Move things & add few pages

mila-iqia · Apr 10, 2024 · dc1c20a · dc1c20a
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diff --git a/.readthedocs.yml b/.readthedocs.yml
@@ -0,0 +1,19 @@
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.9"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+
+python:
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - Sphinx
+        - sphinx-rtd-theme
diff --git a/.vscode/settings.json b/.vscode/settings.json
@@ -0,0 +1,5 @@
+{
+    "githubPullRequests.ignoredPullRequestBranches": [
+        "master"
+    ]
+}
diff --git a/docs/ref-pack.rst → docs/API/ref-pack.rst b/docs/ref-pack.rst → docs/API/ref-pack.rst
diff --git a/docs/sizer.rst → docs/Contributing/BatchSizer.rst b/docs/sizer.rst → docs/Contributing/BatchSizer.rst
diff --git a/docs/config.rst → docs/Contributing/Config.rst b/docs/config.rst → docs/Contributing/Config.rst
diff --git a/docs/dev-usage.rst → docs/Contributing/Debugging.rst b/docs/dev-usage.rst → docs/Contributing/Debugging.rst
diff --git a/docs/Contributing/Design.rst b/docs/Contributing/Design.rst
@@ -0,0 +1,49 @@
+Design
+======
+
+Milabench aims to simulate research workloads for benchmarking purposes.
+
+* Performance is measured as throughput (samples / secs).
+  For example, for a model like resnet the throughput would be image per seconds.
+
+* Single GPU workloads are spawned per GPU to ensure the entire machine is used.
+  Simulating something similar to a hyper parameter search.
+  The performance of the benchmark is the sum of throughput of each processes.
+
+* Multi GPU workloads
+
+* Multi Nodes
+
+
+Run
+===
+
+* Milabench Manager Process
+   * Handles messages from benchmark processes
+   * Saves messages into a file for future analysis
+
+* Benchmark processes
+   * run using ``voir``
+   * voir is configured to intercept and send events during the training process
+   * This allow us to add models from git repositories without modification
+   * voir sends data through a file descriptor that was created by milabench main process
+
+
+What milabench is
+=================
+
+* Training focused
+* milabench show candid performance numbers
+   * No optimization beyond batch size scaling is performed
+   * we want to measure the performance our researcher will see
+     not the performance they could get.
+* pytorch centric
+   * Pytorch has become the defacto library for research
+   * We are looking for accelerator with good maturity that can support
+     this framework with limited code change.
+
+
+What milabench is not
+=====================
+
+* milabench goal is not a performance show case of an accelerator.
diff --git a/docs/instrument.rst → docs/Contributing/Instrumentation.rst b/docs/instrument.rst → docs/Contributing/Instrumentation.rst
diff --git a/docs/new_benchmarks.rst → docs/Contributing/NewBenchmark.rst b/docs/new_benchmarks.rst → docs/Contributing/NewBenchmark.rst
diff --git a/docs/docker.rst → docs/GettingStarted/Docker.rst b/docs/docker.rst → docs/GettingStarted/Docker.rst
@@ -3,6 +3,40 @@ Docker
 
 `Docker Images <https://github.com/mila-iqia/milabench/pkgs/container/milabench>`_ are created for each release. They come with all the benchmarks installed and the necessary datasets. No additional downloads are necessary.
 
+
+Setup
+------
+
+0. Make sure the machine can ssh between each other without passwords
+1. Pull the milabench docker image you would like to run on all machines
+  - ``docker pull``
+1. Create the output directory
+  - ``mkdir -p results``
+2. Create a list of nodes that will participate in the benchmark inside a ``results/system.yaml`` file (see example below)
+  - ``vi results/system.yaml``
+3. Call milabench with by specifying the node list we created.
+  - ``docker ... -v $(pwd)/results:/milabench/envs/runs -v <privatekey>:/milabench/id_milabench milabench run ... --system /milabench/envs/runs/system.yaml``
+
+
+.. code-block:: yaml
+
+   system:
+     sshkey: <privatekey>
+     arch: cuda
+     docker_image: ghcr.io/mila-iqia/milabench:${system.arch}-nightly
+
+     nodes:
+       - name: node1
+         ip: 192.168.0.25
+         main: true
+         port: 8123
+         user: <username>
+
+       - name: node2
+         ip: 192.168.0.26
+         main: false
+         user: <username>
+
 CUDA
 ----
 
@@ -22,16 +56,19 @@ storing the results inside the ``results`` folder on the host machine:
 
 .. code-block:: bash
 
+   export SSH_KEY_FILE=$HOME/.ssh/id_rsa
+
    # Choose the image you want to use
    export MILABENCH_IMAGE=ghcr.io/mila-iqia/milabench:cuda-nightly
 
    # Pull the image we are going to run
    docker pull $MILABENCH_IMAGE
 
    # Run milabench
-   docker run -it --rm --ipc=host --gpus=all      \
-         -v $(pwd)/results:/milabench/envs/runs   \
-         $MILABENCH_IMAGE                         \
+   docker run -it --rm --ipc=host --gpus=all --network host --privileged    \
+         -v $SSH_KEY_FILE:/milabench/id_milabench                           \
+         -v $(pwd)/results:/milabench/envs/runs                             \
+         $MILABENCH_IMAGE                                                   \
          milabench run
 
 ``--ipc=host`` removes shared memory restrictions, but you can also set ``--shm-size`` to a high value instead (at least ``8G``, possibly more).
@@ -63,16 +100,19 @@ For ROCM the usage is similar to CUDA, but you must use a different image and th
 
 .. code-block:: bash
 
+   export SSH_KEY_FILE=$HOME/.ssh/id_rsa
+
    # Choose the image you want to use
    export MILABENCH_IMAGE=ghcr.io/mila-iqia/milabench:rocm-nightly
 
    # Pull the image we are going to run
    docker pull $MILABENCH_IMAGE
 
    # Run milabench
-   docker run -it --rm  --ipc=host                                                  \
+   docker run -it --rm --ipc=host --network host --privileged                       \
          --device=/dev/kfd --device=/dev/dri                                        \
          --security-opt seccomp=unconfined --group-add video                        \
+         -v $SSH_KEY_FILE:/milabench/id_milabench                                   \
          -v /opt/amdgpu/share/libdrm/amdgpu.ids:/opt/amdgpu/share/libdrm/amdgpu.ids \
          -v /opt/rocm:/opt/rocm                                                     \
          -v $(pwd)/results:/milabench/envs/runs                                     \
@@ -90,119 +130,6 @@ For the performance report, it is the same command:
          milabench report --runs /milabench/envs/runs
 
 
-Multi-node benchmark
-^^^^^^^^^^^^^^^^^^^^
-
-There are currently two multi-node benchmarks, ``opt-1_3b-multinode`` (data-parallel) and 
-``opt-6_7b-multinode`` (model-parallel, that model is too large to fit on a single GPU). Here is how to run them:
-
-0. Make sure the machine can ssh between each other without passwords
-1. Pull the milabench docker image you would like to run on all machines
-  - ``docker pull``
-1. Create the output directory
-  - ``mkdir -p results``
-2. Create a list of nodes that will participate in the benchmark inside a ``results/system.yaml`` file (see example below)
-  - ``vi results/system.yaml``
-3. Call milabench with by specifying the node list we created.
-  - ``docker ... -v $(pwd)/results:/milabench/envs/runs -v <privatekey>:/milabench/id_milabench milabench run ... --system /milabench/envs/runs/system.yaml``
-
-.. notes::
-
-   The main node is the node that will be in charge of managing the other worker nodes.
-
-.. code-block:: yaml
-
-   system:
-     sshkey: <privatekey>
-     arch: cuda
-     docker_image: ghcr.io/mila-iqia/milabench:${system.arch}-nightly
-
-     nodes:
-       - name: node1
-         ip: 192.168.0.25
-         main: true
-         port: 8123
-         user: <username>
-      
-       - name: node2
-         ip: 192.168.0.26
-         main: false
-         user: <username>
-
-
-Then, the command should look like this:
-
-.. code-block:: bash
-
-    # On manager-node:
-
-    # Change if needed
-    export SSH_KEY_FILE=$HOME/.ssh/id_rsa
-    export MILABENCH_IMAGE=ghcr.io/mila-iqia/milabench:cuda-nightly
-    docker run -it --rm --gpus all --network host --ipc=host --privileged \
-      -v $SSH_KEY_FILE:/milabench/id_milabench \
-      -v $(pwd)/results:/milabench/envs/runs \
-      $MILABENCH_IMAGE \
-      milabench run --system /milabench/envs/runs/system.yaml \
-      --select multinode
-
-The last line (``--select multinode``) specifically selects the multi-node benchmarks. Omit that line to run all benchmarks.
-
-If you need to use more than two nodes, edit or copy ``system.yaml`` and simply add the other nodes' addresses in ``nodes``. 
-You will also need to update the benchmark definition and increase the max number of nodes by creating a new ``overrides.yaml`` file.
-
-For example, for 4 nodes:
-
-
-.. code-block:: yaml
-
-   # Name of the benchmark. You can also override values in other benchmarks.
-   opt-6_7b-multinode:
-     num_machines: 4
-  
-
-.. code-block:: yaml
-
-   system:
-     arch: cuda
-     docker_image: ghcr.io/mila-iqia/milabench:${system.arch}-nightly
-
-     nodes:
-       - name: node1
-         ip: 192.168.0.25
-         main: true
-         port: 8123
-         user: <username>
-      
-       - name: node2
-         ip: 192.168.0.26
-         main: false
-         user: <username>
-      
-       - name: node3
-         ip: 192.168.0.27
-         main: false
-         user: <username>
-
-       - name: node4
-         ip: 192.168.0.28
-         main: false
-         user: <username>
-
-
-The command would look like
-
-.. code-block:: bash
-
-   docker ... milabench run ... --system /milabench/envs/runs/system.yaml --overrides /milabench/envs/runs/overrides.yaml
-
-
-.. note::
-      The multi-node benchmark is sensitive to network performance. If the mono-node benchmark ``opt-6_7b`` is significantly faster than ``opt-6_7b-multinode`` (e.g. processes more than twice the items per second), this likely indicates that Infiniband is either not present or not used. (It is not abnormal for the multinode benchmark to perform *a bit* worse than the mono-node benchmark since it has not been optimized to minimize the impact of communication costs.)
-
-      Even if Infiniband is properly configured, the benchmark may fail to use it unless the ``--privileged`` flag is set when running the container.
-
-
 Building images
 ---------------