{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](\"https://www.comet.ml/images/logo_comet_light.png\")
\n",
"\n",
"# Comet.ml REST API\n",
"\n",
"*This page is available as an executable or viewable **Jupyter Notebook**:*
\n",
"![](\"https://mybinder.org/badge_logo.svg\")
\n",
"![](\"https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.png\")
\n",
"
\n",
"
\n",
"Comet.ml has an extensive interface to all of your data using a [REST API](https://en.wikipedia.org/wiki/Representational_state_transfer) through [Comet.ml endpoints](https://www.comet.ml/docs/rest-api/endpoints/). Now, you can access this information easily through the Comet.ml Python SDK. Requires version comet_ml version 1.0.40 or greater.\n",
"\n",
"## Setup\n",
"\n",
"To run the following experiments, you'll need to set your COMET_API_KEY and COMET_REST_API_KEY. The easiest way to to this is to set the values in a cell like this:\n",
"\n",
"```python\n",
"import comet_ml\n",
"\n",
"comet_ml.save(api_key=\"...\", rest_api_key=\"...\")\n",
"```\n",
"where you replace the ...'s with your keys.\n",
"\n",
"You can get your COMET_API_KEY under your quickstart link (replace YOUR_USERNAME with your Comet.ml username):\n",
"\n",
"https://www.comet.ml/YOUR_USERNAME/quickstart\n",
"\n",
"You can get your COMET_REST_API_KEY under your settings (replace YOUR_USERNAME with your Comet.ml username):\n",
"\n",
"https://www.comet.ml/YOUR_USERNAME/settings/account\n",
"\n",
"## Quick Overview\n",
"\n",
"To access the REST API through the Comet.ml SDK, you will need to make an API() instance. First, we import the API class and other libraries we will need:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from comet_ml import API\n",
"import comet_ml\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and create the API instance:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"comet_api = API()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the `comet_api` instance, you can get the name of your workspaces:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['comet-demos',\n",
" 'megancomet',\n",
" 'comet-mpm-test',\n",
" 'chasefortier',\n",
" 'maracomet',\n",
" 'development',\n",
" 'jacques-comet',\n",
" 'testingmaracomet',\n",
" 'team-comet-ml',\n",
" 'comet-etsy-workshop']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you reference your workspace by name using comet_api.get(WORKSPACE_NAME), you'll see your projects:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['project-2',\n",
" 'xor',\n",
" 'release-2-0-12c',\n",
" 'tfma-demo',\n",
" 'tfma-demo2',\n",
" 'many-epochs-steps',\n",
" 'comet-test',\n",
" 'output-test',\n",
" 'pytorch',\n",
" 'html-test',\n",
" 'high-dimensional-viz',\n",
" 'pytorch-jeri',\n",
" 'logged-curve',\n",
" 'mnist-001',\n",
" 'multi-gpus-2',\n",
" '3d-point-cloud',\n",
" 'cometr',\n",
" 'robot-controller',\n",
" 'visualizations',\n",
" '1910-01994',\n",
" 'notebook-assets',\n",
" 'bugs',\n",
" 'diy-search',\n",
" 'demo-mnist-005',\n",
" 'keras-bench',\n",
" 'xor2',\n",
" 'model-understanding',\n",
" 'tf-summary',\n",
" 'log-points-3d',\n",
" 'introduction-to-ml',\n",
" 'hydra',\n",
" 'histograms',\n",
" 'release-2-0-12d',\n",
" 'fairseq',\n",
" 'no-histograms',\n",
" 'conx',\n",
" 'large-dataset-analysis',\n",
" 'prj-name',\n",
" 'aitk-network',\n",
" 'xor-histogram',\n",
" 'projector-embeddings',\n",
" 'cepsa-2',\n",
" 'tensorflow-estimator',\n",
" 'test-histogram',\n",
" 'kubeflow',\n",
" 'robot-rl',\n",
" 'databricks',\n",
" 'dataset-profile',\n",
" 'ludwig',\n",
" 'current',\n",
" 'cepsa',\n",
" 'my-project',\n",
" 'tensorflow',\n",
" 'release-3-0-2',\n",
" 'comet-notebooks',\n",
" 'log-test',\n",
" 'optimizer-1',\n",
" 'plotly-confusion-matrix',\n",
" 'my-project2',\n",
" 'notebook-url',\n",
" 'prophet-test',\n",
" 'optimizer-keras',\n",
" 'shakespeare',\n",
" 'huggingface',\n",
" 'transformers',\n",
" 'custom-confusion-matrix',\n",
" 'missing-notebook-link',\n",
" 'histogram-bug',\n",
" 'codecarbon-test',\n",
" 'chainer',\n",
" 'optimizer-test-109',\n",
" 'mnist-014',\n",
" 'optimizer',\n",
" 'histogram-v2',\n",
" 'interactive-figures',\n",
" 'tensorflow-profiling',\n",
" 'comet-custom-barchart',\n",
" 'pytorch-confusion-matrix',\n",
" 'pipelines',\n",
" 'covid-19',\n",
" 'confusion-matrix',\n",
" 'jupyter-experiments',\n",
" 'bounding-box',\n",
" 'google-ml',\n",
" 'asset-metadata-bug-02',\n",
" 'sklearn-demos',\n",
" 'keras',\n",
" 'fastai',\n",
" 'demo-mnist-004',\n",
" 'optimizer-init-test',\n",
" 'colab']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get(\"dsblank\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, get the projects from another user's workspace:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['sparknlp-example',\n",
" 'confusion-matrix',\n",
" 'ludwig',\n",
" 'comet-sagemaker',\n",
" 'outout-tab-test',\n",
" 'fasttext',\n",
" 'comet-notebooks',\n",
" 'shap',\n",
" 'asset-types',\n",
" 'parameter-space-exploration',\n",
" 'hn-performance-prediction',\n",
" 'comet-examples',\n",
" '3d-histograms',\n",
" 'xgboost-example',\n",
" 'home-credit',\n",
" 'histograms']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get(\"cometpublic\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the same method, you can refer to a project by name:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[,\n",
" ,\n",
" ,\n",
" ,\n",
" ,\n",
" ]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get(\"dsblank\", \"keras\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, using the slash delimiter:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[,\n",
" ,\n",
" ,\n",
" ,\n",
" ,\n",
" ]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get(\"dsblank/keras\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And one more level, get the details of an experiment:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get(\"dsblank\", \"keras\", '5dc346a883964bd2b8864c40940fd864')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, again using the slash shorthand:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get(\"dsblank/keras/5dc346a883964bd2b8864c40940fd864\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get the experiment and save it to a variable named `exp`:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"exp = comet_api.get(\"dsblank/keras/51cf6e588a3346cdb560fd0c09d49610\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are a number of items you get from the APIExperiment instance:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on APIExperiment in module comet_ml.api object:\n",
"\n",
"class APIExperiment(comet_ml.experiment.CommonExperiment)\n",
" | APIExperiment(*args, **kwargs)\n",
" | \n",
" | The APIExperiment class is used to access data from the\n",
" | Comet.ml Python API.\n",
" | \n",
" | You can use an instance of the APIExperiment() class to easily\n",
" | access all of your logged experiment information\n",
" | at [Comet.ml](https://www.comet.ml), including metrics, parameters,\n",
" | tags, and assets.\n",
" | \n",
" | Examples:\n",
" | \n",
" | The following examples assume your `COMET_API_KEY` is configured as per\n",
" | [Python Configuration](https://www.comet.ml/docs/python-sdk/advanced/#python-configuration).\n",
" | \n",
" | This example shows looking up an experiment by its URL:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API, APIExperiment\n",
" | \n",
" | ## (assumes api keys are configured):\n",
" | >>> api = API() # can also: API(api_key=\"...\")\n",
" | \n",
" | ## Get an APIExperiment from the API:\n",
" | >>> experiment = api.get(\"cometpublic/comet-notebooks/example 001\")\n",
" | ```\n",
" | \n",
" | You can also make a new experiment using the API:\n",
" | \n",
" | ```python\n",
" | ## Make a new APIExperiment (assumes api keys are configured):\n",
" | >>> experiment = APIExperiment(workspace=\"my-username\",\n",
" | project_name=\"general\")\n",
" | ```\n",
" | \n",
" | Here is an end-to-end snippet to rename a metric. You can use this\n",
" | basic structure for logging new metrics (after the experiment has completed)\n",
" | such as averages or scaling to a baseline.\n",
" | \n",
" | ```python\n",
" | from comet_ml import API\n",
" | \n",
" | WORKSPACE = \"your-comet-id\"\n",
" | PROJECT_NAME = \"general\"\n",
" | EXP_KEY = \"your-experiment-key\"\n",
" | OLD_METRIC_NAME = \"loss\"\n",
" | NEW_METRIC_NAME = \"train_loss\"\n",
" | \n",
" | api = API() # can also: API(api_key=\"...\")\n",
" | \n",
" | experiment = api.get_experiment(WORKSPACE, PROJECT_NAME, EXP_KEY)\n",
" | old_metrics = experiment.get_metrics(OLD_METRIC_NAME)\n",
" | \n",
" | for old_metric in old_metrics:\n",
" | experiment.log_metric(\n",
" | NEW_METRIC_NAME,\n",
" | old_metric[\"metricValue\"],\n",
" | step=old_metric[\"step\"],\n",
" | timestamp=old_metric[\"timestamp\"],\n",
" | )\n",
" | ```\n",
" | \n",
" | For more usage examples, see [Comet Python API examples](../Comet-Python-API/).\n",
" | \n",
" | Method resolution order:\n",
" | APIExperiment\n",
" | comet_ml.experiment.CommonExperiment\n",
" | builtins.object\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __init__(self, *args, **kwargs)\n",
" | Create a new APIExperiment, or use a previous experiment key\n",
" | to access an existing experiment.\n",
" | \n",
" | Examples:\n",
" | ```python\n",
" | # Python API to create a new experiment:\n",
" | # (assumes api keys are configured):\n",
" | >>> experiment = APIExperiment(workspace=WORKSPACE,\n",
" | project_name=PROJECT)\n",
" | \n",
" | # Python API to access an existing experiment:\n",
" | # (assumes api keys are configured):\n",
" | >>> experiment = APIExperiment(previous_experiment=EXPERIMENT_KEY)\n",
" | ```\n",
" | \n",
" | Note: api_key may be defined in environment (COMET_API_KEY)\n",
" | or in a .comet.config file. Additional arguments will be\n",
" | given to API().\n",
" | \n",
" | __repr__(self)\n",
" | Return repr(self).\n",
" | \n",
" | add_tag(self, tag)\n",
" | Append onto an experiment's list of tags.\n",
" | \n",
" | Args:\n",
" | tag: a tag (string)\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.add_tag(\"baseline\")\n",
" | ```\n",
" | \n",
" | add_tags(self, tags)\n",
" | Append onto an experiment's list of tags.\n",
" | \n",
" | Args:\n",
" | tags: a list of tags (strings)\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.add_tags([\"successful\", \"best\"])\n",
" | ```\n",
" | \n",
" | archive(self)\n",
" | Archive this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.archive()\n",
" | ```\n",
" | \n",
" | create_symlink(self, project_name)\n",
" | Create a copy of this experiment in another project\n",
" | in the workspace.\n",
" | \n",
" | Args:\n",
" | project_name: the name of the project with which to create\n",
" | a symlink to this experiment in.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.create_symlink(\"my-other-project\")\n",
" | ```\n",
" | \n",
" | delete_asset(self, asset_id)\n",
" | Delete an experiment's asset.\n",
" | \n",
" | Args:\n",
" | asset_id: the asset id of the asset to delete\n",
" | \n",
" | download_model(self, name, output_path='./', expand=True)\n",
" | Download and save all files from the model.\n",
" | \n",
" | Args:\n",
" | model_name: str, the name of the model\n",
" | output: str, the output directory; defaults to current directory\n",
" | expand: if True, the downloaded zipfile is unzipped; if False, then the zipfile\n",
" | is copied to the output_path\n",
" | \n",
" | download_tensorflow_folder(self, output_path='./', overwrite=False)\n",
" | Download all files logged with `Experiment.log_tensorflow_folder(FOLDER)`.\n",
" | \n",
" | Args:\n",
" | output_path: (str) where to download the files\n",
" | overwrite: (bool) if True, then overwrite any file that exists\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> experiment = comet_ml.Experiment()\n",
" | >>> experiment.log_tensorboard_folder(\"logs\")\n",
" | >>> api = comet_ml.API()\n",
" | >>> api_experiment = api.get_experiment_by_id(experiment.id)\n",
" | >>> api_experiment.download_tensorflow_folder()\n",
" | ```\n",
" | \n",
" | end(self)\n",
" | Method called at end of experiment.\n",
" | \n",
" | get_additional_system_info(self)\n",
" | Get the associated additional system info for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_additional_system_info()\n",
" | []\n",
" | ```\n",
" | \n",
" | get_asset(self, asset_id, return_type='binary', stream=False)\n",
" | Get an asset, given the asset_id.\n",
" | \n",
" | Args:\n",
" | asset_id: (str) the asset ID\n",
" | return_type: (str) the type of object returned. Default is\n",
" | \"binary\". Options: \"binary\", \"text\", or \"response\"\n",
" | stream: (bool) when return_type is \"response\", you can also\n",
" | use stream=True to use the response as a stream\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_asset(\"298378237283728\", return_type=\"json\")\n",
" | {...}\n",
" | ```\n",
" | \n",
" | To use with the streaming option:\n",
" | \n",
" | ```python\n",
" | >>> asset_response = api_experiment.get_asset(\n",
" | ... \"298378237283728\",\n",
" | ... return_type=\"response\",\n",
" | ... stream=True,\n",
" | ... )\n",
" | >>> with open(filename, 'wb') as fd:\n",
" | >>> for chunk in asset_response.iter_content(chunk_size=1024*1024):\n",
" | >>> fd.write(chunk)\n",
" | ```\n",
" | \n",
" | get_asset_list(self, asset_type='all')\n",
" | Get a list of assets associated with the experiment.\n",
" | \n",
" | Args:\n",
" | asset_type: Optional String, type of asset to return. Can be\n",
" | \"all\", \"image\", \"histogram_combined_3d\", \"video\", or \"audio\".\n",
" | \n",
" | Returns a list of dictionaries of asset properties, like:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API\n",
" | >>> api = API()\n",
" | >>> x = api.get(\"myworkspace/project1/experiment_key\")\n",
" | >>> x.get_asset_list()\n",
" | [{'fileName': 'My Filename.png',\n",
" | 'fileSize': 21113,\n",
" | 'runContext': None,\n",
" | 'step': None,\n",
" | 'link': 'https://www.comet.ml/api/asset/download?experimentKey=KEY&assetId=ASSET_ID',\n",
" | 'createdAt': 1565898755830,\n",
" | 'dir': 'assets',\n",
" | 'canView': False,\n",
" | 'audio': False,\n",
" | 'video': False,\n",
" | 'histogram': False,\n",
" | 'image': True,\n",
" | 'type': 'image',\n",
" | 'metadata': None,\n",
" | 'assetId': ASSET_ID}, ...]\n",
" | \n",
" | >>> x.get_asset_list(\"image\")\n",
" | [{'fileName': 'My Filename.png',\n",
" | 'fileSize': 21113,\n",
" | 'runContext': None,\n",
" | 'step': None,\n",
" | 'link': 'https://www.comet.ml/api/asset/download?experimentKey=KEY&assetId=ASSET_ID',\n",
" | 'createdAt': 1565898755830,\n",
" | 'dir': 'assets',\n",
" | 'canView': False,\n",
" | 'audio': False,\n",
" | 'video': False,\n",
" | 'histogram': False,\n",
" | 'image': True,\n",
" | 'type': 'image',\n",
" | 'metadata': None,\n",
" | 'assetId': ASSET_ID}, ...]\n",
" | ```\n",
" | \n",
" | get_code(self)\n",
" | Get the associated source code for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_code()\n",
" | 'import comet_ml\n",
" | experiment = comet_ml.Experiment()\n",
" | experiment.end()'\n",
" | ```\n",
" | \n",
" | get_command(self)\n",
" | Get the associated command-line script and args for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_command()\n",
" | ['keras.py', '--size', '1024', '--log', 'experiment.log']\n",
" | ```\n",
" | \n",
" | get_curve(self, asset_id)\n",
" | Get curve logged with experiment by asset id.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_curve(\"57457745745745774\")\n",
" | {\"name\": \"curve1\", \"x\": [1, 2, 3], \"y\": [4, 5, 6], \"step\": 0}\n",
" | ```\n",
" | \n",
" | get_curves(self)\n",
" | Get all curves logged with experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_curves()\n",
" | [{\"name\": \"curve1\", \"x\": [1, 2, 3], \"y\": [4, 5, 6], \"step\": 0}]\n",
" | ```\n",
" | \n",
" | get_environment_details(self)\n",
" | Deprecated. Use `APIExperiment.get_os_packages()` instead.\n",
" | \n",
" | get_executable(self)\n",
" | Get the associated executable for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_executable()\n",
" | '/usr/local/bin/python'\n",
" | ```\n",
" | \n",
" | get_git_metadata(self)\n",
" | Get the git-metadata associated with this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_git_metadata()\n",
" | {\n",
" | \"branch\": 'refs/heads/master',\n",
" | \"origin\": 'git@github.com:comet-ml/comet-examples.git',\n",
" | \"parent\": '96ff529b4c02e4e0bb92992a7c4ce81275985764',\n",
" | \"root\": 'eec2d16daa057d0cf4c2c49974e6ea51e732a7b2',\n",
" | \"user\": 'user',\n",
" | }\n",
" | ```\n",
" | \n",
" | get_git_patch(self)\n",
" | Get the git-patch associated with this experiment as a zipfile containing an unique file\n",
" | named `zip_file.patch`.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> import io, zipfile\n",
" | >>> zip_patch = io.BytesIO(api_experiment.get_git_patch())\n",
" | >>> archive = zipfile.ZipFile(zip_patch)\n",
" | >>> patch = archive.read(\"git_diff.patch\")\n",
" | >>> patch\n",
" | b'...'\n",
" | ```\n",
" | \n",
" | get_gpu_static_info(self)\n",
" | Get the associated GPU static info for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_gpu_static_info()\n",
" | [{\n",
" | \"gpuIndex\": 0,\n",
" | \"name\": \"GeForce GTX 950\",\n",
" | \"uuid\": \"GPU-cb6c1b39-5a56-6d79-8899-3796f23c6425\",\n",
" | \"totalMemory\": 2090074112,\n",
" | \"powerLimit\": 110000,\n",
" | }, ...]\n",
" | ```\n",
" | \n",
" | get_hostname(self)\n",
" | Get the associated hostname for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_hostname()\n",
" | 'name-of-computer-host'\n",
" | ```\n",
" | \n",
" | get_html(self)\n",
" | Get the HTML associated with this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_html()\n",
" | \"Hello, world!\"\n",
" | ```\n",
" | \n",
" | get_installed_packages(self)\n",
" | Get the associated installed packages for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_installed_packages()\n",
" | ['absl-py==0.8.1', 'adal==1.2.2', 'alabaster==0.7.12', ...]\n",
" | ```\n",
" | \n",
" | get_ip(self)\n",
" | Get the associated IP for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_ip()\n",
" | '175.29.200.91'\n",
" | ```\n",
" | \n",
" | get_machine(self)\n",
" | Get the associated total RAM for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_machine()\n",
" | 'AMD64'\n",
" | ```\n",
" | \n",
" | get_max_memory(self)\n",
" | Get the associated max total memory for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_max_memory()\n",
" | 1024\n",
" | ```\n",
" | \n",
" | get_metadata(self)\n",
" | Get the metadata associated with this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml import APIExperiment\n",
" | >>> api_experiment = APIExperiment(previous_experiment='EXPERIMENT-KEY')\n",
" | >>> api_experiment.get_metadata()\n",
" | {\n",
" | 'archived': False,\n",
" | 'durationMillis': 7,\n",
" | 'endTimeMillis': 1586174765277,\n",
" | 'experimentKey': 'EXPERIMENT-KEY',\n",
" | 'experimentName': None,\n",
" | 'fileName': None,\n",
" | 'filePath': None,\n",
" | 'optimizationId': None,\n",
" | 'projectId': 'PROJECT-ID',\n",
" | 'projectName': 'PROJECT-NAME',\n",
" | 'running': False,\n",
" | 'startTimeMillis': 1586174757596,\n",
" | 'throttle': False,\n",
" | 'workspaceName': 'WORKSPACE-NAME',\n",
" | }\n",
" | ```\n",
" | \n",
" | get_metrics(self, metric=None)\n",
" | Get all of the logged metrics. Optionally, just get the given metric name.\n",
" | \n",
" | Args:\n",
" | metric: Optional. String. If given, filter the metrics by name.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API\n",
" | >>> api = API()\n",
" | >>> x = api.get(\"myworkspace/project1/experiment_key\")\n",
" | \n",
" | >>> x.get_metrics()\n",
" | [{'metricName': 'val_loss',\n",
" | 'metricValue': '0.13101346811652184',\n",
" | 'timestamp': 1558962376383,\n",
" | 'step': 1500,\n",
" | 'epoch': None,\n",
" | 'runContext': None},\n",
" | {'metricName': 'acc',\n",
" | 'metricValue': '0.876',\n",
" | 'timestamp': 1564536453647,\n",
" | 'step': 100,\n",
" | 'epoch': None,\n",
" | 'runContext': None},\n",
" | ...]\n",
" | \n",
" | >>> x.get_metrics(\"acc\")\n",
" | [{'metricName': 'acc',\n",
" | 'metricValue': '0.876',\n",
" | 'timestamp': 1564536453647,\n",
" | 'step': 100,\n",
" | 'epoch': None,\n",
" | 'runContext': None},\n",
" | ...]\n",
" | ```\n",
" | \n",
" | get_metrics_summary(self, metric=None)\n",
" | Return the experiment metrics summary. Optionally, also if you\n",
" | provide the metric name, the function will only return the\n",
" | summary of the metric.\n",
" | \n",
" | Args:\n",
" | metric: optional (string), name of a metric\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API\n",
" | >>> api = API()\n",
" | >>> x = api.get(\"myworkspace/project1/experiment_key\")\n",
" | >>> x.get_metrics_summary()\n",
" | [{'name': 'val_loss',\n",
" | 'valueMax': '0.24951280827820302',\n",
" | 'valueMin': '0.13101346811652184',\n",
" | 'valueCurrent': '0.13101346811652184',\n",
" | 'timestampMax': 1558962367938,\n",
" | 'timestampMin': 1558962367938,\n",
" | 'timestampCurrent': 1558962376383,\n",
" | 'stepMax': 500,\n",
" | 'stepMin': 1500,\n",
" | 'stepCurrent': 1500},\n",
" | ...]\n",
" | \n",
" | >>> api.get_metrics_summary(\"val_loss\")\n",
" | {'name': 'val_loss',\n",
" | 'valueMax': '0.24951280827820302',\n",
" | 'valueMin': '0.13101346811652184',\n",
" | 'valueCurrent': '0.13101346811652184',\n",
" | 'timestampMax': 1558962367938,\n",
" | 'timestampMin': 1558962367938,\n",
" | 'timestampCurrent': 1558962376383,\n",
" | 'stepMax': 500,\n",
" | 'stepMin': 1500,\n",
" | 'stepCurrent': 1500}\n",
" | ```\n",
" | \n",
" | get_model_asset_list(self, model_name)\n",
" | Get an experiment model's asset list by model name.\n",
" | \n",
" | Args:\n",
" | model_name: str, the name of the model\n",
" | \n",
" | Returns: a list of asset dictionaries with these fields:\n",
" | * fileName\n",
" | * fileSize\n",
" | * runContext\n",
" | * step\n",
" | * link\n",
" | * createdAt\n",
" | * dir\n",
" | * canView\n",
" | * audio\n",
" | * histogram\n",
" | * image\n",
" | * type\n",
" | * metadata\n",
" | * assetId\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml import API\n",
" | >>> api = API()\n",
" | >>> api_exp = api.get(\"workspace/project/765643463546345364536453436\")\n",
" | >>> api_exp.get_model_asset_list(\"Model Name\")\n",
" | [\n",
" | {\n",
" | \"assetId\": 74374637463476,\n",
" | \"audio\": False,\n",
" | \"canView\": False,\n",
" | \"createdAt\": 7337347634,\n",
" | \"dir\": \"trained-models\",\n",
" | \"fileName\": \"model.h5\",\n",
" | \"fileSize\": 254654,\n",
" | \"histogram\": False,\n",
" | \"image\": False,\n",
" | \"link\": \"https://link-to-download-asset-file\",\n",
" | \"metadata\": None,\n",
" | \"remote\": False,\n",
" | \"runContext\": \"train\",\n",
" | \"step\": 54,\n",
" | \"type\": \"asset\",\n",
" | }\n",
" | ]\n",
" | ```\n",
" | \n",
" | get_model_data(self, name)\n",
" | Deprecated. Use APIExperiment.get_model_asset_list(model_name) instead.\n",
" | \n",
" | get_model_graph(self)\n",
" | Get the associated graph/model description for this\n",
" | experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_model_graph()\n",
" | {\"class_name\": \"Sequential\", ...}\n",
" | ```\n",
" | \n",
" | get_model_names(self)\n",
" | Get a list of model names associated with this experiment.\n",
" | \n",
" | Returns: list of model names\n",
" | \n",
" | get_name(self)\n",
" | Get the name of the experiment, if one.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_name(\"My Name\")\n",
" | >>> api_experiment.get_name()\n",
" | 'My Name'\n",
" | ```\n",
" | \n",
" | get_network_interface_ips(self)\n",
" | Get the associated network interface IPs for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_network_interface_ips()\n",
" | ['127.0.0.1', '10.0.0.71', ...]\n",
" | ```\n",
" | \n",
" | get_os(self)\n",
" | Get the associated OS for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_os()\n",
" | 'Linux-4.15.0-1059-oem-x86_64-with-Ubuntu-18.04-bionic'\n",
" | ```\n",
" | \n",
" | get_os_packages(self)\n",
" | Get the OS packages for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_os_packages()\n",
" | ['accountsservice=0.6.45-1ubuntu1', 'acl=2.2.52-3build1', 'acpi-support=0.142', ...]\n",
" | ```\n",
" | \n",
" | get_os_release(self)\n",
" | Get the associated OS release for this experiment.\n",
" | \n",
" | Example:\n",
" | ```\n",
" | >>> api_experiment.get_os_release()\n",
" | '8'\n",
" | ```\n",
" | \n",
" | get_os_type(self)\n",
" | Get the associated os type for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_os_type()\n",
" | 'Linux'\n",
" | ```\n",
" | \n",
" | get_others_summary(self, other=None)\n",
" | Get the other items logged in summary form.\n",
" | \n",
" | Args:\n",
" | other: optional, string, the name of the other item\n",
" | logged. If given, return the valueCurrent of\n",
" | the other item. Otherwise, return all other\n",
" | items logged.\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API\n",
" | >>> api = API()\n",
" | >>> x = api.get(\"myworkspace/project1/experiment_key\")\n",
" | >>> x.get_others_summary()\n",
" | [{'name': 'trainable_params',\n",
" | 'valueMax': '712723',\n",
" | 'valueMin': '712723',\n",
" | 'valueCurrent': '712723',\n",
" | 'timestampMax': 1558962363411,\n",
" | 'timestampMin': 1558962363411,\n",
" | 'timestampCurrent': 1558962363411},\n",
" | ...]\n",
" | \n",
" | >>> x.get_others_summary(\"trainable_params\")\n",
" | ['712723']\n",
" | ```\n",
" | \n",
" | get_output(self)\n",
" | Get the associated standard output for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_output()\n",
" | \"Experiment is live\"\n",
" | ```\n",
" | \n",
" | get_parameters_summary(self, parameter=None)\n",
" | Return the experiment parameters summary. Optionally, also if you\n",
" | provide a parameter name, the method will only return the\n",
" | summary of the given parameter.\n",
" | \n",
" | Args:\n",
" | parameter: optional (string), name of a parameter\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API\n",
" | >>> api = API()\n",
" | >>> x = api.get(\"myworkspace/project1/experiment_key\")\n",
" | >>> x.get_parameters_summary()\n",
" | [{'name': 'batch_size',\n",
" | 'valueMax': '120',\n",
" | 'valueMin': '120',\n",
" | 'valueCurrent': '120',\n",
" | 'timestampMax': 1558962363411,\n",
" | 'timestampMin': 1558962363411,\n",
" | 'timestampCurrent': 1558962363411},\n",
" | ...]\n",
" | \n",
" | >>> x.get_parameters_summary(\"batch_size\")\n",
" | {'name': 'batch_size',\n",
" | 'valueMax': '120',\n",
" | 'valueMin': '120',\n",
" | 'valueCurrent': '120',\n",
" | 'timestampMax': 1558962363411,\n",
" | 'timestampMin': 1558962363411,\n",
" | 'timestampCurrent': 1558962363411}\n",
" | ```\n",
" | \n",
" | get_pid(self)\n",
" | Get the pid for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_pid()\n",
" | 34658\n",
" | ```\n",
" | \n",
" | get_processor(self)\n",
" | Get the associated total RAM for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_processor()\n",
" | 'Intel64 Family 6 Model 60 Stepping 3, GenuineIntel\n",
" | ```\n",
" | \n",
" | get_python_version(self)\n",
" | Get the Python version for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_python_version()\n",
" | '3.6.8'\n",
" | ```\n",
" | \n",
" | get_python_version_verbose(self)\n",
" | Get the Python version verbose for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_python_version_verbose()\n",
" | '3.6.8 (default, Oct 7 2019, 12:59:55) \n",
" | [GCC 8.3.0]'\n",
" | ```\n",
" | \n",
" | get_system_details(self)\n",
" | Get the system details associated with this experiment.\n",
" | \n",
" | Returns a dictionary like:\n",
" | \n",
" | ```python\n",
" | {\n",
" | \"experimentKey\": \"someExperimentKey\",\n",
" | \"user\": \"system username\"\n",
" | \"pythonVersion\": \"python version\"\n",
" | \"pythonVersionVerbose\": \"python version with verbose flag\"\n",
" | \"pid\": ,\n",
" | \"osType\": \"os experiment ran on\",\n",
" | \"os\": \"os with version info\",\n",
" | \"ip\": \"ip address\",\n",
" | \"hostname\": \"hostname\",\n",
" | \"gpuStaticInfoList\": [\n",
" | {\n",
" | \"gpuIndex\": ,\n",
" | \"name\": \"name\",\n",
" | \"uuid\": \"someUniqueId\",\n",
" | \"totalMemory\": ,\n",
" | \"powerLimit\": \n",
" | }\n",
" | ],\n",
" | \"logAdditionalSystemInfoList\": [\n",
" | {\n",
" | \"key\": \"someKey\",\n",
" | \"value\": \"someValue\"\n",
" | }\n",
" | ],\n",
" | \"systemMetricNames\": [\"name\", \"anotherName\"],\n",
" | \"maxTotalMemory\": ,\n",
" | \"networkInterfaceIps\": [\"ip\", \"anotherIp\"]\n",
" | \"command\": [\"part1\", \"part2\"],\n",
" | \"executable\": \"The python Exe, if any (in future could be non python executables)\",\n",
" | \"osPackages\": [\"package\", \"anotherPackage\"],\n",
" | \"installedPackages\": [\"package\", \"anotherPackage\"]\n",
" | }\n",
" | ```\n",
" | \n",
" | get_system_metric_names(self)\n",
" | Get the associated system metric names for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_system_metric_names()\n",
" | ['sys.cpu.percent.03', 'sys.cpu.percent.02', 'sys.cpu.percent.01', ...]\n",
" | ```\n",
" | \n",
" | get_tags(self)\n",
" | Get the associated tags for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_tags()\n",
" | [\"best\"]\n",
" | ```\n",
" | \n",
" | get_total_memory(self)\n",
" | Get the associated total RAM for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_total_memory()\n",
" | 1024\n",
" | ```\n",
" | \n",
" | get_user(self)\n",
" | Get the associated user for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_user()\n",
" | 'usename'\n",
" | ```\n",
" | \n",
" | log_additional_system_info(self, key, value)\n",
" | Log additional system information for this experiment.\n",
" | \n",
" | Args:\n",
" | key: (string) the name for this system information\n",
" | value: (any type), the value of the system information\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> experiment.log_additional_system_info(\"some name\": 42)\n",
" | ```\n",
" | \n",
" | log_asset(self, filename, step=None, overwrite=None, context=None, ftype=None, metadata=None)\n",
" | Upload an asset to an experiment.\n",
" | \n",
" | Args:\n",
" | filename: the name of the asset file to upload\n",
" | step: the current step\n",
" | overwrite: if True, overwrite any previous upload\n",
" | context: the current context (e.g., \"train\" or \"test\")\n",
" | ftype: the type of asset (e.g., \"image\", \"histogram_combined_3d\",\n",
" | \"image\", \"audio\", or \"video\")\n",
" | metadata: a JSON object to attach to image\n",
" | \n",
" | Note: don't delete the file until upload is complete\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_asset(\"histogram.json\", ftype=\"histogram_compbined_3d\")\n",
" | ```\n",
" | \n",
" | log_cpu_metrics(self, cpu_metrics, context=None, step=None, epoch=None, timestamp=None)\n",
" | Log an instance of cpu_metrics.\n",
" | \n",
" | Args:\n",
" | cpu_metrics: a list of integer percentages, ordered by cpu\n",
" | context: optional, a run context\n",
" | step: optional, the current step\n",
" | epoch: optional, the current epoch\n",
" | timestamp: optional, current time, in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | # Four CPUs:\n",
" | >>> api_experiment.log_cpu_metrics([25, 50, 10, 45])\n",
" | ```\n",
" | \n",
" | log_curve(self, name, x, y, overwrite=False, step=None)\n",
" | Log timeseries data.\n",
" | \n",
" | Args:\n",
" | name: (str) name of data\n",
" | x: list of x-axis values\n",
" | y: list of y-axis values\n",
" | overwrite: (optional, bool) if True, overwrite previous log\n",
" | step: (optional, int) the step value\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> experiment.log_curve(\"my curve\", x=[1, 2, 3, 4, 5],\n",
" | y=[10, 20, 30, 40, 50])\n",
" | >>> experiment.log_curve(\"my curve\", [1, 2, 3, 4, 5],\n",
" | [10, 20, 30, 40, 50])\n",
" | ```\n",
" | \n",
" | log_gpu_metrics(self, gpu_metrics)\n",
" | Log an instance of gpu_metrics.\n",
" | \n",
" | Args:\n",
" | gpu_metrics: a list of dicts with keys:\n",
" | * gpuId: required, Int identifier\n",
" | * freeMemory: required, Long\n",
" | * usedMemory: required, Long\n",
" | * gpuUtilization: required, Int percentage utilization\n",
" | * totalMemory: required, Long\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_gpu_metrics([{\n",
" | ... \"gpuId\": 1,\n",
" | ... \"freeMemory\": 1024,\n",
" | ... \"usedMemory\": 856,\n",
" | ... \"gpuUtilization\": 25,\n",
" | ... \"totalMemory\": 2056,\n",
" | ... }])\n",
" | ```\n",
" | \n",
" | log_html(self, html, clear=False, timestamp=None)\n",
" | Set, or append onto, an experiment's HTML.\n",
" | \n",
" | Args:\n",
" | html: (string) the HTML text to associate with this experiment\n",
" | clear: (optional, boolean) if True, clear any previously logged HTML\n",
" | timestamp: (optional, number) the current time (in milliseconds)\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_html(\"Hello!\")\n",
" | ```\n",
" | \n",
" | log_image(self, filename, image_name=None, step=None, overwrite=None, context=None)\n",
" | Upload an image asset to an experiment.\n",
" | \n",
" | Args:\n",
" | filename: the name of the image file to upload\n",
" | image_name: the name of the image\n",
" | step: the current step\n",
" | overwrite: if True, overwrite any previous upload\n",
" | context: the current context (e.g., \"train\" or \"test\")\n",
" | \n",
" | Note: don't delete the file until upload is complete\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_image(\"image.png\", \"Weights\")\n",
" | ```\n",
" | \n",
" | log_load_metrics(self, load_avg, context=None, step=None, epoch=None, timestamp=None)\n",
" | Log an instance of system load metrics.\n",
" | \n",
" | Args:\n",
" | load_avg: required, the load average\n",
" | context: optional, the run context\n",
" | step: optional, the current step\n",
" | epoch: optional, the current epoch\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_load_metrics(1.5, \"validate\", 100, 25, 65364346)\n",
" | ```\n",
" | \n",
" | log_metric(self, metric, value, step=None, timestamp=None)\n",
" | Set a metric name/value pair for an experiment.\n",
" | \n",
" | Args:\n",
" | metric: string, the name of the metric\n",
" | value: any type, the value of the metric\n",
" | step: optional integer, the current step\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_metric(\"loss\", 0.698)\n",
" | ```\n",
" | \n",
" | log_metrics(self, metric_dict, step=None, timestamp=None)\n",
" | Set a dictionary of metric name/value pairs for an experiment.\n",
" | \n",
" | Args:\n",
" | metric_dict: dict in the form of {\"metric_name\": value, ...}\n",
" | step: optional integer, the current step\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_metrics({\"loss\": 0.698, \"accuracy\": 0.12})\n",
" | [...]\n",
" | ```\n",
" | \n",
" | log_other(self, key, value, timestamp=None)\n",
" | Set an other key/value pair for an experiment.\n",
" | \n",
" | Args:\n",
" | key: string, the name of the other information\n",
" | value: any time, the value of the other information\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_other(\"key\", value)\n",
" | ```\n",
" | \n",
" | log_output(self, output, context=None, stderr=False, timestamp=None)\n",
" | Log output line(s).\n",
" | \n",
" | Args:\n",
" | output: string representing standard output or error\n",
" | context: optional, the run context\n",
" | stderr: optional, boolean, if True, the lines are standard errors\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_output(\"output line 1\n",
" | output line 2\")\n",
" | ```\n",
" | \n",
" | log_parameter(self, parameter, value, step=None, timestamp=None)\n",
" | Set a parameter name/value pair for an experiment.\n",
" | \n",
" | Args:\n",
" | parameter: string, the name of the parameter\n",
" | value: any type, the value of the parameter\n",
" | step: optional integer, the current step\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_parameter(\"hidden_layer_size\", 64)\n",
" | ```\n",
" | \n",
" | log_parameters(self, param_dict, step=None, timestamp=None)\n",
" | Set a dictionary of parameter name/value pairs for an experiment.\n",
" | \n",
" | Args:\n",
" | param_dict: dict in the form of {\"param_name\": value, ...}\n",
" | step: optional integer, the current step\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_parameters({\"learning_rate\": 0.12, \"layers\": 3})\n",
" | [...]\n",
" | ```\n",
" | \n",
" | log_ram_metrics(self, total_ram, used_ram, context=None, step=None, epoch=None, timestamp=None)\n",
" | Log an instance of RAM metrics.\n",
" | \n",
" | Args:\n",
" | total_ram: required, total RAM available\n",
" | used_ram: required, RAM used\n",
" | context: optional, the run context\n",
" | step: optional, the current step\n",
" | epoch: optional, the current epoch\n",
" | timestamp: optional, the current timestamp in millisconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_ram_metrics(1024, 865, \"train\", 100, 1, 3645346534)\n",
" | ```\n",
" | \n",
" | log_table(self, filename, tabular_data=None, headers=False, **format_kwargs)\n",
" | Log tabular data, including data, csv files, tsv files, and Pandas dataframes.\n",
" | \n",
" | Args:\n",
" | filename: str (required), a filename ending in \".csv\", or \".tsv\" (for tablular\n",
" | data) or \".json\", \".csv\", \".md\", or \".html\" (for Pandas dataframe data).\n",
" | tabular_data: (optional) data that can be interpreted as 2D tabular data\n",
" | or a Pandas dataframe).\n",
" | headers: bool or list, if True, will add column headers automatically\n",
" | if tabular_data is given; if False, no headers will be added; if list\n",
" | then it will be used as headers. Only useful with tabular data (csv, or tsv).\n",
" | format_kwargs: (optional keyword arguments), when passed a Pandas dataframe\n",
" | these keyword arguments are used in the conversion to \"json\", \"csv\",\n",
" | \"md\", or \"html\". See Pandas Dataframe conversion methods (like `to_json()`)\n",
" | for more information.\n",
" | \n",
" | See also:\n",
" | \n",
" | * [pandas.DataFrame.to_json documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html)\n",
" | * [pandas.DataFrame.to_csv documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html)\n",
" | * [pandas.DataFrame.to_html documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_html.html)\n",
" | * [pandas.DataFrame.to_markdown documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_markdown.html)\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_table(\"vectors.tsv\",\n",
" | ... [[\"one\", \"two\", \"three\"],\n",
" | ... [1, 2, 3],\n",
" | ... [4, 5, 6]],\n",
" | ...\n",
" | >>> api_experiment.log_table(\"dataframe.json\", pandas_dataframe)\n",
" | ```\n",
" | \n",
" | register_model(self, model_name, version='1.0.0', workspace=None, registry_name=None, public=None, description=None, comment=None, stages=None)\n",
" | Register an experiment model in the workspace registry.\n",
" | \n",
" | Args:\n",
" | model_name: the name of the experiment model\n",
" | workspace: optional, the name of workspace; defaults to current workspace\n",
" | version: a proper semantic version string; defaults to \"1.0.0\"\n",
" | registry_name: optional, the name of the registered workspace model, if not provided the\n",
" | model_name will be used instead.\n",
" | public: optional, if True, then the model will be publically viewable\n",
" | description: optional, a textual description of the model\n",
" | comment: optional, a textual comment about the model\n",
" | stages: optional, a list of textual tags such as [\"production\",\n",
" | \"staging\"] etc.\n",
" | \n",
" | Returns `{\"registryModelId\": \"ath6ho4eijaexeShahJ9sohQu\", \"registryModelItemId\":\n",
" | \"yoi5saes7ea2vooG2ush1uuwi\"}` if successful.\n",
" | \n",
" | set_code(self, code=None, filename=None)\n",
" | Set the code for this experiment. Pass in either\n",
" | the code as a string, or provide filename.\n",
" | \n",
" | Args:\n",
" | code: string, optional, the source code for this experiment\n",
" | filename: string, optional, the filename for this experiment\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_code(\"import comet_ml\n",
" | experiment = comet_ml.Experiment()\")\n",
" | >>> api_experiment.set_code(filename=\"script.py\")\n",
" | ```\n",
" | \n",
" | set_command(self, command_args_list)\n",
" | Set the command-line (script and args) for this experiment.\n",
" | \n",
" | Args:\n",
" | command_args_list: list of strings, starting with name of script,\n",
" | and followed by arguments.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_command([\"script.py\", \"arg1\", \"arg2\", \"--flag\", \"arg3\"])\n",
" | ```\n",
" | \n",
" | set_end_time(self, end_server_timestamp)\n",
" | Set the end time of an experiment.\n",
" | \n",
" | Args:\n",
" | end_server_timestamp: a timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_end_time(2652656352)\n",
" | ```\n",
" | \n",
" | Note: Time is in milliseconds. If the start time has not\n",
" | been set, it will be set to 1 second before the end\n",
" | time.\n",
" | \n",
" | set_executable(self, executable)\n",
" | Set the executable for this experiment.\n",
" | \n",
" | Args:\n",
" | executable: string, the python executable\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_executable(\"/usr/bin/python3\")\n",
" | ```\n",
" | \n",
" | set_filename(self, filename)\n",
" | Set the path and filename for this experiment.\n",
" | \n",
" | Args:\n",
" | filename: string, the python path and filename\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_filename(\"../src/script.py\")\n",
" | ```\n",
" | \n",
" | set_git_metadata(self, user, root, branch, parent, origin)\n",
" | Set the git metadata for this experiment.\n",
" | \n",
" | Args:\n",
" | user: (string, required) the name of the git user\n",
" | root: (string, required) the name of the git root\n",
" | branch: (string, required) the name of the git branch\n",
" | parent: (string, required) the name of the git parent\n",
" | origin: (string, required) the name of the git origin\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_git_metadata(\"user\", \"root\", \"branch\", \"parent\", \"origin\")\n",
" | ```\n",
" | \n",
" | set_git_patch(self, file_data)\n",
" | Set the git patch for this experiment.\n",
" | \n",
" | Args:\n",
" | file_data: the contents or filename of the git patch file\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_git_patch(\"git.patch\")\n",
" | ```\n",
" | \n",
" | set_gpu_static_info(self, gpu_static_info)\n",
" | Set the GPU static info for this experiment.\n",
" | \n",
" | Args:\n",
" | gpu_static_info: list of dicts containing keys\n",
" | `gpuIndex`, `name`, `uuid`, `totalMemory`, and `powerLimit`\n",
" | and their values.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_gpu_static_info([{\n",
" | \"gpuIndex\": 0,\n",
" | \"name\": \"GeForce GTX 950\",\n",
" | \"uuid\": \"GPU-cb6c1b39-5a56-6d79-8899-3796f23c6425\",\n",
" | \"totalMemory\": 2090074112,\n",
" | \"powerLimit\": 110000,\n",
" | }, ...])\n",
" | ```\n",
" | \n",
" | set_hostname(self, hostname)\n",
" | Set the hostname for this experiment.\n",
" | \n",
" | Args:\n",
" | hostname: string, the hostname of the computer the experiment ran on\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_hostname(\"machine.company.com\")\n",
" | ```\n",
" | \n",
" | set_installed_packages(self, installed_packages)\n",
" | Set the installed Python packages for this experiment.\n",
" | \n",
" | Args:\n",
" | installed_packages: list of strings, a list of the\n",
" | installed Python packages\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_installed_packages([\"comet_ml\", \"matplotlib\"])\n",
" | ```\n",
" | \n",
" | set_ip(self, ip)\n",
" | Set the internet protocol (IP) address for this experiment.\n",
" | \n",
" | Args:\n",
" | ip: string, the internet protocol address\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_ip(\"10.0.0.7\")\n",
" | ```\n",
" | \n",
" | set_machine(self, machine)\n",
" | Set the machine for this experiment.\n",
" | \n",
" | Args:\n",
" | machine: string, the machine type\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> import platform\n",
" | >>> api_experiment.set_machine(platform.machine())\n",
" | ```\n",
" | \n",
" | set_model_graph(self, graph)\n",
" | Set the model graph for this experiment.\n",
" | \n",
" | Args:\n",
" | graph: various types, a representation of the model graph\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_model_graph(model)\n",
" | ```\n",
" | \n",
" | set_name(self, name)\n",
" | Set a name for the experiment. Useful for filtering and searching on Comet.ml.\n",
" | Will shown by default under the `Other` tab.\n",
" | Args:\n",
" | name: String. A name for the experiment.\n",
" | \n",
" | set_network_interface_ips(self, network_interface_ips)\n",
" | Set the network interface ips for this experiment.\n",
" | \n",
" | Args:\n",
" | network_interface_ips: list of strings, of local\n",
" | network interfaces\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_network_interface_ips([\"127.0.0.1\", \"192.168.1.100\"])\n",
" | ```\n",
" | \n",
" | set_os(self, os)\n",
" | Set the OS for this experiment.\n",
" | \n",
" | Args:\n",
" | os: string, the OS platform identifier\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> import platform\n",
" | >>> api_experiment.set_os(platform.platform(aliased=True))\n",
" | ```\n",
" | \n",
" | set_os_packages(self, os_packages)\n",
" | Set the OS packages for this experiment.\n",
" | \n",
" | Args:\n",
" | os_packages: list of strings, the OS package list\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_os_packages(['accountsservice=0.6.45-1ubuntu1', ...])\n",
" | ```\n",
" | \n",
" | set_os_release(self, os_release)\n",
" | Set the OS release for this experiment.\n",
" | \n",
" | Args:\n",
" | os_release: string, the OS release\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> import platform\n",
" | >>> api_experiment.set_os_release(platform.uname()[2])\n",
" | ```\n",
" | \n",
" | set_os_type(self, os_type)\n",
" | Set the OS type for this experiment.\n",
" | \n",
" | Args:\n",
" | os_type: string, the OS type\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_os_type(\"Linux 2.0.1, Ubuntu 16.10\")\n",
" | ```\n",
" | \n",
" | set_pid(self, pid)\n",
" | Set the process ID for this experiment.\n",
" | \n",
" | Args:\n",
" | pid: string, the OS process ID\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_pid(54238)\n",
" | ```\n",
" | \n",
" | set_processor(self, processor)\n",
" | Set the processor for this experiment.\n",
" | \n",
" | Args:\n",
" | processor: string, the processor name\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> import platform\n",
" | >>> api_experiment.set_processor(platform.processor())\n",
" | ```\n",
" | \n",
" | set_python_version(self, python_version)\n",
" | Set the Python version for this experiment.\n",
" | \n",
" | Args:\n",
" | python_version: string, the verbose Python version\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_python_version(\"3.6.7\")\n",
" | ```\n",
" | \n",
" | set_python_version_verbose(self, python_version_verbose)\n",
" | Set the Python version verbose for this experiment.\n",
" | \n",
" | Args:\n",
" | python_version_verbose: string, the verbose Python version\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_python_version_verbose(\"3.6.7, by Anaconda\")\n",
" | ```\n",
" | \n",
" | set_start_time(self, start_server_timestamp)\n",
" | Set the start time of an experiment.\n",
" | \n",
" | Args:\n",
" | start_server_timestamp: a timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_start_time(2652656352)\n",
" | ```\n",
" | \n",
" | Note: Time is in milliseconds. If the end time has not been set\n",
" | it will automatically be set for 1 second after the start\n",
" | time.\n",
" | \n",
" | set_user(self, user)\n",
" | Set the user for this experiment.\n",
" | \n",
" | Args:\n",
" | user: string, the OS username\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_user(\"os-user-name\")\n",
" | ```\n",
" | \n",
" | to_json(self, full=False)\n",
" | The experiment data in JSON-like format.\n",
" | \n",
" | Args:\n",
" | full: bool (optional), if True, get all experiment information.\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> experiment.to_json()\n",
" | ```\n",
" | \n",
" | Returns:\n",
" | \n",
" | ```json\n",
" | {'id': '073e272581ac48c283910a05e5495381',\n",
" | 'name': None,\n",
" | 'workspace': 'testuser',\n",
" | 'project_name': 'test-project-7515',\n",
" | 'archived': False,\n",
" | 'url': 'https://www.comet.ml/testuser/test-project-7515/073e272581ac48c283910a05e54953801',\n",
" | 'duration_millis': 4785,\n",
" | 'start_server_timestamp': 1571318652586,\n",
" | 'end_server_timestamp': 7437457,\n",
" | 'optimization_id': None,\n",
" | }\n",
" | \n",
" | ```python\n",
" | >>> experiment.to_json(full=True)\n",
" | ```\n",
" | \n",
" | Returns:\n",
" | \n",
" | ```json\n",
" | {'id': '073e272581ac48c283910a05e5495380',\n",
" | 'name': None,\n",
" | 'workspace': 'testuser',\n",
" | 'project_name': 'test-project-7515',\n",
" | 'archived': False,\n",
" | 'url': 'https://staging.comet.ml/testuser/test-project-7515/073e272581ac48c283910a05e5495380',\n",
" | 'duration_millis': 4785,\n",
" | 'start_server_timestamp': 1571318652586,\n",
" | 'end_server_timestamp': 7437457,\n",
" | 'optimization_id': None,\n",
" | 'asset_list': [],\n",
" | 'code': 'one\n",
" | two\n",
" | three\n",
" | ',\n",
" | 'html': 'hello\n",
" | world!\n",
" | ',\n",
" | 'metrics': [],\n",
" | 'metrics_summary': [],\n",
" | 'output': 'four\n",
" | five\n",
" | six\n",
" | ',\n",
" | 'parameters_summary': [],\n",
" | 'system_details': {'experimentKey': None,\n",
" | 'user': None,\n",
" | 'pythonVersion': None,\n",
" | 'pythonVersionVerbose': None,\n",
" | 'pid': None,\n",
" | 'osType': None,\n",
" | 'os': None,\n",
" | 'ip': None,\n",
" | 'hostname': None,\n",
" | 'gpuStaticInfoList': [],\n",
" | 'logAdditionalSystemInfoList': [],\n",
" | 'systemMetricNames': [],\n",
" | 'maxTotalMemory': None,\n",
" | 'networkInterfaceIps': None,\n",
" | 'command': None,\n",
" | 'executable': None,\n",
" | 'osPackages': ['lib1', 'lib2', 'lib3'],\n",
" | 'installedPackages': [],\n",
" | 'totalRam': None},\n",
" | 'tags': ['tag6', 'tag7', 'tag8'],\n",
" | 'git_patch': b'',\n",
" | 'git_metadata': {'user': None, 'root': None, 'branch': None, 'parent': None, 'origin': None},\n",
" | }\n",
" | ```\n",
" | \n",
" | update_status(self)\n",
" | Update the status for this experiment. Sends the keep-alive\n",
" | status for it in the UI. The return JSON dictionary contains\n",
" | the recommended interval to send subsequent `update_status()`\n",
" | messages.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.update_status()\n",
" | {'isAliveBeatDurationMillis': 10000,\n",
" | 'gpuMonitorIntervalMillis': 60000,\n",
" | 'cpuMonitorIntervalMillis': 68000}\n",
" | ```\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Readonly properties defined here:\n",
" | \n",
" | key\n",
" | Get the experiment key (the unique id).\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.key\n",
" | \"34637643746374637463476\"\n",
" | ```\n",
" | \n",
" | url\n",
" | Get the url of the experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.url\n",
" | \"https://www.comet.ml/username/34637643746374637463476\"\n",
" | ```\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors defined here:\n",
" | \n",
" | name\n",
" | Get the experiment name.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.name\n",
" | \"laughing_toaster_23454\"\n",
" | ```\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from comet_ml.experiment.CommonExperiment:\n",
" | \n",
" | display(self, clear=False, wait=True, new=0, autoraise=True, tab=None)\n",
" | Show the Comet.ml experiment page in an IFrame in a\n",
" | Jupyter notebook or Jupyter lab, OR open a browser\n",
" | window or tab.\n",
" | \n",
" | Common Args:\n",
" | tab: name of the Tab on Experiment View\n",
" | \n",
" | Note: the Tab name should be one of:\n",
" | * \"artifacts\"\n",
" | * \"assets\"\n",
" | * \"audio\"\n",
" | * \"charts\"\n",
" | * \"code\"\n",
" | * \"confusion-matrices\"\n",
" | * \"histograms\"\n",
" | * \"images\"\n",
" | * \"installed-packages\"\n",
" | * \"metrics\"\n",
" | * \"notes\"\n",
" | * \"parameters\"\n",
" | * \"system-metrics\"\n",
" | * \"text\"\n",
" | \n",
" | For Jupyter environments:\n",
" | \n",
" | Args:\n",
" | clear: to clear the output area, use clear=True\n",
" | wait: to wait for the next displayed item, use\n",
" | wait=True (cuts down on flashing)\n",
" | \n",
" | For non-Jupyter environments:\n",
" | \n",
" | Args:\n",
" | new: open a new browser window if new=1, otherwise re-use\n",
" | existing window/tab\n",
" | autoraise: make the browser tab/window active\n",
" | \n",
" | display_project(self, view_id=None, clear=False, wait=True, new=0, autoraise=True)\n",
" | Show the Comet.ml project page in an IFrame in a\n",
" | Jupyter notebook or Jupyter lab, OR open a browser\n",
" | window or tab.\n",
" | \n",
" | Common Args:\n",
" | view_id: (optional, string) the id of the view to show\n",
" | \n",
" | For Jupyter environments:\n",
" | \n",
" | Args:\n",
" | clear: to clear the output area, use clear=True\n",
" | wait: to wait for the next displayed item, use\n",
" | wait=True (cuts down on flashing)\n",
" | \n",
" | For non-Jupyter environments:\n",
" | \n",
" | Args:\n",
" | new: open a new browser window if new=1, otherwise re-use\n",
" | existing window/tab\n",
" | autoraise: make the browser tab/window active\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors inherited from comet_ml.experiment.CommonExperiment:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
"\n"
]
}
],
"source": [
"help(exp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For example, we can explore the `other` property, which shows items saved with Experiment.log_other(NAME, VALUE):"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'max_loss',\n",
" 'valueMax': '2.429072618484497',\n",
" 'valueMin': '2.429072618484497',\n",
" 'valueCurrent': '2.429072618484497',\n",
" 'timestampMax': 1585647443592,\n",
" 'timestampMin': 1585647443592,\n",
" 'timestampCurrent': 1585647443592,\n",
" 'editable': False},\n",
" {'name': 'Name',\n",
" 'valueMax': 'example 001',\n",
" 'valueMin': 'example 001',\n",
" 'valueCurrent': 'example 001',\n",
" 'timestampMax': 1544452402114,\n",
" 'timestampMin': 1544452402114,\n",
" 'timestampCurrent': 1544452402114,\n",
" 'editable': False},\n",
" {'name': 'storage_size_bytes',\n",
" 'valueMax': '0',\n",
" 'valueMin': '0',\n",
" 'valueCurrent': '0',\n",
" 'timestampMax': 1574708394365,\n",
" 'timestampMin': 1574708394365,\n",
" 'timestampCurrent': 1574708394365,\n",
" 'editable': False},\n",
" {'name': 'trainable_params',\n",
" 'valueMax': '134794',\n",
" 'valueMin': '134794',\n",
" 'valueCurrent': '134794',\n",
" 'timestampMax': 1542738532810,\n",
" 'timestampMin': 1542738532810,\n",
" 'timestampCurrent': 1542738532810,\n",
" 'editable': False}]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exp.get_others_summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example, we see that the experiment has the `Name` \"example 001\". We can use `Name` to also look up experiments:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('51cf6e588a3346cdb560fd0c09d49610', 'example 001')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exp = comet_api.get(\"dsblank/keras/example 001\")\n",
"exp.id, exp.name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perhaps one of the most useful abilities for the REST API is to access your experiment's data in order to create a variation of a plot. To access the raw metric data, use the `get_metrics` property of the experiment:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
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" {'metricName': 'val_acc',\n",
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" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738547808,\n",
" 'step': 4211,\n",
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" 'runContext': None,\n",
" 'offset': 1759},\n",
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" 'runContext': None,\n",
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" {'metricName': 'size',\n",
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" 'offset': 1819},\n",
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" 'step': 4481,\n",
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" 'runContext': None,\n",
" 'offset': 1867},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738548755,\n",
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" 'runContext': None,\n",
" 'offset': 1871},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738548892,\n",
" 'step': 4501,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1882},\n",
" {'metricName': 'size',\n",
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" {'metricName': 'size',\n",
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" 'runContext': None,\n",
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" 'runContext': None,\n",
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" 'timestamp': 1542738549034,\n",
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" 'runContext': None,\n",
" 'offset': 1898},\n",
" {'metricName': 'size',\n",
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" 'runContext': None,\n",
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" 'runContext': None,\n",
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" {'metricName': 'size',\n",
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" 'timestamp': 1542738549198,\n",
" 'step': 4591,\n",
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" 'offset': 1918},\n",
" {'metricName': 'size',\n",
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" 'timestamp': 1542738549230,\n",
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" 'runContext': None,\n",
" 'offset': 1922},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549262,\n",
" 'step': 4611,\n",
" 'epoch': None,\n",
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" 'timestamp': 1542738549294,\n",
" 'step': 4621,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1930},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549328,\n",
" 'step': 4631,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1934},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549356,\n",
" 'step': 4641,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1938},\n",
" {'metricName': 'size',\n",
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" 'runContext': None,\n",
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" {'metricName': 'size',\n",
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" 'step': 4671,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1950},\n",
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" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1954},\n",
" {'metricName': 'size',\n",
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" 'timestamp': 1542738549540,\n",
" 'step': 4691,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1958},\n",
" {'metricName': 'size',\n",
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" 'timestamp': 1542738549569,\n",
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" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1962},\n",
" {'metricName': 'size',\n",
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" 'timestamp': 1542738549600,\n",
" 'step': 4711,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1966},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549631,\n",
" 'step': 4721,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1970},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549661,\n",
" 'step': 4731,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1974},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549694,\n",
" 'step': 4741,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1978},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549725,\n",
" 'step': 4751,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1982},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549759,\n",
" 'step': 4761,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1986},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549794,\n",
" 'step': 4771,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1990},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549823,\n",
" 'step': 4781,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1994},\n",
" {'metricName': 'size',\n",
" 'metricValue': '120',\n",
" 'timestamp': 1542738549857,\n",
" 'step': 4791,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1998},\n",
" ...]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exp.get_metrics()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thus, there were over 2000 metrics logged during the training of this experiment. We can get the first using indexing with an integer:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'metricName': 'val_loss',\n",
" 'metricValue': '0.3130521882325411',\n",
" 'timestamp': 1542738534731,\n",
" 'step': 500,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 221}"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exp.get_metrics()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That shows that the \"acc\" (accuracy) metric had a value of about 0.09 at step 1 of the experiment.\n",
"\n",
"We can also use a string as an index to query all of the dictionaries in `get_metrics_summary` to only give those values at each step, like so:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'metricName': 'acc',\n",
" 'metricValue': '0.09166666865348816',\n",
" 'timestamp': 1542738532966,\n",
" 'step': 1,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 21},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.30000001192092896',\n",
" 'timestamp': 1542738533006,\n",
" 'step': 11,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 25},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.18333333730697632',\n",
" 'timestamp': 1542738533047,\n",
" 'step': 21,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 29},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.3083333373069763',\n",
" 'timestamp': 1542738533080,\n",
" 'step': 31,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 33},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.44999998807907104',\n",
" 'timestamp': 1542738533109,\n",
" 'step': 41,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 37},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.5666666626930237',\n",
" 'timestamp': 1542738533146,\n",
" 'step': 51,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 41},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.625',\n",
" 'timestamp': 1542738533176,\n",
" 'step': 61,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 45},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.6499999761581421',\n",
" 'timestamp': 1542738533205,\n",
" 'step': 71,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 49},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.6583333611488342',\n",
" 'timestamp': 1542738533240,\n",
" 'step': 81,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 53},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.6833333373069763',\n",
" 'timestamp': 1542738533269,\n",
" 'step': 91,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 57},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.7749999761581421',\n",
" 'timestamp': 1542738533299,\n",
" 'step': 101,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 61},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.7166666388511658',\n",
" 'timestamp': 1542738533335,\n",
" 'step': 111,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 65},\n",
" {'metricName': 'acc',\n",
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" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738549262,\n",
" 'step': 4611,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1928},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738549294,\n",
" 'step': 4621,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1932},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9833333492279053',\n",
" 'timestamp': 1542738549328,\n",
" 'step': 4631,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1936},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9833333492279053',\n",
" 'timestamp': 1542738549356,\n",
" 'step': 4641,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1940},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '1.0',\n",
" 'timestamp': 1542738549395,\n",
" 'step': 4651,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1944},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738549429,\n",
" 'step': 4661,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1948},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9750000238418579',\n",
" 'timestamp': 1542738549472,\n",
" 'step': 4671,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1952},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9833333492279053',\n",
" 'timestamp': 1542738549505,\n",
" 'step': 4681,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1956},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9750000238418579',\n",
" 'timestamp': 1542738549540,\n",
" 'step': 4691,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1960},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9750000238418579',\n",
" 'timestamp': 1542738549569,\n",
" 'step': 4701,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1964},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738549600,\n",
" 'step': 4711,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1968},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9750000238418579',\n",
" 'timestamp': 1542738549631,\n",
" 'step': 4721,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1972},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '1.0',\n",
" 'timestamp': 1542738549662,\n",
" 'step': 4731,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1976},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '1.0',\n",
" 'timestamp': 1542738549694,\n",
" 'step': 4741,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1980},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9750000238418579',\n",
" 'timestamp': 1542738549725,\n",
" 'step': 4751,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1984},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9666666388511658',\n",
" 'timestamp': 1542738549759,\n",
" 'step': 4761,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1988},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9750000238418579',\n",
" 'timestamp': 1542738549794,\n",
" 'step': 4771,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1992},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9833333492279053',\n",
" 'timestamp': 1542738549823,\n",
" 'step': 4781,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 1996},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9833333492279053',\n",
" 'timestamp': 1542738549857,\n",
" 'step': 4791,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2000},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9750000238418579',\n",
" 'timestamp': 1542738549890,\n",
" 'step': 4801,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2004},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738549918,\n",
" 'step': 4811,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2008},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9333333373069763',\n",
" 'timestamp': 1542738549951,\n",
" 'step': 4821,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2012},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9833333492279053',\n",
" 'timestamp': 1542738549981,\n",
" 'step': 4831,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2016},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738550016,\n",
" 'step': 4841,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2020},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9666666388511658',\n",
" 'timestamp': 1542738550046,\n",
" 'step': 4851,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2024},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9833333492279053',\n",
" 'timestamp': 1542738550075,\n",
" 'step': 4861,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2028},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9666666388511658',\n",
" 'timestamp': 1542738550114,\n",
" 'step': 4871,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2032},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9583333134651184',\n",
" 'timestamp': 1542738550144,\n",
" 'step': 4881,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2036},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9833333492279053',\n",
" 'timestamp': 1542738550172,\n",
" 'step': 4891,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2040},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9666666388511658',\n",
" 'timestamp': 1542738550206,\n",
" 'step': 4901,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2044},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738550246,\n",
" 'step': 4911,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2048},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9666666388511658',\n",
" 'timestamp': 1542738550276,\n",
" 'step': 4921,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2052},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738550308,\n",
" 'step': 4931,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2056},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738550342,\n",
" 'step': 4941,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2060},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9833333492279053',\n",
" 'timestamp': 1542738550372,\n",
" 'step': 4951,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2064},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9833333492279053',\n",
" 'timestamp': 1542738550406,\n",
" 'step': 4961,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2068},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738550436,\n",
" 'step': 4971,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2072},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738550483,\n",
" 'step': 4981,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2076},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9916666746139526',\n",
" 'timestamp': 1542738550518,\n",
" 'step': 4991,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2080},\n",
" {'metricName': 'acc',\n",
" 'metricValue': '0.9817500071525573',\n",
" 'timestamp': 1542738550643,\n",
" 'step': 5000,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 2087}]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"acc_metrics = exp.get_metrics(\"acc\")\n",
"acc_metrics"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"510"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(acc_metrics)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'metricName': 'acc',\n",
" 'metricValue': '0.09166666865348816',\n",
" 'timestamp': 1542738532966,\n",
" 'step': 1,\n",
" 'epoch': None,\n",
" 'runContext': None,\n",
" 'offset': 21}"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"acc_metrics[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it for a quick overview. Now let's look in detail at each component, and introduce the low-level REST API as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Workspaces\n",
"\n",
"By default, comet_api.get() reports only your workspace names:"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['comet-demos',\n",
" 'megancomet',\n",
" 'comet-mpm-test',\n",
" 'chasefortier',\n",
" 'maracomet',\n",
" 'development',\n",
" 'jacques-comet',\n",
" 'testingmaracomet',\n",
" 'team-comet-ml',\n",
" 'comet-etsy-workshop']"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also interate over those names:"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"comet-demos\n",
"megancomet\n",
"comet-mpm-test\n",
"chasefortier\n",
"maracomet\n",
"development\n",
"jacques-comet\n",
"testingmaracomet\n",
"team-comet-ml\n",
"comet-etsy-workshop\n"
]
}
],
"source": [
"for workspace in comet_api.get():\n",
" print(workspace)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we saw above, you can also access other public workspaces as well:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['sparknlp-example',\n",
" 'confusion-matrix',\n",
" 'ludwig',\n",
" 'comet-sagemaker',\n",
" 'outout-tab-test',\n",
" 'fasttext',\n",
" 'comet-notebooks',\n",
" 'shap',\n",
" 'asset-types',\n",
" 'parameter-space-exploration',\n",
" 'hn-performance-prediction',\n",
" 'comet-examples',\n",
" '3d-histograms',\n",
" 'xgboost-example',\n",
" 'home-credit',\n",
" 'histograms']"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get(\"cometpublic\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Projects\n",
"\n",
"Under get(WORKSPACE_NAME), you'll find the projects:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['sparknlp-example',\n",
" 'confusion-matrix',\n",
" 'ludwig',\n",
" 'comet-sagemaker',\n",
" 'outout-tab-test',\n",
" 'fasttext',\n",
" 'comet-notebooks',\n",
" 'shap',\n",
" 'asset-types',\n",
" 'parameter-space-exploration',\n",
" 'hn-performance-prediction',\n",
" 'comet-examples',\n",
" '3d-histograms',\n",
" 'xgboost-example',\n",
" 'home-credit',\n",
" 'histograms']"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get(\"cometpublic\")"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"project = comet_api.get(\"cometpublic\", \"comet-notebooks\")\n",
"## OR:\n",
"#project = comet_api.get(\"cometpublic/comet-notebooks\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you just print out, or iterate over a project, you get access to the experiment ids:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[,\n",
" ]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"project"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('7092a5e4c362453fb0b3f06785a1d30c', None)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"project[0].id, project[0].name"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('d21f94a1c71841d2961da1e6ddb5ab20', 'example 001')"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"project[1].id, project[1].name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Experiments\n",
"\n",
"Continuing with the dictionary-like access, you can see and iterate over the experiment ids:"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[,\n",
" ]"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comet_api.get(\"cometpublic\", \"comet-notebooks\")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exp = comet_api.get(\"cometpublic\", \"comet-notebooks\", 'd21f94a1c71841d2961da1e6ddb5ab20')\n",
"## OR\n",
"# exp = comet_api.get(\"cometpublic/comet-notebooks/d21f94a1c71841d2961da1e6ddb5ab20\")\n",
"exp"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exp = comet_api.get(\"cometpublic\", \"comet-notebooks\", 'example 001')\n",
"## OR\n",
"## exp = comet_api.get(\"cometpublic/comet-notebooks/example 001\")\n",
"exp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Regular Expression Experiment Name Matching\n",
"\n",
"You can also use regular expressions as the name for the experiment:"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"comet_api.get(\"cometpublic\", \"comet-notebooks\", \"example.*\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Experiment Properties"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this brief dictionary representation, you will see that `other`, `metrics` and `parameters` give a list of names. However, as we saw above, you can get more information through properties of those same names:\n",
"\n",
"names through exp.data[\"properties\"] and more detail at exp.properties:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'f',\n",
" 'valueMax': '/run/user/1000/jupyter/kernel-b1c4403c-c673-4e7f-90dc-853616d231e2.json',\n",
" 'valueMin': '/run/user/1000/jupyter/kernel-b1c4403c-c673-4e7f-90dc-853616d231e2.json',\n",
" 'valueCurrent': '/run/user/1000/jupyter/kernel-b1c4403c-c673-4e7f-90dc-853616d231e2.json',\n",
" 'timestampMax': 1542823692798,\n",
" 'timestampMin': 1542823692798,\n",
" 'timestampCurrent': 1542823692798,\n",
" 'editable': False}]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exp.get_parameters_summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"names through exp.data[\"other\"] and more detail at exp.other:"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('Name', 'example 001')"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exp.get_others_summary()[0][\"name\"], exp.get_others_summary()[0][\"valueCurrent\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"names through exp.data[\"metrics\"] and more detail at exp.metrics:"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'metricName': 'train_loss',\n",
" 'metricValue': '2.3872790336608887',\n",
" 'timestamp': 1542823695820,\n",
" 'step': 0,\n",
" 'epoch': None,\n",
" 'runContext': 'train',\n",
" 'offset': 9},\n",
" {'metricName': 'train_accuracy',\n",
" 'metricValue': '0.0',\n",
" 'timestamp': 1542823699380,\n",
" 'step': 1,\n",
" 'epoch': None,\n",
" 'runContext': 'train',\n",
" 'offset': 11},\n",
" {'metricName': 'train_accuracy',\n",
" 'metricValue': '0.0',\n",
" 'timestamp': 1542823704609,\n",
" 'step': 3,\n",
" 'epoch': None,\n",
" 'runContext': 'train',\n",
" 'offset': 14},\n",
" {'metricName': 'train_val_loss',\n",
" 'metricValue': '2.3066751956939697',\n",
" 'timestamp': 1542823699379,\n",
" 'step': 1,\n",
" 'epoch': None,\n",
" 'runContext': 'train',\n",
" 'offset': 10},\n",
" {'metricName': 'train_val_loss',\n",
" 'metricValue': '2.2724671363830566',\n",
" 'timestamp': 1542823704609,\n",
" 'step': 3,\n",
" 'epoch': None,\n",
" 'runContext': 'train',\n",
" 'offset': 13},\n",
" {'metricName': 'train_curr_epoch',\n",
" 'metricValue': '0',\n",
" 'timestamp': 1542823693885,\n",
" 'step': None,\n",
" 'epoch': None,\n",
" 'runContext': 'train',\n",
" 'offset': 7},\n",
" {'metricName': 'train_curr_epoch',\n",
" 'metricValue': '1',\n",
" 'timestamp': 1542823699380,\n",
" 'step': None,\n",
" 'epoch': None,\n",
" 'runContext': 'train',\n",
" 'offset': 12}]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exp.get_metrics()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can see all of the methods and propeties on an experiment instance:"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on APIExperiment in module comet_ml.api object:\n",
"\n",
"class APIExperiment(comet_ml.experiment.CommonExperiment)\n",
" | APIExperiment(*args, **kwargs)\n",
" | \n",
" | The APIExperiment class is used to access data from the\n",
" | Comet.ml Python API.\n",
" | \n",
" | You can use an instance of the APIExperiment() class to easily\n",
" | access all of your logged experiment information\n",
" | at [Comet.ml](https://www.comet.ml), including metrics, parameters,\n",
" | tags, and assets.\n",
" | \n",
" | Examples:\n",
" | \n",
" | The following examples assume your `COMET_API_KEY` is configured as per\n",
" | [Python Configuration](https://www.comet.ml/docs/python-sdk/advanced/#python-configuration).\n",
" | \n",
" | This example shows looking up an experiment by its URL:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API, APIExperiment\n",
" | \n",
" | ## (assumes api keys are configured):\n",
" | >>> api = API() # can also: API(api_key=\"...\")\n",
" | \n",
" | ## Get an APIExperiment from the API:\n",
" | >>> experiment = api.get(\"cometpublic/comet-notebooks/example 001\")\n",
" | ```\n",
" | \n",
" | You can also make a new experiment using the API:\n",
" | \n",
" | ```python\n",
" | ## Make a new APIExperiment (assumes api keys are configured):\n",
" | >>> experiment = APIExperiment(workspace=\"my-username\",\n",
" | project_name=\"general\")\n",
" | ```\n",
" | \n",
" | Here is an end-to-end snippet to rename a metric. You can use this\n",
" | basic structure for logging new metrics (after the experiment has completed)\n",
" | such as averages or scaling to a baseline.\n",
" | \n",
" | ```python\n",
" | from comet_ml import API\n",
" | \n",
" | WORKSPACE = \"your-comet-id\"\n",
" | PROJECT_NAME = \"general\"\n",
" | EXP_KEY = \"your-experiment-key\"\n",
" | OLD_METRIC_NAME = \"loss\"\n",
" | NEW_METRIC_NAME = \"train_loss\"\n",
" | \n",
" | api = API() # can also: API(api_key=\"...\")\n",
" | \n",
" | experiment = api.get_experiment(WORKSPACE, PROJECT_NAME, EXP_KEY)\n",
" | old_metrics = experiment.get_metrics(OLD_METRIC_NAME)\n",
" | \n",
" | for old_metric in old_metrics:\n",
" | experiment.log_metric(\n",
" | NEW_METRIC_NAME,\n",
" | old_metric[\"metricValue\"],\n",
" | step=old_metric[\"step\"],\n",
" | timestamp=old_metric[\"timestamp\"],\n",
" | )\n",
" | ```\n",
" | \n",
" | For more usage examples, see [Comet Python API examples](../Comet-Python-API/).\n",
" | \n",
" | Method resolution order:\n",
" | APIExperiment\n",
" | comet_ml.experiment.CommonExperiment\n",
" | builtins.object\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __init__(self, *args, **kwargs)\n",
" | Create a new APIExperiment, or use a previous experiment key\n",
" | to access an existing experiment.\n",
" | \n",
" | Examples:\n",
" | ```python\n",
" | # Python API to create a new experiment:\n",
" | # (assumes api keys are configured):\n",
" | >>> experiment = APIExperiment(workspace=WORKSPACE,\n",
" | project_name=PROJECT)\n",
" | \n",
" | # Python API to access an existing experiment:\n",
" | # (assumes api keys are configured):\n",
" | >>> experiment = APIExperiment(previous_experiment=EXPERIMENT_KEY)\n",
" | ```\n",
" | \n",
" | Note: api_key may be defined in environment (COMET_API_KEY)\n",
" | or in a .comet.config file. Additional arguments will be\n",
" | given to API().\n",
" | \n",
" | __repr__(self)\n",
" | Return repr(self).\n",
" | \n",
" | add_tag(self, tag)\n",
" | Append onto an experiment's list of tags.\n",
" | \n",
" | Args:\n",
" | tag: a tag (string)\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.add_tag(\"baseline\")\n",
" | ```\n",
" | \n",
" | add_tags(self, tags)\n",
" | Append onto an experiment's list of tags.\n",
" | \n",
" | Args:\n",
" | tags: a list of tags (strings)\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.add_tags([\"successful\", \"best\"])\n",
" | ```\n",
" | \n",
" | archive(self)\n",
" | Archive this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.archive()\n",
" | ```\n",
" | \n",
" | create_symlink(self, project_name)\n",
" | Create a copy of this experiment in another project\n",
" | in the workspace.\n",
" | \n",
" | Args:\n",
" | project_name: the name of the project with which to create\n",
" | a symlink to this experiment in.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.create_symlink(\"my-other-project\")\n",
" | ```\n",
" | \n",
" | delete_asset(self, asset_id)\n",
" | Delete an experiment's asset.\n",
" | \n",
" | Args:\n",
" | asset_id: the asset id of the asset to delete\n",
" | \n",
" | download_model(self, name, output_path='./', expand=True)\n",
" | Download and save all files from the model.\n",
" | \n",
" | Args:\n",
" | model_name: str, the name of the model\n",
" | output: str, the output directory; defaults to current directory\n",
" | expand: if True, the downloaded zipfile is unzipped; if False, then the zipfile\n",
" | is copied to the output_path\n",
" | \n",
" | download_tensorflow_folder(self, output_path='./', overwrite=False)\n",
" | Download all files logged with `Experiment.log_tensorflow_folder(FOLDER)`.\n",
" | \n",
" | Args:\n",
" | output_path: (str) where to download the files\n",
" | overwrite: (bool) if True, then overwrite any file that exists\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> experiment = comet_ml.Experiment()\n",
" | >>> experiment.log_tensorboard_folder(\"logs\")\n",
" | >>> api = comet_ml.API()\n",
" | >>> api_experiment = api.get_experiment_by_id(experiment.id)\n",
" | >>> api_experiment.download_tensorflow_folder()\n",
" | ```\n",
" | \n",
" | end(self)\n",
" | Method called at end of experiment.\n",
" | \n",
" | get_additional_system_info(self)\n",
" | Get the associated additional system info for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_additional_system_info()\n",
" | []\n",
" | ```\n",
" | \n",
" | get_asset(self, asset_id, return_type='binary', stream=False)\n",
" | Get an asset, given the asset_id.\n",
" | \n",
" | Args:\n",
" | asset_id: (str) the asset ID\n",
" | return_type: (str) the type of object returned. Default is\n",
" | \"binary\". Options: \"binary\", \"text\", or \"response\"\n",
" | stream: (bool) when return_type is \"response\", you can also\n",
" | use stream=True to use the response as a stream\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_asset(\"298378237283728\", return_type=\"json\")\n",
" | {...}\n",
" | ```\n",
" | \n",
" | To use with the streaming option:\n",
" | \n",
" | ```python\n",
" | >>> asset_response = api_experiment.get_asset(\n",
" | ... \"298378237283728\",\n",
" | ... return_type=\"response\",\n",
" | ... stream=True,\n",
" | ... )\n",
" | >>> with open(filename, 'wb') as fd:\n",
" | >>> for chunk in asset_response.iter_content(chunk_size=1024*1024):\n",
" | >>> fd.write(chunk)\n",
" | ```\n",
" | \n",
" | get_asset_list(self, asset_type='all')\n",
" | Get a list of assets associated with the experiment.\n",
" | \n",
" | Args:\n",
" | asset_type: Optional String, type of asset to return. Can be\n",
" | \"all\", \"image\", \"histogram_combined_3d\", \"video\", or \"audio\".\n",
" | \n",
" | Returns a list of dictionaries of asset properties, like:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API\n",
" | >>> api = API()\n",
" | >>> x = api.get(\"myworkspace/project1/experiment_key\")\n",
" | >>> x.get_asset_list()\n",
" | [{'fileName': 'My Filename.png',\n",
" | 'fileSize': 21113,\n",
" | 'runContext': None,\n",
" | 'step': None,\n",
" | 'link': 'https://www.comet.ml/api/asset/download?experimentKey=KEY&assetId=ASSET_ID',\n",
" | 'createdAt': 1565898755830,\n",
" | 'dir': 'assets',\n",
" | 'canView': False,\n",
" | 'audio': False,\n",
" | 'video': False,\n",
" | 'histogram': False,\n",
" | 'image': True,\n",
" | 'type': 'image',\n",
" | 'metadata': None,\n",
" | 'assetId': ASSET_ID}, ...]\n",
" | \n",
" | >>> x.get_asset_list(\"image\")\n",
" | [{'fileName': 'My Filename.png',\n",
" | 'fileSize': 21113,\n",
" | 'runContext': None,\n",
" | 'step': None,\n",
" | 'link': 'https://www.comet.ml/api/asset/download?experimentKey=KEY&assetId=ASSET_ID',\n",
" | 'createdAt': 1565898755830,\n",
" | 'dir': 'assets',\n",
" | 'canView': False,\n",
" | 'audio': False,\n",
" | 'video': False,\n",
" | 'histogram': False,\n",
" | 'image': True,\n",
" | 'type': 'image',\n",
" | 'metadata': None,\n",
" | 'assetId': ASSET_ID}, ...]\n",
" | ```\n",
" | \n",
" | get_code(self)\n",
" | Get the associated source code for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_code()\n",
" | 'import comet_ml\n",
" | experiment = comet_ml.Experiment()\n",
" | experiment.end()'\n",
" | ```\n",
" | \n",
" | get_command(self)\n",
" | Get the associated command-line script and args for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_command()\n",
" | ['keras.py', '--size', '1024', '--log', 'experiment.log']\n",
" | ```\n",
" | \n",
" | get_curve(self, asset_id)\n",
" | Get curve logged with experiment by asset id.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_curve(\"57457745745745774\")\n",
" | {\"name\": \"curve1\", \"x\": [1, 2, 3], \"y\": [4, 5, 6], \"step\": 0}\n",
" | ```\n",
" | \n",
" | get_curves(self)\n",
" | Get all curves logged with experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_curves()\n",
" | [{\"name\": \"curve1\", \"x\": [1, 2, 3], \"y\": [4, 5, 6], \"step\": 0}]\n",
" | ```\n",
" | \n",
" | get_environment_details(self)\n",
" | Deprecated. Use `APIExperiment.get_os_packages()` instead.\n",
" | \n",
" | get_executable(self)\n",
" | Get the associated executable for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_executable()\n",
" | '/usr/local/bin/python'\n",
" | ```\n",
" | \n",
" | get_git_metadata(self)\n",
" | Get the git-metadata associated with this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_git_metadata()\n",
" | {\n",
" | \"branch\": 'refs/heads/master',\n",
" | \"origin\": 'git@github.com:comet-ml/comet-examples.git',\n",
" | \"parent\": '96ff529b4c02e4e0bb92992a7c4ce81275985764',\n",
" | \"root\": 'eec2d16daa057d0cf4c2c49974e6ea51e732a7b2',\n",
" | \"user\": 'user',\n",
" | }\n",
" | ```\n",
" | \n",
" | get_git_patch(self)\n",
" | Get the git-patch associated with this experiment as a zipfile containing an unique file\n",
" | named `zip_file.patch`.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> import io, zipfile\n",
" | >>> zip_patch = io.BytesIO(api_experiment.get_git_patch())\n",
" | >>> archive = zipfile.ZipFile(zip_patch)\n",
" | >>> patch = archive.read(\"git_diff.patch\")\n",
" | >>> patch\n",
" | b'...'\n",
" | ```\n",
" | \n",
" | get_gpu_static_info(self)\n",
" | Get the associated GPU static info for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_gpu_static_info()\n",
" | [{\n",
" | \"gpuIndex\": 0,\n",
" | \"name\": \"GeForce GTX 950\",\n",
" | \"uuid\": \"GPU-cb6c1b39-5a56-6d79-8899-3796f23c6425\",\n",
" | \"totalMemory\": 2090074112,\n",
" | \"powerLimit\": 110000,\n",
" | }, ...]\n",
" | ```\n",
" | \n",
" | get_hostname(self)\n",
" | Get the associated hostname for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_hostname()\n",
" | 'name-of-computer-host'\n",
" | ```\n",
" | \n",
" | get_html(self)\n",
" | Get the HTML associated with this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_html()\n",
" | \"Hello, world!\"\n",
" | ```\n",
" | \n",
" | get_installed_packages(self)\n",
" | Get the associated installed packages for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_installed_packages()\n",
" | ['absl-py==0.8.1', 'adal==1.2.2', 'alabaster==0.7.12', ...]\n",
" | ```\n",
" | \n",
" | get_ip(self)\n",
" | Get the associated IP for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_ip()\n",
" | '175.29.200.91'\n",
" | ```\n",
" | \n",
" | get_machine(self)\n",
" | Get the associated total RAM for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_machine()\n",
" | 'AMD64'\n",
" | ```\n",
" | \n",
" | get_max_memory(self)\n",
" | Get the associated max total memory for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_max_memory()\n",
" | 1024\n",
" | ```\n",
" | \n",
" | get_metadata(self)\n",
" | Get the metadata associated with this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml import APIExperiment\n",
" | >>> api_experiment = APIExperiment(previous_experiment='EXPERIMENT-KEY')\n",
" | >>> api_experiment.get_metadata()\n",
" | {\n",
" | 'archived': False,\n",
" | 'durationMillis': 7,\n",
" | 'endTimeMillis': 1586174765277,\n",
" | 'experimentKey': 'EXPERIMENT-KEY',\n",
" | 'experimentName': None,\n",
" | 'fileName': None,\n",
" | 'filePath': None,\n",
" | 'optimizationId': None,\n",
" | 'projectId': 'PROJECT-ID',\n",
" | 'projectName': 'PROJECT-NAME',\n",
" | 'running': False,\n",
" | 'startTimeMillis': 1586174757596,\n",
" | 'throttle': False,\n",
" | 'workspaceName': 'WORKSPACE-NAME',\n",
" | }\n",
" | ```\n",
" | \n",
" | get_metrics(self, metric=None)\n",
" | Get all of the logged metrics. Optionally, just get the given metric name.\n",
" | \n",
" | Args:\n",
" | metric: Optional. String. If given, filter the metrics by name.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API\n",
" | >>> api = API()\n",
" | >>> x = api.get(\"myworkspace/project1/experiment_key\")\n",
" | \n",
" | >>> x.get_metrics()\n",
" | [{'metricName': 'val_loss',\n",
" | 'metricValue': '0.13101346811652184',\n",
" | 'timestamp': 1558962376383,\n",
" | 'step': 1500,\n",
" | 'epoch': None,\n",
" | 'runContext': None},\n",
" | {'metricName': 'acc',\n",
" | 'metricValue': '0.876',\n",
" | 'timestamp': 1564536453647,\n",
" | 'step': 100,\n",
" | 'epoch': None,\n",
" | 'runContext': None},\n",
" | ...]\n",
" | \n",
" | >>> x.get_metrics(\"acc\")\n",
" | [{'metricName': 'acc',\n",
" | 'metricValue': '0.876',\n",
" | 'timestamp': 1564536453647,\n",
" | 'step': 100,\n",
" | 'epoch': None,\n",
" | 'runContext': None},\n",
" | ...]\n",
" | ```\n",
" | \n",
" | get_metrics_summary(self, metric=None)\n",
" | Return the experiment metrics summary. Optionally, also if you\n",
" | provide the metric name, the function will only return the\n",
" | summary of the metric.\n",
" | \n",
" | Args:\n",
" | metric: optional (string), name of a metric\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API\n",
" | >>> api = API()\n",
" | >>> x = api.get(\"myworkspace/project1/experiment_key\")\n",
" | >>> x.get_metrics_summary()\n",
" | [{'name': 'val_loss',\n",
" | 'valueMax': '0.24951280827820302',\n",
" | 'valueMin': '0.13101346811652184',\n",
" | 'valueCurrent': '0.13101346811652184',\n",
" | 'timestampMax': 1558962367938,\n",
" | 'timestampMin': 1558962367938,\n",
" | 'timestampCurrent': 1558962376383,\n",
" | 'stepMax': 500,\n",
" | 'stepMin': 1500,\n",
" | 'stepCurrent': 1500},\n",
" | ...]\n",
" | \n",
" | >>> api.get_metrics_summary(\"val_loss\")\n",
" | {'name': 'val_loss',\n",
" | 'valueMax': '0.24951280827820302',\n",
" | 'valueMin': '0.13101346811652184',\n",
" | 'valueCurrent': '0.13101346811652184',\n",
" | 'timestampMax': 1558962367938,\n",
" | 'timestampMin': 1558962367938,\n",
" | 'timestampCurrent': 1558962376383,\n",
" | 'stepMax': 500,\n",
" | 'stepMin': 1500,\n",
" | 'stepCurrent': 1500}\n",
" | ```\n",
" | \n",
" | get_model_asset_list(self, model_name)\n",
" | Get an experiment model's asset list by model name.\n",
" | \n",
" | Args:\n",
" | model_name: str, the name of the model\n",
" | \n",
" | Returns: a list of asset dictionaries with these fields:\n",
" | * fileName\n",
" | * fileSize\n",
" | * runContext\n",
" | * step\n",
" | * link\n",
" | * createdAt\n",
" | * dir\n",
" | * canView\n",
" | * audio\n",
" | * histogram\n",
" | * image\n",
" | * type\n",
" | * metadata\n",
" | * assetId\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml import API\n",
" | >>> api = API()\n",
" | >>> api_exp = api.get(\"workspace/project/765643463546345364536453436\")\n",
" | >>> api_exp.get_model_asset_list(\"Model Name\")\n",
" | [\n",
" | {\n",
" | \"assetId\": 74374637463476,\n",
" | \"audio\": False,\n",
" | \"canView\": False,\n",
" | \"createdAt\": 7337347634,\n",
" | \"dir\": \"trained-models\",\n",
" | \"fileName\": \"model.h5\",\n",
" | \"fileSize\": 254654,\n",
" | \"histogram\": False,\n",
" | \"image\": False,\n",
" | \"link\": \"https://link-to-download-asset-file\",\n",
" | \"metadata\": None,\n",
" | \"remote\": False,\n",
" | \"runContext\": \"train\",\n",
" | \"step\": 54,\n",
" | \"type\": \"asset\",\n",
" | }\n",
" | ]\n",
" | ```\n",
" | \n",
" | get_model_data(self, name)\n",
" | Deprecated. Use APIExperiment.get_model_asset_list(model_name) instead.\n",
" | \n",
" | get_model_graph(self)\n",
" | Get the associated graph/model description for this\n",
" | experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_model_graph()\n",
" | {\"class_name\": \"Sequential\", ...}\n",
" | ```\n",
" | \n",
" | get_model_names(self)\n",
" | Get a list of model names associated with this experiment.\n",
" | \n",
" | Returns: list of model names\n",
" | \n",
" | get_name(self)\n",
" | Get the name of the experiment, if one.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_name(\"My Name\")\n",
" | >>> api_experiment.get_name()\n",
" | 'My Name'\n",
" | ```\n",
" | \n",
" | get_network_interface_ips(self)\n",
" | Get the associated network interface IPs for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_network_interface_ips()\n",
" | ['127.0.0.1', '10.0.0.71', ...]\n",
" | ```\n",
" | \n",
" | get_os(self)\n",
" | Get the associated OS for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_os()\n",
" | 'Linux-4.15.0-1059-oem-x86_64-with-Ubuntu-18.04-bionic'\n",
" | ```\n",
" | \n",
" | get_os_packages(self)\n",
" | Get the OS packages for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_os_packages()\n",
" | ['accountsservice=0.6.45-1ubuntu1', 'acl=2.2.52-3build1', 'acpi-support=0.142', ...]\n",
" | ```\n",
" | \n",
" | get_os_release(self)\n",
" | Get the associated OS release for this experiment.\n",
" | \n",
" | Example:\n",
" | ```\n",
" | >>> api_experiment.get_os_release()\n",
" | '8'\n",
" | ```\n",
" | \n",
" | get_os_type(self)\n",
" | Get the associated os type for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_os_type()\n",
" | 'Linux'\n",
" | ```\n",
" | \n",
" | get_others_summary(self, other=None)\n",
" | Get the other items logged in summary form.\n",
" | \n",
" | Args:\n",
" | other: optional, string, the name of the other item\n",
" | logged. If given, return the valueCurrent of\n",
" | the other item. Otherwise, return all other\n",
" | items logged.\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API\n",
" | >>> api = API()\n",
" | >>> x = api.get(\"myworkspace/project1/experiment_key\")\n",
" | >>> x.get_others_summary()\n",
" | [{'name': 'trainable_params',\n",
" | 'valueMax': '712723',\n",
" | 'valueMin': '712723',\n",
" | 'valueCurrent': '712723',\n",
" | 'timestampMax': 1558962363411,\n",
" | 'timestampMin': 1558962363411,\n",
" | 'timestampCurrent': 1558962363411},\n",
" | ...]\n",
" | \n",
" | >>> x.get_others_summary(\"trainable_params\")\n",
" | ['712723']\n",
" | ```\n",
" | \n",
" | get_output(self)\n",
" | Get the associated standard output for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_output()\n",
" | \"Experiment is live\"\n",
" | ```\n",
" | \n",
" | get_parameters_summary(self, parameter=None)\n",
" | Return the experiment parameters summary. Optionally, also if you\n",
" | provide a parameter name, the method will only return the\n",
" | summary of the given parameter.\n",
" | \n",
" | Args:\n",
" | parameter: optional (string), name of a parameter\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> from comet_ml.api import API\n",
" | >>> api = API()\n",
" | >>> x = api.get(\"myworkspace/project1/experiment_key\")\n",
" | >>> x.get_parameters_summary()\n",
" | [{'name': 'batch_size',\n",
" | 'valueMax': '120',\n",
" | 'valueMin': '120',\n",
" | 'valueCurrent': '120',\n",
" | 'timestampMax': 1558962363411,\n",
" | 'timestampMin': 1558962363411,\n",
" | 'timestampCurrent': 1558962363411},\n",
" | ...]\n",
" | \n",
" | >>> x.get_parameters_summary(\"batch_size\")\n",
" | {'name': 'batch_size',\n",
" | 'valueMax': '120',\n",
" | 'valueMin': '120',\n",
" | 'valueCurrent': '120',\n",
" | 'timestampMax': 1558962363411,\n",
" | 'timestampMin': 1558962363411,\n",
" | 'timestampCurrent': 1558962363411}\n",
" | ```\n",
" | \n",
" | get_pid(self)\n",
" | Get the pid for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_pid()\n",
" | 34658\n",
" | ```\n",
" | \n",
" | get_processor(self)\n",
" | Get the associated total RAM for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_processor()\n",
" | 'Intel64 Family 6 Model 60 Stepping 3, GenuineIntel\n",
" | ```\n",
" | \n",
" | get_python_version(self)\n",
" | Get the Python version for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_python_version()\n",
" | '3.6.8'\n",
" | ```\n",
" | \n",
" | get_python_version_verbose(self)\n",
" | Get the Python version verbose for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_python_version_verbose()\n",
" | '3.6.8 (default, Oct 7 2019, 12:59:55) \n",
" | [GCC 8.3.0]'\n",
" | ```\n",
" | \n",
" | get_system_details(self)\n",
" | Get the system details associated with this experiment.\n",
" | \n",
" | Returns a dictionary like:\n",
" | \n",
" | ```python\n",
" | {\n",
" | \"experimentKey\": \"someExperimentKey\",\n",
" | \"user\": \"system username\"\n",
" | \"pythonVersion\": \"python version\"\n",
" | \"pythonVersionVerbose\": \"python version with verbose flag\"\n",
" | \"pid\": ,\n",
" | \"osType\": \"os experiment ran on\",\n",
" | \"os\": \"os with version info\",\n",
" | \"ip\": \"ip address\",\n",
" | \"hostname\": \"hostname\",\n",
" | \"gpuStaticInfoList\": [\n",
" | {\n",
" | \"gpuIndex\": ,\n",
" | \"name\": \"name\",\n",
" | \"uuid\": \"someUniqueId\",\n",
" | \"totalMemory\": ,\n",
" | \"powerLimit\": \n",
" | }\n",
" | ],\n",
" | \"logAdditionalSystemInfoList\": [\n",
" | {\n",
" | \"key\": \"someKey\",\n",
" | \"value\": \"someValue\"\n",
" | }\n",
" | ],\n",
" | \"systemMetricNames\": [\"name\", \"anotherName\"],\n",
" | \"maxTotalMemory\": ,\n",
" | \"networkInterfaceIps\": [\"ip\", \"anotherIp\"]\n",
" | \"command\": [\"part1\", \"part2\"],\n",
" | \"executable\": \"The python Exe, if any (in future could be non python executables)\",\n",
" | \"osPackages\": [\"package\", \"anotherPackage\"],\n",
" | \"installedPackages\": [\"package\", \"anotherPackage\"]\n",
" | }\n",
" | ```\n",
" | \n",
" | get_system_metric_names(self)\n",
" | Get the associated system metric names for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_system_metric_names()\n",
" | ['sys.cpu.percent.03', 'sys.cpu.percent.02', 'sys.cpu.percent.01', ...]\n",
" | ```\n",
" | \n",
" | get_tags(self)\n",
" | Get the associated tags for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_tags()\n",
" | [\"best\"]\n",
" | ```\n",
" | \n",
" | get_total_memory(self)\n",
" | Get the associated total RAM for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_total_memory()\n",
" | 1024\n",
" | ```\n",
" | \n",
" | get_user(self)\n",
" | Get the associated user for this experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.get_user()\n",
" | 'usename'\n",
" | ```\n",
" | \n",
" | log_additional_system_info(self, key, value)\n",
" | Log additional system information for this experiment.\n",
" | \n",
" | Args:\n",
" | key: (string) the name for this system information\n",
" | value: (any type), the value of the system information\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> experiment.log_additional_system_info(\"some name\": 42)\n",
" | ```\n",
" | \n",
" | log_asset(self, filename, step=None, overwrite=None, context=None, ftype=None, metadata=None)\n",
" | Upload an asset to an experiment.\n",
" | \n",
" | Args:\n",
" | filename: the name of the asset file to upload\n",
" | step: the current step\n",
" | overwrite: if True, overwrite any previous upload\n",
" | context: the current context (e.g., \"train\" or \"test\")\n",
" | ftype: the type of asset (e.g., \"image\", \"histogram_combined_3d\",\n",
" | \"image\", \"audio\", or \"video\")\n",
" | metadata: a JSON object to attach to image\n",
" | \n",
" | Note: don't delete the file until upload is complete\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_asset(\"histogram.json\", ftype=\"histogram_compbined_3d\")\n",
" | ```\n",
" | \n",
" | log_cpu_metrics(self, cpu_metrics, context=None, step=None, epoch=None, timestamp=None)\n",
" | Log an instance of cpu_metrics.\n",
" | \n",
" | Args:\n",
" | cpu_metrics: a list of integer percentages, ordered by cpu\n",
" | context: optional, a run context\n",
" | step: optional, the current step\n",
" | epoch: optional, the current epoch\n",
" | timestamp: optional, current time, in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | # Four CPUs:\n",
" | >>> api_experiment.log_cpu_metrics([25, 50, 10, 45])\n",
" | ```\n",
" | \n",
" | log_curve(self, name, x, y, overwrite=False, step=None)\n",
" | Log timeseries data.\n",
" | \n",
" | Args:\n",
" | name: (str) name of data\n",
" | x: list of x-axis values\n",
" | y: list of y-axis values\n",
" | overwrite: (optional, bool) if True, overwrite previous log\n",
" | step: (optional, int) the step value\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> experiment.log_curve(\"my curve\", x=[1, 2, 3, 4, 5],\n",
" | y=[10, 20, 30, 40, 50])\n",
" | >>> experiment.log_curve(\"my curve\", [1, 2, 3, 4, 5],\n",
" | [10, 20, 30, 40, 50])\n",
" | ```\n",
" | \n",
" | log_gpu_metrics(self, gpu_metrics)\n",
" | Log an instance of gpu_metrics.\n",
" | \n",
" | Args:\n",
" | gpu_metrics: a list of dicts with keys:\n",
" | * gpuId: required, Int identifier\n",
" | * freeMemory: required, Long\n",
" | * usedMemory: required, Long\n",
" | * gpuUtilization: required, Int percentage utilization\n",
" | * totalMemory: required, Long\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_gpu_metrics([{\n",
" | ... \"gpuId\": 1,\n",
" | ... \"freeMemory\": 1024,\n",
" | ... \"usedMemory\": 856,\n",
" | ... \"gpuUtilization\": 25,\n",
" | ... \"totalMemory\": 2056,\n",
" | ... }])\n",
" | ```\n",
" | \n",
" | log_html(self, html, clear=False, timestamp=None)\n",
" | Set, or append onto, an experiment's HTML.\n",
" | \n",
" | Args:\n",
" | html: (string) the HTML text to associate with this experiment\n",
" | clear: (optional, boolean) if True, clear any previously logged HTML\n",
" | timestamp: (optional, number) the current time (in milliseconds)\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_html(\"Hello!\")\n",
" | ```\n",
" | \n",
" | log_image(self, filename, image_name=None, step=None, overwrite=None, context=None)\n",
" | Upload an image asset to an experiment.\n",
" | \n",
" | Args:\n",
" | filename: the name of the image file to upload\n",
" | image_name: the name of the image\n",
" | step: the current step\n",
" | overwrite: if True, overwrite any previous upload\n",
" | context: the current context (e.g., \"train\" or \"test\")\n",
" | \n",
" | Note: don't delete the file until upload is complete\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_image(\"image.png\", \"Weights\")\n",
" | ```\n",
" | \n",
" | log_load_metrics(self, load_avg, context=None, step=None, epoch=None, timestamp=None)\n",
" | Log an instance of system load metrics.\n",
" | \n",
" | Args:\n",
" | load_avg: required, the load average\n",
" | context: optional, the run context\n",
" | step: optional, the current step\n",
" | epoch: optional, the current epoch\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_load_metrics(1.5, \"validate\", 100, 25, 65364346)\n",
" | ```\n",
" | \n",
" | log_metric(self, metric, value, step=None, timestamp=None)\n",
" | Set a metric name/value pair for an experiment.\n",
" | \n",
" | Args:\n",
" | metric: string, the name of the metric\n",
" | value: any type, the value of the metric\n",
" | step: optional integer, the current step\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_metric(\"loss\", 0.698)\n",
" | ```\n",
" | \n",
" | log_metrics(self, metric_dict, step=None, timestamp=None)\n",
" | Set a dictionary of metric name/value pairs for an experiment.\n",
" | \n",
" | Args:\n",
" | metric_dict: dict in the form of {\"metric_name\": value, ...}\n",
" | step: optional integer, the current step\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_metrics({\"loss\": 0.698, \"accuracy\": 0.12})\n",
" | [...]\n",
" | ```\n",
" | \n",
" | log_other(self, key, value, timestamp=None)\n",
" | Set an other key/value pair for an experiment.\n",
" | \n",
" | Args:\n",
" | key: string, the name of the other information\n",
" | value: any time, the value of the other information\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_other(\"key\", value)\n",
" | ```\n",
" | \n",
" | log_output(self, output, context=None, stderr=False, timestamp=None)\n",
" | Log output line(s).\n",
" | \n",
" | Args:\n",
" | output: string representing standard output or error\n",
" | context: optional, the run context\n",
" | stderr: optional, boolean, if True, the lines are standard errors\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_output(\"output line 1\n",
" | output line 2\")\n",
" | ```\n",
" | \n",
" | log_parameter(self, parameter, value, step=None, timestamp=None)\n",
" | Set a parameter name/value pair for an experiment.\n",
" | \n",
" | Args:\n",
" | parameter: string, the name of the parameter\n",
" | value: any type, the value of the parameter\n",
" | step: optional integer, the current step\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_parameter(\"hidden_layer_size\", 64)\n",
" | ```\n",
" | \n",
" | log_parameters(self, param_dict, step=None, timestamp=None)\n",
" | Set a dictionary of parameter name/value pairs for an experiment.\n",
" | \n",
" | Args:\n",
" | param_dict: dict in the form of {\"param_name\": value, ...}\n",
" | step: optional integer, the current step\n",
" | timestamp: optional, the current timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_parameters({\"learning_rate\": 0.12, \"layers\": 3})\n",
" | [...]\n",
" | ```\n",
" | \n",
" | log_ram_metrics(self, total_ram, used_ram, context=None, step=None, epoch=None, timestamp=None)\n",
" | Log an instance of RAM metrics.\n",
" | \n",
" | Args:\n",
" | total_ram: required, total RAM available\n",
" | used_ram: required, RAM used\n",
" | context: optional, the run context\n",
" | step: optional, the current step\n",
" | epoch: optional, the current epoch\n",
" | timestamp: optional, the current timestamp in millisconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_ram_metrics(1024, 865, \"train\", 100, 1, 3645346534)\n",
" | ```\n",
" | \n",
" | log_table(self, filename, tabular_data=None, headers=False, **format_kwargs)\n",
" | Log tabular data, including data, csv files, tsv files, and Pandas dataframes.\n",
" | \n",
" | Args:\n",
" | filename: str (required), a filename ending in \".csv\", or \".tsv\" (for tablular\n",
" | data) or \".json\", \".csv\", \".md\", or \".html\" (for Pandas dataframe data).\n",
" | tabular_data: (optional) data that can be interpreted as 2D tabular data\n",
" | or a Pandas dataframe).\n",
" | headers: bool or list, if True, will add column headers automatically\n",
" | if tabular_data is given; if False, no headers will be added; if list\n",
" | then it will be used as headers. Only useful with tabular data (csv, or tsv).\n",
" | format_kwargs: (optional keyword arguments), when passed a Pandas dataframe\n",
" | these keyword arguments are used in the conversion to \"json\", \"csv\",\n",
" | \"md\", or \"html\". See Pandas Dataframe conversion methods (like `to_json()`)\n",
" | for more information.\n",
" | \n",
" | See also:\n",
" | \n",
" | * [pandas.DataFrame.to_json documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html)\n",
" | * [pandas.DataFrame.to_csv documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html)\n",
" | * [pandas.DataFrame.to_html documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_html.html)\n",
" | * [pandas.DataFrame.to_markdown documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_markdown.html)\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.log_table(\"vectors.tsv\",\n",
" | ... [[\"one\", \"two\", \"three\"],\n",
" | ... [1, 2, 3],\n",
" | ... [4, 5, 6]],\n",
" | ...\n",
" | >>> api_experiment.log_table(\"dataframe.json\", pandas_dataframe)\n",
" | ```\n",
" | \n",
" | register_model(self, model_name, version='1.0.0', workspace=None, registry_name=None, public=None, description=None, comment=None, stages=None)\n",
" | Register an experiment model in the workspace registry.\n",
" | \n",
" | Args:\n",
" | model_name: the name of the experiment model\n",
" | workspace: optional, the name of workspace; defaults to current workspace\n",
" | version: a proper semantic version string; defaults to \"1.0.0\"\n",
" | registry_name: optional, the name of the registered workspace model, if not provided the\n",
" | model_name will be used instead.\n",
" | public: optional, if True, then the model will be publically viewable\n",
" | description: optional, a textual description of the model\n",
" | comment: optional, a textual comment about the model\n",
" | stages: optional, a list of textual tags such as [\"production\",\n",
" | \"staging\"] etc.\n",
" | \n",
" | Returns `{\"registryModelId\": \"ath6ho4eijaexeShahJ9sohQu\", \"registryModelItemId\":\n",
" | \"yoi5saes7ea2vooG2ush1uuwi\"}` if successful.\n",
" | \n",
" | set_code(self, code=None, filename=None)\n",
" | Set the code for this experiment. Pass in either\n",
" | the code as a string, or provide filename.\n",
" | \n",
" | Args:\n",
" | code: string, optional, the source code for this experiment\n",
" | filename: string, optional, the filename for this experiment\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_code(\"import comet_ml\n",
" | experiment = comet_ml.Experiment()\")\n",
" | >>> api_experiment.set_code(filename=\"script.py\")\n",
" | ```\n",
" | \n",
" | set_command(self, command_args_list)\n",
" | Set the command-line (script and args) for this experiment.\n",
" | \n",
" | Args:\n",
" | command_args_list: list of strings, starting with name of script,\n",
" | and followed by arguments.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_command([\"script.py\", \"arg1\", \"arg2\", \"--flag\", \"arg3\"])\n",
" | ```\n",
" | \n",
" | set_end_time(self, end_server_timestamp)\n",
" | Set the end time of an experiment.\n",
" | \n",
" | Args:\n",
" | end_server_timestamp: a timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_end_time(2652656352)\n",
" | ```\n",
" | \n",
" | Note: Time is in milliseconds. If the start time has not\n",
" | been set, it will be set to 1 second before the end\n",
" | time.\n",
" | \n",
" | set_executable(self, executable)\n",
" | Set the executable for this experiment.\n",
" | \n",
" | Args:\n",
" | executable: string, the python executable\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_executable(\"/usr/bin/python3\")\n",
" | ```\n",
" | \n",
" | set_filename(self, filename)\n",
" | Set the path and filename for this experiment.\n",
" | \n",
" | Args:\n",
" | filename: string, the python path and filename\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_filename(\"../src/script.py\")\n",
" | ```\n",
" | \n",
" | set_git_metadata(self, user, root, branch, parent, origin)\n",
" | Set the git metadata for this experiment.\n",
" | \n",
" | Args:\n",
" | user: (string, required) the name of the git user\n",
" | root: (string, required) the name of the git root\n",
" | branch: (string, required) the name of the git branch\n",
" | parent: (string, required) the name of the git parent\n",
" | origin: (string, required) the name of the git origin\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_git_metadata(\"user\", \"root\", \"branch\", \"parent\", \"origin\")\n",
" | ```\n",
" | \n",
" | set_git_patch(self, file_data)\n",
" | Set the git patch for this experiment.\n",
" | \n",
" | Args:\n",
" | file_data: the contents or filename of the git patch file\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_git_patch(\"git.patch\")\n",
" | ```\n",
" | \n",
" | set_gpu_static_info(self, gpu_static_info)\n",
" | Set the GPU static info for this experiment.\n",
" | \n",
" | Args:\n",
" | gpu_static_info: list of dicts containing keys\n",
" | `gpuIndex`, `name`, `uuid`, `totalMemory`, and `powerLimit`\n",
" | and their values.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_gpu_static_info([{\n",
" | \"gpuIndex\": 0,\n",
" | \"name\": \"GeForce GTX 950\",\n",
" | \"uuid\": \"GPU-cb6c1b39-5a56-6d79-8899-3796f23c6425\",\n",
" | \"totalMemory\": 2090074112,\n",
" | \"powerLimit\": 110000,\n",
" | }, ...])\n",
" | ```\n",
" | \n",
" | set_hostname(self, hostname)\n",
" | Set the hostname for this experiment.\n",
" | \n",
" | Args:\n",
" | hostname: string, the hostname of the computer the experiment ran on\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_hostname(\"machine.company.com\")\n",
" | ```\n",
" | \n",
" | set_installed_packages(self, installed_packages)\n",
" | Set the installed Python packages for this experiment.\n",
" | \n",
" | Args:\n",
" | installed_packages: list of strings, a list of the\n",
" | installed Python packages\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_installed_packages([\"comet_ml\", \"matplotlib\"])\n",
" | ```\n",
" | \n",
" | set_ip(self, ip)\n",
" | Set the internet protocol (IP) address for this experiment.\n",
" | \n",
" | Args:\n",
" | ip: string, the internet protocol address\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_ip(\"10.0.0.7\")\n",
" | ```\n",
" | \n",
" | set_machine(self, machine)\n",
" | Set the machine for this experiment.\n",
" | \n",
" | Args:\n",
" | machine: string, the machine type\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> import platform\n",
" | >>> api_experiment.set_machine(platform.machine())\n",
" | ```\n",
" | \n",
" | set_model_graph(self, graph)\n",
" | Set the model graph for this experiment.\n",
" | \n",
" | Args:\n",
" | graph: various types, a representation of the model graph\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_model_graph(model)\n",
" | ```\n",
" | \n",
" | set_name(self, name)\n",
" | Set a name for the experiment. Useful for filtering and searching on Comet.ml.\n",
" | Will shown by default under the `Other` tab.\n",
" | Args:\n",
" | name: String. A name for the experiment.\n",
" | \n",
" | set_network_interface_ips(self, network_interface_ips)\n",
" | Set the network interface ips for this experiment.\n",
" | \n",
" | Args:\n",
" | network_interface_ips: list of strings, of local\n",
" | network interfaces\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_network_interface_ips([\"127.0.0.1\", \"192.168.1.100\"])\n",
" | ```\n",
" | \n",
" | set_os(self, os)\n",
" | Set the OS for this experiment.\n",
" | \n",
" | Args:\n",
" | os: string, the OS platform identifier\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> import platform\n",
" | >>> api_experiment.set_os(platform.platform(aliased=True))\n",
" | ```\n",
" | \n",
" | set_os_packages(self, os_packages)\n",
" | Set the OS packages for this experiment.\n",
" | \n",
" | Args:\n",
" | os_packages: list of strings, the OS package list\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_os_packages(['accountsservice=0.6.45-1ubuntu1', ...])\n",
" | ```\n",
" | \n",
" | set_os_release(self, os_release)\n",
" | Set the OS release for this experiment.\n",
" | \n",
" | Args:\n",
" | os_release: string, the OS release\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> import platform\n",
" | >>> api_experiment.set_os_release(platform.uname()[2])\n",
" | ```\n",
" | \n",
" | set_os_type(self, os_type)\n",
" | Set the OS type for this experiment.\n",
" | \n",
" | Args:\n",
" | os_type: string, the OS type\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_os_type(\"Linux 2.0.1, Ubuntu 16.10\")\n",
" | ```\n",
" | \n",
" | set_pid(self, pid)\n",
" | Set the process ID for this experiment.\n",
" | \n",
" | Args:\n",
" | pid: string, the OS process ID\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_pid(54238)\n",
" | ```\n",
" | \n",
" | set_processor(self, processor)\n",
" | Set the processor for this experiment.\n",
" | \n",
" | Args:\n",
" | processor: string, the processor name\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> import platform\n",
" | >>> api_experiment.set_processor(platform.processor())\n",
" | ```\n",
" | \n",
" | set_python_version(self, python_version)\n",
" | Set the Python version for this experiment.\n",
" | \n",
" | Args:\n",
" | python_version: string, the verbose Python version\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_python_version(\"3.6.7\")\n",
" | ```\n",
" | \n",
" | set_python_version_verbose(self, python_version_verbose)\n",
" | Set the Python version verbose for this experiment.\n",
" | \n",
" | Args:\n",
" | python_version_verbose: string, the verbose Python version\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_python_version_verbose(\"3.6.7, by Anaconda\")\n",
" | ```\n",
" | \n",
" | set_start_time(self, start_server_timestamp)\n",
" | Set the start time of an experiment.\n",
" | \n",
" | Args:\n",
" | start_server_timestamp: a timestamp in milliseconds\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_start_time(2652656352)\n",
" | ```\n",
" | \n",
" | Note: Time is in milliseconds. If the end time has not been set\n",
" | it will automatically be set for 1 second after the start\n",
" | time.\n",
" | \n",
" | set_user(self, user)\n",
" | Set the user for this experiment.\n",
" | \n",
" | Args:\n",
" | user: string, the OS username\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.set_user(\"os-user-name\")\n",
" | ```\n",
" | \n",
" | to_json(self, full=False)\n",
" | The experiment data in JSON-like format.\n",
" | \n",
" | Args:\n",
" | full: bool (optional), if True, get all experiment information.\n",
" | \n",
" | Examples:\n",
" | \n",
" | ```python\n",
" | >>> experiment.to_json()\n",
" | ```\n",
" | \n",
" | Returns:\n",
" | \n",
" | ```json\n",
" | {'id': '073e272581ac48c283910a05e5495381',\n",
" | 'name': None,\n",
" | 'workspace': 'testuser',\n",
" | 'project_name': 'test-project-7515',\n",
" | 'archived': False,\n",
" | 'url': 'https://www.comet.ml/testuser/test-project-7515/073e272581ac48c283910a05e54953801',\n",
" | 'duration_millis': 4785,\n",
" | 'start_server_timestamp': 1571318652586,\n",
" | 'end_server_timestamp': 7437457,\n",
" | 'optimization_id': None,\n",
" | }\n",
" | \n",
" | ```python\n",
" | >>> experiment.to_json(full=True)\n",
" | ```\n",
" | \n",
" | Returns:\n",
" | \n",
" | ```json\n",
" | {'id': '073e272581ac48c283910a05e5495380',\n",
" | 'name': None,\n",
" | 'workspace': 'testuser',\n",
" | 'project_name': 'test-project-7515',\n",
" | 'archived': False,\n",
" | 'url': 'https://staging.comet.ml/testuser/test-project-7515/073e272581ac48c283910a05e5495380',\n",
" | 'duration_millis': 4785,\n",
" | 'start_server_timestamp': 1571318652586,\n",
" | 'end_server_timestamp': 7437457,\n",
" | 'optimization_id': None,\n",
" | 'asset_list': [],\n",
" | 'code': 'one\n",
" | two\n",
" | three\n",
" | ',\n",
" | 'html': 'hello\n",
" | world!\n",
" | ',\n",
" | 'metrics': [],\n",
" | 'metrics_summary': [],\n",
" | 'output': 'four\n",
" | five\n",
" | six\n",
" | ',\n",
" | 'parameters_summary': [],\n",
" | 'system_details': {'experimentKey': None,\n",
" | 'user': None,\n",
" | 'pythonVersion': None,\n",
" | 'pythonVersionVerbose': None,\n",
" | 'pid': None,\n",
" | 'osType': None,\n",
" | 'os': None,\n",
" | 'ip': None,\n",
" | 'hostname': None,\n",
" | 'gpuStaticInfoList': [],\n",
" | 'logAdditionalSystemInfoList': [],\n",
" | 'systemMetricNames': [],\n",
" | 'maxTotalMemory': None,\n",
" | 'networkInterfaceIps': None,\n",
" | 'command': None,\n",
" | 'executable': None,\n",
" | 'osPackages': ['lib1', 'lib2', 'lib3'],\n",
" | 'installedPackages': [],\n",
" | 'totalRam': None},\n",
" | 'tags': ['tag6', 'tag7', 'tag8'],\n",
" | 'git_patch': b'',\n",
" | 'git_metadata': {'user': None, 'root': None, 'branch': None, 'parent': None, 'origin': None},\n",
" | }\n",
" | ```\n",
" | \n",
" | update_status(self)\n",
" | Update the status for this experiment. Sends the keep-alive\n",
" | status for it in the UI. The return JSON dictionary contains\n",
" | the recommended interval to send subsequent `update_status()`\n",
" | messages.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.update_status()\n",
" | {'isAliveBeatDurationMillis': 10000,\n",
" | 'gpuMonitorIntervalMillis': 60000,\n",
" | 'cpuMonitorIntervalMillis': 68000}\n",
" | ```\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Readonly properties defined here:\n",
" | \n",
" | key\n",
" | Get the experiment key (the unique id).\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.key\n",
" | \"34637643746374637463476\"\n",
" | ```\n",
" | \n",
" | url\n",
" | Get the url of the experiment.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.url\n",
" | \"https://www.comet.ml/username/34637643746374637463476\"\n",
" | ```\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors defined here:\n",
" | \n",
" | name\n",
" | Get the experiment name.\n",
" | \n",
" | Example:\n",
" | \n",
" | ```python\n",
" | >>> api_experiment.name\n",
" | \"laughing_toaster_23454\"\n",
" | ```\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from comet_ml.experiment.CommonExperiment:\n",
" | \n",
" | display(self, clear=False, wait=True, new=0, autoraise=True, tab=None)\n",
" | Show the Comet.ml experiment page in an IFrame in a\n",
" | Jupyter notebook or Jupyter lab, OR open a browser\n",
" | window or tab.\n",
" | \n",
" | Common Args:\n",
" | tab: name of the Tab on Experiment View\n",
" | \n",
" | Note: the Tab name should be one of:\n",
" | * \"artifacts\"\n",
" | * \"assets\"\n",
" | * \"audio\"\n",
" | * \"charts\"\n",
" | * \"code\"\n",
" | * \"confusion-matrices\"\n",
" | * \"histograms\"\n",
" | * \"images\"\n",
" | * \"installed-packages\"\n",
" | * \"metrics\"\n",
" | * \"notes\"\n",
" | * \"parameters\"\n",
" | * \"system-metrics\"\n",
" | * \"text\"\n",
" | \n",
" | For Jupyter environments:\n",
" | \n",
" | Args:\n",
" | clear: to clear the output area, use clear=True\n",
" | wait: to wait for the next displayed item, use\n",
" | wait=True (cuts down on flashing)\n",
" | \n",
" | For non-Jupyter environments:\n",
" | \n",
" | Args:\n",
" | new: open a new browser window if new=1, otherwise re-use\n",
" | existing window/tab\n",
" | autoraise: make the browser tab/window active\n",
" | \n",
" | display_project(self, view_id=None, clear=False, wait=True, new=0, autoraise=True)\n",
" | Show the Comet.ml project page in an IFrame in a\n",
" | Jupyter notebook or Jupyter lab, OR open a browser\n",
" | window or tab.\n",
" | \n",
" | Common Args:\n",
" | view_id: (optional, string) the id of the view to show\n",
" | \n",
" | For Jupyter environments:\n",
" | \n",
" | Args:\n",
" | clear: to clear the output area, use clear=True\n",
" | wait: to wait for the next displayed item, use\n",
" | wait=True (cuts down on flashing)\n",
" | \n",
" | For non-Jupyter environments:\n",
" | \n",
" | Args:\n",
" | new: open a new browser window if new=1, otherwise re-use\n",
" | existing window/tab\n",
" | autoraise: make the browser tab/window active\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors inherited from comet_ml.experiment.CommonExperiment:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
"\n"
]
}
],
"source": [
"help(exp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For example, just like when creating and logging data, you can also use the `.display()` method to show the Comet.ml page for that experiment right in the notebook:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"exp.display()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can get an existing experiment:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"e = comet_api.get(\"dsblank\", \"chainer\", \"596d91ae1dbc420c9b13a3ced858de3c\")\n",
"API_KEY = comet_ml.get_config()[\"comet.api_key\"]\n",
"ee = comet_ml.ExistingExperiment(API_KEY, previous_experiment=e.id)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can make changes to the saved data using the existing experiment:\n",
"\n",
"https://www.comet.ml/docs/python-sdk/ExistingExperiment/"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"ee.end()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Examples\n",
"\n",
"Comet.ml is working on a query API which will allow highly efficient queries of your data. However, you can also write your own query of sorts.\n",
"\n",
"Here is some code that prints out the names of experiments that have associated HTML (this can take a long time if you have many experiments):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"for workspace in comet_api.get():\n",
" print(\"processing workspace\", workspace, \"...\")\n",
" for project in comet_api.get(workspace):\n",
" print(\" processing project\", project, \"...\")\n",
" for exp_id in comet_api.get(workspace, project):\n",
" exp = comet_api.get(workspace, project, exp_id)\n",
" if exp.html != None:\n",
" print(\"found html in %s!\" % exp.key)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a function that will find the first experiment that has associated images:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"def find_image():\n",
" for workspace in comet_api.get():\n",
" for project in comet_api.get(workspace):\n",
" for exp_id in comet_api.get(workspace, project):\n",
" exp = comet_api.get(workspace, project, exp_id)\n",
" if exp.images != []:\n",
" return exp"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"find_image()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we get the experiment API and explore the `.images` property:"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'APIExperiment' object has no attribute 'get_images'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mc:\\Users\\Mara\\Documents\\comet-examples\\notebooks\\Comet-REST-API.ipynb Cell 77\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m comet_api\u001b[39m.\u001b[39;49mget(\u001b[39m'\u001b[39;49m\u001b[39mdsblank/pytorch/3b56611892b7447aa8c4486a6eeb27d0\u001b[39;49m\u001b[39m'\u001b[39;49m)\u001b[39m.\u001b[39;49mget_images()\n",
"\u001b[1;31mAttributeError\u001b[0m: 'APIExperiment' object has no attribute 'get_images'"
]
}
],
"source": [
"comet_api.get('dsblank/pytorch/3b56611892b7447aa8c4486a6eeb27d0').get_images()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can get a URL for the image, and display it in the notebook:"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'https://s3.amazonaws.com/comet.ml/4ykmnOHJFZkrRD36KDrTXvHJk.svg'"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = comet_api.get('dsblank/pytorch/3b56611892b7447aa8c4486a6eeb27d0').get_asset_list(asset_type=\"image\")[0][\"link\"]\n",
"url"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Image"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"![](\"https://s3.amazonaws.com/comet.ml/4ykmnOHJFZkrRD36KDrTXvHJk.svg\"/) "
],
"text/plain": [
""
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(url=url)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's write a short program that will find the run with the best accuracy given a workspace/project string:"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"def find_best_run(project):\n",
" runs = []\n",
" for exp_id in comet_api.get(project):\n",
" exp = comet_api.get(project, experiment=exp_id)\n",
" accs = [x[\"valueMax\"] for x in exp.get_metrics() if x[\"name\"] == \"acc\"]\n",
" if len(accs) > 0:\n",
" runs.append([float(accs[0]), exp])\n",
" if runs:\n",
" return sorted(runs, key=lambda v: v[0], reverse=True)[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"find_best_run(\"cometpublic/fasttext\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Can we get all of the `hidden_size` parameter values for the experiments in dsblank/pytorch?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"[[p[\"valueCurrent\"] for p in exp.get_parameters()] for exp in comet_api.get(\"dsblank/pytorch\")]"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"experiments = [[(exp, \"hidden_size\", int(param[\"valueCurrent\"])) \n",
" for param in exp.get_parameters_summary() \n",
" if param[\"name\"] == \"hidden_size\"]\n",
" for exp in comet_api.get(\"dsblank/pytorch\")]\n",
"experiments = [e[0] for e in experiments if len(e) > 0]"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(,\n",
" 'hidden_size',\n",
" 128)"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"experiments[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assets\n",
"\n",
"To get an asset, you need to get the asset_id. You can see all of the assets related to a project using the `APIExperiment.asset_list`:"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"def find_asset():\n",
" for ws in comet_api.get():\n",
" for pj in comet_api.get(ws):\n",
" for exp in comet_api.get(ws, pj):\n",
" if exp.get_asset_list() != []:\n",
" return (exp, exp.get_asset_list())\n",
"exp, elist = find_asset()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From there, you can use the `APIExperiment.get_asset(asset_id)` method to get the asset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"h5 = exp.get_asset(\"a6c75ebcfd344c06a4934b97641ea87e\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We hope that this gives you some ideas of how you can use the Comet REST API!"
]
}
],
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"kernelspec": {
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"language": "python",
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|