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--- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/docs/docs/blog/colossalai_parallelism/README.md b/docs/docs/blog/colossalai_parallelism/README.md new file mode 100644 index 0000000..02c4d25 --- /dev/null +++ b/docs/docs/blog/colossalai_parallelism/README.md @@ -0,0 +1,161 @@ +# 并行优化策略: 利用Colossal-AI进行训练任务并行优化 + +[Colossal-AI](https://github.com/hpcaitech/ColossalAI) 框架中包含了一系列训练时的并行优化策略,其中包括了张量并行,模型并行、数据并行和Zero等优化策略。在本个实例中,我们将演示如何便捷地使用我们Colossal-AI云平台框架使用这些并行策略训练LLaMA2 7B模型。 + +Tags:  Colossal-AI,训练并行, LLaMA2 + +## 运行环境要求 + +镜像:推荐使用官方镜像 notebook-colossalai-torch210-cu118 + +GPU规格:推荐使用H800 (4块及以上) + +## 什么是并行策略 + +- 数据并行 + + 数据并行是最常见的并行形式,因为它很简单。在数据并行训练中,数据集被分割成几个碎片,每个碎片被分配到一个设备上。这相当于沿批次维度对训练过程进行并行化。每个设备将持有一个完整的模型副本,并在分配的数据集碎片上进行训练。在反向传播之后,模型的梯度将被全部减少,以便在不同设备上的模型参数能够保持同步。 + +- 模型并行 + + 在数据并行训练中,一个明显的特点是每个 GPU 持有整个模型权重的副本。这就带来了冗余问题。另一种并行模式是模型并行,即模型被分割并分布在一个设备阵列上。通常有两种类型的并行:张量并行和流水线并行。张量并行是在一个操作中进行并行计算,如矩阵-矩阵乘法。流水线并行是在各层之间进行并行计算。因此,从另一个角度来看,张量并行可以被看作是层内并行,流水线并行可以被看作是层间并行。 + +- 优化器相关的并行 + + 另一种并行方法和优化器相关,目前这种并行最流行的方法是 ZeRO,即零冗余优化器。 ZeRO 在三个层面上工作,以消除内存冗余(ZeRO需要进行fp16训练)。 + + Level 1: 优化器状态在各进程中被划分。 + + Level 2: 用于更新模型权重的32位梯度也被划分,因此每个进程只存储与其优化器状态划分相对应的梯度。 + + Level 3: 16位模型参数在各进程中被划分。 + +## 1. 准备数据库 + +与原始的LLaMA不同,我们使用[RedPajama](https://www.together.xyz/blog/redpajama)数据集,这是LLaMA训练数据集的复制,包含超过1.2万亿个令牌。完整的数据集在磁盘上未解压缩约为5TB,在下载时约为3TB压缩。 + +您可以通过[Hugging Face](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)下载一个更小、更易消化的随机样本。如果您只想尝试预训练脚本,可以使用RedPajama的10亿个令牌样本子集,该子集可在[Hugging Face](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample)上找到。下载完成后,您可以将数据库上传至云平台。 + +RedPajama-Data-1T 包含七个数据片段: + +| | RedPajama | LLaMA | +|---------------|--------------|---------------| +| CommonCrawl | 8780亿 | 8520亿 | +| C4 | 1750亿 | 1900亿 | +| Github | 590亿 | 1000亿 | +| Books | 260亿 | 250亿 | +| ArXiv | 280亿 | 330亿 | +| Wikipedia | 240亿 | 250亿 | +| StackExchange | 200亿 | 270亿 | +| 总计 | 1.2万亿 | 1.25万亿 | + +在此样例中,我们使用预置好的 RedPajama-Data-1T-Sample 数据集,稍后我们会将此数据集挂载在我们的任务上。 + +## 2. 准备项目文件 + +我们需要克隆[Colossal-AI](https://github.com/hpcaitech/ColossalAI)代码仓库。在此样例中,我们使用预置好的项目文件,稍后我们会将此项目挂载在我们的任务上。 + + +## 2. 启动任务 + +1. 在控制台中选择`任务`选项,点击 `新任务`; + +2. 填写对应的任务名称和描述; + +3. 挂载数据集:将之前准备好的 `RedPajama-Data-1T` 数据集挂载到 Container 中,在这个例子里,模型被挂载到了 `/mnt/dataset`; + +3. 挂载项目:将之前准备好的 Colossal AI 项目 `ColossalAI-Parallel-Train-LLaMA-Example` 文件挂载到 Container 中,在这个例子里,项目被挂载到了 `/mnt/project`; + +4. 启动命令:填入我们项目中默认的启动命令 `bash /mnt/project/train.sh`。同时您可以在云平台页面通过设置环境变量选择此次训练配置,如并行策略,Flash Attention加速,epoch大小等。如果您想了解启动命令是如何启动服务的,或者想自定义启动命令,请参考 `3.启动命令`; + + 在本例中,我们进行zero2的优化策略,利用8卡进行 max_length为2k,batch_size为1的 LLaMA2 7b模型训练 + +5. 镜像设置:选择官方镜像 `colossalai 0.3.4`; + +6. 显卡配置:推荐选择 `NVIDA-H800`,GPU 数量设置为大于等于 `4`; + +7. 最后点击 `创建`,启动任务; + +![infernce_api_create](./images/task_create.jpg) + + +## 3. 启动命令 + +1. 在本例中,LLaMA2 的python训练脚本位于 `ColossalAI/examples/language/llama2/`, 具体的可传入参数列表如下: + +- Model configuration: `-c`, `--config`. `7b`, `13b`, `30b` and `65b` are supported for LLaMA-1, `7b`, `13b`, and `70b` are supported for LLaMA-2. +- Booster plugin: `-p`, `--plugin`. `gemini`, `gemini_auto`, `zero2`, `hybrid_parallel` and `zero2_cpu` are supported. For more details, please refer to [Booster plugins](https://colossalai.org/docs/basics/booster_plugins). +- Dataset path: `-d`, `--dataset`. The default dataset is `togethercomputer/RedPajama-Data-1T-Sample`. It support any dataset from `datasets` with the same data format as RedPajama. +- Number of epochs: `-e`, `--num_epochs`. The default value is 1. +- Local batch size: `-b`, `--batch_size`. Batch size per GPU. The default value is 2. +- Learning rate: `--lr`. The default value is 3e-4. +- Weight decay: `-w`, `--weight_decay`. The default value is 0.1. +- Warmup steps: `-s`, `--warmup_steps`. The default value is 2000. +- Gradient checkpointing: `-g`, `--gradient_checkpoint`. The default value is `False`. This saves memory at the cost of speed. You'd better enable this option when training with a large batch size. +- Max length: `-l`, `--max_length`. The default value is 4096. +- Mixed precision: `-x`, `--mixed_precision`. The default value is "fp16". "fp16" and "bf16" are supported. +- Save interval: `-i`, `--save_interval`. The interval (steps) of saving checkpoints. The default value is 1000. +- Checkpoint directory: `-o`, `--save_dir`. The directory path to save checkpoints. The default value is `checkpoint`. +- Checkpoint to load: `-f`, `--load`. The checkpoint path to load. The default value is `None`. +- Gradient clipping: `--gradient_clipping`. The default value is 1.0. +- Tensorboard log directory: `-t`, `--tensorboard_dir`. The directory path to save tensorboard logs. The default value is `tb_logs`. +- Flash attention: `-a`, `--flash_attention`. If you want to use flash attention, you must install `flash-attn`. The default value is `False`. This is helpful to accelerate training while saving memory. We recommend you always use flash attention. + +2. 以上参数中,值得注意的是优化策略的选择的参数`plugin`,其中包含了: + +- Torch DDP:它是 torch.nn.parallel.DistributedDataParallel 的封装,可用于使用数据并行性训练模型。 +- Torch FSD:它是 torch.distributed.fsdp.FullyShardedDataParallel 的封装,可用于使用 zero-dp 训练模型。 +- Low Level Zero:它封装了 colossalai.zero.low_level.LowLevelZeroOptimizer,并可用于使用 zero-dp 训练模型。它仅支持 zero 阶段 1 和阶段 2。 +- Gemini:它封装了 Gemini,实现了基于分块和异构内存管理的 Zero-3。 +- Hybrid Parallel:它提供了一个整洁的接口,集成了 Shardformer、管道管理器、混合精度训练、TorchDDP 和 Zero 阶段 1/2 功能。使用此插件,Transformer 模型可以轻松地以任何组合的张量并行、管道并行和数据并行(DDP/Zero)高效训练,以及各种加速和内存节省的优化工具。有关支持的并行策略和优化工具的详细信息在下面的部分中解释。 + +如果您选择了Hybrid Parallel,您可以更改python训练脚本中第181行到第190行的HybridParallelPlugin配置代码,更改tp_size和pp_size以更改张量并行和管道并行的大小。HybridParallelPlugin是通过shardformer实现张量并行,在该插件中,可设置tp_size确定张量并行组的大小,此外,还有多个参数可设置张量并行时的优化特性: + +enable_all_optimization(布尔类型,可选项):是否启用Shardformer支持的所有优化方法,目前所有优化方法包括融合归一化、flash attention和JIT。默认为False。 enable_fused_normalization(布尔类型,可选项):是否在Shardformer中启用融合归一化。默认为False。 enable_flash_attention(布尔类型,可选项):是否在Shardformer中启用flash attention。默认为False。 enable_jit_fused(布尔类型,可选项):是否在Shardformer中启用JIT。默认为False。 enable_sequence_parallelism(布尔类型):是否在Shardformer中启用序列并行性。默认为False。 enable_sequence_overlap(布尔类型):是否在Shardformer中启用序列重叠性。默认为False。 + +HybridParallelPlugin通过设置pp_size确定流水线并行组的大小,num_microbatches设置流水线并行时将整个batch划分为小batch的数量,microbatch_size可设置小batch的大小,插件会优先使用num_microbatches来确定micro batch的配置。 HybridParallelPlugin的设置示例如下: + +``` +plugin = HybridParallelPlugin( + tp_size=4, + pp_size=2, + num_microbatches=None, + microbatch_size=1, + enable_jit_fused=False, + zero_stage=0, + precision="fp32", + initial_scale=1, + ) +``` + +3. 在本例中,我们用如下启动命令运行python训练脚本,其位于`./train.sh`。如您想进行更改相关配置,可更改此文件。 +``` +#!/usr/bin/env bash +SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +DATASET_DIR="/mnt/dataset" +OUTPUT_DIR="/output" + +TRAINING_DATASET_DIR=${DATASET_DIR}/RedPajama-Data-1T-Sample +TENSORBOARD_OUTPUT_DIR=${OUTPUT_DIR}/tensorboard +CHECKPOINT_SAVE_DIR=${OUTPUT_DIR}/checkpoint +SAVE_MODEL_PATH=${CHECKPOINT_SAVE_DIR} + +mkdir -p ${TENSORBOARD_OUTPUT_DIR} +mkdir -p ${SAVE_MODEL_PATH} + +cd ColossalAI +cd ColossalAI/examples/language/llama2/ + +colossalai run --nproc_per_node ${NPROC_PER_NODE} ${SCRIPT_DIR}/pretrain.py \ + --plugin ${strategy} \ + --dataset ${TRAINING_DATASET_DIR} \ + --save_dir ${SAVE_MODEL_PATH} \ + --num_epochs ${num_epochs} \ + --batch_size ${batch_size} \ + --max_length ${max_len} \ + save_interval ${save_interval}\ + --lr ${lr} \ + --log_dir ${TENSORBOARD_OUTPUT_DIR} \ +``` + +LLaMA2 将会就此开始训练,你同时也可以在项目页面查看输出和结果。 diff --git a/docs/docs/blog/colossalai_parallelism/images/task_create.jpg b/docs/docs/blog/colossalai_parallelism/images/task_create.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0c6ba8a34bdd574f683ca7050265eb019358e997 GIT binary patch literal 287600 zcmbTdcUTkM);p~wwN+`Tn5l?}h-lQ+p1mX@ zqJ|I=k=9d?5srWo@mGn6t|>b`eX6Ve^eKm~7s%ep)sBcr?Om!7xv}0LL!MdE2W3a% 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"blog/inference_api_llama2_70b_vllm/README", + ], + }, ], contactSidebar: [ `contact/${ diff --git a/docusaurus/docusaurus.config.js b/docusaurus/docusaurus.config.js index 4f1660d..b54ff46 100644 --- a/docusaurus/docusaurus.config.js +++ b/docusaurus/docusaurus.config.js @@ -107,7 +107,6 @@ const config = { position: "right", label: "Documentation", }, - { to: "/blog", label: "Blog", position: "right" }, ], }, footer: { @@ -120,10 +119,6 @@ const config = { label: "Tutorial", to: "/docs/intro", }, - { - label: "Blog", - to: "/blog", - }, ], }, { diff --git a/scripts/preview.sh b/scripts/preview.sh index 3b6ee59..808365e 100755 --- a/scripts/preview.sh +++ b/scripts/preview.sh @@ -24,7 +24,7 @@ mkdir -p $DOCUSAURUS_DIR/blog # replace the docs and blog cp -r $ROOT_DIR/docs/docs/* $DOCUSAURUS_DIR/docs -cp -r $ROOT_DIR/docs/blog/* $DOCUSAURUS_DIR/blog +#cp -r $ROOT_DIR/docs/blog/* $DOCUSAURUS_DIR/blog cp -r $ROOT_DIR/docs/sidebars.js $DOCUSAURUS_DIR/ From 3cbd95cd4688be689c4ce1d0fbe4dba4f154f4ba Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Thu, 29 Feb 2024 18:24:47 +0800 Subject: [PATCH 2/4] add advanced blog tutorial --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 875b02b..563fea3 100644 --- a/.gitignore +++ b/.gitignore @@ -165,3 +165,7 @@ cython_debug/ # cache .cache/ + +# IDE +.idea/ +.vscode/ \ No newline at end of file From 226a0af329dabc3402891b63cf44535a01cdcd87 Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Tue, 5 Mar 2024 18:37:01 +0800 Subject: [PATCH 3/4] add inference_api_tensorrt_triton --- .../inference_api_tensorrt_triton/README.md | 108 ++++++++++++++++++ .../images/infernce_api_create.jpg | Bin 0 -> 359563 bytes docs/sidebars.js | 1 + scripts/build.sh | 3 +- 4 files changed, 110 insertions(+), 2 deletions(-) create mode 100644 docs/docs/blog/inference_api_tensorrt_triton/README.md create mode 100644 docs/docs/blog/inference_api_tensorrt_triton/images/infernce_api_create.jpg diff --git a/docs/docs/blog/inference_api_tensorrt_triton/README.md b/docs/docs/blog/inference_api_tensorrt_triton/README.md new file mode 100644 index 0000000..1184196 --- /dev/null +++ b/docs/docs/blog/inference_api_tensorrt_triton/README.md @@ -0,0 +1,108 @@ +# 推理API使用: 利用 Triton 推理服务器 部署 TensorRT 模型 + +[TensorRT](https://github.com/NVIDIA/TensorRT)是一个有助于在NVIDIA图形处理单元(GPU)上高性能推理c++库。它旨在与TesnsorFlow、Caffe、Pytorch以及MXNet等训练框架以互补的方式进行工作,专门致力于在GPU上快速有效地进行网络推理。 + +[Triton推理服务器](https://github.com/triton-inference-server/server)是一种开源推理服务软件,可以简化AI推理。Triton使团队能够从多个深度学习和机器学习框架(包括TensorRT、TensorFlow、PyTorch、ONNX、OpenVINO、Python、RAPIDS FIL等)部署任何AI模型。 + +这个快速入门指南展示了如何使用 Colossal-AI 云平台在 Triton 推理服务器上使用 TensorRT 加速部署一个简单的 DenseNet 模型。让我们逐步讨论如何在云平台上使用 TensorRT 优化模型、在 Triton 推理服务器上部署它,并构建一个客户端来查询该模型的过程。 + +Tags:  推理API,TensorRT,Triton Server + +## 运行环境要求 + +镜像:推荐使用官方镜像 triton-server-24.1 + +GPU规格:推荐使用H800 (1块) + +## 1. 准备TensorRT模型 + +可使用云平台内置的 DenseNet TensorRT 模型,或从 [Triton Server TensorRT Conversion](https://github.com/NVIDIA/TensorRT/tree/main/quickstart/deploy_to_triton#step-1-optimize-your-model-with-tensorrt) 中按步骤转换获得TensorRT 模型,并上传到 Colossal-AI 云平台。 + + +## 2. 启动推理API + +1. 在控制台中选择`推理API`选项,点击 `创建新的API`; + +2. 填写对应的API名称和描述; + +3. 挂载文件:将之前准备好的 DenseNet TensorRT 模型挂载到 Container 中,在这个例子里,模型被挂载到了 `/mnt/model/densenet_tensorrt`; + +4. 启动命令:填入 `tritonserver --model-repository=/mnt/model --http-port 8080`,在这行命令中,我们使用了1块 GPU 部署 Triton 的推理API服务; + +5. 镜像设置:选择官方镜像 `triton-server`; + +6. 显卡配置:推荐选择 `NVIDA-H800`,每个副本 GPU 数量设置为 `1`; + +7. 最后点击 `创建`,启动API推理服务,等待2-3分钟api服务完成启动; + +![infernce_api_create](./images/infernce_api_create.jpg) + +## 3. 测试推理API + +1. 待API创建完成后,从页面获`API URL`,如 `http://inference-api-77.colossal-ai.platform.luchentech.com`; + +2. 向此`API URL`发送如下示例请求,注意url应放置前一步获取的`API URL` +```bash +docker pull nvcr.io/nvidia/tritonserver:24.01-py3-sdk +docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:24.01-py3-sdk + +/workspace/install/bin/image_client -url http://inference-api-77.colossal-ai.platform.luchentech.com -m densenet_tensorrt -c 3 -s INCEPTION /workspace/images/mug.jpg +``` + +3. 或者也可以使用 python client 发送请求: + +安装相关python环境 +```bash +wget -O img1.jpg "https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg" +pip install torchvision +pip install attrdict +pip install nvidia-pyindex +pip install tritonclient[all] +``` + +运行下方python脚本 +```python +import numpy as np +from torchvision import transforms +from PIL import Image +import tritonclient.http as httpclient +from tritonclient.utils import triton_to_np_dtype + +def dn50_preprocess(img_path="img1.jpg"): + img = Image.open(img_path) + preprocess = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + return preprocess(img).numpy() + +transformed_img = dn50_preprocess() + +# Setup a connection with the Triton Inference Server. +triton_client = httpclient.InferenceServerClient(url="http://inference-api-77.colossal-ai.platform.luchentech.com") + +# Specify the names of the input and output layer(s) of our model. +test_input = httpclient.InferInput("input", transformed_img.shape, datatype="FP32") +test_input.set_data_from_numpy(transformed_img, binary_data=True) + +test_output = httpclient.InferRequestedOutput("output", binary_data=True, class_count=1000) + +# Querying the server +results = triton_client.infer(model_name="densenet_tensorrt", inputs=[test_input], outputs=[test_output]) +test_output_fin = results.as_numpy('output') + +print(test_output_fin[:5]) + +``` + +4. API 服务器会处理请求, 并返回如下格式的结果: +``` +Request 0, batch size 1 +Image '/workspace/images/mug.jpg': + 15.346230 (504) = COFFEE MUG + 13.224326 (968) = CUP + 10.422965 (505) = COFFEEPOT +``` + diff --git a/docs/docs/blog/inference_api_tensorrt_triton/images/infernce_api_create.jpg b/docs/docs/blog/inference_api_tensorrt_triton/images/infernce_api_create.jpg new file mode 100644 index 0000000000000000000000000000000000000000..708b5629aaf76ed7dd7c26db041e41cbfd5f4dbc GIT binary patch literal 359563 zcmeFZX;c$w*Df5}ww+pSLRtZQT-~H`6b5bI z?tsBULg^8NgYG{b^`ZQ@uU;on z(g*0ZH3GUhG}(2~c-@(QjI9>NC;l;ZSr|vr&(WcCjx3BLNQC{+_#`y8I`f~#C;rno z;9SJQ`Ki!3E+J{0h(7B^m7Jw7Iq5uBg_L93JZYQLZh9q9WZ;C<_R-i@#m8Xz`L|%U3O5v22kUbpDcW7Js*NncniX`s+61y|WDrjm$G{ z$yF=t9gjz{C5qOaKYI`W^3lXQPnB(NjkjzilEQzx{%XC6yH8HZ?FZ@)4o-U*QJLix zm9H)KMqId8was!D^1#6p(c@OX@4n3CqtJ)`$YR}q?rsLn>zjqy{cC!QzJcZj-F-Q9 z_?RB-n?>Jz^BuH(yK>2r@4j6MgAVI`yLQQDd;IZ??|u%yE&rfzPWbKC_|kPdcb~{~ zCq~>|X0U|>oXlc9P`uqAK>wY+%K=@kG&C~adPn8xbe<6@Ilx_BSoF0W_Wfe$=6Z|u 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zvr3u3Za**%@Wi?aTm)Yj8WjpWDv)XSanFXfCfu2>UG4mcn%$Le`1@qiu`+k+#60{! z0JDtgHh`ZXKDpjXqnOfiuzW%#rX9T;O5tq3$DO|5I-+}uSI182IOf9{z{qb41BTvI z&a7P)`n6vb%d zIq#ASO>+y>uAKrrqCrkA*T$0bt_!gRI_UWI)dA+uS&Wk6?2YSXkd63~>V_`UVSTI1T3R$ zhX=qs2LdR!L1NKJ0ymb`8Kh0!0;O_GK|Ceju9 Date: Tue, 5 Mar 2024 18:50:51 +0800 Subject: [PATCH 4/4] Delete .idea directory --- .idea/.gitignore | 8 -------- .idea/Cloud-Platform-Docs.iml | 8 -------- .idea/inspectionProfiles/profiles_settings.xml | 6 ------ .idea/misc.xml | 7 ------- .idea/modules.xml | 8 -------- .idea/vcs.xml | 6 ------ 6 files changed, 43 deletions(-) delete mode 100644 .idea/.gitignore delete mode 100644 .idea/Cloud-Platform-Docs.iml delete mode 100644 .idea/inspectionProfiles/profiles_settings.xml delete mode 100644 .idea/misc.xml delete mode 100644 .idea/modules.xml delete mode 100644 .idea/vcs.xml diff --git a/.idea/.gitignore b/.idea/.gitignore deleted file mode 100644 index 1c2fda5..0000000 --- a/.idea/.gitignore +++ /dev/null @@ -1,8 +0,0 @@ -# Default ignored files -/shelf/ -/workspace.xml -# Editor-based HTTP Client requests -/httpRequests/ -# Datasource local storage ignored files -/dataSources/ -/dataSources.local.xml diff --git a/.idea/Cloud-Platform-Docs.iml b/.idea/Cloud-Platform-Docs.iml deleted file mode 100644 index d9e6024..0000000 --- a/.idea/Cloud-Platform-Docs.iml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml deleted file mode 100644 index 105ce2d..0000000 --- a/.idea/inspectionProfiles/profiles_settings.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml deleted file mode 100644 index ea56748..0000000 --- a/.idea/misc.xml +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml deleted file mode 100644 index 6aee249..0000000 --- a/.idea/modules.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml deleted file mode 100644 index c8397c9..0000000 --- a/.idea/vcs.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - - - \ No newline at end of file