Diviner is a serverless machine learning and hyperparameter tuning platform. Diviner runs studies on behalf of a user; each study comprises a set of hyperparameters (e.g., learning rate, data augmentation policy, loss functions) and instructions for how to instantiate a trial based on a set of concrete hyperparameter values. Diviner then manages trial execution, book-keeping, and hyperparameter optimization based on past trials.
Diviner can be used as a scriptable tool or in Go programs through a Go package.
- build status:
- issues: github.com/grailbio/diviner/issues
- API docs: godoc.org/github.com/grailbio/diviner
- install diviner:
GO111MODULE=on go get -u github.com/grailbio/diviner/cmd/diviner
Diviner defines a data model that is rooted in user-defined studies. A study contains all the information needed to conduct a number of trials, including a set of hyperparameters over which to conduct the study. A trial is an instantiation of a set of valid hyperparameter values. A run is a trial attempt; runs may fail or be retried.
Diviner stores studies and runs in an underlying database, keyed by study names. The database is used to construct leaderboards that show the best-performing hyperparameter combinations for a study. The database can also be used to query pending runs and detailed information about each.
The diviner tool interprets study definitions written in Starlark. The study definitions include the hyperparameters definitions and a function that determines how to conduct a run based on a set of parameter values, selected by an optimizer (called an oracle).
In this example, we'll run a hyperparameter search on the example PyTorch convolutional neural network on the MNIST dataset.
By default, Diviner uses the dynamoDB table called "diviner".
To set this up, we have to run diviner create-table before we
begin to use Diviner.
$ diviner create-table
Now we're ready to define our study and instruct Diviner how to run trials.
First, create a file called mnist.dv.
We are interested in running training on GPU instances in AWS EC2,
so the first thing we do is to define an ec2 system for the trainers to run:
ec2gpu = ec2system(
"ec2gpu",
region="us-west-2",
ami="ami-01a4e5be5f289dd12",
security_group="SECURITY_GROUP",
instance_type="p3.2xlarge",
disk_space=100,
data_space=500,
on_demand=True,
flavor="ubuntu")
The AMI named here is the AWS DLAMI in us-west-2 as of this writing. The security group should give external access to port 443 (HTTPS).
We'll run our examples on p3.2xlarge instances, which are the smallest GPU instances provided by EC2.
Next, we'll define a function that is called with a set of parameter values. The function, run_mnist, returns a run configuration based on the provided parameter values. The run config defines how a trial is to be run given these parameters. In our case, we keep it very simple: we use the pytorch-provided docker image to invoke the mnist example with the provided parameter values.
def run_mnist(params):
return run_config(
system=ec2gpu,
script="""
nvidia-docker run -i pytorch/pytorch bash -x <<EOF
git clone https://github.com/pytorch/examples.git
python examples/mnist/main.py \
--batch-size %(batch_size)d \
--lr %(learning_rate)f \
--epochs %(epochs)d 2>&1 | \
awk '/^Test set:/ {gsub(",", "", \$5); print "METRICS: loss="\$5} {print}'
EOF
""" % params)
The only thing of note here is that we're using awk to pull out the test losses reported by the PyTorch trainer and formatting them in the manner expected by Diviner. (Any line in the process stdout beginning with "METRICS: " and followed by a set of key=value pairs are interpeted by Diviner as metrics reported by the trial.) The run config also defines the system definition on which to run the trial.
Now that we have a system definition and a run function, we can define our study. The study declares the set of parameters and their ranges. In this case, we're using discrete parameter ranges, but users can also define continuous ranges. Starlark intrinsics available in Diviner are documented here. We define the study's objective to minimize the metric "loss" (as reported by the runner, above). The oracle, grid_search, defines how the parameter space is to be explored. Grid searching exhaustively explores the parameter space.
study(
name="mnist",
params={
"epochs": discrete(1, 10, 50, 100),
"batch_size": discrete(32, 64, 128, 256),
"learning_rate": discrete(0.001, 0.01, 0.1, 1.0),
},
objective=minimize("loss"),
oracle=grid_search,
run=run_mnist,
)
Finally, we can now run the study. We run the study in "streaming" mode,
meaning that new trials are started as soon as capacity allows. The -trials
argument determines how many trials may be run in parallel. (And in our case,
how many EC2 GPU instances are created at a time.)
$ diviner run -stream -trials 5 mnist.dv
While the study is running, we can query the database. For example, to see the current set of trials running:
$ diviner list -runs -state pending -s
mnist:9 6:02PM 11m59s pending running: Train Epoch: 16 [45120/60000 (75%)] Loss: 0.171844
mnist:10 6:08PM 5m29s pending running: Train Epoch: 9 [14080/60000 (23%)] Loss: 0.178012
mnist:11 6:09PM 5m14s pending running:
mnist:12 6:09PM 5m14s pending running: Train Epoch: 5 [56320/60000 (94%)] Loss: 0.380566
mnist:13 6:09PM 4m59s pending running: Train Epoch: 7 [19200/60000 (32%)] Loss: 0.069407
(The last column(s) in this output shows the last line printed to standard output.) We can also examine the details of particular run. Runs are named by the study and an index.
$ diviner info mnist:9
run mnist:9:
state: pending
created: 2019-10-25 18:02:14 -0700 PDT
runtime: 13m29.972611766s
restarts: 0
replicate: 0
values:
batch_size: 32
epochs: 50
learning_rate: 0.001
metrics:
loss: 0.0398
Here we can see the parameter values used in this run,
the latest reported metrics, and some other relevant metadata.
Passing -v to diviner info gives even more detail, including
all of the reported metrics and the rendered script.
$ diviner info -v mnist:9
run mnist:9:
state: pending
created: 2019-10-25 18:02:14 -0700 PDT
runtime: 13m59.973265371s
restarts: 0
replicate: 0
values:
batch_size: 32
epochs: 50
learning_rate: 0.001
metrics[0]:
loss: 0.2996
metrics[1]:
loss: 0.1848
...
metrics[17]:
loss: 0.0398
script:
nvidia-docker run -i pytorch/pytorch bash -x <<EOF
git clone https://github.com/pytorch/examples.git
python examples/mnist/main.py --batch-size 32 --lr 0.001000 --epochs 50 2>&1 | awk '/^Test set:/ {gsub(",", "", \$5); print "METRICS: loss="\$5} {print}'
EOF
After Diviner has accumulated a number of trials, we can request the current leaderboard:
$ diviner leaderboard mnist
study replicates loss batch_size epochs learning_rate
mnist:21 0 0.0255 32 10 0.01
mnist:28 0 0.0265 256 50 0.01
mnist:9 0 0.027 32 50 0.001
mnist:14 0 0.0285 64 100 0.001
mnist:27 0 0.029 128 50 0.01
mnist:13 0 0.0298 32 100 0.001
...
This tells us the best hyperparameters (so far) for this MNIST classification task is batch_size=32, epochs=10, and learning_rate=0.01.
In addition to tracking studies and runs, Diviner maintains logs for each run. This can be useful when debugging issues or monitoring ongoing jobs. For example to view the logs of the best entry in the above study:
$ diviner logs mnist:21
diviner: started run (try 1) at 2019-10-25 18:41:57.84792 -0700 PDT on https://ec2-52-88-125-113.us-west-2.compute.amazonaws.com/
+ git clone https://github.com/pytorch/examples.git
Cloning into 'examples'...
+ python examples/mnist/main.py --batch-size 32 --lr 0.010000 --epochs 10
+ awk '/^Test set:/ {gsub(",", "", $5); print "METRICS: loss="$5} {print}'
9920512it [00:01, 8438927.61it/s]
32768it [00:00, 138802.83it/s]
1654784it [00:00, 2387828.32it/s]
8192it [00:00, 53052.53it/s] Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz
Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz
Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz
Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw
Processing...
Done!
Train Epoch: 1 [0/60000 (0%)] Loss: 2.301286
Train Epoch: 1 [320/60000 (1%)] Loss: 2.246368
Train Epoch: 1 [640/60000 (1%)] Loss: 2.143235
Train Epoch: 1 [960/60000 (2%)] Loss: 2.020995
Train Epoch: 1 [1280/60000 (2%)] Loss: 1.889598
Train Epoch: 1 [1600/60000 (3%)] Loss: 1.387788
Train Epoch: 1 [1920/60000 (3%)] Loss: 1.103807
...