docs: reorganize README and add conda migration files
This commit is contained in:
parent
48e57490f2
commit
16910a76e2
233
README.md
233
README.md
|
|
@ -1,71 +1,208 @@
|
|||
# Deeplearning 使用说明
|
||||
|
||||
## Preliminary
|
||||
1. 对于每一个类，将数据如下处理, 保存成xlsx或者xls文件
|
||||
## 1. 项目约定
|
||||
|
||||
| | | | |
|
||||
|-------|-------|-------|-------|
|
||||
| arbitrary value | value | arbitrary value | vlaue |
|
||||
| arbitrary value | value | arbitrary value | vlaue |
|
||||
### 1.1 输入数据格式
|
||||
每一类数据建议保存为 `xlsx/xls`。读取时默认取偶数列（索引 1,3,5...）作为特征，奇数列内容可忽略。
|
||||
|
||||
即偶数列为一次循环的数据，奇数列为任意值即可
|
||||
示意：
|
||||
|
||||
2. 配置conda环境
|
||||
> pass
|
||||
| 任意值 | 特征值 | 任意值 | 特征值 |
|
||||
|---|---|---|---|
|
||||
| arbitrary value | value | arbitrary value | value |
|
||||
| arbitrary value | value | arbitrary value | value |
|
||||
|
||||
### 1.2 目录约定
|
||||
训练数据放在 `Static/`，输出结果放在 `Result/`。
|
||||
|
||||
## Quickly Start
|
||||
1. 将项目文件夹编辑成**日期+项目名**
|
||||
2. 编辑好label名称，label名称命名变成英文或者数字
|
||||
>例如：”PDMS“， ”1“ 等, , 如果你的每一个类，下面又多个子特征则可以建立一个文件夹，在创建神经网络类的时候将**isDir**参数改成True即可.
|
||||
>详细如下图：
|
||||
>2.1. 如果只有一类特征
|
||||
> 
|
||||
>2.2. 如果有多类特征
|
||||
>
|
||||
3. 将准备好的文件夹移动到**Static**文件夹中（没有就建立），如果没有 **Result** 建立一个**Result**文件夹用来存放结果
|
||||
推荐目录：
|
||||
|
||||
4. 读取数据：
|
||||
```python
|
||||
# 以MaterialDiv为例
|
||||
projet_name = '20241009MaterialDiv'
|
||||
label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood']
|
||||
# 使用库 divSet 划分训练集和数据集
|
||||
data = load_data(projet_name, label_names, isDir=False, fileClass='xlsx')
|
||||
```text
|
||||
.
|
||||
├─ Static/
|
||||
│ └─ 20241009MaterialDiv/
|
||||
└─ Result/
|
||||
```
|
||||
|
||||
5. 创建神经网络类
|
||||
## 2. Conda 环境迁移
|
||||
|
||||
环境文件在 `conda_env/`：
|
||||
|
||||
- `conda_env/environment.portable.yml`：通用迁移（推荐）
|
||||
- `conda_env/environment.lock.txt`：精确锁定（同系统/同架构优先）
|
||||
- `conda_env/env.yml`：历史文件
|
||||
|
||||
### 2.1 创建环境
|
||||
|
||||
```bash
|
||||
# 方式1（推荐）：通用创建
|
||||
conda env create -f conda_env/environment.portable.yml
|
||||
conda activate Deeplearning
|
||||
|
||||
# 方式2：精确复现
|
||||
conda create -n Deeplearning --file conda_env/environment.lock.txt
|
||||
conda activate Deeplearning
|
||||
|
||||
# 验证
|
||||
python -V
|
||||
python -c "import torch; print(torch.__version__)"
|
||||
```
|
||||
|
||||
### 2.2 同名环境已存在时
|
||||
|
||||
```bash
|
||||
# 方式A：保留旧环境，改名创建
|
||||
conda env create -f conda_env/environment.portable.yml -n Deeplearning_v2
|
||||
conda activate Deeplearning_v2
|
||||
|
||||
# 或者（lock 方式）
|
||||
conda create -n Deeplearning_v2 --file conda_env/environment.lock.txt
|
||||
conda activate Deeplearning_v2
|
||||
```
|
||||
|
||||
```bash
|
||||
# 方式B：删除旧环境后重建（谨慎）
|
||||
conda env remove -n Deeplearning
|
||||
conda env create -f conda_env/environment.portable.yml
|
||||
conda activate Deeplearning
|
||||
```
|
||||
|
||||
### 2.3 重新导出环境
|
||||
|
||||
```bash
|
||||
conda env export -n Deeplearning --no-builds > conda_env/environment.portable.yml
|
||||
conda list -n Deeplearning --explicit > conda_env/environment.lock.txt
|
||||
```
|
||||
|
||||
## 3. 快速开始
|
||||
|
||||
### 3.1 准备数据
|
||||
1. 将数据目录命名为 `日期+项目名`，例如 `20241009MaterialDiv`。
|
||||
2. 准备 `label_names`（建议英文或数字）。
|
||||
3. 将数据目录放入 `Static/`。
|
||||
|
||||
### 3.2 数据目录模板
|
||||
|
||||
单文件模式（`isDir=False`）：
|
||||
|
||||
```text
|
||||
Static/
|
||||
20241009MaterialDiv/
|
||||
Acrlic.xlsx
|
||||
Ecoflex.xlsx
|
||||
PDMS.xlsx
|
||||
PLA.xlsx
|
||||
Wood.xlsx
|
||||
```
|
||||
|
||||
多子特征模式（`isDir=True`）：
|
||||
|
||||
```text
|
||||
Static/
|
||||
20241009MaterialDiv/
|
||||
Acrlic/
|
||||
sample_01.xlsx
|
||||
sample_02.xlsx
|
||||
Ecoflex/
|
||||
sample_01.xlsx
|
||||
sample_02.xlsx
|
||||
PDMS/
|
||||
sample_01.xlsx
|
||||
sample_02.xlsx
|
||||
PLA/
|
||||
sample_01.xlsx
|
||||
sample_02.xlsx
|
||||
Wood/
|
||||
sample_01.xlsx
|
||||
sample_02.xlsx
|
||||
```
|
||||
|
||||
命名规则（重要）：
|
||||
|
||||
- `label_names` 中每一项必须与文件名（`isDir=False`）或子文件夹名（`isDir=True`）完全一致（区分大小写）。
|
||||
- `label_names` 顺序就是标签编码顺序，训练结果和混淆矩阵按该顺序展示。
|
||||
|
||||
示例：
|
||||
|
||||
```python
|
||||
label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood']
|
||||
```
|
||||
|
||||
对应关系：
|
||||
|
||||
```text
|
||||
Acrlic <-> Acrlic.xlsx 或 Acrlic/
|
||||
Ecoflex <-> Ecoflex.xlsx 或 Ecoflex/
|
||||
PDMS <-> PDMS.xlsx 或 PDMS/
|
||||
PLA <-> PLA.xlsx 或 PLA/
|
||||
Wood <-> Wood.xlsx 或 Wood/
|
||||
```
|
||||
|
||||
### 3.3 训练示例
|
||||
|
||||
```python
|
||||
from Qtorch.Models.Qmlp import Qmlp
|
||||
from Qfunctions.divSet import divSet
|
||||
from Qfunctions.loaData import load_data
|
||||
from Qfunctions.saveToxlsx import save_to_xlsx
|
||||
|
||||
projet_name = '20241009MaterialDiv'
|
||||
label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood']
|
||||
|
||||
# 读取数据
|
||||
data = load_data(projet_name, label_names, isDir=False, fileClass='xlsx')
|
||||
|
||||
# 划分训练/测试集
|
||||
X_train, X_test, y_train, y_test, encoder = divSet(
|
||||
data=data,
|
||||
labels=label_names,
|
||||
test_size=0.3
|
||||
)
|
||||
|
||||
# 构建模型
|
||||
model = Qmlp(
|
||||
X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
|
||||
X_train=X_train,
|
||||
X_test=X_test,
|
||||
y_train=y_train,
|
||||
y_test=y_test,
|
||||
hidden_layers=[128],
|
||||
dropout_rate=0
|
||||
)
|
||||
```
|
||||
|
||||
6. 训练并获取数据
|
||||
```python
|
||||
# 训练与导出结果
|
||||
pca_2d, pca_3d = model.get_PCA()
|
||||
|
||||
model.fit(300)
|
||||
|
||||
cm = model.get_cm()
|
||||
cmn = model.get_cmn()
|
||||
epoch_data = model.get_epoch_data()
|
||||
|
||||
save_to_xlsx(project_name=projet_name, file_name="pca_2d", data=pca_2d)
|
||||
save_to_xlsx(project_name=projet_name, file_name="pca_3d", data=pca_3d)
|
||||
save_to_xlsx(project_name=projet_name, file_name="cm", data=cm )
|
||||
save_to_xlsx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data)
|
||||
save_to_xlsx(project_name=projet_name, file_name='pca_2d', data=pca_2d)
|
||||
save_to_xlsx(project_name=projet_name, file_name='pca_3d', data=pca_3d)
|
||||
save_to_xlsx(project_name=projet_name, file_name='cm', data=cm)
|
||||
save_to_xlsx(project_name=projet_name, file_name='cmn', data=cmn)
|
||||
save_to_xlsx(project_name=projet_name, file_name='acc_and_loss', data=epoch_data)
|
||||
```
|
||||
## Advanced
|
||||
### loadData 处理数据工具的使用
|
||||
||参数类型|默认值|参数作用|
|
||||
|
||||
## 4. load_data 参数说明
|
||||
|
||||
| 参数 | 类型 | 默认值 | 说明 |
|
||||
|---|---|---|---|
|
||||
|folder|str|必填项|指定数据存放在Static下的哪个文件夹|
|
||||
|lableNames| list| 必填项| 指定每一个类的label名称, 既可以用来读取相应的文件，也可以用来给label排序|
|
||||
|isDir| bool| True| 若是上文Quickly Strat章节2.1情况需要改成False，2.2情况则是True|
|
||||
|fileClass| str| 'xlsx'| 数据文件的后缀|
|
||||
> tips: 数据读取是按照一下情况读取的(2.1和2.2是Quickly Start章节的2.1和2.2简称)：
|
||||
> 2.1情况的第一类数据读取的地址是 ./Static/folder/labelsNames[0].xlsx, 其他类同理
|
||||
> 2.2情况的第二类数据读取的地址是 ./Static/folder/labelsNames[0]/*.xlsx, 其他同理
|
||||
### Qmlp 模型使用
|
||||
> pass
|
||||
| folder | str | 必填 | `Static/` 下的数据目录名 |
|
||||
| labelNames | list | 必填 | 类别名称列表，用于读取和排序标签 |
|
||||
| isDir | bool | True | `False` 对应单文件模式，`True` 对应多子特征模式 |
|
||||
| fileClass | str | xlsx | 数据文件后缀 |
|
||||
|
||||
读取路径规则：
|
||||
|
||||
- 单文件模式：`./Static/folder/labelNames[i].xlsx`
|
||||
- 多子特征模式：`./Static/folder/labelNames[i]/*.xlsx`
|
||||
|
||||
## 5. 常见问题
|
||||
|
||||
### 5.1 找不到文件
|
||||
优先检查：
|
||||
|
||||
- `label_names` 与文件/文件夹是否同名
|
||||
- `isDir` 是否与目录结构匹配
|
||||
- 文件后缀是否与 `fileClass` 一致
|
||||
|
|
|
|||
|
|
@ -0,0 +1,163 @@
|
|||
# This file may be used to create an environment using:
|
||||
# $ conda create --name <env> --file <this file>
|
||||
# platform: linux-64
|
||||
# created-by: conda 26.1.1
|
||||
@EXPLICIT
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-mkl.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.11.26-h06a4308_0.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-cudart-12.4.127-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-cupti-12.4.127-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-nvrtc-12.4.127-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-nvtx-12.4.127-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-opencl-12.4.127-0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcublas-12.4.2.65-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcufft-11.2.0.44-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcufile-1.9.1.3-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcurand-10.3.5.147-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcusolver-11.6.0.99-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcusparse-12.3.0.142-0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libgfortran5-11.2.0-h1234567_1.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/libnpp-12.2.5.2-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libnvfatbin-12.4.127-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libnvjitlink-12.4.99-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libnvjpeg-12.3.1.89-0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/pybind11-abi-5-hd3eb1b0_0.conda
|
||||
https://conda.anaconda.org/pytorch/noarch/pytorch-mutex-1.0-cuda.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/noarch/tzdata-2024a-h04d1e81_0.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-libraries-12.4.0-0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-11.2.0-h00389a5_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-runtime-12.4.0-0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda
|
||||
https://conda.anaconda.org/pytorch/linux-64/pytorch-cuda-12.4-hc786d27_6.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/cudatoolkit-11.5.1-hcf5317a_9.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.2-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/gmp-6.2.1-h295c915_3.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/icu-73.1-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/jpeg-9e-h5eee18b_3.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/lame-3.100-h7b6447c_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/lerc-3.0-h295c915_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libbrotlicommon-1.0.9-h5eee18b_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libdeflate-1.17-h5eee18b_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libiconv-1.16-h5eee18b_3.conda
|
||||
https://conda.anaconda.org/pytorch/linux-64/libjpeg-turbo-2.0.0-h9bf148f_0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libtasn1-4.19.0-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libunistring-0.9.10-h27cfd23_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libwebp-base-1.3.2-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.15-h7f8727e_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/openh264-2.1.1-h4ff587b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.15-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/tbb-2021.8.0-hdb19cb5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/yaml-0.2.5-h7b6447c_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/intel-openmp-2023.1.0-hdb19cb5_46306.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libbrotlidec-1.0.9-h5eee18b_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libbrotlienc-1.0.9-h5eee18b_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libcups-2.4.2-h2d74bed_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libedit-3.1.20230828-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libidn2-2.3.4-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libllvm14-14.0.6-hdb19cb5_3.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libpng-1.6.39-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libxml2-2.13.1-hfdd30dd_2.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/llvm-openmp-14.0.6-h9e868ea_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/nettle-3.7.3-hbbd107a_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pcre2-10.42-hebb0a14_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h39e8969_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.5-hc292b87_2.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/brotli-bin-1.0.9-h5eee18b_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/freetype-2.12.1-h4a9f257_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/gnutls-3.6.15-he1e5248_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/krb5-1.20.1-h143b758_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libclang13-14.0.6-default_he11475f_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libglib-2.78.4-hdc74915_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libtiff-4.5.1-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libxkbcommon-1.0.1-h097e994_2.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mkl-2023.1.0-h213fc3f_46344.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/brotli-1.0.9-h5eee18b_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/cyrus-sasl-2.1.28-h52b45da_1.conda
|
||||
https://conda.anaconda.org/pytorch/linux-64/ffmpeg-4.3-hf484d3e_0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/fontconfig-2.14.1-h55d465d_3.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/glib-tools-2.78.4-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/lcms2-2.12-h3be6417_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libclang-14.0.6-default_hc6dbbc7_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libpq-12.17-hdbd6064_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/openjpeg-2.5.2-he7f1fd0_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.4-h5148396_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/brotli-python-1.0.9-py312h6a678d5_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/certifi-2024.12.14-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/charset-normalizer-3.3.2-pyhd3eb1b0_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/debugpy-1.6.7-py312h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/et_xmlfile-1.1.0-py312h06a4308_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/filelock-3.13.1-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/glib-2.78.4-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/idna-3.7-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/joblib-1.4.2-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/kiwisolver-1.4.4-py312h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/markupsafe-2.1.3-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mkl-service-2.4.0-py312h5eee18b_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mpmath-1.3.0-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mysql-5.7.24-h721c034_2.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/networkx-3.3-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/packaging-24.1-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pillow-10.4.0-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/ply-3.11-py312h06a4308_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pyopengl-3.1.1a1-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pyparsing-3.0.9-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pyqt5-sip-12.13.0-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pysocks-1.7.1-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2023.3-pyhd3eb1b0_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pytz-2024.1-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pyyaml-6.0.1-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/setuptools-72.1.0-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/threadpoolctl-3.5.0-py312he106c6f_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/tornado-6.4.1-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/tqdm-4.66.5-py312he106c6f_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/typing_extensions-4.11.0-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/unicodedata2-15.1.0-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/xlrd-2.0.1-pyhd3eb1b0_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/dbus-1.13.18-hb2f20db_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/fonttools-4.51.0-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/gstreamer-1.14.1-h5eee18b_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/jinja2-3.1.4-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/numpy-base-1.26.4-py312h0da6c21_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/openpyxl-3.1.5-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/python-dateutil-2.9.0post0-py312h06a4308_2.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/sip-6.7.12-py312h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/sympy-1.12-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/urllib3-2.2.2-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/gst-plugins-base-1.14.1-h6a678d5_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/requests-2.32.3-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h53bd1ea_10.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.10-py312h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/bottleneck-1.3.7-py312ha883a20_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/contourpy-1.2.0-py312hdb19cb5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-3.8.4-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-base-3.8.4-py312h526ad5a_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mkl_fft-1.3.8-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mkl_random-1.2.4-py312hdb19cb5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.26.4-py312hc5e2394_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/numexpr-2.8.7-py312hf827012_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/scipy-1.13.1-py312hc5e2394_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pandas-2.2.2-py312h526ad5a_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/scikit-learn-1.5.1-py312h526ad5a_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/seaborn-0.13.2-py312h06a4308_0.conda
|
||||
https://conda.anaconda.org/pytorch/linux-64/pytorch-2.4.0-py3.12_cuda12.4_cudnn9.1.0_0.tar.bz2
|
||||
https://conda.anaconda.org/pytorch/linux-64/torchaudio-2.4.0-py312_cu124.tar.bz2
|
||||
https://conda.anaconda.org/pytorch/linux-64/torchtriton-3.0.0-py312.tar.bz2
|
||||
https://conda.anaconda.org/pytorch/linux-64/torchvision-0.19.0-py312_cu124.tar.bz2
|
||||
|
|
@ -0,0 +1,179 @@
|
|||
name: Deeplearning
|
||||
channels:
|
||||
- defaults
|
||||
- nvidia
|
||||
- pytorch
|
||||
dependencies:
|
||||
- _libgcc_mutex=0.1
|
||||
- _openmp_mutex=5.1
|
||||
- blas=1.0
|
||||
- bottleneck=1.3.7
|
||||
- brotli=1.0.9
|
||||
- brotli-bin=1.0.9
|
||||
- brotli-python=1.0.9
|
||||
- bzip2=1.0.8
|
||||
- ca-certificates=2024.11.26
|
||||
- certifi=2024.12.14
|
||||
- charset-normalizer=3.3.2
|
||||
- contourpy=1.2.0
|
||||
- cuda-cudart=12.4.127
|
||||
- cuda-cupti=12.4.127
|
||||
- cuda-libraries=12.4.0
|
||||
- cuda-nvrtc=12.4.127
|
||||
- cuda-nvtx=12.4.127
|
||||
- cuda-opencl=12.4.127
|
||||
- cuda-runtime=12.4.0
|
||||
- cudatoolkit=11.5.1
|
||||
- cycler=0.11.0
|
||||
- cyrus-sasl=2.1.28
|
||||
- dbus=1.13.18
|
||||
- debugpy=1.6.7
|
||||
- et_xmlfile=1.1.0
|
||||
- expat=2.6.2
|
||||
- ffmpeg=4.3
|
||||
- filelock=3.13.1
|
||||
- fontconfig=2.14.1
|
||||
- fonttools=4.51.0
|
||||
- freetype=2.12.1
|
||||
- glib=2.78.4
|
||||
- glib-tools=2.78.4
|
||||
- gmp=6.2.1
|
||||
- gnutls=3.6.15
|
||||
- gst-plugins-base=1.14.1
|
||||
- gstreamer=1.14.1
|
||||
- icu=73.1
|
||||
- idna=3.7
|
||||
- intel-openmp=2023.1.0
|
||||
- jinja2=3.1.4
|
||||
- joblib=1.4.2
|
||||
- jpeg=9e
|
||||
- kiwisolver=1.4.4
|
||||
- krb5=1.20.1
|
||||
- lame=3.100
|
||||
- lcms2=2.12
|
||||
- ld_impl_linux-64=2.38
|
||||
- lerc=3.0
|
||||
- libbrotlicommon=1.0.9
|
||||
- libbrotlidec=1.0.9
|
||||
- libbrotlienc=1.0.9
|
||||
- libclang=14.0.6
|
||||
- libclang13=14.0.6
|
||||
- libcublas=12.4.2.65
|
||||
- libcufft=11.2.0.44
|
||||
- libcufile=1.9.1.3
|
||||
- libcups=2.4.2
|
||||
- libcurand=10.3.5.147
|
||||
- libcusolver=11.6.0.99
|
||||
- libcusparse=12.3.0.142
|
||||
- libdeflate=1.17
|
||||
- libedit=3.1.20230828
|
||||
- libffi=3.4.4
|
||||
- libgcc-ng=11.2.0
|
||||
- libgfortran-ng=11.2.0
|
||||
- libgfortran5=11.2.0
|
||||
- libglib=2.78.4
|
||||
- libgomp=11.2.0
|
||||
- libiconv=1.16
|
||||
- libidn2=2.3.4
|
||||
- libjpeg-turbo=2.0.0
|
||||
- libllvm14=14.0.6
|
||||
- libnpp=12.2.5.2
|
||||
- libnvfatbin=12.4.127
|
||||
- libnvjitlink=12.4.99
|
||||
- libnvjpeg=12.3.1.89
|
||||
- libpng=1.6.39
|
||||
- libpq=12.17
|
||||
- libstdcxx-ng=11.2.0
|
||||
- libtasn1=4.19.0
|
||||
- libtiff=4.5.1
|
||||
- libunistring=0.9.10
|
||||
- libuuid=1.41.5
|
||||
- libwebp-base=1.3.2
|
||||
- libxcb=1.15
|
||||
- libxkbcommon=1.0.1
|
||||
- libxml2=2.13.1
|
||||
- llvm-openmp=14.0.6
|
||||
- lz4-c=1.9.4
|
||||
- markupsafe=2.1.3
|
||||
- matplotlib=3.8.4
|
||||
- matplotlib-base=3.8.4
|
||||
- mkl=2023.1.0
|
||||
- mkl-service=2.4.0
|
||||
- mkl_fft=1.3.8
|
||||
- mkl_random=1.2.4
|
||||
- mpmath=1.3.0
|
||||
- mysql=5.7.24
|
||||
- ncurses=6.4
|
||||
- nettle=3.7.3
|
||||
- networkx=3.3
|
||||
- numexpr=2.8.7
|
||||
- numpy=1.26.4
|
||||
- numpy-base=1.26.4
|
||||
- openh264=2.1.1
|
||||
- openjpeg=2.5.2
|
||||
- openpyxl=3.1.5
|
||||
- openssl=3.0.15
|
||||
- packaging=24.1
|
||||
- pandas=2.2.2
|
||||
- pcre2=10.42
|
||||
- pillow=10.4.0
|
||||
- pip=24.2
|
||||
- ply=3.11
|
||||
- pybind11-abi=5
|
||||
- pyopengl=3.1.1a1
|
||||
- pyparsing=3.0.9
|
||||
- pyqt=5.15.10
|
||||
- pyqt5-sip=12.13.0
|
||||
- pysocks=1.7.1
|
||||
- python=3.12.4
|
||||
- python-dateutil=2.9.0post0
|
||||
- python-tzdata=2023.3
|
||||
- pytorch=2.4.0
|
||||
- pytorch-cuda=12.4
|
||||
- pytorch-mutex=1.0
|
||||
- pytz=2024.1
|
||||
- pyyaml=6.0.1
|
||||
- qt-main=5.15.2
|
||||
- readline=8.2
|
||||
- requests=2.32.3
|
||||
- scikit-learn=1.5.1
|
||||
- scipy=1.13.1
|
||||
- seaborn=0.13.2
|
||||
- setuptools=72.1.0
|
||||
- sip=6.7.12
|
||||
- six=1.16.0
|
||||
- sqlite=3.45.3
|
||||
- sympy=1.12
|
||||
- tbb=2021.8.0
|
||||
- threadpoolctl=3.5.0
|
||||
- tk=8.6.14
|
||||
- torchaudio=2.4.0
|
||||
- torchtriton=3.0.0
|
||||
- torchvision=0.19.0
|
||||
- tornado=6.4.1
|
||||
- tqdm=4.66.5
|
||||
- typing_extensions=4.11.0
|
||||
- tzdata=2024a
|
||||
- unicodedata2=15.1.0
|
||||
- urllib3=2.2.2
|
||||
- wheel=0.43.0
|
||||
- xlrd=2.0.1
|
||||
- xz=5.4.6
|
||||
- yaml=0.2.5
|
||||
- zlib=1.2.13
|
||||
- zstd=1.5.5
|
||||
- pip:
|
||||
- autopep8==2.3.1
|
||||
- basedpyright==1.16.0
|
||||
- black==24.8.0
|
||||
- click==8.1.7
|
||||
- fsspec==2024.6.1
|
||||
- graphviz==0.20.3
|
||||
- greenlet==3.0.3
|
||||
- msgpack==1.0.8
|
||||
- mypy-extensions==1.0.0
|
||||
- nodejs-wheel-binaries==20.16.0
|
||||
- pathspec==0.12.1
|
||||
- platformdirs==4.2.2
|
||||
- pycodestyle==2.12.1
|
||||
- pynvim==0.5.0
|
||||
Loading…
Reference in New Issue