Import CSV
Importing and logging data into W&B
The below table becomes this Weights & Biases Dashboard after conversion
Experiment | Model Name | Notes | Tags | Num Layers | Final Train Acc | Final Val Acc | Training Losses |
|---|---|---|---|---|---|---|---|
Experiment 1 | mnist-300-layers | Overfit way too much on training data | [latest] | 300 | 0.99 | 0.90 | [0.55, 0.45, 0.44, 0.42, 0.40, 0.39] |
Experiment 2 | mnist-250-layers | Current best model | [prod, best] | 250 | 0.95 | 0.96 | [0.55, 0.45, 0.44, 0.42, 0.40, 0.39] |
Experiment 3 | mnist-200-layers | Did worse than the baseline model. Need to debug | [debug] | 200 | 0.76 | 0.70 | [0.55, 0.45, 0.44, 0.42, 0.40, 0.39] |
... | ... | ... | ... | ... | ... | ... | |
Experiment N | mnist-X-layers | NOTES | ... | ... | ... | ... | [..., ...] |
In some cases, you may have your experiment details in a CSV file. W&B can take this CSV and easily convert it into an experiment run. That may include:
- A name for the experiment run
- Configurations needed for your experiment (with the added benefit of being able to utilize our Sweeps Hyperparameter Tuning)
run = wandb.init(project=PROJECT_NAME, name=run_name, tags=tags, notes=notes, config=config)
As an experiment runs, you'll want to log every instance of your metrics so they are available to view, query, and analyze with W&B. This is done via a
run.log() command:run.log({key: val})
Once an experiment reaches the end, you may want to log final summary metrics to define the outcome of the run. This can be done with W&B using
define_metric, but in this example case, we will be directly adding the summary metrics to our run with run.summary.update():run.summary.update(summaries)
Below is the full example script that will turn the above table into a W&B Dashboard:
FILENAME = "experiments.csv"
loaded_experiment_df = pd.read_csv(FILENAME)
PROJECT_NAME = "Converted Experiments"
EXPERIMENT_NAME_COL = "Experiment"
NOTES_COL = "Notes"
TAGS_COL = "Tags"
CONFIG_COLS = ["Num Layers"]
SUMMARY_COLS = ["Final Train Acc", "Final Val Acc"]
METRIC_COLS = ["Training Losses"]
pyth
for i, row in loaded_experiment_df.iterrows():
run_name = row[EXPERIMENT_NAME_COL]
notes = row[NOTES_COL]
tags = row[TAGS_COL]
config = {}
for config_col in CONFIG_COLS:
config[config_col] = row[config_col]
metrics = {}
for metric_col in METRIC_COLS:
metrics[metric_col] = row[metric_col]
summaries = {}
for summary_col in SUMMARY_COLS:
summaries[summary_col] = row[summary_col]
run = wandb.init(project=PROJECT_NAME, name=run_name,\
tags=tags, notes=notes, config=config)
for key, val in metrics.items():
if isinstance(val, list):
for _val in val:
run.log({key: _val})
else:
run.log({key: val})
run.summary.update(summaries)
run.finish()
Using Artifacts we can also directly log our
.csv file to W&B for reuse across projects and experiments:# Read our CSV into a new DataFrame
new_iris_dataframe = pd.read_csv("iris_dataframe.csv")
# Convert the DataFrame into a W&B Table
# NOTE: Tables will have a row limit of 10000 but...
iris_table = wandb.Table(dataframe=new_iris_dataframe)
# Add the table to an Artifact to increase the row limit to 200000 and make it easier to reuse!
iris_table_artifact = wandb.Artifact("iris_artifact", type="dataset")
iris_table_artifact.add(iris_table, "iris_table")
# We will also log the raw csv file within an artifact to preserve our data
iris_table_artifact.add_file("iris_dataframe.csv")
# Start a W&B run to log data
run = wandb.init(project="Tables-Quickstart")
# Log the table to visualize with a run...
run.log({"iris": iris_table})
# and Log as an Artifact to increase the available row limit!
run.log_artifact(iris_table_artifact)
# Finish the run (useful in notebooks)
run.finish()
Last modified 3mo ago