opentelemetry-exporter-otlp-proto-grpc |
1.26.0 |
opentelemetry-exporter-otlp-proto-http |
1.26.0 |
opentelemetry-exporter-prometheus |
0.47b0 |
opentelemetry-proto |
1.26.0 |
opentelemetry-sdk |
1.26.0 |
opentelemetry-semantic-conventions |
0.47b0 |
opentelemetry-semantic-conventions-ai |
0.4.13 |
outlines |
0.1.11 |
outlines_core |
0.1.26 |
패키징 |
24.2 |
pandas |
2.2.3 |
parso |
0.8.5 |
partd |
1.4.2 |
partial-json-parser |
0.2.1.1.post6 |
pastel |
0.2.1 |
patsy |
1.0.1 |
peft |
0.17.1 |
pexpect |
4.9.0 |
pillow |
10.4.0 |
platformdirs |
4.4.0 |
plotly |
6.3.0 |
prometheus-fastapi-instrumentator |
7.1.0 |
prometheus_client |
0.22.1 |
prompt_toolkit |
3.0.52 |
propcache |
0.3.2 |
prophet |
1.1.7 |
proto-plus |
1.26.1 |
프로토콜 버퍼 |
4.25.8 |
psutil |
7.0.0 |
ptyprocess |
0.7.0 |
pure_eval |
0.2.3 |
py-cpuinfo |
9.0.0 |
py-spy |
0.4.1 |
py4j |
0.10.9.7 |
pyarrow |
19.0.1 |
pyasn1 |
0.6.1 |
pyasn1_modules |
0.4.2 |
pycountry |
24.6.1 |
pycparser |
2.22 |
pydantic |
2.11.7 |
pydantic-extra-types |
2.10.5 |
pydantic-settings |
2.10.1 |
pydantic_core |
2.33.2 |
pydeck |
0.9.1 |
Pygments |
2.19.2 |
PyJWT |
2.10.1 |
pylev |
1.4.0 |
pylibcudf-cu12 |
25.6.0 |
pylibraft-cu12 |
25.6.0 |
pymc |
5.25.1 |
pynvjitlink-cu12 |
0.7.0 |
pynvml |
12.0.0 |
pyOpenSSL |
25.1.0 |
pyparsing |
3.2.3 |
pysimdjson |
6.0.2 |
pystan |
3.10.0 |
pytensor |
2.31.7 |
python-dateutil |
2.9.0.post0 |
python-dotenv |
1.1.1 |
python-json-logger |
3.3.0 |
python-multipart |
0.0.20 |
pytimeparse |
1.1.8 |
pytz |
2025.2 |
PyYAML |
6.0.2 |
pyzmq |
27.0.2 |
raft-dask-cu12 |
25.6.0 |
rapids-dask-dependency |
25.6.0 |
rapids-logger |
0.1.1 |
ray |
2.47.1 |
referencing |
0.36.2 |
regex |
2025.7.34 |
요청 |
2.32.5 |
retrying |
1.4.2 |
rich |
13.9.4 |
rich-toolkit |
0.15.0 |
rignore |
0.6.4 |
rmm-cu12 |
25.6.0 |
rpds-py |
0.27.1 |
rsa |
4.9.1 |
s3fs |
2025.3.0 |
s3transfer |
0.13.1 |
safetensors |
0.6.2 |
scikit-learn |
1.5.2 |
scipy |
1.15.3 |
seaborn |
0.13.2 |
sentencepiece |
0.2.1 |
sentry-sdk |
2.35.1 |
shap |
0.48.0 |
shellingham |
1.5.4 |
six |
1.17.0 |
slicer |
0.0.8 |
smart_open |
7.3.0.post1 |
smmap |
5.0.2 |
sniffio |
1.3.1 |
snowbooks |
1.76.7rc1 |
Snowflake |
1.7.0 |
snowflake-connector-python |
3.17.2 |
snowflake-ml-python |
1.11.0 |
snowflake-snowpark-python |
1.37.0 |
snowflake-telemetry-python |
0.7.1 |
snowflake._legacy |
1.0.1 |
snowflake.core |
1.7.0 |
snowpark-connect |
0.20.3 |
sortedcontainers |
2.4.0 |
sqlglot |
27.9.0 |
sqlparse |
0.5.3 |
stack-data |
0.6.3 |
stanio |
0.5.1 |
starlette |
0.47.3 |
statsmodels |
0.14.5 |
Streamlit |
1.39.1 |
sympy |
1.13.1 |
tblib |
3.1.0 |
tenacity |
9.1.2 |
threadpoolctl |
3.6.0 |
tiktoken |
0.11.0 |
tokenizers |
0.21.4 |
toml |
0.10.2 |
tomlkit |
0.13.3 |
toolz |
1.0.0 |
torch |
2.6.0+cu126 |
torchaudio |
2.6.0+cu126 |
torchvision |
0.21.0+cu126 |
tornado |
6.5.2 |
tqdm |
4.67.1 |
traitlets |
5.14.3 |
transformers |
4.51.3 |
treelite |
4.4.1 |
triton |
3.2.0 |
typer |
0.16.1 |
typing-inspection |
0.4.1 |
typing_extensions |
4.15.0 |
tzdata |
2025.2 |
tzlocal |
5.3.1 |
ucx-py-cu12 |
0.44.0 |
ucxx-cu12 |
0.44.0 |
urllib3 |
2.5.0 |
uvicorn |
0.35.0 |
uvloop |
0.21.0 |
virtualenv |
20.34.0 |
vllm |
0.8.5.post1 |
watchdog |
5.0.3 |
watchfiles |
1.1.0 |
wcwidth |
0.2.13 |
webargs |
8.7.0 |
websockets |
15.0.1 |
Werkzeug |
3.1.3 |
wrapt |
1.17.3 |
xarray |
2025.6.1 |
xarray-einstats |
0.8.0 |
xformers |
0.0.29.post2 |
xgboost |
2.1.4 |
xgboost-ray |
0.1.19 |
xgrammar |
0.1.18 |
xxhash |
3.5.0 |
yarl |
1.20.1 |
zict |
3.0.0 |
zipp |
3.23.0 |
최적화된 학습¶
Container Runtime offers a set of distributed training APIs, including distributed versions of LightGBM, PyTorch,
and XGBoost, that take full advantage of the available resources in the container environment. These are found in the
snowflake.ml.modeling.distributors namespace. The APIs of the distributed classes are similar to those of the
standard versions.
이러한 APIs에 대한 자세한 내용은 API 참조 섹션을 참조하십시오.
XGBoost¶
기본 XGBoost 클래스는 snowflake.ml.modeling.distributors.xgboost.XGBEstimator 입니다. 관련 클래스에는 다음이 포함됩니다.
snowflake.ml.modeling.distributors.xgboost.XGBScalingConfig
For an example of working with this API, see the XGBoost on GPU example notebook in the Snowflake Container Runtime GitHub repository.
LightGBM¶
기본 LightGBM 클래스는 snowflake.ml.modeling.distributors.lightgbm.LightGBMEstimator 입니다. 관련 클래스에는 다음이 포함됩니다.
snowflake.ml.modeling.distributors.lightgbm.LightGBMScalingConfig
For an example of working with this API, see the LightGBM on GPU example notebook in the Snowflake Container Runtime GitHub repository.
PyTorch¶
기본 PyTorch 클래스는 snowflake.ml.modeling.distributors.pytorch.PyTorchDistributor 입니다. 관련 클래스 및 함수는 다음을 포함합니다.
snowflake.ml.modeling.distributors.pytorch.WorkerResourceConfigsnowflake.ml.modeling.distributors.pytorch.PyTorchScalingConfigsnowflake.ml.modeling.distributors.pytorch.Contextsnowflake.ml.modeling.distributors.pytorch.get_context
For an example of working with this API, see the PyTorch on GPU example notebook in the Snowflake Container Runtime GitHub repository.
다음 단계¶
To try the notebook using Container Runtime, see Notebooks on Container Runtime.