我是新手git-lfs
,以下是我配置并尝试推送的步骤git-lfs
git clone git@gitlab.example.com:group/project.git
将 tar.gz lfs 文件从不同的 repo 复制到我当前的 repo,然后
git lfs install
git lfs track "*.tar.gz"
git add .gitattributes
git commit -m "message"
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同时git push origin master
:
我面临的错误:
(missing) code.tar.gz (f2b4bf22bcb011fef16f80532247665d15edbb9051***)
Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done.
hint: Your push was rejected due to missing or corrupt local objects.
hint: You can disable this check with: 'git config lfs.allowincompletepush true'
error: failed to push some refs to 'git@gitlab.example.com:group/project.git' …
Run Code Online (Sandbox Code Playgroud) 我刚刚开始学习 Pytorch 并创建了我的第一个 CNN。该数据集包含 3360 张 RGB 图像,我将它们转换为[3360, 3, 224, 224]
张量。数据和标签在dataset(torch.utils.data.TensorDataset)
. 下面是训练代码。
def train_net():
dataset = ld.load()
data_iter = Data.DataLoader(dataset, batch_size=168, shuffle=True)
net = model.VGG_19()
summary(net, (3, 224, 224), device="cpu")
loss_func = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9, dampening=0.1)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)
for epoch in range(5):
print("epoch:", epoch + 1)
train_loss = 0
for i, data in enumerate(data_iter, 0):
x, y = data
print(x.dtype)
optimizer.zero_grad()
out = net(x)
loss = loss_func(out, y)
loss.backward()
optimizer.step() …
Run Code Online (Sandbox Code Playgroud) 我想发布一个带有缩略图的嵌入内容,以及我电脑上的本地文件。每当我尝试运行此代码时,图像都会发送到嵌入之外。
embedVar = discord.Embed(title="title",
description="desc.",
color=0X19A6FF)
file = discord.File("file_location/file.png", filename="image.png")
embedVar.set_thumbnail(url="attachment://file_location/file.png")
await message.channel.send(file=file, embed=embedVar)
Run Code Online (Sandbox Code Playgroud) 我有一个输入和一个按钮,我需要在按下按钮时保存文本输入的值。
dcc.Input(id='username', value='Initial Value', type='text'),
html.Button(id='submit-button', children='Submit'),
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我的回调中遗漏了一些东西吗?
@app.callback(Output('output_div','children' ),
[Input('submit-button')],
[State('input-element', 'value')],
[Event('submit-button', 'click'])
def update_output(input_element):
print(input_element)
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谢谢
我对docker完全陌生,我需要在docker环境中运行代码。
我在构建 Dockerfile 时遇到错误:
我通过 hyper-V 运行 Ubuntu 20.04,当我构建 Dockerfile 时,我收到以下消息:
Step 4/20 : RUN curl -o ~/miniconda.sh -O https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh && chmod +x ~/miniconda.sh && ~/miniconda.sh -b -p /opt/conda && rm ~/miniconda.sh && /opt/conda/bin/conda install numpy pyyaml scipy ipython mkl && /opt/conda/bin/conda install -c soumith magma-cuda90 && /opt/conda/bin/conda clean -ya <br />
---> Running in 9758f4fe60a4 <br />
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 …
Run Code Online (Sandbox Code Playgroud) AWS Elastic Beanstalk 失败并出现来自守护程序的错误响应:
您无法删除正在运行的容器 c911e29b2ce2283bae300645bf2f034f7bf51c07b7ec72c6ebfa133d531a6129。
在尝试移除或强制移除之前停止容器
错误日志如下:
2020/04/26 07:15:31.279931 [INFO] Running command /bin/sh -c docker rmi `docker images -aq`
2020/04/26 07:15:31.369738 [ERROR] Error response from daemon: conflict: unable to delete 274bac0a2621 (cannot be forced) - image has dependent child images
Error response from daemon: conflict: unable to delete 6040329cd021 (cannot be forced) - image has dependent child images
Error response from daemon: conflict: unable to delete 1020a81a3f38 (cannot be forced) - image is being used by running container c911e29b2ce2 …
Run Code Online (Sandbox Code Playgroud) 我知道 Ansible 在 Linux 上运行,但如果使用 WinRm 远程管理 Windows 系统。我是否可以判断(检测)远程主机是否是 Windows 而不是 Linux?
class Base(object):
def __init__(self):
self.fname="MS"
self.lname="Dhoni"
class Child(Base):
def __init__(self):
self.fname="kohli"
super(Base).__init__()
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上面代码中 super 方法的用途是什么甚至评论super(Base).__init__()
我正在获取输出kohli
请解释
我对 Docker 完全陌生。我想使用 Docker 安装Transmission(即我不想使用linuxserver.io/transmission)
目前我的Dockerfile
是:
RUN apt-get update -y
RUN apt-get install transmission-daemon -y
EXPOSE 9091 51413/tcp 51413/udp
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但是如何给出settings.json文件呢?如何运行我的 Dockerfile?我需要docker-compose.yml
文件吗?
先感谢您 :)
transmission docker dockerfile docker-compose transmission-daemon
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