Login nodes. To connect to these, you would typically
use a remote shell connection. The most usual tool to do so is SSH. You’ll hear
and read a lot about this tool. Imagine it as a very long (and somewhat
magical) extension cord which connects the computer you are using now, such as
your laptop, to a remote computer’s terminal shell. You might already know what
a terminal shell is if you ever used the command line.

The compute nodes
In the field of artificial intelligence, you will usually be on the hunt for
GPUs. In most clusters, the compute nodes are the ones with GPU capacity.
While there is a general paradigm to tend towards a homogeneous configuration
for nodes, this is not always possible in the field of artificial intelligence
as the hardware evolve rapidly as is being complemented by new hardware and so
on. Hence, you will often read about computational node classes. Some of which
might have different GPU models or even no GPU at all. For the Mila cluster you
will find this information in the Node profile description section. For
now, you should note that is important to keep in mind that you should be aware
of which nodes your code is running on.  More on that later.

The storage nodes
Some computers on a cluster function to only store and serve files.  While the
name of these computers might matter to some, as a user, you’ll only be
concerned about the path to the data. More on that in the Processing data section.

Different nodes for different uses
It is important to note here the difference in intended uses between the
compute nodes and the login nodes. While the compute nodes are meant for heavy
computation, the login nodes are not.
The login nodes however are used by everyone who uses the cluster and care must
be taken not to overburden these nodes. Consequently, only very short and light
processes should be run on these otherwise the cluster may become inaccessible.
In other words, please refrain from executing long or compute intensive
processes on login nodes because it affects all other users. In some cases, you
will also find that doing so might get you into trouble.

Data parallelism
The first technique is called data parallelism (aka task
parallelism in formal computer science). You simply run lots of
processes each handling a portion of the data you want to
process. This is by far the easiest technique to use and should be
favored whenever possible. A common example of this is
hyperparameter optimisation.
For really small computations the time to setup multiple processes
might be longer than the processing time and lead to waste. This can
be addressed by bunching up some of the processes together by doing
sequential processing of sub-partitions of the data.
For the cluster systems it is also inadvisable to launch thousands of
jobs and even if each job would run for a reasonable amount of time
(several minutes at minimum), it would be best to make larger groups
until the amount of jobs is in the low hundreds at most.
Finally another thing to keep in mind is that the transfer bandwidth
is limited between the filesystems (see Filesystem concerns)
and the compute nodes and if you run too many jobs using too much data
at once they may end up not being any faster because they will spend
their time waiting for data to arrive.

Model parallelism
The second technique is called model parallelism (which doesn’t
have a single equivalent in formal computer science). It is used
mostly when a single instance of a model will not fit in a computing
resource (such as the GPU memory being too small for all the
parameters).
In this case, the model is split into its constituent parts, each
processed independently and their intermediate results communicated
with each other to arrive at a final result.
This is generally harder but necessary to work with larger, more
powerful models like GPT.

Communication concerns
The main difference of these two approaches is the need for
communication between the multiple processes. Some common training
methods, like stochastic gradient descent sit somewhere between the
two, because they require some communication, but not a lot. Most
people classify it as data parallelism since it sits closer to that
end.
In general for data parallelism tasks or tasks that communicate
infrequently it doesn’t make a lot of difference where the processes
sit because the communication bandwidth and latency will not have a
lot of impact on the time it takes to complete the job.  The
individual tasks can generally be scheduled independently.
On the contrary for model parallelism you need to pay more attention
to where your tasks are.  In this case it is usually required to use
the facilities of the workload manager to group the tasks so that they
are on the same machine or machines that are closely linked to ensure
optimal communication.  What is the best allocation depends on the
specific cluster architecture available and the technologies it
support (such as InfiniBand,
RDMA,
NVLink or others)

Filesystem concerns
When working on a cluster, you will generally encounter several
different filesystems.  Usually there will be names such as ‘home’,
‘scratch’, ‘datasets’, ‘projects’, ‘tmp’.
The reason for having different filesystems available instead of a
single giant one is to provide for different use cases. For example,
the ‘datasets’ filesystem would be optimized for fast reads but have
slow write performance. This is because datasets are usually written
once and then read very often for training.
Different filesystems have different performance levels. For instance, backed
up filesystems (such as $PROJECT in Digital Research Alliance of Canada
clusters) provide more space and can handle large files but cannot sustain
highly parallel accesses typically required for high speed model training.
The set of filesystems provided by the cluster you are using should be
detailed in the documentation for that cluster and the names can
differ from those above. You should pay attention to their recommended
use case in the documentation and use the appropriate filesystem for
the appropriate job. There are cases where a job ran hundreds of times
slower because it tried to use a filesystem that wasn’t a good fit for
the job.
One last thing to pay attention to is the data retention policy for
the filesystems. This has two subpoints: how long is the data kept
for, and are there backups.
Some filesystems will have a limit on how long they keep their
files. Typically the limit is some number of days (like 90 days) but
can also be ‘as long as the job runs’ for some.
As for backups, some filesystems will not have a limit for data, but
will also not have backups. For those it is important to maintain a
copy of any crucial data somewhere else. The data will not be
purposefully deleted, but the filesystem may fail and lose all or part
of its data. If you have any data that is crucial for a paper or your
thesis keep an additional copy of it somewhere else.

Cluster software modules
Modules are small files which modify your environment variables to point to
specific versions of various software and libraries. For instance, a module
might provide the python command to point to Python 3.7, another might
activate CUDA version 11.0, another might provide the torch package, and so
on.
For more information, see The module command.

Containers
Containers are a special form of isolation of software and its dependencies. A
container is essentially a lightweight virtual machine: it encapsulates a
virtual file system for a full OS installation, as well as a separate network
and execution environment.
For example, you can create an Ubuntu container in which you install various
packages using apt, modify settings as you would as a root user, and so on,
but without interfering with your main installation. Once built, a container can
be run on any compatible system.
For more information, see Using containers on clusters.

Python Virtual environments
A virtual environment in Python is a local, isolated environment in which you
can install or uninstall Python packages without interfering with the global
environment (or other virtual environments). In order to use a virtual
environment, you first have to activate it.
For more information, see Virtual environments.

Intended audience
This documentation is mainly intended for Mila researchers having access to the
Mila cluster. This access is determined by your researcher status. See
Roles and authorizations for more information. The core of the
information with this purpose can be found in the following section:
Computing infrastructure and policies.
However, we also aim to provide more general information which can be useful
outside the scope of using the Mila cluster. For instance, more general theory
on computational considerations and such. In this perspective, we hope the
documentation can be of use for all of Mila members.

Connecting to the cluster
VSCode cannot be used to edit code on the login nodes, because it is a heavy
enough process (a node process, plus the language server, linter, and
possibly other plugins depending on your configured environment) that there is a
risk of overloading the login nodes if too many researchers did it at the same
time.
Therefore, to use VSCode on the cluster, you first need to allocate a compute
node, then connect to that node.
The milatools package provides a command to make the operation easier. More
info can be found here.

Activating an environment
Reference
To activate a conda or pip environment, you can open the command palette with
Ctrl+Shift+P and type “Python: Select interpreter”. This will prompt you for the
path to the Python executable for your environment.

Tip
If you already have the environment activated in a terminal session, you can
run the command which python to get the path for this environment. This
path can be pasted into the interpreter selection prompt in VSCode to use
that same environment.

Troubleshooting

“Cannot reconnect”
When connecting to multiple compute nodes (and/or from multiple computers), some
instances may crash with that message because of conflicts in the lock files
VSCode installs in ~/.vscode-server (which is shared on all compute nodes).
To fix this issue, you can change this setting in your settings.json file:
{ "remote.SSH.lockfilesInTmp": true }

This will store the necessary lockfiles in /tmp on the compute nodes (which
are local to the node).

Debugger timeouts
Sometimes, slowness on the compute node or the networked filesystem might cause
the VSCode debugger to timeout when starting a remote debug process. As a quick
fix, you can add this to your ~/.bashrc or ~/.profile or equivalent
resource file for your preferred shell, to increase the timeout delay to 500
seconds:
export DEBUGPY_PROCESS_SPAWN_TIMEOUT=500

seconds:

It is opt-in and must be requested via
CCDB manually. Compute resources in Niagara are not assigned to jobs on a per-CPU,
but on a per-node basis.

Beluga
Beluga is a cluster located at ÉTS in Montreal. It
uses SLURM to schedule jobs. Its full documentation can be found here, and its current status
here.
You can access Beluga via ssh:
ssh <user>@beluga.computecanada.ca
Where <user> is the username you created previously (see Account Creation).

Launching Jobs
Users must specify the resource allocation Group Name using the flag
--account=rrg-bengioy-ad.  To launch a CPU-only job:
sbatch --time=1:0:0 --account=rrg-bengioy-ad job.sh

Note
The account name will differ based on your affiliation.

To launch a GPU job:
sbatch --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1 job.sh
And to get an interactive session, use the salloc command:
salloc --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1
The full documentation for jobs launching on Beluga can be found here.

Beluga nodes description
Each GPU node consists of:

40 CPU cores
186 GB RAM
4 GPU NVIDIA V100 (16GB)

Tip
You should ask for max 10 CPU cores and 32 GB of RAM per GPU you are
requesting (as explained here),
otherwise, your job will count for more than 1 allocation, and will take
more time to get scheduled.

Beluga Storage

Storage
Path
Usage

$HOME
/home/<user>/

Code
Specific libraries

$HOME/projects
/project/rpp-bengioy

Compressed raw datasets

$SCRATCH
/scratch/<user>

Processed datasets
Experimental results
Logs of experiments

$SLURM_TMPDIR

Temporary job results

They are roughly listed in order of increasing performance and optimized for
different uses:

The $HOME folder on NFS is appropriate for codes and libraries which are
small and read once. Do not write experiemental results here!
The $HOME/projects folder should only contain compressed raw datasets
(processed datasets should go in $SCRATCH). We have a limit on the
size and number of file in $HOME/projects, so do not put anything else
there.  If you add a new dataset there (make sure it is readable by every
member of the group using chgrp -R rpp-bengioy <dataset>).
The $SCRATCH space can be used for short term storage. It has good
performance and large quotas, but is purged regularly (every file that has
not been used in the last 3 months gets deleted, but you receive an email
before this happens).
$SLURM_TMPDIR points to the local disk of the node on which a job is
running. It should be used to copy the data on the node at the beginning of
the job and write intermediate checkpoints. This folder is cleared after each
job.

When an experiment is finished, results should be transferred back to Mila
servers.
More details on storage can be found here.

Modules
Many software, such as Python or MATLAB are already compiled and available on
Beluga through the module command and its subcommands. Its full
documentation can be found here.

module avail
Displays all the available modules

module load <module>
Loads <module>

module spider <module>
Shows specific details about <module>

In particular, if you with to use Python 3.6 you can simply do:
module load python/3.6

Tip
If you wish to use Python on the cluster, we strongly encourage you to
read Alliance Python Documentation, and in particular the Pytorch and/or Tensorflow pages.

The cluster has many Python packages (or wheels), such already compiled for
the cluster. See here for the
details. In particular, you can browse the packages by doing:
avail_wheels <wheel>
Such wheels can be installed using pip. Moreover, the most efficient way to use
modules on the cluster is to build your environnement inside your job.
See the script example below.

Script Example
Here is a sbatch script that follows good practices on Beluga:
 1#!/bin/bash
 2#SBATCH --account=rrg-bengioy-ad         # Yoshua pays for your job
 3#SBATCH --cpus-per-task=6                # Ask for 6 CPUs
 4#SBATCH --gres=gpu:1                     # Ask for 1 GPU
 5#SBATCH --mem=32G                        # Ask for 32 GB of RAM
 6#SBATCH --time=3:00:00                   # The job will run for 3 hours
 7#SBATCH -o /scratch/<user>/slurm-%j.out  # Write the log in $SCRATCH
 8
 9# 1. Create your environement locally
10module load python/3.6
11virtualenv --no-download $SLURM_TMPDIR/env
12source $SLURM_TMPDIR/env/bin/activate
13pip install --no-index torch torchvision
14
15# 2. Copy your dataset on the compute node
16# IMPORTANT: Your dataset must be compressed in one single file (zip, hdf5, ...)!!!
17cp $SCRATCH/<dataset.zip> $SLURM_TMPDIR
18
19# 3. Eventually unzip your dataset
20unzip $SLURM_TMPDIR/<dataset.zip> -d $SLURM_TMPDIR
21
22# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
23#    and look for the dataset into $SLURM_TMPDIR
24python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
25
26# 5. Copy whatever you want to save on $SCRATCH
27cp $SLURM_TMPDIR/<to_save> $SCRATCH

Using CometML and Wandb
The compute nodes for Beluga don’t have access to the internet,
but there is a special module that can be loaded in order to allow
training scripts to access some specific servers, which includes
the necessary servers for using CometML and Wandb (“Weights and Biases”).
module load httpproxy
More documentation about this can be found here.

Graham
Graham is a cluster located at University of Waterloo. It uses SLURM to schedule
jobs. Its full documentation can be found here, and its current status here.
You can access Graham via ssh:
ssh <user>@graham.computecanada.ca
Since its structure is similar to Beluga, please look at the Beluga
documentation, as well as relevant parts of the Digital Research Alliance of
Canada Documentation.

Note
For GPU jobs the ressource allocation Group Name is the same as Beluga, so you should use the flag --account=rrg-bengioy-ad for GPU jobs.

Cedar
Cedar is a cluster located at Simon Fraser University. It uses SLURM to schedule
You can access Cedar via ssh:
ssh <user>@cedar.computecanada.ca

Note
However, we don’t have any CPU priority on Cedar, in this case you can
use --account=def-bengioy for CPU. Thus, it might take some time before
they start.

Niagara
Niagara is a cluster located at University of Toronto. It uses SLURM to schedule
You can access Niagara via ssh:
ssh <user>@niagara.computecanada.ca

FAQ

What to do with  ImportError: /lib64/libm.so.6: version GLIBC_2.23 not found?
The structure of the file system is different than a classical Linux, so your
code has trouble finding libraries. See how to install binary packages.

Disk quota exceeded error on /project file systems
You have files in /project with the wrong permissions. See how to change
permissions.

Current allocation description
Clusters of the Alliance are shared with researchers across the country.
Allocations are given by the Alliance to selected research groups to ensure to
a minimal amount of computational resources throughout the year.
Depending on your affiliation, you will have access to different allocations. If
you are a student at University of Montreal, you can have access to the
rrg-bengioy-ad allocation described below. For students from other
universities, you should ask your advisor to know which allocations you could
have access to.
From the Alliance’s documentation: An allocation is an amount of resources
that a research group can target for use for a period of time, usually a year.
To be clear, it is not a maximal amount of resources that can be used
simultaneously, it is a weighting factor of the workload manager to balance
jobs. For instance, even though we are allocated 400 GPU-years across all
clusters, we can use more or less than 400 GPUs simultaneously depending on the
history of usage from our group and other groups using the cluster at a given
period of time. Please see the Alliance’s documentation for
more information on how allocations and resource scheduling are configured for
these installations.
The table below provides information on the allocation for
rrg-bengioy-ad for the period which spans from April 2022 to
April 2023. Note that there are no special allocations for GPUs on
Graham and therefore jobs with GPUs should be submitted with the
account def-bengioy.

Cluster
CPUs
GPUs

#
account
Model
#
SLURM type specifier
account

Beluga
238
rrg-bengioy-ad
V100-16G
77
v100

Cedar
34
V100-32G
138
v100l

Graham
34
various
–
–
def-bengioy

Narval
34
A100-40G
185
a100

Account Creation
To access the Alliance clusters you have to first create an account at
https://ccdb.computecanada.ca. Use a password with at least 8 characters, mixed
case letters, digits and special characters. Later you will be asked to create
another password with those rules, and it’s really convenient that the two
password are the same.
Then, you have to apply for a role at
https://ccdb.computecanada.ca/me/add_role, which basically means telling the
Alliance that you are part of the lab so they know which cluster you can have
access to, and track your usage.
You will be asked for the CCRI (See screenshot below). Please reach out to your
sponsor to get the CCRI.

You will need to wait for your sponsor to accept before being able to login
to the Alliance clusters.

Beluga
here.

Note

186 GB RAM

Tip

Storage
Path
Usage

$HOME

Code

$SCRATCH

job.

servers.

Modules

Tip

 8
14
18
21
25

Graham

Note

Cedar

Note

Niagara

Beluga
here.

Note

186 GB RAM

Tip

Storage
Path
Usage

$HOME

Code

$SCRATCH

job.

servers.

Modules

Tip

 8
14
18
21
25

Note

186 GB RAM

Tip

Storage
Path
Usage

$HOME

Code

$SCRATCH

job.

servers.

Modules

Tip

 8
14
18
21
25

Graham

Note

Cedar

Note

Niagara

FAQ

Roles and authorizations
There are mainly two types of researchers statuses at Mila :

Core researchers
Affiliated researchers

This is determined by Mila policy. Core researchers have access to the Mila
computing cluster. See your supervisor’s Mila status to know what is your own
status.

Overview of available computing resources at Mila
The Mila cluster is to be used for regular development and relatively small
number of jobs (< 5). It is a heterogeneous cluster. It uses
SLURM to schedule jobs.

Mila cluster versus Digital Research Alliance of Canada clusters
There are a lot of commonalities between the Mila cluster and the clusters from
Digital Research Alliance of Canada (the Alliance). At the time being, the
Alliance clusters where we have a large allocation of resources are beluga,
cedar, graham and narval. We also have comparable computational resources
in the Mila cluster, with more to come.
The main distinguishing factor is that we have more control over our own
cluster than we have over the ones at the Alliance. Notably, also, the compute
nodes in the Mila cluster all have unrestricted access to the Internet, which
is not the case in general for the Alliance clusters (although cedar does
allow it).
At the current time of this writing (June 2021), Mila students are advised to
use a healthy diet of a mix of Mila and Alliance clusters. This is especially
true in times when your favorite cluster is oversubscribed, because you can
easily switch over to a different one if you are used to it.

Guarantees about one GPU as absolute minimum
There are certain guarantees that the Mila cluster tries to honor when it comes
to giving at minimum one GPU per student, all the time, to be used in
interactive mode. This is strictly better than “one GPU per student on average”
because it’s a floor meaning that, at any time, you should be able to ask for
your GPU, right now, and get it (although it might take a minute for the
request to be processed by SLURM).
Interactive sessions are possible on the Alliance clusters, and there are
generally special rules that allow you to get resources more easily if you
request them for a very short duration (for testing code before queueing long
jobs). You do not get the same guarantee as on the Mila cluster, however.

allow it).

Node profile description

Name
GPU
CPUs
Sockets
Cores/Socket
Threads/Core
Memory (GB)
TmpDisk (TB)
Arch
Slurm Features

Model
Mem
#
GPU Arch and Memory

GPU Compute Nodes

cn-a[001-011]
RTX8000
48
8
40
2
20
1
384
3.6
x86_64
turing,48gb

cn-b[001-005]
V100
32
8
40
2
20
1
384
3.6
x86_64
volta,nvlink,32gb

cn-c[001-040]
RTX8000
48
8
64
2
32
1
384
3
x86_64

cn-g[001-026]
A100
80
4
64
2
32
1
1024
7
x86_64
ampere,nvlink,80gb

DGX Systems

cn-d[001-002]
A100
40
8
128
2
64
1
1024
14
x86_64
ampere,nvlink,40gb

cn-d[003-004]
A100
80
8
128
2
64
1
2048
28
x86_64

cn-e[002-003]
V100
32
8
40
2
20
1
512
7
x86_64
volta,32gb

CPU Compute Nodes

cn-f[001-004]

32
1
32
1
256
10
x86_64
rome

cn-h[001-004]

64
2
32
1
768
7
x86_64
milan

Legacy GPU Compute Nodes

kepler5
V100
16
2
16
2
4
2
256
3.6
x86_64
volta,16gb

TITAN RTX

rtx[1,3-5,7]
titanrtx
24
2
20
1
10
2
128
0.93
x86_64
turing,24gb

Special nodes and outliers

DGX A100
DGX A100 nodes are NVIDIA appliances with 8 NVIDIA A100 Tensor Core GPUs. Each
GPU has 40 GB of memory, for a total of 320 GB per appliance. The GPUs are
interconnected via 6 NVSwitches which allows 4.8 TB/s bi-directional bandwidth.
In order to run jobs on a DGX A100, add the flags below to your Slurm
commands:
--gres=gpu:a100:<number> --reservation=DGXA100

MIG
MIG (Multi-Instance GPU)
is an NVIDIA technology allowing certain GPUs to be
partitioned into multiple instances, each of which has a roughly proportional
amount of compute resources, device memory and bandwidth to that memory.
NVIDIA supports MIG on its A100 GPUs and allows slicing the A100 into up to 7
instances. Although this can theoretically be done dynamically, the SLURM job
scheduler does not support doing so in practice as it does not model
reconfigurable resources very well. Therefore, the A100s must currently be
statically partitioned into the required number of instances of every size
expected to be used.
The cn-g series of nodes include A100-80GB GPUs. One third have been
configured to offer regular (non-MIG mode) a100l GPUs. The other two-thirds
have been configured in MIG mode, and offer the following profiles:

Name
GPU
Cluster-wide

Model
Memory
Compute
#

a100l.1g.10gb
a100l.1
A100
10GB 
(1/8th)
1/7th
of full
72

a100l.2g.20gb
a100l.2
A100
20GB 
(2/8th)
2/7th
of full
108

a100l.3g.40gb
a100l.3
A100
40GB 
(4/8th)
3/7th
of full
72

And can be requested using a SLURM flag such as --gres=gpu:a100l.1
The partitioning may be revised as needs and SLURM capabilities evolve. Other
MIG profiles exist and could be introduced.

Warning
MIG has a number of important limitations,
most notably that a GPU in MIG mode does not support graphics APIs
(OpenGL/Vulkan), nor P2P over NVLink and PCIe. We have therefore chosen to
limit every MIG job to exactly one MIG slice and no more. Thus,
--gres=gpu:a100l.3 will work (and request a size-3 slice of an
a100l GPU) but --gres=gpu:a100l.1:3 (with :3 requesting
three size-1 slices) will not.

AMD

Warning
As of August 20 2019 the GPUs had to return back to AMD.  Mila will get
more samples. You can join the amd slack channels to get the latest
information

Mila has a few node equipped with MI50 GPUs.
srun --gres=gpu -c 8 --reservation=AMD --pty bash

 first time setup of AMD stack
conda create -n rocm python=3.6
conda activate rocm

pip install tensorflow-rocm
pip install /wheels/pytorch/torch-1.1.0a0+d8b9d32-cp36-cp36m-linux_x86_64.whl

DGX A100
commands:

MIG

Name
GPU

Model
Memory
Compute
#

a100l.1
A100
10GB 
(1/8th)
1/7th
of full
72

a100l.2
A100
20GB 
(2/8th)
2/7th
of full
108

a100l.3
A100
40GB 
(4/8th)
3/7th
of full
72

Warning

AMD

Warning

DGX A100
commands:

MIG

Name
GPU

Model
Memory
Compute
#

a100l.1
A100
10GB 
(1/8th)
1/7th
of full
72

a100l.2
A100
20GB 
(2/8th)
2/7th
of full
108

a100l.3
A100
40GB 
(4/8th)
3/7th
of full
72

Warning

AMD

Warning

Data sharing policies

Note
/network/scratch aims to support
Access Control Lists (ACLs)
to allow collaborative work on rapidly changing data, e.g. work in process
datasets, model checkpoints, etc…

/network/projects aims to offer a collaborative
space for long-term projects. Data that should be kept for a longer period then
90 days can be stored in that location but first a request to Mila’s helpdesk has to be made to create the project
directory.

Monitoring
Every compute node on the Mila cluster has a Netdata
monitoring daemon allowing you to get a sense of the state of the node.
This information is exposed in two ways:

For every node, there is a web interface from Netdata itself at <node>.server.mila.quebec:19999.
This is accessible only when using the Mila wifi or through SSH tunnelling.

SSH tunnelling: on your local machine, run

ssh -L 19999:<node>.server.mila.quebec:19999 -p 2222
login.server.mila.quebec
or ssh -L 19999:<node>.server.mila.quebec:19999 mila if you have
already setup your SSH Login,

then open http://localhost:19999 in your browser.

The Mila dashboard at dashboard.server.mila.quebec
exposes aggregated statistics with the use of grafana.
These are collected internally to an instance of prometheus.

In both cases, those graphs are not editable by individual users,
but they provide valuable insight into the state of the whole cluster
or the individual nodes.
One of the important uses is to collect data about the health
of the Mila cluster and to sound the alarm if outages occur
(e.g. if the nodes crash or if GPUs mysteriously become unavailable for SLURM).

Example with Netdata on cn-c001
For example, if we have a job running on cn-c001, we can type
cn-c001.server.mila.quebec:19999 in a browser address bar and the following
page will appear.

Example watching the CPU/RAM/GPU usage
Given that compute nodes are generally shared
with other users who are also running jobs at the same time and
consuming resources, this is not generally a good way to profile your code
in fine details.
However, it can still be a very useful source of information
for getting an idea of whether the machine that you requested is being
used in its full capacity.
Given how expensive the GPUs are, it generally makes sense to try to
make sure that this resources is always kept busy.

CPU
iowait (pink line): High values means your model is waiting on IO a lot (disk or network).

CPU RAM
You can see how much CPU RAM is being used by your script in practice,
considering the amount that you requested (e.g. `sbatch --mem=8G ...`).
GPU usage is generally more important to monitor than CPU RAM.
You should not cut it so close to the limit that your experiments randomly fail
because they run out of RAM. However, you should not request blindly 32GB of RAM
when you actually require only 8GB.

GPU
Monitors the GPU usage using an nvidia-smi plugin for Netdata.
Under the plugin interface, select the GPU number which was allocated to
you. You can figure this out by running echo $SLURM_JOB_GPUS on the
allocated node or, if you have the job ID,
scontrol show -d job YOUR_JOB_ID | grep 'GRES' and checking IDX
You should make sure you use the GPUs to their fullest capacity.
Select the biggest batch size if possible to increase GPU memory usage and
the GPU computational load.
Spawn multiple experiments if you can fit many on a single GPU.
Running 10 independent MNIST experiments on a single GPU will probably take
less than 10x the time to run a single one. This assumes that you have more
experiments to run, because nothing is gained by gratuitously running experiments.
You can request a less powerful GPU and leave the more powerful GPUs
to other researchers who have experiments that can make best use of them.
Sometimes you really just need a k80 and not a v100.

Other users or jobs
If the node seems unresponsive or slow,
it may be useful to check what other tasks are
running at the same time on that node.
This should not be an issue in general,
but in practice it is useful to be able to
inspect this to diagnose certain problems.

Example with Mila dashboard

CPU

CPU RAM

GPU

Storage

Path
Performance
Usage
Quota (Space/Files)
Backup
Auto-cleanup

/network/datasets/
High

Curated raw datasets (read only)

$HOME or /home/mila/<u>/<username>/
Low

Personal user space
Specific libraries, code, binaries

100GB/1000K
Daily
no

$SCRATCH or /network/scratch/<u>/<username>/
High

Optimized for small Files

no
no
90 days

Highest

High speed disk for temporary job
results

4TB/-
no
at job end

/network/projects/<groupname>/
Fair

Shared space to facilitate
collaboration between researchers
Long-term project storage

200GB/1000K
Daily
no

$ARCHIVE or /network/archive/<u>/<username>/
Low

Long-term personal storage

500GB
no
no

Note
The $HOME file system is backed up once a day. For any file
restoration request, file a request to Mila’s IT support with the path to the file or directory to
restore, with the required date.

Warning
Currently there is no backup system for any other file systems of
the Mila cluster. Storage local to personal computers, Google Drive and other
related solutions should be used to backup important data

$HOME
$HOME is appropriate for codes and libraries which are small and read once,
as well as the experimental results that would be needed at a later time (e.g.
the weights of a network referenced in a paper).
Quotas are enabled on $HOME for both disk capacity (blocks) and number of
files (inodes). The limits for blocks and inodes are respectively 100GiB and 1
million per user. The command to check the quota usage from a login node is:
beegfs-ctl --cfgFile=/etc/beegfs/home.d/beegfs-client.conf --getquota --uid $USER

$SCRATCH
$SCRATCH can be used to store processed datasets, work in progress datasets
or temporary job results. Its block size is optimized for small files which
minimizes the performance hit of working on extracted datasets.

Note
Auto-cleanup: this file system is cleared on a weekly basis,
files not used for more than 90 days will be deleted.

$SLURM_TMPDIR
running. It should be used to copy the data on the node at the beginning of the
job and write intermediate checkpoints. This folder is cleared after each job.

projects
projects can be used for collaborative projects. It aims to ease the
sharing of data between users working on a long-term project.
Quotas are enabled on projects for both disk capacity (blocks) and number
of files (inodes). The limits for blocks and inodes are respectively 200GiB and
1 million per user and per group.

Note
It is possible to request higher quota limits if the project requires
it. File a request to Mila’s IT support.

$ARCHIVE
$ARCHIVE purpose is to store data other than datasets that has to be kept
long-term (e.g.  generated samples, logs, data relevant for paper submission).
$ARCHIVE is only available on the login nodes. Because this file system
is tuned for large files, it is recommended to archive your directories. For
example, to archive the results of an experiment in
$SCRATCH/my_experiment_results/, run the commands below from a login node:
cd $SCRATCH
tar cJf $ARCHIVE/my_experiment_results.tar.xz --xattrs my_experiment_results
Disk capacity quotas are enabled on $ARCHIVE. The soft limit per user is
500GB, the hard limit is 550GB. The grace time is 7 days. This means that one
can use more than 500GB for 7 days before the file system enforces quota.
However, it is not possible to use more than 550GB.
The command to check the quota usage from a login node is df:
df -h $ARCHIVE

Note
There is NO backup of this file system.

datasets
datasets contains curated datasets to the benefit of the Mila community.
To request the addition of a dataset or a preprocessed dataset you think could
benefit the research of others, you can fill this form. Datasets can also be browsed from the
web : Mila Datasets
Datasets in datasets/restricted are restricted and require an explicit
request to gain access. Please submit a support ticket mentioning the dataset’s
access group (ex.: scannet_users), your cluster’s username and the
approbation of the group owner. You can find the dataset’s access group by
listing the content of /network/datasets/restricted with the ls command.
Those datasets are mirrored to the Alliance clusters in
~/projects/rrg-bengioy-ad/data/curated/ if they follow Digital Research
Alliance of Canada’s good practices on data.
To list the local datasets on an Alliance cluster, you can execute the
following command:
ssh [CLUSTER_LOGIN] -C "projects/rrg-bengioy-ad/data/curated/list_datasets_cc.sh"

$HOME

$SCRATCH

Note

projects

Note

$ARCHIVE

Note

datasets

this is the favored method since the bandwidth can
be limited to prevent impacting the usage of the cluster: rsync
Digital Research Alliance of Canada: Globus

Quick Start
Users first need login access to the cluster. It is
recommended to install milatools which will help in the set up of the
ssh configuration needed to securely and easily connect to the
cluster.

mila code
milatools also makes it easy to run and debug code on the Mila cluster. Using
the mila code command will allow you to use VSCode on the server. Simply run:
mila code path/on/cluster

The details of the command can be found on the github page of the package. Note that you need to
first setup your ssh configuration using mila init before the mila code
command can be used. The initialisation of the ssh configuration is explained
here and on the github page of the package.

mila code

Logging in to the cluster
To access the Mila Cluster clusters, you will need a Mila account. Please contact
Mila systems administrators if you don’t have it already. Our IT support service
is available here: https://it-support.mila.quebec/
You will also need to complete and return an IT Onboarding Training to get
access to the cluster.  Please refer to the Mila Intranet for more
informations:
https://sites.google.com/mila.quebec/mila-intranet/it-infrastructure/it-onboarding-training
IMPORTANT : Your access to the Cluster is granted based on your status at
Mila (for students, your status is the same as your main supervisor’ status),
and on the duration of your stay, set during the creation of your account. The
following have access to the cluster : Current Students of Core Professors -
Core Professors - Staff

SSH Login
You can access the Mila cluster via ssh:
# Generic login, will send you to one of the 4 login nodes to spread the load
ssh <user>@login.server.mila.quebec -p 2222

# To connect to a specific login node, X in [1, 2, 3, 4]
ssh <user>@login-X.login.server.mila.quebec -p 2222
Four login nodes are available and accessible behind a load balancer. At each
connection, you will be redirected to the least loaded login-node.
The ECDSA, RSA and ED25519 fingerprints for Mila’s login nodes are:
SHA256:baEGIa311fhnxBWsIZJ/zYhq2WfCttwyHRKzAb8zlp8 (ECDSA)
SHA256:Xr0/JqV/+5DNguPfiN5hb8rSG+nBAcfVCJoSyrR0W0o (RSA)
SHA256:gfXZzaPiaYHcrPqzHvBi6v+BWRS/lXOS/zAjOKeoBJg (ED25519)

Important
Login nodes are merely entry points to the cluster. They give you access
to the compute nodes and to the filesystem, but they are not meant to run
anything heavy. Do not run compute-heavy programs on these nodes,
because in doing so you could bring them down, impeding cluster access for
everyone.
This means no training or experiments, no compiling programs, no Python
scripts, but also no zip of a large folder or anything that demands a
sustained amount of computation.
Rule of thumb: never run a program that takes more than a few seconds on
a login node.

Note
In a similar vein, you should not run VSCode remote SSH instances directly
on login nodes, because even though they are typically not very
computationally expensive, when many people do it, they add up! See
Visual Studio Code for specific instructions.

mila init
To make it easier to set up a productive environment, Mila publishes the
milatools package, which defines a mila init command which will
automatically perform some of the below steps for you. You can install it with
pip and use it, provided your Python version is at least 3.8:
$ pip install milatools
$ mila init

SSH Config
The login nodes support the following authentication mechanisms:
publickey,keyboard-interactive.  If you would like to set an entry in your
.ssh/config file, please use the following recommendation:
Host mila
    User YOUR-USERNAME
    Hostname login.server.mila.quebec
    PreferredAuthentications publickey,keyboard-interactive
    Port 2222
    ServerAliveInterval 120
    ServerAliveCountMax 5

Then you can simply write ssh mila to connect to a login node. You will also
be able to use mila with scp, rsync and other such programs.

Tip
You can run commands on the login node with ssh directly, for example
ssh mila squeue -u '$USER' (remember to put single quotes around any
$VARIABLE you want to evaluate on the remote side, otherwise it will be
evaluated locally before ssh is even executed).

Passwordless login
To save you some repetitive typing it is highly recommended to set up public
key authentication, which means you won’t have to enter your password every time
you connect to the cluster.
# ON YOUR LOCAL MACHINE
# You might already have done this in the past, but if you haven't:
ssh-keygen  # Press ENTER 3x

# Copy your public key over to the cluster
# You will need to enter your password
ssh-copy-id mila

Connecting to compute nodes
If (and only if) you have a job running on compute node “cnode”, you are
allowed to SSH to it directly, if for some reason you need a second terminal.
That session will be automatically ended when your job is relinquished.
First, however, you need to have
password-less ssh either with a key present in your home or with an
ssh-agent. To generate a key pair on the login node:
# ON A LOGIN NODE
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
chmod 600 ~/.ssh/authorized_keys
chmod 700 ~/.ssh

Then from the login node you can write ssh <node>. From your local
machine, you can use ssh -J mila USERNAME@<node> (-J represents a “jump”
through the login node, necessary because the compute nodes are behind a
firewall).
If you wish, you may also add the following wildcard rule in your .ssh/config:
Host *.server.mila.quebec !*login.server.mila.quebec
    HostName %h
    ProxyJump mila

This will let you connect to a compute node with ssh <node>.server.mila.quebec.

SSH Login

Important
everyone.

Note

mila init
$ mila init

Host mila
    Port 2222

Tip

Running your code

SLURM commands guide

Basic Usage
The SLURM documentation
provides extensive information on the available commands to query the cluster
status or submit jobs.
Below are some basic examples of how to use SLURM.

Submitting jobs

Batch job
In order to submit a batch job, you have to create a script containing the main
command(s) you would like to execute on the allocated resources/nodes.
 2#SBATCH --job-name=test
 3#SBATCH --output=job_output.txt
 4#SBATCH --error=job_error.txt
 5#SBATCH --ntasks=1
 6#SBATCH --time=10:00
 7#SBATCH --mem=100Gb
 8
 9module load python/3.5
10python my_script.py

Your job script is then submitted to SLURM with sbatch (ref.)
sbatch job_script
sbatch: Submitted batch job 4323674
The working directory of the job will be the one where your executed sbatch.

Tip
Slurm directives can be specified on the command line alongside sbatch or
inside the job script with a line starting with #SBATCH.

Interactive job
Workload managers usually run batch jobs to avoid having to watch its
progression and let the scheduler run it as soon as resources are available. If
you want to get access to a shell while leveraging cluster resources, you can
submit an interactive jobs where the main executable is a shell with the
srun/salloc (srun/salloc) commands
salloc
Will start an interactive job on the first node available with the default
resources set in SLURM (1 task/1 CPU).  srun accepts the same arguments as
sbatch with the exception that the environment is not passed.

Tip
To pass your current environment to an interactive job, add
--preserve-env to srun.

salloc can also be used and is mostly a wrapper around srun if provided
without more info but it gives more flexibility if for example you want to get
an allocation on multiple nodes.

Job submission arguments
In order to accurately select the resources for your job, several arguments are
available. The most important ones are:

Argument
Description

-n, –ntasks=<number>
The number of task in your script, usually =1

-c, –cpus-per-task=<ncpus>
The number of cores for each task

-t, –time=<time>
Time requested for your job

–mem=<size[units]>
Memory requested for all your tasks

–gres=<list>
Select generic resources such as GPUs for your job: --gres=gpu:GPU_MODEL

Tip
Always consider requesting the adequate amount of resources to improve the
scheduling of your job (small jobs always run first).

Checking job status
To display jobs currently in queue, use squeue and to get only your jobs type
squeue -u $USER
JOBID   USER          NAME    ST  START_TIME         TIME NODES CPUS TRES_PER_NMIN_MEM NODELIST (REASON) COMMENT
133     my_username   myjob   R   2019-03-28T18:33   0:50     1    2        N/A  7000M node1 (None) (null)

Note
The maximum number of jobs able to be submitted to the system per user is 1000 (MaxSubmitJobs=1000)
at any given time from the given association. If this limit is reached, new submission requests
will be denied until existing jobs in this association complete.

Removing a job
To cancel your job simply use scancel
scancel 4323674

Partitioning
Since we don’t have many GPUs on the cluster, resources must be shared as fairly
as possible.  The --partition=/-p flag of SLURM allows you to set the
priority you need for a job.  Each job assigned with a priority can preempt jobs
with a lower priority: unkillable > main > long. Once preempted, your job is
killed without notice and is automatically re-queued on the same partition until
resources are available. (To leverage a different preemption mechanism, see the
Handling preemption)

Flag
Max Resource Usage
Max Time
Note

--partition=unkillable
6  CPUs, mem=32G,  1 GPU
2 days

--partition=unkillable-cpu
2  CPUs, mem=16G
2 days
CPU-only jobs

--partition=short-unkillable
24 CPUs, mem=128G, 4 GPUs
3 hours (!)
Large but short jobs

--partition=main
8  CPUs, mem=48G,  2 GPUs
5 days

--partition=main-cpu
8  CPUs, mem=64G
5 days

--partition=long
no limit of resources
7 days

--partition=long-cpu
7 days

Warning
Historically, before the 2022 introduction of CPU-only nodes (e.g. the cn-f
series), CPU jobs ran side-by-side with the GPU jobs on GPU nodes. To prevent
them obstructing any GPU job, they were always lowest-priority and preemptible.
This was implemented by automatically assigning them to one of the now-obsolete
partitions cpu_jobs, cpu_jobs_low or cpu_jobs_low-grace.
Do not use these partition names anymore. Prefer the *-cpu partition
names defined above.
For backwards-compatibility purposes, the legacy partition names are translated
to their effective equivalent long-cpu, but they will eventually be removed
entirely.

Note
As a convenience, should you request the unkillable, main or long
partition for a CPU-only job, the partition will be translated to its -cpu
equivalent automatically.

For instance, to request an unkillable job with 1 GPU, 4 CPUs, 10G of RAM and
12h of computation do:
sbatch --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable <job.sh>
You can also make it an interactive job using salloc:
salloc --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable
The Mila cluster has many different types of nodes/GPUs. To request a specific
type of node/GPU, you can add specific feature requirements to your job
submission command.
To access those special nodes you need to request them explicitly by adding the
flag --constraint=<name>.  The full list of nodes in the Mila Cluster can be
accessed Node profile description.
Example:
To request a machine with 2 GPUs using NVLink, you can use
sbatch -c 4 --gres=gpu:2 --constraint=nvlink

Feature
Particularities

12GB/16GB/24GB/32GB/48GB
Request a specific amount of GPU memory

volta/turing/ampere
Request a specific GPU architecture

nvlink
Machine with GPUs using the NVLink interconnect technology

Information on partitions/nodes
sinfo (ref.) provides most of the
information about available nodes and partitions/queues to submit jobs to.
Partitions are a group of nodes usually sharing similar features. On a
partition, some job limits can be applied which will override those asked for a
job (i.e. max time, max CPUs, etc…)
To display available partitions, simply use
sinfo
PARTITION AVAIL TIMELIMIT NODES STATE  NODELIST
batch     up     infinite     2 alloc  node[1,3,5-9]
batch     up     infinite     6 idle   node[10-15]
cpu       up     infinite     6 idle   cpu_node[1-15]
gpu       up     infinite     6 idle   gpu_node[1-15]
To display available nodes and their status, you can use
sinfo -N -l
NODELIST    NODES PARTITION STATE  CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
node[1,3,5-9]   2 batch     allocated 2    246    16000     0  (null)   (null)
node[2,4]       2 batch     drain     2    246    16000     0  (null)   (null)
node[10-15]     6 batch     idle      2    246    16000     0  (null)   (null)
...
And to get statistics on a job running or terminated, use sacct with some of
the fields you want to display
sacct --format=User,JobID,Jobname,partition,state,time,start,end,elapsed,nnodes,ncpus,nodelist,workdir -u $USER
     User        JobID    JobName  Partition      State  Timelimit               Start                 End    Elapsed   NNodes      NCPUS        NodeList              WorkDir
--------- ------------ ---------- ---------- ---------- ---------- ------------------- ------------------- ---------- -------- ---------- --------------- --------------------
my_usern+ 2398         run_extra+      batch    RUNNING 130-05:00+ 2019-03-27T18:33:43             Unknown 1-01:07:54        1         16 node9           /home/mila/my_usern+
my_usern+ 2399         run_extra+      batch    RUNNING 130-05:00+ 2019-03-26T08:51:38             Unknown 2-10:49:59        1         16 node9           /home/mila/my_usern+
Or to get the list of all your previous jobs, use the --start=YYYY-MM-DD flag. You can check sacct(1) for further information about additional time formats.
sacct -u $USER --start=2019-01-01
scontrol (ref.) can be used to
provide specific information on a job (currently running or recently terminated)
scontrol show job 43123
JobId=43123 JobName=python_script.py
UserId=my_username(1500000111) GroupId=student(1500000000) MCS_label=N/A
Priority=645895 Nice=0 Account=my_username QOS=normal
JobState=RUNNING Reason=None Dependency=(null)
Requeue=1 Restarts=3 BatchFlag=1 Reboot=0 ExitCode=0:0
RunTime=2-10:41:57 TimeLimit=130-05:00:00 TimeMin=N/A
SubmitTime=2019-03-26T08:47:17 EligibleTime=2019-03-26T08:49:18
AccrueTime=2019-03-26T08:49:18
StartTime=2019-03-26T08:51:38 EndTime=2019-08-03T13:51:38 Deadline=N/A
PreemptTime=None SuspendTime=None SecsPreSuspend=0
LastSchedEval=2019-03-26T08:49:18
Partition=slurm_partition AllocNode:Sid=login-node-1:14586
ReqNodeList=(null) ExcNodeList=(null)
NodeList=node2
BatchHost=node2
NumNodes=1 NumCPUs=16 NumTasks=1 CPUs/Task=16 ReqB:S:C:T=0:0:*:*
TRES=cpu=16,mem=32000M,node=1,billing=3
Socks/Node=* NtasksPerN:B:S:C=1:0:*:* CoreSpec=*
MinCPUsNode=16 MinMemoryNode=32000M MinTmpDiskNode=0
Features=(null) DelayBoot=00:00:00
OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
WorkDir=/home/mila/my_username
StdErr=/home/mila/my_username/slurm-43123.out
StdIn=/dev/null
StdOut=/home/mila/my_username/slurm-43123.out
Power=
Or more info on a node and its resources
scontrol show node node9
NodeName=node9 Arch=x86_64 CoresPerSocket=4
CPUAlloc=16 CPUTot=16 CPULoad=1.38
AvailableFeatures=(null)
ActiveFeatures=(null)
Gres=(null)
NodeAddr=10.252.232.4 NodeHostName=mila20684000000 Port=0 Version=18.08
OS=Linux 4.15.0-1036 #38-Ubuntu SMP Fri Dec 7 02:47:47 UTC 2018
RealMemory=32000 AllocMem=32000 FreeMem=23262 Sockets=2 Boards=1
State=ALLOCATED+CLOUD ThreadsPerCore=2 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
Partitions=slurm_partition
BootTime=2019-03-26T08:50:01 SlurmdStartTime=2019-03-26T08:51:15
CfgTRES=cpu=16,mem=32000M,billing=3
AllocTRES=cpu=16,mem=32000M
CapWatts=n/a
CurrentWatts=0 LowestJoules=0 ConsumedJoules=0
ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s

Useful Commands

sallocGet an interactive job and give you a shell. (ssh like) CPU only

salloc --gres=gpu:1 -c 2 --mem=12000Get an interactive job with one GPU, 2 CPUs and 12000 MB RAM

sbatchstart a batch job (same options as salloc)

sattach --pty <jobid>.0Re-attach a dropped interactive job

sinfostatus of all nodes

sinfo -Ogres:27,nodelist,features -tidle,mix,allocList GPU type and FEATURES that you can request

savail(Custom) List available gpu

scancel <jobid>Cancel a job

squeuesummary status of all active jobs

squeue -u $USERsummary status of all YOUR active jobs

squeue -j <jobid>summary status of a specific job

squeue -Ojobid,name,username,partition,state,timeused,nodelist,gres,tresstatus of all jobs including requested resources (see the SLURM squeue doc for all output options)

scontrol show job <jobid>Detailed status of a running job

sacct -j <job_id> -o NodeListGet the node where a finished job ran

sacct -u $USER -S <start_time> -E <stop_time>Find info about old jobs

sacct -oJobID,JobName,User,Partition,Node,StateList of current and recent jobs

Special GPU requirements
Specific GPU architecture and memory can be easily requested through the
--gres flag by using either

--gres=gpu:architecture:number
--gres=gpu:memory:number
--gres=gpu:model:number

Example:
To request 1 GPU with at least 16GB of memory use
sbatch -c 4 --gres=gpu:16gb:1
The full list of GPU and their features can be accessed here.

Example script
Here is a sbatch script that follows good practices on the Mila cluster:
 2
 3#SBATCH --partition=unkillable                           # Ask for unkillable job
 4#SBATCH --cpus-per-task=2                                # Ask for 2 CPUs
 5#SBATCH --gres=gpu:1                                     # Ask for 1 GPU
 6#SBATCH --mem=10G                                        # Ask for 10 GB of RAM
 7#SBATCH --time=3:00:00                                   # The job will run for 3 hours
 8#SBATCH -o /network/scratch/<u>/<username>/slurm-%j.out  # Write the log on scratch
 9
10# 1. Load the required modules
11module --quiet load anaconda/3
12
13# 2. Load your environment
14conda activate "<env_name>"
15
16# 3. Copy your dataset on the compute node
17cp /network/datasets/<dataset> $SLURM_TMPDIR
18
19# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
20#    and look for the dataset into $SLURM_TMPDIR
21python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
22
23# 5. Copy whatever you want to save on $SCRATCH
24cp $SLURM_TMPDIR/<to_save> /network/scratch/<u>/<username>/

Batch job
 8

Tip

salloc

Tip

Argument

Tip

Note

Batch job
 8

Tip

salloc

Tip

Batch job
 8

Tip

salloc

Tip

Argument

Tip

Note

Flag
Max Time
Note

2 days

2 days

3 hours (!)

5 days

5 days

7 days

7 days

Warning
entirely.

Note

Example:

Feature

nvlink

sinfo
sinfo -N -l
...
Power=

sinfo
sinfo -N -l
...
Power=

Example:

 2
 9
12
15
18
22

print(torch.__file__)'
cvmfs/ai.mila.quebec/apps/x86_64/debian/pytorch/python3.7-cuda10.1-cudnn7.6-v1.5.0/lib/python3.7/site-packages/torch/__init__.py'

On using containers
Another option for creating portable code is Using containers on clusters.
Containers are a popular approach at deploying applications by packaging a lot
of the required dependencies together. The most popular tool for this is
Docker, but Docker cannot be used on the Mila
cluster (nor the other clusters from Digital Research Alliance of Canada).
One popular mechanism for containerisation on a computational cluster is called
Singularity.
This is the recommended approach for running containers on the
Mila cluster. See section Singularity for more details.

Virtual environments
environment (or other virtual environments). It usually lives in a directory
(location varies depending on whether you use venv, conda or poetry). In order
to use a virtual environment, you have to activate it. Activating an
environment essentially sets environment variables in your shell so that:

python points to the right Python version for that environment (different
virtual environments can use different versions of Python!)
python looks for packages in the virtual environment
pip install installs packages into the virtual environment
Any shell commands installed via pip install are made available

To run experiments within a virtual environment, you can simply activate it
in the script given to sbatch.

Pip/Virtualenv
Pip is the preferred package manager for Python and each cluster provides
several Python versions through the associated module which comes with pip. In
order to install new packages, you will first have to create a personal space
for them to be stored.  The preferred solution (as it is the preferred solution
on Digital Research Alliance of Canada clusters) is to use virtual
environments.
First, load the Python module you want to use:
module load python/3.8
Then, create a virtual environment in your home directory:
python -m venv $HOME/<env>
Where <env> is the name of your environment. Finally, activate the environment:
source $HOME/<env>/bin/activate
You can now install any Python package you wish using the pip command, e.g.
pytorch:
pip install torch torchvision
Or Tensorflow:
pip install tensorflow-gpu

Conda
Another solution for Python is to use miniconda or anaconda which are also available through the module
command: (the use of Conda is not recommended for Digital Research Alliance of
Canada clusters due to the availability of custom-built packages for pip)
module load miniconda/3
=== Module miniconda/3 loaded ===]
o enable conda environment functions, first use:
To create an environment (see here
for details) using a specific Python version, you may write:
conda create -n <env> python=3.9
Where <env> is the name of your environment. You can now activate it by doing:
conda activate <env>
You are now ready to install any Python package you want in this environment.
For instance, to install PyTorch, you can find the Conda command of any version
you want on pytorch’s website, e.g:
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
If you make a lot of environments and install/uninstall a lot of packages, it
can be good to periodically clean up Conda’s cache:
conda clean --all

pytorch:

Conda

The module command
For a list of available modules, simply use:
-------------------------------------------------------------------------------------------------------------- Global Aliases ---------------------------------------------------------------------------------------------------------------
  cuda/10.0 -> cudatoolkit/10.0    cuda/9.2      -> cudatoolkit/9.2                                 pytorch/1.4.1       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.1    tensorflow/1.15 -> python/3.7/tensorflow/1.15
  cuda/10.1 -> cudatoolkit/10.1    mujoco-py     -> python/3.7/mujoco-py/2.0                        pytorch/1.5.0       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.0    tensorflow/2.2  -> python/3.7/tensorflow/2.2
  cuda/10.2 -> cudatoolkit/10.2    mujoco-py/2.0 -> python/3.7/mujoco-py/2.0                        pytorch/1.5.1       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1
  cuda/11.0 -> cudatoolkit/11.0    pytorch       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1    tensorflow          -> python/3.7/tensorflow/2.2
  cuda/9.0  -> cudatoolkit/9.0     pytorch/1.4.0 -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.0    tensorflow-cpu/1.15 -> python/3.7/tensorflow/1.15

-------------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Core ---------------------------------------------------------------------------------------------------
  Mila       (S,L)    anaconda/3 (D)    go/1.13.5        miniconda/2        mujoco/1.50        python/2.7    python/3.6        python/3.8           singularity/3.0.3    singularity/3.2.1    singularity/3.5.3 (D)
  anaconda/2          go/1.12.4         go/1.14   (D)    miniconda/3 (D)    mujoco/2.0  (D)    python/3.5    python/3.7 (D)    singularity/2.6.1    singularity/3.1.1    singularity/3.4.2

------------------------------------------------------------------------------------------------ /cvmfs/config.mila.quebec/modules/Compiler -------------------------------------------------------------------------------------------------
  python/3.7/mujoco-py/2.0

-------------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Cuda ---------------------------------------------------------------------------------------------------
  cuda/10.0/cudnn/7.3        cuda/10.0/nccl/2.4         cuda/10.1/nccl/2.4     cuda/11.0/nccl/2.7        cuda/9.0/nccl/2.4     cudatoolkit/9.0     cudatoolkit/10.1        cudnn/7.6/cuda/10.0/tensorrt/7.0
  cuda/10.0/cudnn/7.5        cuda/10.1/cudnn/7.5        cuda/10.2/cudnn/7.6    cuda/9.0/cudnn/7.3        cuda/9.2/cudnn/7.6    cudatoolkit/9.2     cudatoolkit/10.2        cudnn/7.6/cuda/10.1/tensorrt/7.0
  cuda/10.0/cudnn/7.6 (D)    cuda/10.1/cudnn/7.6 (D)    cuda/10.2/nccl/2.7     cuda/9.0/cudnn/7.5 (D)    cuda/9.2/nccl/2.4     cudatoolkit/10.0    cudatoolkit/11.0 (D)    cudnn/7.6/cuda/9.0/tensorrt/7.0

------------------------------------------------------------------------------------------------ /cvmfs/config.mila.quebec/modules/Pytorch --------------------------------------------------------------------------------------------------
  python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.4.1    python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.5.1 (D)    python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.0
  python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.5.0    python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.1        python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1 (D)

----------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Tensorflow ------------------------------------------------------------------------------------------------
  python/3.7/tensorflow/1.15    python/3.7/tensorflow/2.0    python/3.7/tensorflow/2.2 (D)
Modules can be loaded using the load command:
To search for a module or a software, use the command spider:
module spider search_term
E.g.: by default, python2 will refer to the os-shipped installation of python2.7 and python3 to python3.6.
If you want to use python3.7 you can type:
module load python3.7

Available Software
Modules are divided in 5 main sections:

Section

Core
Base interpreter and software (Python, go, etc…)

Compiler
Interpreter-dependent software (see the note below)

Cuda
Toolkits, cudnn and related libraries

Pytorch/Tensorflow
Pytorch/TF built with a specific Cuda/Cudnn
version for Mila’s GPUs (see the related paragraph)

Note
Modules which are nested (../../..) usually depend on other software/module
loaded alongside the main module.  No need to load the dependent software,
the complex naming scheme allows an automatic detection of the dependent
module(s):
i.e.: Loading cudnn/7.6/cuda/9.0/tensorrt/7.0 will load cudnn/7.6 and
cuda/9.0 alongside
python/3.X is a particular dependency which can be served through
python/3.X or anaconda/3 and is not automatically loaded to let the
user pick his favorite flavor.

Singularity

Overview

What is Singularity?
Running Docker on SLURM is a security problem (e.g. running as root, being able
to mount any directory).  The alternative is to use Singularity, which is a
popular solution in the world of HPC.
There is a good level of compatibility between Docker and Singularity,
and we can find many exaggerated claims about able to convert containers
from Docker to Singularity without any friction.
Oftentimes, Docker images from DockerHub are 100% compatible with Singularity,
and they can indeed be used without friction, but things get messy when
we try to convert our own Docker build files to Singularity recipes.

Links to official documentation

official Singularity user guide (this is the one you
will use most often)
official Singularity admin guide

Overview of the steps used in practice
Most often, the process to create and use a Singularity container is:

on your Linux computer (at home or work)

select a Docker image from DockerHub (e.g. pytorch/pytorch)
make a recipe file for Singularity that starts with that DockerHub image
build the recipe file, thus creating the image file (e.g. my-pytorch-image.sif)
test your singularity container before send it over to the cluster
rsync -av my-pytorch-image.sif <login-node>:Documents/my-singularity-images

on the login node for that cluster

queue your jobs with sbatch ...
(note that your jobs will copy over the my-pytorch-image.sif to $SLURM_TMPDIR
and will then launch Singularity with that image)
do something else while you wait for them to finish
queue more jobs with the same my-pytorch-image.sif,
reusing it many times over

In the following sections you will find specific examples or tips to accomplish
in practice the steps highlighted above.

Nope, not on MacOS
Singularity does not work on MacOS, as of the time of this writing in 2021.
Docker does not actually run on MacOS, but there Docker silently installs a
virtual machine running Linux, which makes it a pleasant experience,
and the user does not need to care about the details of how Docker does it.
Given its origins in HPC, Singularity does not provide that kind of seamless
experience on MacOS, even though it’s technically possible to run it
inside a Linux virtual machine on MacOS.

Where to build images
Building Singularity images is a rather heavy task, which can take 20 minutes
if you have a lot of steps in your recipe. This makes it a bad task to run on
the login nodes of our clusters, especially if it needs to be run regularly.
On the Mila cluster, we are lucky to have unrestricted internet access on the compute
nodes, which means that anyone can request an interactive CPU node (no need for GPU)
and build their images there without problem.

Warning
Do not build Singularity images from scratch every time your run a
job in a large batch.  This will be a colossal waste of GPU time as well as
internet bandwidth.  If you setup your workflow properly (e.g. using bind
paths for your code and data), you can spend months reusing the same
Singularity image my-pytorch-image.sif.

Building the containers
Building a container is like creating a new environment except that containers
are much more powerful since they are self-contained systems.  With
singularity, there are two ways to build containers.
The first one is by yourself, it’s like when you got a new Linux laptop and you
don’t really know what you need, if you see that something is missing, you
install it. Here you can get a vanilla container with Ubuntu called a sandbox,
you log in and you install each packages by yourself.  This procedure can take
time but will allow you to understand how things work and what you need. This is
recommended if you need to figure out how things will be compiled or if you want
to install packages on the fly. We’ll refer to this procedure as singularity
sandboxes.
The second way is more like you know what you want, so you write a list of
everything you need, you send it to singularity and it will install everything
for you. Those lists are called singularity recipes.

First way: Build and use a sandbox
You might ask yourself: On which machine should I build a container?
First of all, you need to choose where you’ll build your container. This
operation requires memory and high cpu usage.

Warning
Do NOT build containers on any login nodes !

(Recommended for beginner) If you need to use apt-get, you should build
the container on your laptop with sudo privileges. You’ll only need to
install singularity on your laptop. Windows/Mac users can look there and
Ubuntu/Debian users can use directly:

sudo apt-get install singularity-container

If you can’t install singularity on your laptop and you don’t need
apt-get, you can reserve a cpu node on the Mila cluster to build your
container.

In this case, in order to avoid too much I/O over the network, you should define
the singularity cache locally:

export SINGULARITY_CACHEDIR=$SLURM_TMPDIR

If you can’t install singularity on your laptop and you want to use
apt-get, you can use singularity-hub to build your containers and read
Recipe_section.

Download containers from the web
Hopefully, you may not need to create containers from scratch as many have been
already built for the most common deep learning software. You can find most of
them on dockerhub.
Go on dockerhub and select the container you want to pull.
For example, if you want to get the latest PyTorch version with GPU support
(Replace runtime by devel if you need the full Cuda toolkit):
singularity pull docker://pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime
Or the latest TensorFlow:
singularity pull docker://tensorflow/tensorflow:latest-gpu-py3
Currently the pulled image pytorch.simg or tensorflow.simg is read-only
meaning that you won’t be able to install anything on it.  Starting now, PyTorch
will be taken as example. If you use TensorFlow, simply replace every
pytorch occurrences by tensorflow.

How to add or install stuff in a container
The first step is to transform your read only container
pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg in a writable version that will
allow you to add packages.

Warning
Depending on the version of singularity you are using, singularity
will build a container with the extension .simg or .sif. If you’re using
.sif files, replace every occurences of .simg by .sif.

Tip
If you want to use apt-get you have to put sudo ahead of the
following commands

This command will create a writable image in the folder pytorch.
singularity build --sandbox pytorch pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg
Then you’ll need the following command to log inside the container.
singularity shell --writable -H $HOME:/home pytorch
Once you get into the container, you can use pip and install anything you need
(Or with apt-get if you built the container with sudo).

Warning
Singularity mounts your home folder, so if you install things into
the $HOME of your container, they will be installed in your real
$HOME!

You should install your stuff in /usr/local instead.

Creating useful directories
One of the benefits of containers is that you’ll be able to use them across
different clusters. However for each cluster the datasets and experiments
folder location can be different. In order to be invariant to those locations,
we will create some useful mount points inside the container:
mkdir /dataset
mkdir /tmp_log
mkdir /final_log
From now, you won’t need to worry anymore when you write your code to specify
where to pick up your dataset. Your dataset will always be in /dataset
independently of the cluster you are using.

Testing
If you have some code that you want to test before finalizing your container,
you have two choices.  You can either log into your container and run Python
code inside it with:
singularity shell --nv pytorch
Or you can execute your command directly with
singularity exec --nv pytorch Python YOUR_CODE.py

Tip
—nv allows the container to use gpus. You don’t need this if you
don’t plan to use a gpu.

Warning
Don’t forget to clear the cache of the packages you installed in
the containers.

Creating a new image from the sandbox
Once everything you need is installed inside the container, you need to convert
it back to a read-only singularity image with:
singularity build pytorch_final.simg pytorch

Second way: Use recipes
A singularity recipe is a file including specifics about installation software,
environment variables, files to add, and container metadata.  It is a starting
point for designing any custom container. Instead of pulling a container and
installing your packages manually, you can specify in this file the packages
you want and then build your container from this file.
Here is a toy example of a singularity recipe installing some stuff:
################# Header: Define the base system you want to use ################
# Reference of the kind of base you want to use (e.g., docker, debootstrap, shub).
Bootstrap: docker
# Select the docker image you want to use (Here we choose tensorflow)
From: tensorflow/tensorflow:latest-gpu-py3

################# Section: Defining the system #################################
# Commands in the %post section are executed within the container.
%post
        echo "Installing Tools with apt-get"
        apt-get update
        apt-get install -y cmake libcupti-dev libyaml-dev wget unzip
        apt-get clean
        echo "Installing things with pip"
        pip install tqdm
        echo "Creating mount points"
        mkdir /dataset
        mkdir /tmp_log
        mkdir /final_log

# Environment variables that should be sourced at runtime.
%environment
        # use bash as default shell
        SHELL=/bin/bash
        export SHELL

A recipe file contains two parts: the header and sections. In the
header you specify which base system you want to use, it can be any docker
or singularity container. In sections, you can list the things you want to
install in the subsection post or list the environment’s variable you need
to source at each runtime in the subsection environment. For a more detailed
description, please look at the singularity documentation.
In order to build a singularity container from a singularity recipe file, you
should use:
sudo singularity build <NAME_CONTAINER> <YOUR_RECIPE_FILES>

Warning
You always need to use sudo when you build a container from a
recipe. As there is no access to sudo on the cluster, a personal computer or
the use singularity hub is needed to build a container

Build recipe on singularity hub
Singularity hub allows users to build containers from recipes directly on
singularity-hub’s cloud meaning that you don’t need to build containers by
yourself.  You need to register on singularity-hub and link your
singularity-hub account to your GitHub account, then:

Create a new github repository.
Add a collection on singularity-hub and select the github repository your created.
Clone the github repository on your computer.
$ git clone <url>

Write the singularity recipe and save it as a file named Singularity.
Git add Singularity, commit and push on the master branch
$ git add Singularity
$ git commit
$ git push origin master

At this point, robots from singularity-hub will build the container for you, you
will be able to download your container from the website or directly with:
singularity pull shub://<github_username>/<repository_name>

Example: Recipe with OpenAI gym, MuJoCo and Miniworld
Here is an example on how you can use a singularity recipe to install complex
environment such as OpenAI gym, MuJoCo and Miniworld on a PyTorch based
container. In order to use MuJoCo, you’ll need to copy the key stored on the
Mila cluster in /ai/apps/mujoco/license/mjkey.txt to your current directory.
#This is a dockerfile that sets up a full Gym install with test dependencies

# Here we ll build our container upon the pytorch container
From: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime

# Now we'll copy the mjkey file located in the current directory inside the container's root
# directory
%files
        mjkey.txt

# Then we put everything we need to install
%post
        export PATH=$PATH:/opt/conda/bin
        apt -y update && \
        apt install -y keyboard-configuration && \
        apt install -y \
        python3-dev \
        python-pyglet \
        python3-opengl \
        libhdf5-dev \
        libjpeg-dev \
        libboost-all-dev \
        libsdl2-dev \
        libosmesa6-dev \
        patchelf \
        ffmpeg \
        xvfb \
        openjdk-8-jdk \
        wget \
        git \
        unzip && \
        apt clean && \
        rm -rf /var/lib/apt/lists/*
        pip install h5py

        # Download Gym and MuJoCo
        mkdir /Gym && cd /Gym
        git clone https://github.com/openai/gym.git || true && \
        mkdir /Gym/.mujoco && cd /Gym/.mujoco
        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
        unzip mjpro150_linux.zip && \
        wget https://www.roboti.us/download/mujoco200_linux.zip && \
        unzip mujoco200_linux.zip && \
        mv mujoco200_linux mujoco200

        # Export global environment variables
        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
        # Install Python dependencies
        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
        pip install -r requirements.txt
        # Install Gym and MuJoCo
        cd /Gym/gym
        pip install -e '.[all]'
        # Change permission to use mujoco_py as non sudoer user
        chmod -R 777 /opt/conda/lib/python3.6/site-packages/mujoco_py/
        pip install --upgrade minerl

# Export global environment variables
        export SHELL=/bin/sh
        export PATH=/Gym/gym/.tox/py3/bin:$PATH

%runscript
        exec /bin/sh "$@"

Here is the same recipe but written for TensorFlow:

# Here we ll build our container upon the tensorflow container

%files
        mjkey.txt

%post
        python3-setuptools \
        patchelf \
        ffmpeg \
        xvfb \
        wget \
        git \
        unzip && \

        chmod -R 777 /usr/local/lib/python3.5/dist-packages/mujoco_py/

        # Then install miniworld
        cd /usr/local/
        git clone https://github.com/maximecb/gym-miniworld.git
        cd gym-miniworld
        pip install -e .

        export SHELL=/bin/bash

        exec /bin/bash "$@"

Keep in mind that those environment variables are sourced at runtime and not at
build time. This is why, you should also define them in the %post section
since they are required to install MuJoCo.

Using containers on clusters

How to use containers on clusters
On every cluster with Slurm, datasets and intermediate results should go in
$SLURM_TMPDIR while the final experiment results should go in $SCRATCH.
In order to use the container you built, you need to copy it on the cluster you
want to use.

Warning
You should always store your container in $SCRATCH !

Then reserve a node with srun/sbatch, copy the container and your dataset on the
node given by SLURM (i.e in $SLURM_TMPDIR) and execute the code
<YOUR_CODE> within the container <YOUR_CONTAINER> with:
singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ $SLURM_TMPDIR/<YOUR_CONTAINER> python <YOUR_CODE>
Remember that /dataset, /tmp_log and /final_log were created in the
previous section. Now each time, we’ll use singularity, we are explicitly
telling it to mount $SLURM_TMPDIR on the cluster’s node in the folder
/dataset inside the container with the option -B such that each dataset
downloaded by PyTorch in /dataset will be available in $SLURM_TMPDIR.
This will allow us to have code and scripts that are invariant to the cluster
environment. The option -H specify what will be the container’s home. For
example, if you have your code in $HOME/Project12345/Version35/ you can
specify -H $HOME/Project12345/Version35:/home, thus the container will only
have access to the code inside Version35.
If you want to run multiple commands inside the container you can use:
singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ \
   -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ \
   $SLURM_TMPDIR/<YOUR_CONTAINER> bash -c 'pwd && ls && python <YOUR_CODE>'

Example: Interactive case (srun/salloc)
Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and
<YOUR_DATASET> to $SLURM_TMPDIR
0. Get an interactive session
srun --gres=gpu:1
1. Copy your container on the compute node
rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
2. Copy your dataset on the compute node
rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
Then use singularity shell to get a shell inside the container
3. Get a shell in your environment
singularity shell --nv \
        -H $HOME:/home \
        -B $SLURM_TMPDIR:/dataset/ \
        -B $SLURM_TMPDIR:/tmp_log/ \
        -B $SCRATCH:/final_log/ \
        $SLURM_TMPDIR/<YOUR_CONTAINER>
4. Execute your code
python <YOUR_CODE>
or use singularity exec to execute <YOUR_CODE>.
3. Execute your code
singularity exec --nv \
        $SLURM_TMPDIR/<YOUR_CONTAINER> \
        python <YOUR_CODE>
You can create also the following alias to make your life easier.
alias my_env='singularity exec --nv \
        $SLURM_TMPDIR/<YOUR_CONTAINER>'
This will allow you to run any code with:
my_env python <YOUR_CODE>

Example: sbatch case
You can also create a sbatch script:
:linenos:

#!/bin/bash
#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
#SBATCH --gres=gpu:1              # Ask for 1 GPU
#SBATCH --mem=10G                 # Ask for 10 GB of RAM
#SBATCH --time=0:10:00            # The job will run for 10 minutes

# 1. Copy your container on the compute node
# 2. Copy your dataset on the compute node
# 3. Executing your code with singularity
        python "<YOUR_CODE>"
# 4. Copy whatever you want to save on $SCRATCH
rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH

Issue with PyBullet and OpenGL libraries
If you are running certain gym environments that require pyglet, you may
encounter a problem when running your singularity instance with the Nvidia
drivers using the --nv flag. This happens because the --nv flag also
provides the OpenGL libraries:
libGL.so.1 => /.singularity.d/libs/libGL.so.1
libGLX.so.0 => /.singularity.d/libs/libGLX.so.0

If you don’t experience those problems with pyglet, you probably don’t need
to address this. Otherwise, you can resolve those problems by apt-get install
-y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making
sure that your LD_LIBRARY_PATH points to those libraries before the ones in
/.singularity.d/libs.
        # ...
        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH

Mila cluster
On the Mila cluster $SCRATCH is not yet defined, you should add the
experiment results you want to keep in /network/scratch/<u>/<username>/. In
order to use the sbatch script above and to match other cluster environment’s
names, you can define $SCRATCH as an alias for
/network/scratch/<u>/<username> with:
echo "export SCRATCH=/network/scratch/${USER:0:1}/$USER" >> ~/.bashrc
Then, you can follow the general procedure explained above.

Digital Research Alliance of Canada
Using singularity on Digital Research Alliance of Canada is similar except that
you need to add Yoshua’s account name and load singularity. Here is an example
of a sbatch script using singularity on compute Canada cluster:

Warning
You should use singularity/2.6 or singularity/3.4. There is a bug
in singularity/3.2 which makes gpu unusable.

 2#SBATCH --account=rpp-bengioy     # Yoshua pays for your job
 3#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
 4#SBATCH --gres=gpu:1              # Ask for 1 GPU
 5#SBATCH --mem=32G                 # Ask for 32 GB of RAM
 6#SBATCH --time=0:10:00            # The job will run for 10 minutes
 7#SBATCH --output="/scratch/<user>/slurm-%j.out" # Modify the output of sbatch
 8
 9# 1. You have to load singularity
10module load singularity
11# 2. Then you copy the container to the local disk
12rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
13# 3. Copy your dataset on the compute node
14rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
15# 4. Executing your code with singularity
16singularity exec --nv \
17        -H $HOME:/home \
18        -B $SLURM_TMPDIR:/dataset/ \
19        -B $SLURM_TMPDIR:/tmp_log/ \
20        -B $SCRATCH:/final_log/ \
21        $SLURM_TMPDIR/<YOUR_CONTAINER> \
22        python "<YOUR_CODE>"
24rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH

Overview

Warning

Warning

Warning

Warning

Tip

Warning
$HOME!

Testing

Tip

Warning

%post

Warning

%files
        mjkey.txt

%post
        patchelf \
        ffmpeg \
        xvfb \
        wget \
        git \
        unzip && \

%files
        mjkey.txt

%post
        patchelf \
        ffmpeg \
        xvfb \
        wget \
        git \
        unzip && \

Warning

Warning

Tip

Warning
$HOME!

Testing

Tip

Warning

Warning

Tip

Warning
$HOME!

Testing

Tip

Warning

%post

Warning

%files
        mjkey.txt

%post
        patchelf \
        ffmpeg \
        xvfb \
        wget \
        git \
        unzip && \

%files
        mjkey.txt

%post
        patchelf \
        ffmpeg \
        xvfb \
        wget \
        git \
        unzip && \

%files
        mjkey.txt

%post
        patchelf \
        ffmpeg \
        xvfb \
        wget \
        git \
        unzip && \

%files
        mjkey.txt

%post
        patchelf \
        ffmpeg \
        xvfb \
        wget \
        git \
        unzip && \

Warning

:linenos:

        # ...

Warning

 8

Warning

:linenos:

        # ...

Warning

 8

:linenos:

        # ...

Warning

 8

Sharing Data with ACLs
Regular permissions bits are extremely blunt tools: They control access through
only three sets of bits owning user, owning group and all others. Therefore,
access is either too narrow (0700 allows access only by oneself) or too wide
(770 gives all permissions to everyone in the same group, and 777 to
literally everyone).
ACLs (Access Control Lists) are an expansion of the permissions bits that allow
more fine-grained, granular control of accesses to a file. They can be used to
permit specific users access to files and folders even if conservative default
permissions would have denied them such access.
As an illustrative example, to use ACLs to allow $USER (oneself) to
share with $USER2 (another person) a “playground” folder hierarchy in
Mila’s scratch filesystem at a location

/network/scratch/${USER:0:1}/$USER/X/Y/Z/...

in a safe and secure fashion that allows both users to read, write, execute,
search and delete each others’ files:

1. Grant oneself permissions to access any future files/folders created
by the other (or oneself)
(-d renders this permission a “default” / inheritable one)

setfacl -Rdm user:${USER}:rwx  /network/scratch/${USER:0:1}/$USER/X/Y/Z/

Note
The importance of doing this seemingly-redundant step first is that files
and folders are always owned by only one person, almost always their
creator (the UID will be the creator’s, the GID typically as well). If that
user is not yourself, you will not have access to those files unless the
other person specifically gives them to you – or these files inherited a
default ACL allowing you full access.
This is the inherited, default ACL serving that purpose.

2. Grant the other permission to access any future files/folders created

setfacl -Rdm user:${USER2}:rwx /network/scratch/${USER:0:1}/$USER/X/Y/Z/

3. Grant the other permission to access any existing files/folders created
by oneself.
Such files and folders were created before the new default ACLs were added
above and thus did not inherit them from their parent folder at the moment of
their creation.

setfacl -Rm  user:${USER2}:rwx /network/scratch/${USER:0:1}/$USER/X/Y/Z/

Note
The purpose of granting permissions first for future files and then for
existing files is to prevent a race condition whereby after the first
setfacl command the other person could create files to which the
second setfacl command does not apply.

4. Grant another permission to search through one’s hierarchy down to the
shared location in question.

Non-recursive (!!!!)
May also grant :rx in unlikely event others listing your folders on the
path is not troublesome or desirable.

setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/X/Y/
setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/X/
setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/

Note
In order to access a file, all folders from the root (/) down to the
parent folder in question must be searchable (+x) by the concerned user.
This is already the case for all users for folders such as /,
/network and /network/scratch, but users must explicitly grant access
to some or all users either through base permissions or by adding ACLs, for
at least /network/scratch/${USER:0:1}/$USER, $HOME and subfolders.
To bluntly allow all users to search through a folder (think twice!),
the following command can be used:
chmod a+x /network/scratch/${USER:0:1}/$USER/

Note
For more information on setfacl and path resolution/access checking,
consider the following documentation viewing commands:

man setfacl
man path_resolution

Viewing and Verifying ACLs
getfacl /path/to/folder/or/file
           1:  # file: somedir/
           2:  # owner: lisa
           3:  # group: staff
           4:  # flags: -s-
           5:  user::rwx
           6:  user:joe:rwx               #effective:r-x
           7:  group::rwx                 #effective:r-x
           8:  group:cool:r-x
           9:  mask::r-x
          10:  other::r-x
          11:  default:user::rwx
          12:  default:user:joe:rwx       #effective:r-x
          13:  default:group::r-x
          14:  default:mask::r-x
          15:  default:other::---

Note

man getfacl

Note

Contributing datasets
If a dataset could help the research of others at Mila, this form can be filled to request its addition
to /network/datasets.

Publicly share a Mila dataset
Mila offers two ways to publicly share a Mila dataset:

Academic Torrent
Google Drive

Note that these options are not mutually exclusive and both can be used.

Academic Torrent
Mila hosts/seeds some datasets created by the Mila community through Academic
Torrent. The first step is to create an
account and a torrent file.
Then drop the dataset in /network/scratch/.transit_datasets and send the
Academic Torrent URL to Mila’s helpdesk. If
the dataset does not reside on the Mila cluster, only the Academic Torrent URL
would be needed to proceed with the initial download. Then you can delete /
stop sharing your copy.

Note

Avoid mentioning dataset in the name of the dataset
Avoid capital letters, special charaters (including spaces) in files and
directories names. Spaces can be replaced by hyphens (-).
Multiple archives can be provided to spread the data (e.g. dataset splits,
raw data, extra data, …)

Generate a .torrent file to be uploaded to Academic Torrent
The command line / Python utility torrentool can be used to create a
DATASET_NAME.torrent file:
# Install torrentool
python3 -m pip install torrentool click
# Change Directory to the location of the dataset to be hosted by Mila
cd /network/scratch/.transit_datasets
torrent create --tracker https://academictorrents.com/announce.php DATASET_NAME

The resulting DATASET_NAME.torrent can then be used to register a new dataset
on Academic Torrent.

Warning

The creation of a DATASET_NAME.torrent file requires the computation of
checksums for the dataset content which can quickly become CPU-heavy. This
process should not be executed on a login node

Download a dataset from Academic Torrent
Academic Torrent provides a Python API to easily download a dataset
from it’s registered list:
# Install the Python API with:
# python3 -m pip install academictorrents
import academictorrents as at
mnist_path = at.get("323a0048d87ca79b68f12a6350a57776b6a3b7fb", datastore="~/scratch/.academictorrents-datastore") # Download the mnist dataset

Note
Current needs have been evaluated to be for a download speed of about 10
MB/s. This speed can be higher if more users also seeds the dataset.

Google Drive
Only a member of the staff team can upload to Mila’s Google Drive
which requires to first drop the dataset in
/network/scratch/.transit_datasets. Then, contact Mila’s helpdesk and provide the following informations:

directory containing the archived dataset (zip is favored) in
/network/scratch/.transit_datasets
the name of the dataset
a licence in .txt format. One of the the creative common licenses can be used. It is
recommended to at least have the Attribution option. The No Derivatives
option is discouraged unless the dataset should not be modified by others.
MD5 checksum of the archive
the arXiv and GitHub URLs (those can be sent later if the article is still in
the submission process)
instructions to know if the dataset needs to be unziped, untared or
else before uploading to Google Drive

Note

Download a dataset from Mila’s Google Drive with  gdown
The utility gdown is a simple utility to
download data from Google Drive from the command line shell or in a Python
script and requires no setup.

Warning
A limitation however is that it uses a shared client id which can cause a
quota block when too many users uses it in the same day. It is described in
a GitHub issue.

Download a dataset from Mila’s Google Drive with rclone
Rclone is a command line program to manage files on
cloud storage. In the context of a Google Drive remote, it allows to specify a
client id to avoid sharing with other users which avoid quota limits. Rclone
describes the creation of a client id in its documentaton. Once this is done, a
remote for Mila’s Google Drive can be configured from the command line:
rclone config create mila-gdrive drive client_id XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.apps.googleusercontent.com \
    client_secret XXXXXXXXXXXXX-XXXXXXXXXX \
    scope 'drive.readonly' \
    root_folder_id 1peJ6VF9wQ-LeETgcdGxu1e4fo28JbtUt \
    config_is_local false \
    config_refresh_token false

The remote can then be used to download a dataset:
rclone copy --progress mila-gdrive:DATASET_NAME/ ~/scratch/datasets/DATASET_NAME/

Rclone is available from the conda channel conda-forge.

Digital Object Identifier (DOI)
It is recommended to get a DOI to reference the dataset. A DOI is a permanent
id/URL which prevents losing references of online scientific data.
https://figshare.com can be used to create a DOI:

Go in My Data
Create an item by clicking Create new item
Check Metadata record only at the top
Fill the metadata fields

Then reference the dataset using https://doi.org like this:
https://doi.org/10.6084/m9.figshare.2066037

Note

Warning

Note

Note

Warning

Note

Warning

Note

Warning

Note

Note

Warning

Warning

Data Transmission using Globus Connect Personal
Mila doesn’t own a Globus license but if the source or destination provides a
Globus account, like Digital Research Alliance of Canada for example, it’s
possible to setup Globus Connect Personal to create a personal endpoint on the
Mila cluster by following the Globus guide to Install, Configure, and
Uninstall Globus Connect Personal for Linux.
This endpoint can then be used to transfer data to and from the Mila cluster.

JupyterHub
JupyterHub is a platform connected to SLURM to start a JupyterLab
session as a batch job then connects it when the allocation has been granted.
It does not require any ssh tunnel or port redirection, the hub acts as a proxy
server that will redirect you to a session as soon as it is available.
It is currently available for Mila clusters and some Digital Research Alliance
of Canada (Alliance) clusters.

Cluster
Address
Login type

Mila Local
https://jupyterhub.server.mila.quebec
Google Oauth

Alliance
https://docs.alliancecan.ca/wiki/JupyterHub
DRAC login

Warning
Do not forget to close the JupyterLab session! Closing the window leaves
running the session and the SLURM job it is linked to.
To close it, use the hub menu and then Control Panel > Stop my server

Note
For Mila Clusters:
mila.quebec account credentials should be used to login and start a
JupyterLab session.

Access Mila Storage in JupyterLab
Unfortunately, JupyterLab does not allow the navigation to parent directories of
$HOME. This makes some file systems like /network/datasets or
$SLURM_TMPDIR unavailable through their absolute path in the interface. It
is however possible to create symbolic links to those resources. To do so, you
can use the ln -s command:
ln -s /network/datasets $HOME

Note that $SLURM_TMPDIR is a directory that is dynamically created for each
job so you would need to recreate the symbolic link every time you start a
JupyterHub session:
ln -sf $SLURM_TMPDIR $HOME

Advanced SLURM usage and Multiple GPU jobs

Handling preemption
On the Mila cluster, jobs can preempt one-another depending on their priority
(unkillable>high>low) (See the Slurm documentation)
The default preemption mechanism is to kill and re-queue the job automatically
without any notice. To allow a different preemption mechanism, every partition
have been duplicated (i.e. have the same characteristics as their counterparts)
allowing a 120sec grace period before killing your job but don’t requeue
it automatically: those partitions are referred by the suffix: -grace
(main-grace, long-grace, main-cpu-grace, long-cpu-grace).
When using a partition with a grace period, a series of signals consisting of
first SIGCONT and SIGTERM then SIGKILL will be sent to the SLURM
job.  It’s good practice to catch those signals using the Linux trap command
to properly terminate a job and save what’s necessary to restart the job.  On
each cluster, you’ll be allowed a grace period before SLURM actually kills
your job (SIGKILL).
The easiest way to handle preemption is by trapping the SIGTERM signal
 1#SBATCH --ntasks=1
 2#SBATCH ....
 3
 4exit_script() {
 5    echo "Preemption signal, saving myself"
 6    trap - SIGTERM # clear the trap
 7    # Optional: sends SIGTERM to child/sub processes
 8    kill -- -$$
 9}
10
11trap exit_script SIGTERM
12
13# The main script part
14python3 my_script

Note

Requeuing:
The Slurm scheduler on the cluster does not allow a grace period before
preempting a job while requeuing it automatically, therefore your job will
be cancelled at the end of the grace period.
To automatically requeue it, you can just add the sbatch command inside
your exit_script function.

Packing jobs

Sharing a GPU between processes
srun, when used in a batch job is responsible for starting tasks on the
allocated resources (see srun) SLURM batch script
1#SBATCH --ntasks-per-node=2
2#SBATCH --output=myjob_output_wrapper.out
3#SBATCH --ntasks=2
4#SBATCH --gres=gpu:1
5#SBATCH --cpus-per-task=4
6#SBATCH --mem=18G
7srun -l --output=myjob_output_%t.out python script args

This will run Python 2 times, each process with 4 CPUs with the same arguments
--output=myjob_output_%t.out will create 2 output files appending the task
id (%t) to the filename and 1 global log file for things happening outside
the srun command.
Knowing that, if you want to have 2 different arguments to the Python program,
you can use a multi-prog configuration file: srun -l --multi-prog silly.conf
0  python script firstarg
1  python script secondarg

Or by specifying a range of tasks
0-1  python script %t

%t being the taskid that your Python script will parse.  Note the -l on the
srun command: this will prepend each line with the taskid (0:, 1:)

Sharing a node with multiple GPU 1process/GPU
On Digital Research Alliance of Canada, several nodes, especially nodes with
largeGPU (P100) are reserved for jobs requesting the whole node, therefore
packing multiple processes in a single job can leverage faster GPU.
If you want different tasks to access different GPUs in a single allocation you
need to create an allocation requesting a whole node and using srun with a
subset of those resources (1 GPU).
Keep in mind that every resource not specified on the srun command while
inherit the global allocation specification so you need to split each resource
in a subset (except –cpu-per-task which is a per-task requirement)
Each srun represents a job step (%s).
Example for a GPU node with 24 cores and 4 GPUs and 128G of RAM
Requesting 1 task per GPU
 2#SBATCH --nodes=1-1
 3#SBATCH --ntasks-per-node=4
 4#SBATCH --output=myjob_output_wrapper.out
 5#SBATCH --gres=gpu:4
 6#SBATCH --cpus-per-task=6
 7srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args1 &
 8srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args2 &
 9srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args3 &
10srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args4 &
11wait

This will create 4 output files:

JOBID-step-0.out
JOBID-step-1.out
JOBID-step-2.out
JOBID-step-3.out

Sharing a node with multiple GPU & multiple processes/GPU
Combining both previous sections, we can create a script requesting a whole node
with four GPUs, allocating 1 GPU per srun and sharing each GPU with multiple
processes
Example still with a 24 cores/4 GPUs/128G RAM
Requesting 2 tasks per GPU
 3#SBATCH --ntasks-per-node=8
 6#SBATCH --cpus-per-task=3
 7srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
 8srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
 9srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
10srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
11wait

--exclusive is important to specify subsequent step/srun to bind to different cpus.
This will produce 8 output files, 2 for each step:

JOBID-step-0-task-0.out
JOBID-step-0-task-1.out
JOBID-step-1-task-0.out
JOBID-step-1-task-1.out
JOBID-step-2-task-0.out
JOBID-step-2-task-1.out
JOBID-step-3-task-0.out
JOBID-step-3-task-1.out

Running nvidia-smi in silly.conf, while parsing the output, we can see 4
GPUs allocated and 2 tasks per GPU
cat JOBID-step-* | grep Tesla
0: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
1: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
0: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
1: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
0: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
1: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
0: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |
1: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |

 3
 9}
10
12

Note

11wait

processes
11wait

11wait

processes
11wait

Multiple Nodes

Data Parallel

Request 3 nodes with at least 4 GPUs each.
 2
 3# Number of Nodes
 4#SBATCH --nodes=3
 5
 6# Number of tasks. 3 (1 per node)
 7#SBATCH --ntasks=3
 8
 9# Number of GPU per node
10#SBATCH --gres=gpu:4
11#SBATCH --gpus-per-node=4
12
13# 16 CPUs per node
14#SBATCH --cpus-per-gpu=4
15
16# 16Go per nodes (4Go per GPU)
17#SBATCH --mem=16G
18
19# we need all nodes to be ready at the same time
20#SBATCH --wait-all-nodes=1
21
22# Total resources:
23#   CPU: 16 * 3 = 48
24#   RAM: 16 * 3 = 48 Go
25#   GPU:  4 * 3 = 12
26
27# Setup our rendez-vous point
28RDV_ADDR=$(hostname)
29WORLD_SIZE=$SLURM_JOB_NUM_NODES
30# -----
31
32srun -l torchrun \
33   --nproc_per_node=$SLURM_GPUS_PER_NODE\
34   --nnodes=$WORLD_SIZE\
35   --rdzv_id=$SLURM_JOB_ID\
36   --rdzv_backend=c10d\
37   --rdzv_endpoint=$RDV_ADDR\
38   training_script.py

You can find below a pytorch script outline on what a multi-node trainer could look like.
import os
import torch.distributed as dist

class Trainer:
   def __init__(self):
      self.local_rank = None
      self.chk_path = ...
      self.model = ...

   @property
   def device_id(self):
      return self.local_rank

   def load_checkpoint(self, path):
      self.chk_path = path
      # ...

   def should_checkpoint(self):
      # Note: only one worker saves its weights
      return self.global_rank == 0 and self.local_rank == 0

   def save_checkpoint(self):
      if self.chk_path is None:
            return

      # Save your states here
      # Note: you should save the weights of self.model not ddp_model
      # ...

   def initialize(self):
      self.global_rank = int(os.environ.get("RANK", -1))
      self.local_rank = int(os.environ.get("LOCAL_RANK", -1))

      assert self.global_rank >= 0, 'Global rank should be set (Only Rank 0 can save checkpoints)'
      assert self.local_rank >= 0, 'Local rank should be set'

      dist.init_process_group(backend="gloo|nccl")

   def sync_weights(self, resuming=False):
      if resuming:
            # in the case of resuming all workers need to load the same checkpoint
            self.load_checkpoint()

            # Wait for everybody to finish loading the checkpoint
            dist.barrier()
            return

      # Make sure all workers have the same initial weights
      # This makes the leader save his weights
      if self.should_checkpoint():
            self.save_checkpoint()

      # All workers wait for the leader to finish
      dist.barrier()

      # All followers load the leader's weights
      if not self.should_checkpoint():

      # Leader waits for the follower to load the weights

   def dataloader(self, dataset, batch_size):
      train_sampler = ElasticDistributedSampler(dataset)
      train_loader = DataLoader(
            dataset,
            batch_size=batch_size,
            num_workers=4,
            pin_memory=True,
            sampler=train_sampler,
      )
      return train_loader

   def train_step(self):
      # Your batch processing step here
      # ...
      pass

   def train(self, dataset, batch_size):
      self.sync_weights()

      ddp_model = torch.nn.parallel.DistributedDataParallel(
            self.model,
            device_ids=[self.device_id],
            output_device=self.device_id
      )

      loader = self.dataloader(dataset, batch_size)

      for epoch in range(100):
            for batch in iter(loader):
               self.train_step(batch)

               if self.should_checkpoint():
                  self.save_checkpoint()

def main():
   trainer = Trainer()
   trainer.load_checkpoint(path)
   tainer.initialize()

   trainer.train(dataset, batch_size)

Note
To bypass Python GIL (Global interpreter lock) pytorch spawn one process for each GPU.
In the example above this means at least 12 processes are spawn, at least 4 on each node.

 2
 5
 8
12
15
18
21
26
30# -----
31

import os

   @property

      # ...

            return

      # ...

            return

            dataset,
      )

      # ...
      pass

      )

Note

Frequently asked questions (FAQs)

Connection/SSH issues

I’m getting connection refused while trying to connect to a login node
Login nodes are protected against brute force attacks and might ban your IP if
it detects too many connections/failures. You will be automatically unbanned
after 1 hour. For any further problem, please submit a support ticket.

Shell issues

How do I change my shell ?
By default you will be assigned /bin/bash as a shell. If you would like to
change for another one, please submit a support ticket.

SLURM issues

How can I get an interactive shell on the cluster ?
Use salloc [--slurm_options] without any executable at the end of the
command, this will launch your default shell on an interactive session. Remember
that an interactive session is bound to the login node where you start it so you
could risk losing your job if the login node becomes unreachable.

How can I reset my cluster password ?
To reset your password, please submit a support ticket.
Warning: your cluster password is the same as your Google Workspace account. So,
after reset, you must use the new password for all your Google services.

srun: error: –mem and –mem-per-cpu are mutually exclusive
You can safely ignore this, salloc has a default memory flag in case you
don’t provide one.

How can I see where and if my jobs are running ?
Use squeue -u YOUR_USERNAME to see all your job status and locations.
To get more info on a running job, try scontrol show job #JOBID

Unable to allocate resources: Invalid account or account/partition combination specified
Chances are your account is not setup properly. You should submit a support ticket.

How do I cancel a job?

To cancel a specific job, use scancel #JOBID
To cancel all your jobs (running and pending), use scancel -u YOUR_USERNAME
To cancel all your pending jobs only, use scancel -t PD

How can I access a node on which one of my jobs is running ?
You can ssh into a node on which you have a job running, your ssh connection
will be adopted by your job, i.e.  if your job finishes your ssh connection will
be automatically terminated. In order to connect to a node, you need to have
ssh-agent. You can generate a key on the login node like this:
ssh-keygen (3xENTER)

I’m getting Permission denied (publickey) while trying to connect to a node
See previous question

Where do I put my data during a job ?
Your /home as well as the datasets are on shared file-systems, it is
recommended to copy them to the $SLURM_TMPDIR to better process them and
leverage higher-speed local drives. If you run a low priority job subject to
preemption, it’s better to save any output you want to keep on the shared file
systems, because the $SLURM_TMPDIR is deleted at the end of each job.

slurmstepd: error: Detected 1 oom-kill event(s) in step #####.batch cgroup
You exceeded the amount of memory allocated to your job, either you did not
request enough memory or you have a memory leak in your process. Try increasing
the amount of memory requested with --mem= or --mem-per-cpu=.

fork: retry: Resource temporarily unavailable
You exceeded the limit of 2000 tasks/PIDs in your job, it probably means there
is an issue with a sub-process spawning too many processes in your script. For
any help with your software, please submit a support ticket.

PyTorch issues

I randomly get INTERNAL ASSERT FAILED at "../aten/src/ATen/MapAllocator.cpp":263
You are using PyTorch 1.10.x and hitting #67864,
for which the solution is PR #72232
merged in PyTorch 1.11.x. For an immediate fix, consider the following compilable Gist:
hack.cpp.
Compile the patch to hack.so and then export LD_PRELOAD=/absolute/path/to/hack.so
before executing the Python process that import torch a broken PyTorch 1.10.
For Hydra users who are using the submitit launcher plug-in, the env_set key cannot
be used to set LD_PRELOAD in the environment as it does so too late at runtime. The
dynamic loader reads LD_PRELOAD only once and very early during the startup of any
process, before the variable can be set from inside the process. The hack must therefore
be injected using the setup key in Hydra YAML config file:
hydra:
  launcher:
    setup:
      - export LD_PRELOAD=/absolute/path/to/hack.so

hack.cpp.
hydra:
  launcher:
    setup:

hack.cpp.
hydra:
  launcher:
    setup: