JS7 JobScheduler
JS7 JobScheduler, an Open Source Workload Automation System, is designed for performance and resilience. JS7 implements state-of-the-art security standards. It offers unlimited performance for parallel executions of jobs and workflows.
JS7 provides cross-platform job execution and managed file transfer. It supports complex dependencies without the need for coding. The JS7 REST-API allows automation of inventory management and job control.
JS7 can operate thousands of Agents across any platform in parallel.
Platforms
- Cloud scheduling for Docker®, OpenShift®, Kubernetes® etc.
- True multi-platform scheduling on premises, for Windows®, Linux®, AIX®, Solaris®, macOS® etc.
- Hybrid cloud and on-premises use
User Interface
- Modern GUI with no-code approach for inventory management, monitoring, and control using web browsers
- Near-real-time information provides immediate visibility to status changes, log outputs of jobs and workflows.
- Multi-client functionality, role-based access management
- OIDC authentication and LDAP integration
High Availability
- Redundancy & Resilience based on asynchronous design and autonomous Agents
- Clustering of all JS7 Products, automatic fail-over and manual switch-over
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Greatmail
Reliable cloud-based email hosting includes features such as spam filtering, antivirus protection, ample storage, and webmail access. It seamlessly integrates with Outlook as well as various other POP3/IMAP clients. For users with high sending needs, it offers a robust SMTP service designed for responsible senders. Additionally, there is an outbound relay service tailored for transactional emails, marketing campaigns, newsletters, and diverse applications. The infrastructure supports dedicated email servers, clustering, and multiple IP load balancing to accommodate high-volume senders effectively. With a fixed monthly fee, users benefit from unlimited sending capabilities and reputation monitoring. Greatmail stands out as an email service provider (ESP) focused on delivering business-class email hosting, SMTP hosting, and specialized email servers. Furthermore, for ISPs, software developers, and cloud architects, we also create custom solutions featuring dedicated IP servers and load-balanced configurations with multiple servers to meet specific processing needs. This commitment to adaptability ensures that all clients receive the best possible service, tailored to their unique requirements.
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AWS ParallelCluster
AWS ParallelCluster is a free, open-source tool designed for efficient management and deployment of High-Performance Computing (HPC) clusters within the AWS environment. It streamlines the configuration of essential components such as compute nodes, shared filesystems, and job schedulers, while accommodating various instance types and job submission queues. Users have the flexibility to engage with ParallelCluster using a graphical user interface, command-line interface, or API, which allows for customizable cluster setups and oversight. The tool also works seamlessly with job schedulers like AWS Batch and Slurm, making it easier to transition existing HPC workloads to the cloud with minimal adjustments. Users incur no additional costs for the tool itself, only paying for the AWS resources their applications utilize. With AWS ParallelCluster, users can effectively manage their computing needs through a straightforward text file that allows for the modeling, provisioning, and dynamic scaling of necessary resources in a secure and automated fashion. This ease of use significantly enhances productivity and optimizes resource allocation for various computational tasks.
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Bright Cluster Manager
Bright Cluster Manager offers a variety of machine learning frameworks including Torch, Tensorflow and Tensorflow to simplify your deep-learning projects.
Bright offers a selection the most popular Machine Learning libraries that can be used to access datasets. These include MLPython and NVIDIA CUDA Deep Neural Network Library (cuDNN), Deep Learning GPU Trainer System (DIGITS), CaffeOnSpark (a Spark package that allows deep learning), and MLPython.
Bright makes it easy to find, configure, and deploy all the necessary components to run these deep learning libraries and frameworks. There are over 400MB of Python modules to support machine learning packages. We also include the NVIDIA hardware drivers and CUDA (parallel computer platform API) drivers, CUB(CUDA building blocks), NCCL (library standard collective communication routines).
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