wandb: Tracking run with wandb version 0.15.5 wandb: W&B syncing is set to `offline` in this directory. wandb: Run `wandb online` or set WANDB_MODE=online to enable cloud syncing. /home/zhangmengjie/anaconda3/envs/torch1/lib/python3.7/site-packages/gym/envs/registration.py:556: UserWarning: WARN: The environment Ant-v2 is out of date. You should consider upgrading to version `v4`. f"The environment {id} is out of date. You should consider " Error compiling Cython file: ------------------------------------------------------------ ... See c_warning_callback, which is the C wrapper to the user defined function ''' global py_warning_callback global mju_user_warning py_warning_callback = warn mju_user_warning = c_warning_callback ^ ------------------------------------------------------------ /home/zhangmengjie/anaconda3/envs/torch1/lib/python3.7/site-packages/mujoco_py/cymj.pyx:92:23: Cannot assign type 'void (const char *) except * nogil' to 'void (*)(const char *) noexcept nogil' Error compiling Cython file: ------------------------------------------------------------ ... See c_warning_callback, which is the C wrapper to the user defined function ''' global py_error_callback global mju_user_error py_error_callback = err_callback mju_user_error = c_error_callback ^ ------------------------------------------------------------ /home/zhangmengjie/anaconda3/envs/torch1/lib/python3.7/site-packages/mujoco_py/cymj.pyx:127:21: Cannot assign type 'void (const char *) except * nogil' to 'void (*)(const char *) noexcept nogil' Compiling /home/zhangmengjie/anaconda3/envs/torch1/lib/python3.7/site-packages/mujoco_py/cymj.pyx because it changed. [1/1] Cythonizing /home/zhangmengjie/anaconda3/envs/torch1/lib/python3.7/site-packages/mujoco_py/cymj.pyx wandb: Waiting for W&B process to finish... (failed 1). wandb: You can sync this run to the cloud by running: wandb: wandb sync /home/zhangmengjie/PID/Python/ERL-Re2-main/wandb/offline-run-20230721_165346-awq1hazo wandb: Find logs at: ./wandb/offline-run-20230721_165346-awq1hazo/logs

根据错误信息，您在使用Wandb库时，将同步模式设置为了`offline`，因此无法进行云端同步。要解决这个问题，您可以执行以下操作之一：

1. 在命令行中运行 `wandb online` 命令，将同步模式切换为在线模式。
2. 在代码中添加以下代码行，将同步模式切换为在线模式：

import wandb
wandb.init(mode='online')

这两种方法都将启用云端同步，使您能够将运行结果上传到Wandb云端。

另外，关于您提到的Mujoco编译错误，请确保您已经正确安装了Mujoco和相应的依赖项，并且已经按照官方文档提供的步骤进行配置。如果问题仍然存在，请参考相关文档或联系Mujoco的支持团队以获取进一步的帮助。

最后，您还收到了关于环境`Ant-v2`版本过时的警告。如果可能的话，建议将环境升级到`v4`版本以解决此警告。

希望这些信息能对您有所帮助！如果您还有其他问题，请随时提问。

相关问题

wandb pytorch

Wandb (Weights & Biases) is a popular tool used for tracking and visualizing machine learning experiments. It provides a Python library that can be integrated with PyTorch, among other deep learning frameworks.

To use Wandb with PyTorch, you will need to install the wandb library first by running the following command:

pip install wandb

Once installed, you can import and initialize wandb in your PyTorch script or notebook. Here's an example:

import wandb
import torch
import torch.nn as nn
import torch.optim as optim

# Initialize wandb
wandb.init(project="your-project-name", entity="your-username")

# Define your PyTorch model
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        # Add your model layers here

    def forward(self, x):
        # Define the forward pass of your model here
        pass

# Instantiate your model
model = MyModel()

# Define your loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
for epoch in range(num_epochs):
    # Training code goes here
    
    # Log metrics to wandb
    wandb.log({"loss": loss.item(), "accuracy": accuracy})

    # Validation code goes here

# Save the trained model
torch.save(model.state_dict(), "model.pth")

# Finish wandb run
wandb.finish()

In this example, wandb.init() initializes the wandb run and connects it to your project. You can specify the project name and your username or organization in the arguments. Throughout your training loop or experiment, you can use wandb.log() to log metrics such as loss and accuracy. Finally, wandb.finish() is called to finish the wandb run.

You can then view and analyze your experiment results on the Wandb platform.

怎么替换wandb中的

你可以使用以下步骤替换wandb：

1. 导入所需的库：

import mlflow
from mlflow.tracking import MlflowClient

2. 初始化MLflow客户端：

mlflow.set_tracking_uri("your_mlflow_tracking_uri")
client = MlflowClient()

3. 创建一个实验：

experiment_name = "your_experiment_name"
experiment_id = client.create_experiment(experiment_name)

4. 设置活动实验：

mlflow.set_experiment(experiment_name)

5. 启动训练：

with mlflow.start_run() as run:
    # 执行训练过程

6. 在训练完成后，保存训练结果：

# 保存模型
model_path = "your_model_path"
mlflow.pytorch.save_model(model, model_path)

# 记录模型信息
mlflow.log_artifact(model_path, artifact_path="models")

7. 查看训练结果和模型的MLflow UI链接：

experiment_url = f"your_mlflow_tracking_uri/#/experiments/{experiment_id}"
run_url = f"{experiment_url}/runs/{run.info.run_id}"
print(f"Experiment URL: {experiment_url}")
print(f"Run URL: {run_url}")