Traditional (HPC) systems provide a standard preset environment to support scientific computation. However, HPC development needs to provide support for more and more diverse applications, such as artificial intelligence and big data. The standard preset environment can no longer meet these diverse requirements. If users still run these emerging applications on HPC systems, they need to manually maintain the specific dependencies (libraries, environment variables, and so on) of their applications. This increases the development and deployment burden for users. Moreover, the multi-user mode brings about privacy problems among users. Containers like Docker and Singularity can encapsulate the job's , but in a highly customized HPC system, cross-environment application deployment of Docker and Singularity is limited. The introduction of container images also imposes a maintenance burden on system administrators. Facing the above-mentioned problems, in this paper we propose a self-deployed (SDEE) for HPC. SDEE combines the advantages of traditional virtualization and modern containers. SDEE provides an isolated and customizable environment (similar to a virtual machine) to the user. The user is the root user in this environment. The user develops and debugs the application and deploys its special dependencies in this environment. Then the user can load the job to compute nodes directly through the traditional HPC job management system. The job and its dependencies are analyzed, packaged, deployed, and executed automatically. This process enables transparent and rapid job deployment, which not only reduces the burden on users, but also protects user privacy. Experiments show that the overhead introduced by SDEE is negligible and lower than those of both Docker and Singularity.