This DGX Best Practices Guide provides recommendations to help administrators and users administer and manage DGX products.

1. DGX-1 Best Practices

NVIDIA has created the DGX-1 as an appliance to make administration and operation as simple as possible. However, like any computational resource it still requires administration. This section discusses some of the best practices around configuring and administering a single DGX-1 or several DGX-1 appliances.

There is also some discussion about how to plan for external storage, networking, and other configuration aspects for the DGX-1.

1.1. Storage

For deep learning to be effective and to take full advantage of the DGX-1, the various aspects of the DGX-1 have to be balanced. This includes storage and IO. This is particularly important for feeding data to the GPUs to keep them busy and dramatically reduce run times for models. This section presents some best practices for storage within and outside of, the DGX-1. It also talks about storage considerations as the number of DGX-1 units are scaled out.

1.1.1. Internal Storage

The first storage consideration is storage within the DGX-1 itself. For the best possible performance, a NFS read cache has been included in the DGX-1 appliance using the Linux cacheFS capability. It uses four SSD’s in a RAID-0 group. The drives are connected to a dedicated hardware RAID controller.

Deep learning I/O patterns typically consist of multiple iterations of reading the training data. The first pass through the data is sometimes referred to the cold start. Subsequent passes through the data can avoid rereading the data from the filesystem if adequate local caching is provided on the node. If you can estimate the maximum size of your data, you can architect your system to provide enough cache so that the data only needs to be read once during any training job. A set of very fast SSD disks can provide an inexpensive and scalable way of providing adequate caching for your applications. The DGX-1 read cache was created for precisely this purpose.

The purpose of this cache is for storing training and validation data for reading by the frameworks. During the first epoch of training a framework, the training data is read and used to start training the model. The NFS cache is a read cache so that all of the data that is read for the first epoch is cached on the RAID-0 group. Subsequent reads of the data are done from the NFS cache and not the central repository that was used in the first epoch. As a result, the IO is much faster after the first epoch.

For training the best model possible, the input data is randomized. This adds some additional statistical noise to the training and also keeps the model from being “overfit” on the training data (in other words, trained very well on the training data but doesn’t do well on the validation data). Randomizing the order of the data for training puts pressure on the data access. The IO pattern becomes random oriented rather than streaming oriented. The DGX-1 NFS cache is SSD based with a very high level of random IOPs performance.

The benefit of adequate caching is that your external filesystem does not have to provide maximum performance during a cold start (the first epoch), since this first pass through the data is only a small part of the overall training time. For example, typical training sessions can iterate over the data 100 times. If we assume a 5x slower read access time during the first cold start iteration vs the remaining iterations with cached access, then the total run time of training increases by the following amount.
  • 5x slower shared storage 1st iteration + 99 local cached storage iterations
    • > 4% increase in runtime over 100 iterations

Even if your external file system cannot sustain peak training IO performance, it has only a small impact on overall training time. This should be considered when creating your storage system to allow you to develop the most cost-effective storage systems for your workloads.

By default, the DGX-1 comes with four SSD devices connected to the RAID controller.
There are more slots that are open in the DGX-1 for other drives but you cannot put additional drives into the system without voiding your warranty.

1.1.2. External Storage

As an organization scales out their GPU enabled data center, there are many shared storage technologies which pair well with GPU applications. Since the performance of a GPU enabled server is so much greater than a traditional CPU server, special care needs to be taken to ensure the performance of your storage system is not a bottleneck to your workflow.

Different data types require different considerations for efficient access from filesystems. For example:
  • Running parallel HPC applications may require the storage technology to support multiple processes accessing the same files simultaneously.
  • To support accelerated analytics, storage technologies often need to support many threads with quick access to small pieces of data.
  • For vision based deep learning, accessing images or video used in classification, object detection or segmentation may require high streaming bandwidth, fast random access, or fast memory mapped (mmap()) performance.
  • For other deep learning techniques, such as recurrent networks, working with text or speech can require any combination of fast bandwidth with random and small files.

HPC workloads typically drive high simultaneous multi-system write performance and benefit greatly from traditional scalable parallel file system solutions. You can size HPC storage and network performance to meet the increased dense compute needs of GPU servers. It is not uncommon to see per-node performance increases from between 10-40x for a 4 GPU system vs a CPU system for many HPC applications.

Data Analytics workloads, similar to HPC, drive high simultaneous access, but are more read focused than HPC. Again, it is important to size Data Analytics storage to match the dense compute performance of GPU servers. As you adopt accelerated analytics technologies such as GPU-enabled in-memory databases, make sure that you can populate the database from your data warehousing solution quickly to minimize startup time when you change database schemas. This may require a network with 10 Gbe for greater performance. To support clients at this rate, you may have to revisit your data warehouse architecture to identify and eliminate bottlenecks.

Deep learning is a fast evolving computational paradigm and it is important to know what your requirements are in the near and long term to properly architect a storage system. The ImageNet database is often used as a reference when benchmarking deep learning frameworks and networks. The resolution of the images in ImageNet are 256x256. However, it is more common to find images at 1080p or 4k. Images in 1080p resolution are 30 times larger than those in ImageNet. Images in 4k resolution are 4 times larger than that (120X the size of ImageNet images). Uncompressed images are 5-10 times larger than compressed images. If your data cannot be compressed for some reason, for example if you are using a custom image formats, the bandwidth requirements increase dramatically.

For AI-Driven Storage, it is suggested that you make use of deep learning framework features that build databases and archives versus accessing small files directly; reading and writing many small files will reduce performance on the network and local file systems. Storing files in formats such as HDF5, LMDB or LevelDB can reduce metadata access to the filesystem helping performance. However, these formats can lead to their own challenges with additional memory overhead or requiring support for fast mmap() performance. All this means that you should plan to be able to read data at 150-200 MB/s per GPU for files at 1080p resolution. Consider more if you are working with 4k or uncompressed files. NFS Storage

NFS can provide a good starting point for AI workloads on small GPU server configurations with properly sized storage and network bandwidth. NFS based solutions can scale well for larger deployments, but be aware of possible single node and aggregate bandwidth requirements and make sure that matches your vendor of choice. As you scale your data center to need more than 10 GB/s or your data center grows to hundreds or thousands of nodes, other technologies may be more efficient and scale better.

Generally, it is a good idea to start with NFS using one or more of the 10 Gb/s Ethernet connections on the DGX-1. After this is configured, it is recommended that you run your applications and check if IO performance is a bottleneck. Typically, NFS over 10Gb/s Ethernet provides up to 1.25 GB/s of IO throughput for large block sizes. If, in your testing, you see NFS performance that is significantly lower than this, check the network between the NFS server and the DGX-1 to make sure there are no bottlenecks (for example, a 1 GigE network connection somewhere, a misconfigured NFS server, or a smaller MTU somewhere in the network).

There are a number of online articles, such as this one, that list some suggestions for tuning NFS performance on both the client and the server. For example:
  • Increasing Read, Write buffer sizes
  • TCP optimizations including larger buffer sizes
  • Increasing the MTU size to 9000
  • Sync vs. Async
  • NFS Server options
  • Increasing the number of NFS server daemons
  • Increasing the amount of NFS server memory
Linux is very flexible and by default most distributions are conservative about their choice of IO buffer sizes since the amount of memory on the client system is unknown. A quick example is increasing the size of the read buffers on the DGX-1 (the NFS client). This can be achieved with the following system parameters:
  • net.core.rmem_max=67108864
  • net.core.rmem_default=67108864
  • net.core.optmem_max=67108864

The values after the variable are example values (they are in bytes). You can change these values on the NFS client and the NFS server, and then run experiments to determine if the IO performance improves.

The previous examples are for the kernel read buffer values. You can also do the same thing for the write buffers where you use wmem instead rmem.

You can also tune the TCP parameters in the NFS client to make them larger. For example, you could change the net.ipv4.tcp_rmem=”4096 87380 33554432” system parameter.

This changes the TCP buffer size, for ipv4, to 4,096 bytes as a minimum, 87,380 bytes as the default, and 33,554,432 bytes as the maximum.

If you can control the NFS server, one suggestion is to increases the number of NFS daemons on the server. By default, NFS only starts with eight nfsd processes (eight threads), which, given that CPUs today have very large core counts, is not really enough.

You can find the number of NFS daemons in two ways. The first is to look at the process table and count the number of NFS processes via the $ ps -aux | grep nfs command.

The second way is to look at the NFS config file (for example, /etc/sysconfig/nfs) for an entry that says RPCNFSDCOUNT. This tells you the number of NFS daemons for the server.

If the NFS server has a large number of cores and a fair amount of memory, you can increase RPCNFSDCOUNT. There are cases where good performance has been achieved using 256 on an NFS server with 16 cores and 128GB of memory.

You should also increase RPCNFSDCOUNT when you have a large number of NFS clients performing I/O at the same time. For this situation, it is recommended that you should also increase the amount of memory on the NFS server to a larger number, such as 128 or 256GB. Don't forget that if you change the value of RPCNFSDCOUNT, you will have to restart NFS for the change to take effect.

One way to determine whether more NFS threads helps performance is to check the data in /proc/net/rpc/nfs entry for the load on the NFS daemons. The output line that starts with th lists the number of threads, and the last 10 numbers are a histogram of the number of seconds the first 10% of threads were busy, the second 10%, and so on.

Ideally, you want the last two numbers to be zero or close to zero, indicating that the threads are busy and you are not "wasting" any threads. If the last two numbers are fairly high, you should add NFS daemons, because the NFS server has become the bottleneck. If the last two, three, or four numbers are zero, then some threads are probably not being used.

One other option, while a little more complex, can prove to be useful if the IO pattern becomes more write intensive. If you are not getting the IO performance you need, change the mount behavior on the NFS clients from “sync” to “async”.
By default, NFS file systems are mounted as “sync” which means the NFS client is told the data is on the NFS server after it has actually been written to the storage indicating the data is safe. Some systems will respond that the data is safe if it has made it to the write buffer on the NFS server and not the actual storage.

Switching from “sync” to “async” means that the NFS server responds to the NFS client that the data has been received when the data is in the NFS buffers on the server (in other words, in memory). The data hasn’t actually been written to the storage yet, it’s still in memory. Typically, writing to the storage is much slower than writing to memory, so write performance with “async” is much faster than with “sync”. However, if, for some reason, the NFS server goes down before the data in memory is written to the storage, then the data is lost.

If you try using “async” on the NFS client (in other words, the DGX-1), ensure that the data on the NFS server is replicated somewhere else so that if the server goes down, there is always a copy of the original data. The reason is if the NFS clients are using “async” and the NFS server goes down, data that is in memory on the NFS server will be lost and cannot be recovered.

NFS “async” mode is very useful for write IO, both streaming (sequential) and random IO. It is also very useful for “scratch” file systems where data is stored temporarily (in other words, not permanent storage or storage that is not replicated or backed up).

If you find that the IO performance is not what you expected and your applications are spending a great deal of time waiting for data, then you can also connect NFS to the DGX-1 over InfiniBand using IPoIB (IP over IB). This is part of the DGX-1 software stack and can be easily configured. The main point is that the NFS server should be InfiniBand attached as well as the NFS clients. This can greatly improve IO performance. Parallel File Systems

Other network file systems that require the installation of additional software or modification of the kernel itself are not supported by NVIDIA. This includes file systems such as Lustre, BeeGFS, General Parallel File System (formerly known as GPFS), and Gluster among others. These file systems can improve the aggregate IO performance as well as the reliability (fault tolerance).
If you require technical support from NVIDIA for your DGX-1, it is possible, although unlikely, that NVIDIA would ask you to uninstall the parallel file system and revert the kernel back to a baseline kernel, to help debug the problem. Scaling Out Recommendations

Based on the general IO patterns of deep learning frameworks (see External Storage), below are suggestions for storage needs based on the use case. These are suggestions only and are to be viewed as general guidelines.
Table 1. Scaling out suggestions and guidelines
Use Case Adequate Read Cache? Network Type Recommended Network File System Options
Data Analytics NA 10 Gbe Object-Storage, NFS, or other system with good multithreaded read and small file performance
HPC NA 10/40/100 GBe, InfiniBand NFS or HPC targeted filesystem with support for large numbers of clients and fast single-node performance
DL, 256x256 images yes 10 Gbe NFS or storage with good small file support
DL, 1080p images yes 10/40 Gbe, InfiniBand High-end NFS, HPC filesystem or storage with fast streaming performance
DL, 4k images yes 40 Gbe, InfiniBand HPC filesystem, high-end NFS or storage with fast streaming performance capable of 3+ GB/s per node
DL, uncompressed Images yes InfiniBand, 40/100 Gbe HPC filesystem, high-end NFS or storage with fast streaming performance capable of 3+ GB/s per node
DL, Datasets that are not cached no InfiniBand, 10/40/100 Gbe Same as above, aggregate storage performance must scale to meet the all applications simultaneously

As always, it is best to understand your own applications’ requirements to architect the optimal storage system.

Lastly, this discussion has focused only on performance needs. Reliability, resiliency and manageability are as important as the performance characteristics. When choosing between different solutions that meet your performance needs, make sure that you have considered all aspects of running a storage system and the needs of your organization to select the solution that will provide the maximum overall value.

1.2. Authenticating Users

To make the DGX useful, users need to be added to the system in some fashion so they can be authenticated to use the system. Generally, this is referred to as user authentication. There are several different ways this can be accomplished, however, each method has its own pros and cons.

1.2.1. Local

The first way is to create users directly on the DGX-1 server using the useradd command. Let’s assume you want to add a user dgxuser. You would first add the user via the following command.
$ useradd -m -s /bin/bash dgxuser
Where -s refers to the default shell for the user and -m creates the user’s home directory. After creating the user you need to add them to the docker group on the DGX.
$ sudo usermod -aG docker dgxuser

This adds the user dgxuser to the group docker which is required for running Docker containers on the DGX.

Using authentication on the DGX is simple but not without its issues. First, there have been occasions when an OS upgrade on the DGX requires the reformatting of all the drives in the appliance. If this happens, you first must make sure all user data is copied somewhere off the DGX-1 before the upgrade. Second, you will have to recreate the users and add them to the docker group and copy their home data back to the DGX-1. This adds work and time to upgrading the system.
Important: Moreover, there is no RAID-1 on the OS drive so if it fails, you will lose all the users and everything in the home directories. It is highly recommended that you backup the pertinent files on the DGX-1 as well as /home for the users.

1.2.2. NIS Vs NIS+

Another authentication option is to use NIS or NIS+. In this case, the DGX-1 would be a client in the NIS/NIS+ configuration. As with using local authentication as previously discussed, there is the possibility that the OS drive in the DGX-1 could be overwritten during an upgrade (not all upgrades reformat the drives, but it’s possible). This means that the administrator may have to reinstall the NIS configuration on the DGX-1.

Also, remember that the DGX-1 has a single OS drive. If this drive fails, the administrator will have to re-configure the NIS/NIS+ configuration, therefore, backups are encouraged.
Note: It is possible that if, in the unlikely event that technical support for the DGX-1 is needed, the NVIDIA engineers may require the administrator to disconnect from the NIS/NIS+ server.

1.2.3. LDAP

A third option for authentication is LDAP (Lightweight Directory Access Protocol). It has become very popular in the clustering world, particularly for Linux. You can configure LDAP on the DGX-1 for user information and authentication from an LDAP server. However, as with NIS, there are possible repercussions.
  • The first is that the OS drive is a single drive. If the drive fails, you will have to rebuild the LDAP configuration (backups are highly recommended).
  • The second is that, as previously mentioned, if, in the unlikely event of needing tech support, you may be asked to disconnect the DGX-1 from the LDAP server so that the system can be triaged.

1.2.4. Active Directory

One other option for user authentication is connecting the DGX-1 to an Active Directory (AD) server. This may require the system administrator to install some extra tools into the DGX-1. This means that this approach should also include the two cautions that were repeated before where the single OS drive may be reformatted for an upgrade or that it may fail (again, backups are highly recommended). It also means that in the unlikely case of needing to involve NVIDIA technical support, you may be asked to take the system off the AD network and remove any added software (this is unlikely but possible).

1.3. Monitoring

Being able to monitor your systems is the first step in being able to manage them. NVIDIA provides some very useful command line tools that can be used specifically for monitoring the GPUs.

1.3.1. DCGM

NVIDIA Data Center GPU Manager™ (DCGM) simplifies GPU administration in the data center. It improves resource reliability and uptime, automates administrative tasks, and helps drive overall infrastructure efficiency. It can perform the following tasks with very low overhead on the appliance.
  • Active health monitoring
  • Diagnostics
  • System validation
  • Policies
  • Power and clock management
  • Group configuration and accounting

The DCGM Toolkit comes with a User Guide that explains how to use the command-line tool called dcgmi, as well as an API Guide (there is no GUI with DCGM). In addition to the command-line tool, DCGM also comes with headers and libraries for writing your own tools in Python or C.

Rather than treat each GPU as a separate resource, DCGM allows you to group them and then apply policies or tuning options to the group. This also includes being able to run diagnostics on the group.

There are several best practices for using DCGM with the DGX-1 appliance. The first is that the command line tool can run diagnostics on the GPUs. You could create a simple cron job on the DGX-1 to check the GPUs and store the results either into a simple flat file or into a simple database.

There are three levels of diagnostics that can be run starting with level 1.
  • Level 1 runs in just a few seconds.
  • Level 3 takes about 4 minutes to run. An example of the output from running a level 3 diagnostic is below.
    Figure 1. Levels of diagnostics Levels of diagnostics

It is fairly easy to parse this output looking for Error in the output. You can easily send an email or raise some other alert if an Error is discovered.

A second best practice for utilizing DCGM is if you have a resource manager (in other words, a job scheduler) installed. Before the user’s job is run, the resource manager can usually perform what is termed a prologue. That is, any system calls before the user’s job is executed. This is a good place to run a quick diagnostic and also use DCGM to start gathering statistics on the job. Below is an example of statistics gathering for a particular job:
Figure 2. Statistics gathering Statistics gathering

When the user’s job is complete, the resource manager can run something called an epilogue. This is a place where the system can run some system calls for doing such things as cleaning up the environment or summarizing the results of the run including the GPU stats as from the above command. Consult the user’s guide to learn more about stats with DCGM.

If you create a set of prologue and epilogue scripts that run diagnostics you might want to consider storing the results in a flat file or a simple database. This allows you to keep a history of the diagnostics of the GPUs so you can pinpoint any issues (if there are any).

A third way to effectively use DCGM is to combine it with a parallel shell tool such as pdsh. With a parallel shell you can run the same command across all of the nodes in a cluster or a specific subset of nodes. You can use it to run dcgmi to run diagnostics across several DGX-1 appliances or a combination of DGX-1 appliances and non-GPU enabled systems. You can easily capture this output and store it in a flat file or a database. Then you can parse the output and create warnings or emails based on the output.

Having all of this diagnostic output is also an excellent source of information for creating reports regarding topics such as utilization.

For more information about DCGM, see NVIDIA Data Center GPU Manager Simplifies Cluster Administration.

1.3.2. Using ctop For Monitoring

Containers can make monitoring a little more challenging than the classic system monitoring. One of the classic tools used by system administrators is top. By default, top displays the load on the system as well as the ordered list of processes on the system.

There is a top-like tool for Docker containers and runC, named ctop. It lists real-time metrics for more than one container and is easy to install and update the resource usage for the running containers.
Attention: ctop runs on a single DGX-1 only. Most likely you will have to log into the specific node and run ctop. A best practice is to use tmux and create a pane for ctop for each DGX-1 if the number of systems is fairly small (approximately less than 10).

1.3.3. Monitoring A Specific DGX Using nvidia-smi

As previously discussed, DCGM is a great tool for monitoring GPUs across multiple nodes. Sometimes, a system administrator may want to monitor a specific DGX in real-time. An easy way to do this is to login into the DGX and run nvidia-smi in conjunction with the watch command.

For example, you could run the command watch -n 1 nvidia-smi that runs the nvidia-smi command every second (-n 1 means to run the command with 1 second intervals). You could also add the -d option to watch so that it highlights changes or differences since the last time it was run. This allows you to easily see what has changed.

Just like ctop, you can use nvidia-smi and watch in a pane in a tmux terminal to keep an eye on a relatively small number of DGX servers.

1.4. Managing Resources

One of the common questions from DGX-1 customers is how can they effectively share the DGX-1 between users without any inadvertent problems or data exchange. The generic phrase for this is resource management, the tools are called resource managers. They can also be called schedulers or job schedulers. These terms are oftentimes used interchangeably.

You can view everything on the DGX as a resource. This includes memory, CPUs, GPUs, and even storage. Users submit a request to the resource manager with their requirements and the resource manager assigns the resources to the user if they are available and not being used. Otherwise, the resource manager puts the request in a queue to wait for the resources to become available. When the resources are available, the resource manager assigns the resources to the user request.

Resource management so that users can effectively share a centralized resource (in this case, the DGX-1 appliance) has been around a long time. There are many open-source solutions, mostly from the HPC world, such as PBS Pro, Torque, SLURM, Openlava, SGE, HTCondor, and Mesos. There are also commercial resource management tools such as UGE and IBM Spectrum LSF.

For more information about getting started, see Job scheduler.

If you haven’t used job scheduling before you should perform some simple experiments first to understand how it works. For example, take a single server and install the resource manager. Then try running some simple jobs using the cores on the server.

1.4.1. SLURM Example

As an example, SLURM is installed and configured on a DGX-1 or DGX Station. The first step is to plan how you want to use the DGX system. The first, and by far the easiest configuration, is to assume that a user gets exclusive access to the entire node. In the case the user gets the entire DGX, access to all 8 GPUs and all cores is given. No other users can use the resources while the first user is using them.

The second way, is to make the GPUs a consumable resource. The user will then ask for the number of GPUs they need ranging from 1 to 8.

There are two public git repositories containing information on SLURM and GPUs, that can help you get started with scheduling jobs.
Note: You may have to configure SLURM to match your specifications.

At a high level, there are two basic options for configuring SLURM with GPU’s and DGX systems. The first is to use what is called exclusive mode access and the second allows each GPU to be scheduled independently of the others. Simple GPU Scheduling With Exclusive Node Access

If you're not interested in allowing simultaneous multiple jobs per compute node, you many not necessarily need to make SLURM aware of the GPUs in the system, and the configuration can be greatly simplified.

One way of scheduling GPUs without making use of GRES (Generic Resource Scheduling) is to create partitions or queues for logical groups of GPUs. For example, grouping nodes with P100 GPUs into a P100 partition would result in something like the following:
$ sinfo -s
p100     up   infinite         4/9/3/16  node[212-213,215-218,220-229]
The corresponding partition configuration via the SLURM configuration file, slurm.conf, would be something like the following:
PartitionName=p100 Default=NO DefaultTime=01:00:00 State=UP Nodes=node[212-213,215-218,220-229]

If a user requests a node from the p100 partition, then they would have access to all of the resources in that node, and other users would not. This is what is called exclusive access.

This approach can be advantageous if you are concerned that sharing resources might result in performance issues on the node or if you are concerned about overloading the node resources. For example, in the case of a DGX-1, if you think multiple users might overwhelm the 8TB NFS read cache, then you might want to consider using exclusive mode. Of if you are concerned that the users may use all of the physical memory causing page swapping with a corresponding reduction in performance, then exclusive mode might be useful. Scheduling Resources At The Per GPU Level

A second option for using SLURM, is to treat the GPUs like a consumable resource and allow users to request them in integer units (i.e. 1, 2, 3, etc.). SLURM can be made aware of GPUs as a consumable resource to allow jobs to request any number of GPU’s. This feature requires job accounting to be enabled first; for more info, see Accounting and Resource Limits. A very quick overview is below.

The SLURM configuration file, slurm.conf, needs parameters set to enable cgroups for resource management and GPU resource scheduling. An example is the following:
# General

# Scheduling

# Logging and Accounting
DebugFlags=CPU_Bind,gres                # show detailed information in Slurm logs about GPU binding and affinity
The partition information in slurm.conf defines the available GPUs for each resource. Here is an example:
# Partitions
NodeName=slurm-node-0[0-1] Gres=gpu:2 CPUs=10 Sockets=1 CoresPerSocket=10 ThreadsPerCore=1 RealMemory=30000 State=UNKNOWN
PartitionName=compute Nodes=ALL Default=YES MaxTime=48:00:00 DefaultTime=04:00:00 MaxNodes=2 State=UP DefMemPerCPU=3000
The way that resource management is enforced is through cgroups. The cgroups configuration require a separate configuration file, cgroup.conf, such as the following:

To schedule GPU resources requires a configuration file to define the available GPUs and their CPU affinity. An example configuration file, gres.conf, is below:
Name=gpu File=/dev/nvidia0 CPUs=0-4
Name=gpu File=/dev/nvidia1 CPUs=5-9
To run a job utilizing GPU resources requires using the --gres flag with the srun command. For example, to run a job requiring a single GPU the following srun command can be used.
$ srun --gres=gpu:1 nvidia-smi

You also may want to restrict memory usage on shared nodes so that a user doesn’t cause swapping with other user or system processes. A convenient way to do this is with memory cgroups.

Using memory cgroups can be used to restrict jobs to allocated memory resources requires setting kernel parameters. On Ubuntu systems this is configurable via the file /etc/default/grub.
GRUB_CMDLINE_LINUX="cgroup_enable=memory swapaccount=1"

1.5. Networking

Networking DGX-1 appliances is an important topic because of the need to provide data to the GPUs for processing. GPUs are remarkably faster than CPUs for many tasks, particularly deep learning. Therefore, the network principles used for connecting CPU servers may not be sufficient for DGX-1 appliances. This is particularly important as the number of DGX-1 appliances grows over time.

To understand best practices for networking the DGX-1 and for planning for future growth, it is best to start with a brief review of the DGX-1 appliance itself. Recall that the DGX-1 comes with four EDR InfiniBand cards (100 Gb/s each) and two 10Gb/s Ethernet cards (copper). These networking interfaces can be used for connecting the DGX-1 to the network for both communications and storage.
Figure 3. Networking interfaces Networking interfaces

Notice that every two GPUs are connected to a single PCIe switch that is on the system board. The switch also connects to an InfiniBand (IB) network card. To reduce latency and improve throughput, and network traffic from these two GPUs should go to the associated IB card. This is why there are four IB cards in the DGX-1 appliance.

1.5.1. InfiniBand Networking

If you want to use the InfiniBand (IB) network to connect DGX appliances, theoretically, you only have to use one of the IB cards. However, this will push data traffic over the QPI link between the CPUs, which is a very slow link for GPU traffic (i.e. it becomes a bottleneck). A better solution would be to use two IB cards, one connected to each CPU. This could be IB0 and IB2, or IB1 and IB3, or IB0 and IB3, or IB1 and IB2. This would greatly reduce the traffic that has to traverse the QPI link. The best performance is always going to be using all four of the IB links to an IB switch.

The best approach for using IB links to connect all four IB cards to an IB fabric. This will result in the best performance (full bisectional bandwidth and lowest latency) if you are using multiple DGX appliances for training.

Typically, the smallest IB switch comes with 36-ports. This means a single IB switch could accommodate nine (9) DGX-1 appliances using all four IB cards. This allows 400 Gb/s of bandwidth from the DGX-1 to the switch.

If your applications do not need the bandwidth between DGX-1 appliances, you can use two IB connections per DGX-1 as mentioned previously. This allows you to connect up to 18 DGX-1 appliances to a single 36-port IB switch.
Note: It is not recommended to use only a single IB card, but if for some reason that is the configuration, then you can connect up to 36 DGX-1 appliances to a single switch.

For larger numbers of DGX-1 appliances, you will likely have to use two levels of switching. The classic HPC configuration is to use 36-port IB switches for the first level (sometimes called leaf switches) and connect them to a single large core switch, which is sometimes called a director class switch. The largest director class InfiniBand switch has 648 ports. You can use more than one core switch but the configuration will get rather complex. If this is something you are considering, please contact your NVIDIA sales team for a discussion.

For two tiers of switching, if all four IB cards per DGX-1 appliance are used to connect to a 36-port switch, and there is no over-subscription, the largest number of DGX-1 appliances per switch is 4. This is 4 ports from each DGX-1 into the switch for a total of 16. Then, there are 16 uplinks from the leaf switch to the core switch (the director class switch). A total of 40x 36-port leaf switches can be connected to the 648-port core switch (648/16). This results in 160 DGX-1 appliances being connected with full bi-sectional bandwidth.

You can also use what is termed over-subscription in designing the IB network. Over-subscription means that the bandwidth from an uplink is less than the bandwidth coming into the unit (in other words, poorer bandwidth performance). If we use 2:1 over-subscription from the DGX-1 appliances to the first level of switches (36-port leaf switches), then each DGX-1 appliance is only using two IB cards to connect to the switches. This results in less bandwidth than if we used all four cards and also higher latency.

If we keep the network bandwidth from the leaf switches to the core directory switch as 1:1 (in other words, no over-subscription, full bi-sectional bandwidth), then we can put nine (9) DGX-1 appliances into a single leaf switch (a total of 18 ports into the leaf switch from the DGX appliances and 18 uplink ports to the core switch). The result is that a total of 36 leaf switches can be connected to the core switch. This allows a grand total of 324 DGX-1 appliances to be connected together.

You can tailor the IB network even further by using over-subscription from the leaf switches to the core switch. This can be done using four IB connections to a leaf switch from each DGX appliance and then doing 2:1 over-subscription to the core switch or even using two IB connections to the leaf switches and then 2:1 over-subscription to the core switch. These designs are left up to the user to determine but if this is something you want to consider, please contact your NVIDIA sales team for a discussion.

Another important aspect of InfiniBand networking is the Subnet Manager (SM). The SM simply manages the IB network. There is one SM that manages the IB fabric at any one time but you can have other SM’s running and ready to take over if the first SM crashes. Choosing how many SM’s to run and where to run them can have a major impact on the design of the cluster.

The first decision to make is where you want to run the SM’s. They can be run on the IB switches if you desire. This is called hardware SM since it runs on the switch hardware. The advantage of this is that you do not need any other servers which could also run the SM. Running the SM on a node is called a software SM. A disadvantage to running a hardware SM is that if the IB traffic is large, the SM could have a difficult time. For lots of IB traffic and for larger networks, it is a best practice to use a software SM on a dedicated server.

The second decision to make is how many SM’s you want to run. At a minimum, you will have to run one SM. The least expensive solution is to run a single hardware SM. This will work fine for small clusters of DGX-1 appliances (perhaps 2-4). As the number of units grow, you will want to consider running two SM’s at the same time to get HA (High Availability) capability. The reason you want HA is that more users are on the cluster and having it go down has a larger impact than just a small number of appliances.

As the number of appliances grow, consider running the SM’s on dedicated servers (software SM). You will also want to run at least two SM’s for the cluster. Ideally, this means two dedicated servers for the SM’s, but there may be a better solution that solves some other problems; a master node.

1.5.2. Ethernet Networking

Each DGX-1 system comes with two 10Gb/s NICs. These can be used to connect the systems to the local network for a variety of functions such as logins and storage traffic. As a starting point, it is recommended to push NFS traffic over these NICs to the DGX-1. You should monitor the impact of IO on the performance of your models in this configuration.

If you need to go to more than one level of Ethernet switching to connect all of the DGX-1 units and the storage, be careful of how you configure the network. More than likely, you will have to enable the spanning tree protocol to prevent loops in the network. The spanning tree protocol can impact network performance, therefore, you could see a decrease in application performance.

The InfiniBand NICs that come with the DGX-1 can also be used as Ethernet NICs running TCP. The ports on the cards are QSFP28 so you can plug them into a compatible Ethernet network or a compatible InfiniBand network. You will have to add some software to the appliance and change the networking but you can use the NICs as 100GigE Ethernet cards.

For more information, see Switch InfiniBand and Ethernet in DGX-1.

1.5.3. Bonded NICs

The DGX-1 provides two 10GbE ports. Out of the factory these two ports are not bonded but they can be bonded if desired. In particular, VLAN Tagged, Bonded NICs across the two 10 GbE cards can be accomplished.

Before bonding the NICs together, ensure you are familiar with the following:
  • Ensure your network team is involved because you will need to choose a bonding mode for the NICs.
  • Ensure you have a working network connection to pull down the VLAN packages. To do so, first setup a basic, single NIC network (no VLAN/bonding) connection and download the appropriate packages. Then, reconfigure the switch for LACP/VLANs.
Tip: Since the networking goes up and down throughout this process, it's easier to work from a remote console.
The process below walks through the steps of an example for bonding the two NICs together.
  1. Edit the /etc/network/interfaces file to setup an interface on a standard network so that we can access required packages.
    auto em1
    	iface em1 inet static
  2. Bring up the updated interface.
    sudo ifdown em1 && sudo ifup em1
  3. Pull down the required bonding and VLAN packages.
    sudo apt-get install vlan
    sudo apt-get install ifenslave
  4. Shut down the networking.
    sudo stop networking
  5. Add the following lines to /etc/modules to load appropriate drivers.
    sudo echo "8021q" >> /etc/modules
    sudo echo "bonding" >> /etc/modules
  6. Load the drivers.
    sudo modprobe 8021q
    sudo modprobe bonding
  7. Reconfigure your /etc/network/interfaces file. There are some configuration parameters that will be customer network dependent and you will want to work with one of your network engineers.
    The following example creates a bonded network over em1/em2 with IP and VLAN ID 430. You specify the VLAN ID in the NIC name (bond0.###). Also notice that this example uses a bond-mode of 4. Which mode you use is up to you and your situation.
    auto lo
    iface lo inet loopback
    # The following 3 sections create the bond (bond0) and associated network ports (em1, em2)
    auto bond0
    iface bond0 inet manual
    bond-mode 4
    bond-miimon 100
    bond-slaves em1 em2
    auto em1
    iface em1 inet manual
    bond-master bond0
    bond-primary em1
    auto em2
    iface em2 inet manual
    bond-master bond0
    # This section creates a VLAN on top of the bond.  The naming format is device.vlan_id
    auto bond0.430
    iface bond0.430 inet static
    dns-search company.net
    vlan-raw-device bond0
  8. Restart the networking.
    sudo start networking
  9. Bring up the bonded interfaces.
    ifup bond0
  10. Engage your network engineers to re-configure LACP and VLANs on switch.
  11. Test the configuration.

1.6. SSH Tunneling

Some environments are not configured or limit access (firewall or otherwise) to computer nodes within an intranet. When running a container with a service or application exposed on a port, such as DIGITS, remote access must be enabled on the remote system to that port on the DGX-1. The following steps use PuTTY to create SSH tunnel from a remote system into the DGX-1. If you are using an SSH utility, one can set up tunneling via the -L option.
Note: A PuTTY SSH tunnel session must be up, logged in, and running for tunnel to function. SSH tunnels are commonly used for the following applications (with listed port numbers).
Table 2.
Application Port Notes
DIGITS 5000 If multiple users, each selects own port
VNC Viewer 5901, 6901 5901 for VNC app, 6901 for web app
To create an SSH Tunnel session with PuTTY, perform the following steps:
  1. Run the PuTTY application.
  2. In the Host Name field, enter the host name you want to connect to.
  3. In the Saved Sessions section, enter a name to save the session under and click Save.
  4. Click Category > Connection, click + next to SSH to expand the section.
  5. Click Tunnels for Tunnel configuration.
  6. Add the DIGITS port for forwarding.
    1. In the Source Port section, enter 5000, which is the port you need to forward for DIGITS.
  7. In the Destination section, enter localhost:5000 for the local port that you will connect to.
  8. Click Add to save the added Tunnel.
  9. In the Category section, click Session.
  10. In the Saved Sessions section, click the name you previously created, then click Save to save the added Tunnels.
To use PuTTY with tunnels, perform the following steps:
  1. Run the PuTTY application.
  2. In the Saved Sessions section, select the Save Session that you created.
  3. Click Load.
  4. Click Open to start session and login. The SSH tunnel is created and you can connect to a remote system via tunnel. As an example, for DIGITS, you can start a web browser and connect to http://localhost:5000.

1.7. Master Node

A master node, also sometimes called a head node, is a very useful server within a cluster. Typically, it runs the cluster management software, the resource manager, and any monitoring tools that are used. For smaller clusters, it is also used as a login node for users to create and submit jobs.

For clusters of any size that include the DGX-1, a master node can be very helpful. It allows the DGX-1 to focus solely on computing rather than any interactive logins or post-processing that users may be doing. As the number of nodes in a cluster increases, it is recommended to use a master node.

It is recommended to size the master node for things such as:
  • Interactive user logins
  • Resource management (running a job scheduler)
  • Graphical pre-processing and post-processing
    • Consider a GPU in the master node for visualization
  • Cluster monitoring
  • Cluster management

Since the master node becomes an important part of the operation of the cluster, consider using RAID-1 for the OS drive in the master node as well as redundant power supplies. This can help improve the uptime of the master node.

For smaller clusters, you can also use the master node as an NFS server by adding storage and more memory to the master node and NFS export the storage to the cluster clients. For larger clusters, it is recommended to have dedicated storage, either NFS or a parallel file system.

For InfiniBand networks, the master node can also be used for running the software SM. If you want some HA for the SM, run the primary SM on the master node and use an SM on the IB switch as a secondary SM (hardware SM).

As the cluster grows, it is recommended to consider splitting the login and data processing functions from the master node to one or more dedicated login nodes. This is also true as the number of users grows. You can run the primary SM on the master node and other SM’s on the login nodes. You could even use the hardware SM’s on the switches as backups.





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