> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gpuhub.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Overview
> Our **Cloud GPU Container Instances** is a container that uses Docker technology for resource allocation and isolation. It has the advantages of lower performance loss and higher efficiency compared to virtual machine instances.
Docker is not supported within the container instance.
## About System Disk
The instance's system disk and data disk are generally local SSDs, with a small number being cloud disks.
Local data disks offer good performance but lack redundant copies.
Due to the inherent lifespan limitations of local disks, there is a probability of failure.
Important data should be regularly backed up to [file storage](/data/file-storage) or locally.
Besides, GPUhub does not provide reliability guarantees or services for such storage products.
## Instance GPU/CPU/Memory Allocation
In GPUhub the method for renting GPUs involves selecting a physical host and then creating an instance with N GPU cards on that host. The allocation logic for memory and CPU configurations is based on a multiple of the number of GPUs.
For example, if the CPU and memory allocation rules on the host are 32GB/GPU and 8 cores/GPU, then:
* Renting 1 GPU would result in an instance configuration of **1 GPU, 8 CPU cores, and 32GB of memory**.
* Renting 2 GPUs would result in an instance configuration of **2 GPUs, 16 CPU cores, and 64GB of memory**,
* and so on.
Therefore, when you rent an instance, you should choose an appropriate physical host based on your needs and then select the number of GPUs required.
## Directories within the Instance
To check the disk usage within the instance, please execute the command `source /root/.bashrc` in the terminal.
| Name | Path | Size | Performance | Description |
| ------------------- | ----------------------------------------------------------------------------------- | ----------------------------------------- | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| System Disk | Root directory / and all subdirectories (except for the special paths listed below) | 30GB | Local disk, Fast | Data will not be lost when the instance is shut down. System dependencies and Python packages are typically installed here. It can also be used to store small amounts of data such as code. The system disk is migrated when the [instance is migrated](), and it is included in the image when [saving an image](). |
| Data Disk | /root/gpuhub-tmp | Starting from 50GB, can be expanded | Local disk, Fast | Data will not be lost when the instance is shut down. Suitable for storing data with high read/write I/O requirements. Data on the data disk cannot be saved to an image. If you need to migrate data from the data disk, please refer to the [instance migration process (within the same region)](). |
| GPUhub File Storage | /root/autodl-fs | Free 20GB, billed for additional capacity | Network disk, Moderate | Enables file synchronization and sharing between different instances within the same region. |
| Public Data | /root/gpuhub-pub | Read-only | Network disk, Slow | Directory for commonly used public data and models provided by the platform. Read-only, does not support writing files. |
### Further Explanation of the System Disk
In the Linux operating system, almost everything exists in the form of folders or files, and these folders/files share a common root directory `/`. If we install the Linux operating system on a disk A (with no other partitions), the root directory will contain the following default system directories:
```bash bash theme={null}
$ ls /
bin etc lib mnt proc run tmp var boot dev home root srv sys usr
```
By default, writing files to any directory ultimately writes to disk A. In an GPUhub instance, the system disk refers to this disk A where the system is installed. Writing files to any location under the root directory `/` means writing to the system disk. However, there are exceptions.
* For example, if there is another new disk B and we want to use it in the Linux system, we can mount this disk device to a specific directory (assuming the mount directory is `/root/gpuhub-tmp`).
In this case, writing "folders/files" to the `/root/gpuhub-tmp` directory means writing to disk B, not disk A. Here, you should understand the boundary between the system disk and the data disk in GPUhub.
* For example, system software installed in GPUhub instances and Python dependencies installed using the built-in Miniconda are all installed on the system disk.
## JupyterLab Working Directory
The working path of JupyterLab is `/root`. The data disk, network disk, and public data directory are all located under the `/root` directory.
Therefore, when you enter JupyterLab, you can directly see these directories created by the platform by default. If you need to view the root directory `/`, you can enter the terminal and use command-line operations.